Tutorial Windografher
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Transcript of Tutorial Windografher
![Page 1: Tutorial Windografher](https://reader035.fdocuments.in/reader035/viewer/2022082201/563dbb1c550346aa9aaa5c82/html5/thumbnails/1.jpg)
Windographer is a wind data analysis program. It reads raw data files, does advanced statistical
processing of the data, produces a variety of graphs for visualizing the data, and provides tools for quality
control of the data.
Windographer allows you to open three types of raw data files: text files, NRG Systems data logger (.RWD) files, and Microsoft Excel (.xls) files. When you open one or more raw data files, Windographer
creates a Windographer document and stores a copy of the data from each file in the document.
Windographer never modifies the data in your original data files.
� To open a file, choose Open from the File menu.
� To create a new empty data set, choose New from the File menu.
� To append data to an existing data set, choose Append from the File menu.
Getting Started
To familiarize yourself with Windographer, you can open the sample file included with the software by navigating to the sample data folder in the Windographer folder, which by default is \Application
Data\Mistaya\Windographer\SampleData. Note that the Application Data folder is hidden by default. If you
cannot see the folder in Windows, try changing the Folder Options to make hidden files visible. You can also download the sample file from www.mistaya.ca/windographer/NapoleonND.zip.
The following articles will help you get started using Windographer:
� Importing raw data files
� Importing .RWD files
� Exporting graphs
� Exporting tables
� Exporting data
Features
When you open a file, Windographer displays the data in many graphs, including:
� time series graphs
� wind roses
� daily profiles
� probability distribution function
� cumulative distribution function
� scatterplots
Windographer also performs a statistical analysis on the data set and calculates such quantities as:
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� turbulence intensity
� power law exponent
� surface roughness
� wind power density
� wind power class
� Weibull k
The Data menu provides access to some more advanced features, such as:
� the gap filling module
� the quality control module
� the turbulence analysis module
� the wind shear analysis module
� the extreme wind analysis module
The Tools menu provides access to some additional tools that you might find useful.
Written by: Tom Lambert Contact: [email protected]
Last modified: May 6, 2008
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A raw data file is a text file, Excel file, or RWD file containing time series data that you want to analyze in Windographer. We have designed the software with wind resource data in mind, but you can use it to
analyze other types of time series data as well.
You can import a raw data file into Windographer in two ways:
� File > Open imports a raw data file into a new Windographer document.
� File > Append adds data from one or more raw data files into an existing Windographer document.
For details, please see the articles on importing raw data files and appending data.
Raw Data File Types
Windographer can open three types of raw data files:
� Text files, also called ASCII files or flat files, can contain data elements delimited by tabs, commas,
spaces, or semi-colons. Text files often have .txt or .csv extensions, but Windographer can open
text files with any extension.
� Excel files may have .xls or .xlsx extensions. To open Excel files, you must have Microsoft Excel
2000, 2002, 2003, or 2007 installed on your computer. If you open an Excel file that contains more
than one worksheet, Windographer will ask you which worksheet contains the data you want to import.
� NRG System files must have the extension .RWD. To import .RWD files, you must have the
Symphonie Data Retriever (SDR) software installed on your computer.
Format Requirements for Raw Data Files
A raw data file can contain any number of rows and columns. Each row must correspond to one time step.
Several examples appear below. These examples show text files, but similar requirements apply to Excel
files.
The example below shows a tab-delimited text file containing wind speed data at 10m, 30m, and 40m above ground, wind direction data at 30m and 40m above ground, and temperature data. When importing
this file, Windographer would automatically detect that the column names appear in line 6, the units
appear in line 7, and the numeric data begin on line 8. It would also recognize that the time step is ten minutes. Thanks to the helpful column names, it would also recognize the instrument heights for the
speed and direction data, and the fact that the last column contains temperature data. (If the column
names were less informative, you would need to specify the instruments heights and identify the last
column as the temperature column in the Configure Data Set window.) It would also read the latitude, longitude, and elevation from the file header.
Raw Data Files
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When importing raw data files, Windographer attempts to identify data column types (wind speed or direction, standard deviation of wind speed or direction, temperature, pressure) by referring both to the
names and to the statistical characteristics of the data columns. The statistical analysis works well if the
raw data file contains at least a few weeks of data, but for data sets covering less than one week Windographer resorts to analyzing the column names only. Longer data sets have an additional
advantage: Windographer can more reliably recognize the date and time when the data set covers more
transitions from one day to the next and one month to the next.
Windographer also refers to the data column names to detect associations between columns. In the
example below, it would recognize that the 'WS40' data column contains the mean wind speed at 40m, and that the 'WS40_SD' data column contains the standard deviation of the wind speed at 40m. If the
columns had less informative names such as 'Column1', 'Column2', and so on, then in the Configure Data
Set window you would need to indicate both that the measurement height of the first wind speed column is 40m, and that the third column contains the associated standard deviation data.
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Tip: If you have control over the format of your raw data files and you want the data import process to work as automatically as possible, you should use
informative column names so that Windographer can automatically identify
measurement heights, column types, column associations, and so on.
Date/Time Formats
Windographer accepts many different date formats. The year can appear as a two-digit or a four-digit
number. The day can be specified by day of the year or by month and day. The year, month, and day can
appear in separate columns or in a single column. When the year, month and day appear in a single column, they can be separated by dashes, periods, or forward slashes, and the year, month, and day can
be in any order. They can also be all together with no separators. In the example below, the last column
contains the date in MMDDYY format:
Windographer can also read dates that include the names or abbreviations of months, such as in the
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following example
The data and time information can also be together in one column with no delimiters, as in the following
example:
The time-of-day data can be in HH, HH:MM, or HH:MM:SS format, with or without the colon delimiters.
The hours and minutes must appear in the same column, not in separate columns. By default, Windographer assumes that the time stamp in the raw data file indicates the start of the time step, but in
the Configure Data Set window you can change that to the end or the middle.
If the file does not contain date/time data or Windographer cannot recognize the data/time data, a
window will appear asking you to specify the start date and time, along with the length of time step. In this case, Windographer will assume the data set contains no gaps.
Gaps
Windographer can read data files containing any number of gaps (missing elements). It will recognize that that data elements are missing if they are blank or contain alphabetic characters. The example below
shows a file that uses blank elements to indicate gaps:
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In the example below, gaps appear as '-n/a-' rather than as blanks:
Windographer will also recognize when entire time steps are simply missing from the data file, as in the
example below where all of June 29 and 30, and parts of June 28 and July 1, are missing:
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Tip: Many data loggers record certain very high or very low numbers (such as -
9999) to indicate missing or erroneous measurements. You can filter these values out using the filter settings in the Configure Data Set window. You can also fill the
resulting gaps using the Fill Gaps function or the Quality Control window.
See also
Importing raw data files
Importing .RWD files
Appending data
Filling gaps
Configure Data Set window
Quality Control window
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 28, 2008
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You can import a raw data file into Windographer in two ways. File > Open imports a single raw data file into a new Windographer document, whereas File > Append adds data from one or many raw data files
into an existing Windographer document.
When you import a raw data file, Windographer analyzes the data and tries to determine:
� the number of rows and columns of data
� the name and units of each data column
� the start time and the length of time step
� the type of data that each data column contains (speed, direction, temperature, etc.)
� the measurement height of each wind speed and wind direction sensor
� any column associations (e.g. a standard deviation column associated with a wind speed data column)
� the latitude, longitude, and elevation
If Windographer cannot determine the state time or the time step, it will ask you to specify them. If it detects any data elements that appear to be out of chrolological order, or that do not appear to conform
to the time step, it will display the Date/Time Anomalies window to alert you to the irregularities and to
give you some control over how to handle them.
Windographer then displays the Configure Data Set window so you can verify all of the pieces of
information that it has automatically determined, and make any necessary changes. When you click OK to close the Configure Data Set window, Windographer displays the data set in the main window. If you save
the data set to a .windog file, Windographer will store the data and the configuration information in
the .windog file. When you open the .windog file in the future, you will not have to specify the configuration information, but the Configure Data Set window is always available in the Data menu if you
want to make changes.
See also
Raw data files
Appending data
Configure Data Set window
Date/Time Anomalies window
Written by: Tom Lambert Contact: [email protected]
Last modified: April 29, 2008
Importing Raw Data Files
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.RWD files are binary-format raw wind data files written by the Symphonie Data Logger software by NRG Systems. You can open .RWD files in Windographer using File > Open or File > Append, provided you
have the Symphonie Data Recorder software installed on your computer. You can download the SDR
software for free from NRG Systems website, www.nrgsystems.com.
Tip: Make sure that the SDR software is not already running on your computer
when you try importing an .RWD file into Windographer.
Note that you can open many .RWD files at once with File > Append. Hold down the Shift key or the Control key to select multiple files:
See also
Raw data files
Importing raw data files
Appending data
Written by: Tom Lambert Contact: [email protected]
Last modified: April 29, 2008
Importing .RWD Files
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To append data from one or more raw data files to an existing Windographer document, choose Append from the File menu. You can use the append facility both to add data to the existing data columns, or to
add new data columns.
Appending Rows of Data
Windographer will add data to an existing data column if a column name in the appended data set
matches a column name in the existing data set. The diagram below illustrates a common scenario, where the appended data has the same data structure as the existing data set. Because the column names
match, Windographer simply adds data to the existing data columns:
If new data become available one day at a time or one month at a time, you can repeatedly use this
process to build up the main data set from many smaller data files. The appended time steps do not have to immediately follow the existing time steps as they do in the above diagram; they can be earlier or later
than the main data set, or even overlap with the main data set. Windographer will place the new data in
the correct chronological order according to the time stamps in the appended data set, and it will adjust the start or end date of the main data set if necessary.
Appending Columns of Data
If a column name in the appended data set does not match that of any existing column, Windographer will
add a data column to hold the new data. The diagram below illustrates a scenario where the existing data set contains one column and the appended data set contains another, so the resulting data set contains
two columns. The appended data covers a few more time steps than does the existing data set, so
Windographer lengthens the data set accordingly, and leaves the new time steps blank in the existing data column.
Appending Data
Main Data Set
Date/Time Col1 Col2
070429 05:40 8.10 287
070429 05:50 8.14 268
070429 06:00 7.53 250
070429 06:10 7.45 236
070429 06:20 6.49 232
070429 06:30 5.43 225
070429 06:40 4.81 222
070429 06:50 4.57 225
Appended Data Set
Date/Time Col1 Col2
070429 07:00 4.84 223
070429 07:10 4.65 219
070429 07:20 4.65 218
070429 07:30 4.98 217
070429 07:40 5.24 227
070429 07:50 5.62 264
append
Resulting Data Set
Date/Time Col1 Col2
070429 05:40 8.10 287
070429 05:50 8.14 268
070429 06:00 7.53 250
070429 06:10 7.45 236
070429 06:20 6.49 232
070429 06:30 5.43 225
070429 06:40 4.81 222
070429 06:50 4.57 225
070429 07:00 4.84 223
070429 07:10 4.65 219
070429 07:20 4.65 218
070429 07:30 4.98 217
070429 07:40 5.24 227
070429 07:50 5.62 264
Main Data Set
Date/Time Col1
Appended Data Set
Date/Time Col2
Resulting Data Set
Date/Time Col1 Col2
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You might use this approach to aggregate data from different data loggers or even altogether different
sources.
Tip: Before appending data, Windographer displays the Configure Data Set
window so that you can verify the structure of the data set you are about to append. That is your chance to make sure the data column names are correct so
that the process works as you want it to.
Appending Overlapping Data
If a column in the appended data set has the same name as a column in the existing data set, and if it
overlaps the existing column in time, Windographer will refer to the settings in the Options window to decide how to handle the overlapping data. You can specify that the appended data always overwrites the
existing data, or that it never overwrites the existing data. A third alternative is to overwrite existing data
only where the existing data is missing. In that case, the appended data can fill gaps in the existing data, but no existing data values will be overwritten. A fourth alternative is to overwrite existing data at all
times except where the appended data is missing. This is the default setting, but you can change it in the
Tools > Options window.
Appending Multiple Raw Data Files
Note that in the Append One or More Files window, you can select multiple files by holding down the Shift key or the Control key when you click on files:
070429 05:40 8.10
070429 05:50 8.14
070429 06:00 7.53
070429 06:10 7.45
070429 06:20 6.49
070429 06:30 5.43
070429 06:40 4.81
070429 06:50 4.57
070429 07:00 4.84
070429 07:10 4.65
070429 07:20 4.65
070429 05:40 287
070429 05:50 268
070429 06:00 250
070429 06:10 236
070429 06:20 232
070429 06:30 225
070429 06:40 222
070429 06:50 225
070429 07:00 223
070429 07:10 219
070429 07:20 218
070429 07:30 217
070429 07:40 227
070429 07:50 264
append
070429 05:40 8.10 287
070429 05:50 8.14 268
070429 06:00 7.53 250
070429 06:10 7.45 236
070429 06:20 6.49 232
070429 06:30 5.43 225
070429 06:40 4.81 222
070429 06:50 4.57 225
070429 07:00 4.84 223
070429 07:10 4.65 219
070429 07:20 4.65 218
070429 07:30 217
070429 07:40 227
070429 07:50 264
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When you select multiple files to append, Windographer will assume that they all share the same data structure. It will only display the Configure Data Set window once, regardless of how many files you have
selected to append.
Templates
In the Configure Data Set window, you can use templates to store data structure information to a file,
then apply it to other data files. When you append data to an existing data set, Windographer will automatically apply the main data set's template to the appended data set. If the appended data set has a
different data structure and you do not want Windographer to apply the main data set's template, you can
remove the template by choosing Remove Template:
When you remove a template, Windographer will analyze the appended data set to determine its data
structure. You can then make any necessary changes before closing the Configure Data Set window.
When you apply a template, Windographer will ask whether you want to continue to apply that template
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automatically whenever appending data to the current Windographer document. If you click yes, then
Windographer will store that template and apply it, rather than the main data set template, whenever you append data to that Windographer document. Doing this can save time in a case where you repeatedly
append new data files to an existing data set as they become available, but the data structure of the new
data files does not exactly match that of the existing data set. This might occur if, for example, you
change the instrument arrangement on a meteorological tower so that it now has a different number of sensors than before, or one sensor is at at different height than before.
See also
Raw data files
Importing raw data files
Templates
Configure Data Set window
Written by: Tom Lambert
Contact: [email protected] Last modified: April 29, 2007
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A template is a small file encapsulating all the properties of all the data columns in a data set. You can save a template from one data set and apply it to another data set that you import into Windographer
using File > Open or File > Append. Templates can save time if you repeatedly open data files with the
same data structure, because rather than entering all the properties of each data column individually, you
can simply apply the template.
To save or apply a template, click the Template button on the Data Columns tab of the Configure Data Set window.
When you apply a template, Windographer asks you whether you want to automatically apply that
template every time you append data to that data set. If you click yes, then Windographer will store that
template and apply it each time you append to that data set. Otherwise it will automatically apply the template from the existing data set to the appended data set. If you do not want to apply a template, you
can remove the template by choosing Remove Template from the Template button.
See also
Importing raw data files
Appending data
Configure Data Set window
Written by: Tom Lambert Contact: [email protected]
Last modified: April 29, 2008
Templates
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To export data to a text file, choose Export Data from the File menu:
The Export Data window allows you to export data in one of four formats: time series, WAsP tab file,
WindSim .wws file, or WindSim .tws file. The following sections describe each of these formats. You can
export the final three formats only if your data set contains both wind speed and wind direction data.
Time Series
A time series data file contains one time step of data per line. On the time series tab, You can choose which data columns to export, and whether to export all or only a subset of the time steps in the data set.
You can also choose the data interval, which is the length of the time step, for the exported data.
Tip: If you choose a longer data interval than that of the original data set,
Windographer calculates the average of each exported data column in each of the
longer time steps. It exports vector means for wind direction data, and arithmetic means for all other types of data.
You can also indicate whether you want the export file to include the date and time for each time step, and if so you have several choices regarding format. The Time stamp indicates control lets you choose
whether the exported date and time values refer to the start or the end of the time steps.
The File delimiter control lets you specify whether to use tab characters, spaces, commas, or some other
character to separate the values in the exported file. The Missing element control lets you enter the text
string that appears in the place of any missing element or gap. By default, Windographer leaves missing
elements blank, but you may instead want to use some code like '-9999' or 'N/A' to appear in place of missing elements.
A preview of the exported file appears at the bottom of the tab. The preview
capacity does not function if the time step of the exported data differs from that of
Exporting Data
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the data file. The export process will still work correctly in that case, but the
necessary calculations make the preview process impractically slow.
WAsP Tab File
A tab file is a text file with a particular format specifying the frequency distribution by wind speed and diretion sector. The file type originated with the wind flow model WAsP. The WAsP documentation gives
detailed information about the tab file format.
Windographer exports tab files that indicate frequency by direction sector and wind speed bin. If the data
set contains multiple wind speed and direction sensors, you can choose the speed and direction sensors
upon which to base the statistics. You can also choose the number of wind direction sectors and whether to base the calculations on all time steps or only on a subset of the time steps in the data set.
A preview of the tab file appears in the window. The columns correspond to the direction sectors and the
rows correspond to the wind speed bins. The frequencies are in per mille, and the frequencies in each
column sum to 1000.
WindSim WWS File
A .wws file is similar to a tab file in that it specifies the frequency histogram versus wind speed and
direction sector. But the .wws file has a specific header format and contains fractional frequency values
rather than per mille values. The wind flow model WindSim accepts data in this format. The WindSim documentation provides detailed information about the format of .wws files.
When exporting a .wws file, you can choose the wind speed and wind direction sensors on which to base
the frequency calculations, the number of direction sectors, and whether to include all time steps or just a
subset of the time steps in the data set.
WindSim TWS File
The .tws format is a second format read by the wind flow model WindSim. The .tws file contains time series data for one wind speed sensor and one wind direction sensor. If your data file contains multiple
wind speed or direction sensors, you can choose the ones to export. You can also choose to export all or
just a subset of the time steps in the data set.
See also
Exporting graphs
Exporting tables
Written by: Tom Lambert
Contact: [email protected] Last modified: October 2, 2008
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From any graph in Windographer, you can export the image or the underlying data with a right-click:
Copying the image to the clipboard
To copy the image to the clipboard, choose either Copy Bitmap or Copy Metafile. Then paste the image into the application of your choice. Use the bitmap format if you plan to paste the image into a bitmap editing
application like Paint. Use the metafile format if you plan to print the image or scale it to a larger size.
Metafiles use a vector format that scales smoothly and looks better at the high resolution of a printed
document. A bitmap image will look pixelated and coarse when scaled up or printed.
Exporting the image to a file
To save the image to a file, choose Save As PNG or Save As Metafile. Portable Network Graphics (.PNG)
files use a highly compressed bitmap format that results in very small files with no distortion. Use the PNG
format if you plan to email the image or put it on a website. Virtually all browsers, graphics programs, and word processors can open .PNG files. As mentioned above, metafiles are preferable if you plan to print the
image or scale it up or down.
Tip: To avoid resizing the exported image, size the graph appropriately in
Windographer before you export the image. Almost all graphs in Windographer
size themselves according to the window in which they appear. Resize the window
to resize the graph.
Exporting the data to a file
To export the plotted data to a tab-delimited text file, choose Export Data. You can import the resulting
text file into a spreadsheet or other data processing applicaiton.
See also
Exporting data
Exporting tables
Exporting Graphs
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Windographer allows you to export the contents of any table to a text file. To do so, right-click the table and choose Export Table from the menu:
Once you export the contents of the table to a text file, you can import the text file into a spreadsheet for further analysis, or into a word processor for inclusion in a report.
See also
Exporting data
Exporting graphs
Written by: Tom Lambert Contact: [email protected]
Last modified: March 7, 2007
Exporting Tables
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The Summary tab shows an overview of the data set. At the left Windographer lists some general properties of the data set:
The Summary tab also displays the following four graphs:
Right-click any graph in Windographer to change its properties, copy the image to the clipboard, or export
it to a file. For more information please refer to the article on exporting graphs.
Summary Tab
Variable Description
Data set properties
LatitudeNorth-south location on the globe. Windographer reads latitude from raw data files, and you can edit it in the Configure
Data Set window.
LongitudeEast-west location on the globe. Windographer reads longitude from raw data files, and you can edit it in the Configure Data Set window.
Elevation The elevation (altitude) of the site above sea level.
Start date The date and time of the beginning of the first time step in the data set.
End date The date and time of the end of the last time step in the data set.
Duration The length of time over which the data set extends.
Time step The time interval between successive recorded observations in the data file.
Calm threshold The threshold value below which Windographer interprets wind speeds as calm.
Environmental conditions
Mean temperature The average air temperature over the entire data set.
Mean pressure The average air pressure over the entire data set.
Mean air density The average air density over the entire data set.
Air density ratio The mean air density divided by standard air density of 1.225 kg/m³.
Wind power coefficients
Power density at
50mThe average wind power density at 50m above ground.
Wind power class The class into which the wind power density at 50m falls.
Wind shear coefficients
Power law exponent
The value of the exponent that makes the power law profile best fit the measured wind shear profile.
Surface roughness The value of surface roughness that makes the logarithmic law profile best fit the measured wind shear profile.
Roughness class A dimensionless number based on the surface roughness.
Roughness
descriptionA description of a terrain typified by the calculated value of surface roughness.
Graph Description
Wind Shear Profile
This graph shows the average wind speed at each height above ground, as well as the best-fit logarithmic profile and power law profile. More detail on the wind shear is available on the Wind Shear Analysis window.
Wind Rose
This polar plot shows the frequency with which the wind blows from each direction sector, or the average wind speed in each
direction sector. Click the button at the top right to modify the properties of the wind rose. More options are available on the Wind Rose tab.
Seasonal Wind Speed Profile
This graph shows the average wind speed in each month of the year for each height above ground. In data sets spanning
multiple years, the monthly averages include values from all years. See the Time Series tab and monthly statistics table for data on each individual month.
Daily Wind
Speed Profile
This graph shows the average wind speed in hour of the year for each height above ground. See the Daily Profile tab for the
average daily profile by month.
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See also
Time Series tab
Wind Rose tab
Daily Profile tab
PDF tab
CDF tab
Scatterplot tab
Boxplot tab
DMap tab
Tables tab
Configure Data Set window
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 29, 2008
![Page 23: Tutorial Windografher](https://reader035.fdocuments.in/reader035/viewer/2022082201/563dbb1c550346aa9aaa5c82/html5/thumbnails/23.jpg)
The Time Series tab displays one or two graphs with which you can plot any of the data columns (including calculated columns) in the data set. Using the buttons at the top of the page you can choose
to display the measured data themselves, or the daily, monthly, or annual averages of those data.
(Average wind direction values refer to vector averages.) Using the scroll bar and zoom buttons at the
bottom of the page you can plot any portion of the data set.
Use the checkboxes on the right side of the window to choose which data columns you want to display. Data checked in the first column will be displayed in the upper graph. You can plot data columns with one
type of units on the left y-axis, and a second type of units on the right y-axis. The data column that is
select first will be displayed on the left. In the example below, the user has chosen to display temperature and air density, with different units shown on the left and right y-axis of the top graph. The lower graph
displays three wind speed columns. Note that Windographer has disabled the checkboxes of the first
graph corresponding to data columns with different units. To plot the direction on the top graph, the user can simply uncheck the temperature box and then select the direction.
Use the scroll bar below the graph to move the graph forward or backward in time, and click the zoom
buttons to the right of the scroll bar to zoom in or out. The third zoom button zooms out to display all the
data.
Time Series Tab
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Click and drag on the graph to select an interval on which to zoom:
Right-click the graph to change its properties, copy the image to the clipboard, or export it to a file. For
more information please refer to the article on exporting graphs.
See also
Summary tab
Wind Rose tab
Daily Profile tab
PDF tab
CDF tab
Scatterplot tab
Boxplot tab
DMap tab
Tables tab
Reports tab
![Page 26: Tutorial Windografher](https://reader035.fdocuments.in/reader035/viewer/2022082201/563dbb1c550346aa9aaa5c82/html5/thumbnails/26.jpg)
The Wind Rose tab allows you to create many types of polar plots related to the wind direction. This page appears only if you have identified at least one wind direction data column on the Configure Data Set
window.
On the left side of the Wind Rose tab are several controls that allow you to specify the type and properties
of the wind rose(s) that Windographer creates. The radio buttons at the top allow you to choose between
the four main types of wind roses. If you choose Frequency by direction, Windographer will create a wind rose showing the frequency with which the wind direction falls within each direction sector. If the data set
contains data from two or more wind direction sensors, you can use the drop-down box labeled Direction sensor to choose the wind direction sensor on which you would like Windographer to base the wind
frequency rose. The example below shows that the wind blows most often from the WSW direction:
In wind frequency roses, the radius indicates frequency. In the above example, the frequency with which
the wind blows from the WSW direction (at a wind speed above the calm threshold) is about 7%. The least frequent wind direction (about 1%) is the N direction. At the top right of each wind frequency rose
Windographer indicates the calm frequency, or the frequency with which the wind speed is equal to or less
than the calm threshold. In the example above the calm frequency is 6%. Windographer does not include these calm winds in the wind rose diagram. To determining whether the wind speed in a particular time
step is sufficiently high to include that time step's wind direction value in a wind rose, Windographer
refers to the wind speed sensor that is closest in height to the chosen wind direction sensor.
If you choose Mean value by direction Windographer will create a different kind of wind rose showing the
mean (average) value of a particular data column wind direction. The example below plots the average wind speed at 50m versus wind direction. The wind rose indicates that that winds from the WSW direction
tend to be the strongest, with an average wind speed of over 10 m/s. Winds from the NE direction tend to
be the lightest, averaging less than 4 m/s.
Wind Rose Tab
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You can plot any data column you wish with this kind of wind rose. Use the drop-down box labeled Data column to choose the data column you wish to plot. And as with wind frequency roses, use the drop-down
box labeled Direction sensor to choose a wind direction sensor. The example below plots the average solar
radiation versus wind direction. This diagram shows that the conditions tend to be very sunny when winds blow from north and east, and very cloudy when winds blow from the south and west. The average solar
radiation is near zero when the wind is blowing from the NW.
The type of wind rose Windographer creates when you choose Total value by direction is exclusively for
plotting the total amount of wind energy coming from each direction sector. The example below shows
that about 45% of the total amount of energy in the wind comes from the WSW direction, with another
20% coming from the W direction, and another 16% coming from the SW direction.
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If you choose Scatterplot Windographer will create another kind of polar plot, drawing a small X mark for
every data point. As with the plots of mean value by direction, you use the drop-down boxes to select the
data column and direction sensor. The example below plots the wind speed at 50m versus the wind
direction. In this example the strongest winds all blow from the west.
With the Year and Month drop-down boxes, you can choose to plot data for a specific year and/or a
specific month. If you select All from both drop-down boxes, Windographer will plot all the data from the entire data set. If you select all years and one particular month, Windographer will plot data from that
month for all years in the data set. For example, if you choose April in a four-year data set, the graph will
include data from all four Aprils. If you select one particular year and all months, Windographer will plot
data from only that year. If you select a particular year and a particular month, Windographer will plot data for that single month.
Under the heading Display, if you choose Single wind rose Windographer will create a single wind rose
diagram using all data for the selected time frame. If you choose By month Windographer will create
twelve separate diagrams, one for each month of the year (this option is not applicable if you have chosen
a specific month from the Month drop-down box. If you choose By time of day Windographer will create twelve separate diagrams, one for each two-hour segment of the day. The checkbox labeled Use common scale allows you to choose whether all twelve wind roses should use the same scale to facilitate
comparison.
The remaining controls on the left side of the Wind Rose tab affect the appearance of the wind rose
diagrams. From the Drawing style radio buttons, choose the style you prefer: either Point-to-point, shown
![Page 29: Tutorial Windografher](https://reader035.fdocuments.in/reader035/viewer/2022082201/563dbb1c550346aa9aaa5c82/html5/thumbnails/29.jpg)
below on the left, Pie slices, shown in the center, or Outlined pie slices, shown on the right.
The slider bar labeled Direction sectors lets you experiment with different numbers of direction sectors.
The optimal number of direction sectors depends on the resolution and character of the wind direction
data, the size of the diagram, and your own preferences.
Tip: You can set the default drawing style and number of sectors for wind rose
diagrams in Tools > Options.
Right-click any wind rose to copy the image to the clipboard or export it to a file. For more information
please see the article on exporting graphs.
See also
Summary tab
Time Series tab
Daily Profile tab
PDF tab
CDF tab
Scatterplot tab
Boxplot tab
DMap tab
Tables tab
Reports tab
![Page 30: Tutorial Windografher](https://reader035.fdocuments.in/reader035/viewer/2022082201/563dbb1c550346aa9aaa5c82/html5/thumbnails/30.jpg)
Exporting graphs
Options window
Calm threshold
Written by: Tom Lambert
Contact: [email protected]
Last modified: January 28, 2008
![Page 31: Tutorial Windografher](https://reader035.fdocuments.in/reader035/viewer/2022082201/563dbb1c550346aa9aaa5c82/html5/thumbnails/31.jpg)
The Daily Profile tab displays the average daily profile of one or more data columns. The drop-down box in the top left corner of the page lets you choose which data column to plot. If you choose Wind speeds,
Windographer plots the average daily profile of every wind speed data column in the data set. The other
data columns appear individually in the drop-down box.
With the Year and Month drop-down boxes, you can choose to plot data for a specific year and/or a
specific month. If you select All from both drop-down boxes, Windographer will plot all the data from the entire data set. If you select all years and one particular month, Windographer will plot data from that
month for all years in the data set. For example, if you choose April in a four-year data set, the graph will
include data from all four Aprils. If you select one particular year and all months, Windographer will plot
data from only that year. If you select a particular year and a particular month, Windographer will plot data for that single month.
If you select All from the Month drop-down box, you can choose to display the data in a single plot or in
twelve separate plots, one for each month of the year.
To calculate the average daily profile of a set of data points, Windographer finds the average value of all
of the points that occur within the hour of 12:00am to 1:00am, then all those that occur within the hour of 1:00am to 2:00am, and so on for each of the 24 hours of the day.
Tip: The daily profile table displays similar data in tabular form. You can create the daily profile table on the Tables tab of the main window.
Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For
information about exporting graphs from Windographer, please refer to the article on exporting graphs.
See also
Daily Profile table
Summary tab
Time Series tab
Wind Rose tab
PDF tab
CDF tab
Daily Profile Tab
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Scatterplot tab
Boxplot tab
DMap tab
Tables tab
Exporting graphs
Written by: Tom Lambert Contact: [email protected]
Last modified: April 24, 2008
![Page 33: Tutorial Windografher](https://reader035.fdocuments.in/reader035/viewer/2022082201/563dbb1c550346aa9aaa5c82/html5/thumbnails/33.jpg)
The PDF tab displays a graph of the probability distribution function (abbreviated PDF) of a data column. The graph shows the frequency with which the variable falls within different bins. The example
below shows that the wind speed falls within the range of 0 to 1 m/s about 4% of the time,1 to 2 m/s
about 6.6% of the time, 2 to 3 m/s about 11% of the time, and so on.
From the Data column drop-down box at the top of the PDF tab, choose the data column you would like to
plot.
Filtering by direction
With the Wind direction sensor and Wind direction sector drop-down boxes, you can choose to create the
PDF for only those time steps when the wind blows in a certain direction. The Sectors drop-down box
allows you to experiment with different numbers of direction sectors. If you select All from the Wind
Direction sector box, Windographer will not apply any wind direction filtering. In the filter example below, the only data included in the PDF graph is when wind direction at the 10m sensor is between 75 and 105
degrees.
Filtering by date
With the Year and Month drop-down boxes, you can choose to plot data for a specific year and/or a specific month. If you select All from both drop-down boxes, Windographer will plot all the data from the
entire data set. If you select all years and one particular month, Windographer will plot data from that
month for all years in the data set. For example, if you choose April in a four-year data set, the graph will
include data from all four Aprils. If you select one particular year and all months, Windographer will plot data from only that year. If you select a particular year and a particular month, Windographer will plot
data for that single month.
PDF Tab
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Other PDF Features
Windographer automatically chooses a reasonable bin size, but you use the Bin size input box to specify
another value.
Use the radio buttons to choose whether to create a single PDF graph for the entire data column, or
twelve different graphs showing the data separately for each month of the year. If you create a PDF of a wind speed column, Windographer also plots the best-fit Weibull distribution so you can see how well it
fits the actual distribution of wind speeds.
Tip: You can see similar data in tabular format by creating a Frequency by Bin
table or a Frequency by Bin and Direction on the Tables tab.
Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For
information about exporting graphs from Windographer, please refer to the article on exporting graphs.
See also
probability distribution function
Weibull distribution
Frequency by Bin table
Frequency by Bin and Direction table
Summary tab
Time Series tab
Wind Rose tab
Daily Profile tab
CDF tab
Scatterplot tab
Boxplot tab
DMap tab
Tables tab
Reports tab
Exporting graphs
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The CDF tab displays the cumulative distribution function (abbreviated CDF) of one or more data columns. The cumulative distribution function represents the probability that a variable is less than or
equal to a particular value. The example below shows that the wind speed is below 20 km/hr about 32%
of the time, and below 40 km/hr about 85% of the time.
With the Year and Month drop-down boxes, you can choose to plot data for a specific year and/or a
specific month. If you select All from both drop-down boxes, Windographer will plot all the data from the entire data set. If you select all years and one particular month, Windographer will plot data from that
month for all years in the data set. For example, if you choose April in a four-year data set, the graph will
include data from all four Aprils. If you select one particular year and all months, Windographer will plot data from only that year. If you select a particular year and a particular month, Windographer will plot
data for that single month.
Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For
information about exporting graphs from Windographer, please refer to the article on exporting graphs.
See also
Summary tab
CDF Tab
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Time Series tab
Wind Rose tab
Daily Profile tab
PDF tab
Scatterplot tab
Boxplot tab
DMap tab
Tables tab
Exporting graphs
Written by: Tom Lambert
Contact: [email protected] Last modified: May 15, 2006
![Page 38: Tutorial Windografher](https://reader035.fdocuments.in/reader035/viewer/2022082201/563dbb1c550346aa9aaa5c82/html5/thumbnails/38.jpg)
The Scatterplot tab allows you to plot one data column versus another. Choose the data column to plot on the y-axis from the Plot drop-down box, and the data column to plot on the x-axis from the versus drop-
down box. The scatterplot will display data for all time steps that contain valid data in both the x-axis
column and the y-axis column, and that satisfy any filter conditions that you impose.
Filtering by Wind Direction
With the drop-down boxes labeled Wind direction sensor and Wind direction sector, you can create a scatterplot for only those time steps when the wind blows in a certain direction. The Sectors drop-down
box allows you to experiment with different numbers of direction sectors. If you select All from the Wind
Direction sector box, Windographer will not apply any wind direction filtering. Note that when you filter by
direction sector, the scatterplot will exclude any time step that does not contain valid data in the wind direction sensor on which you have chosen to base the directional filtering.
To display only data corresponding to a west wind, for example, you could use the following filter settings.
The scatterplot would therefore only include time steps in which the 10m wind direction sensor reports a
value between 255° and 285°.
Filtering by Date
With the drop-down boxes labeled Year and Month, you can choose to plot data for a specific year and/or a
specific month. If you select All from both drop-down boxes, Windographer will not filter by month or
year. If you select all years and one particular month, Windographer will plot data from that month for all
years in the data set. For example, if you choose April in a four-year data set, the graph will include data from the month of April for all four years. If you select one particular year and all months, Windographer
will plot data from all months in only that year. If you select a particular year and a particular month,
Windographer will plot data for that single month.
The example below shows the wind speed at 45m above ground versus the wind speed at 10m above
ground in December. Normally the 45m wind speed should almost always exceed the 10m wind speed, but this scatterplot shows that the opposite occurred in many time steps. (These points appear highlighted
in red.) Another thing that does not look right is that in several time steps one anemometer is recording
near-zero wind speeds while the other is recording moderate wind speeds, between 3 m/s and 12 m/s. (These points appear highlighted in yellow.) Both of these features of the scatterplot seem to indicate a
problem with the data set. Below we will investigate further using another capability of the the Scatterplot
tab, the ability to filter by a data column.
Scatterplot Tab
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Filtering by Data Column
For some scatterplots, you may want to filter the data according to the value of a particular data column. You can do that by clicking the checkbox labeled Data column, choosing the data column from the drop-
down box, and then specifying the minimum and/or maximum values for that data column. (This is an
inclusive range, meaning Windographer will include data greater than or equal to the minimum value, and less than or equal to the maximum value.)
For example, if you suspected that the errant points in the above scatterplot were due to the effects of
anemometer icing, you might want to filter those data according to temperature. To display data only for
times when the temperature was above freezing, you could use the following filter settings:
When we apply that filter to show only above-freezing data, all of the extraordinary data points disappear
from the scatterplot, as shown below. This is evidence that icing is indeed to blame for the apparent
problems in the December data. With the Quality Control window you could investigate further and even
remove problem segments from the data set.
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Line of best fit
The checkbox allows you to select whether you want the line of best fit to appear on the graph. Windographer calculates the line of best fit by performing a linear least squares regression. To the right of
the checkbox, Windographer displays the equation of the line of best fit, along with the r2 value which
indicates the goodness of fit. The r2 value is a number between 0 and 1. The closer it is to one, the better the fit. When the line of best fit is shown, Windographer also allows you to force the zero intercept.
Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For
information about exporting graphs from Windographer, please refer to the article on exporting graphs.
See also
Summary tab
Time Series tab
Wind Rose tab
Daily Profile tab
PDF tab
![Page 41: Tutorial Windografher](https://reader035.fdocuments.in/reader035/viewer/2022082201/563dbb1c550346aa9aaa5c82/html5/thumbnails/41.jpg)
CDF tab
Boxplot tab
DMap tab
Tables tab
Reports tab
Quality Control window
Exporting graphs
Written by: Tom Lambert Contact: [email protected]
Last modified: February 24, 2009
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The Boxplot tab graphically displays, for each month of the year and for the overall data set, the following five statistical measures:
� the mean
� the average daily high and average daily low
� the maximum and minimum values
Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For
information about exporting graphs from Windographer, please refer to the article on exporting graphs.
See also
Summary tab
Time Series tab
Wind Rose tab
Daily Profile tab
PDF tab
CDF tab
Scatterplot tab
DMap tab
Tables tab
Exporting graphs
Written by: Tom Lambert
Contact: [email protected]
Last modified: May 15, 2006
Boxplot Tab
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The DMap tab plots a data column in a matrix format, with day of the year on the x-axis and hour of the day on the y-axis. Each time step of the year is therefore represented by a tiny rectangular area of the
graph. The colour of that rectangular area indicates the value of the variable in that time step. This format
is often useful for identifying daily and seasonal patterns in the data. The example below shows the hourly
solar radation over one year. Several features of this solar resource are evident in this graph: the sun shines during the day and not at night, summer days are longer than winter days, the maximum intensity
of solar radiation is higher in the summer than in the winter, afternoons tend to be cloudier than mornings
during the summer, and entire days with very low solar radiation can happen at any time of the year.
DMap is short for 'data map'.
Right-click a DMap to change its properties, copy the image to the clipboard, or export it to a file. For
information about exporting graphs from Windographer, please refer to the article on exporting graphs.
See also
Summary tab
Time Series tab
Wind Rose tab
Daily Profile tab
PDF tab
CDF tab
Scatterplot tab
Boxplot tab
Tables tab
DMap Tab
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The Data Set Summary table is one of the tables you can create on the Tables tab of Windographer's main window. It contains the following information.
The same information appears on the Summary tab.
Right click on this or any table to export it to a text file or to copy it to the clipboard:
See also
Summary tab
Tables tab
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 25, 2008
Data Set Summary Table
Variable Description
Latitude You enter the latitude on the Data Set tab of the Configure Data Set window.
Longitude You enter the longitude on the Data Set tab of the Configure Data Set window.
Elevation Altitude above sea level. You enter the elevation on the Data Set tab of the Configure Data Set window.
Start date The time of the beginning of the first time step.
End date The time of the end of the last time step.
Duration The time interval between the start date and the end date.
Length of time step The time interval between the start and end of each time step.
Calm threshold The threshold wind speed for inclusion in a wind rose.
Mean temperature The mean air temperature over the entire data set.
Mean pressure The mean air pressure over the entire data set.
Mean air density The mean air density over the entire data set.
Power density at 50m The mean wind power density at 50m above ground.
Wind power class The class into which falls the wind power density at 50m.
Power law exponent A measure of the overall wind shear.
Surface roughness A measure of the overall wind shear.
Roughness class The class into which falls the surface roughness.
Roughness class The type of terrain typified by a similar value of surface roughness.
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The Wind Speed Sensor Summary table is one of the tables you can create on the Tables tab of Windographer's main window It contains the following information:
Right click on this or any table to export it to a text file or to copy it to the clipboard:
See also
Tables tab
Written by: Tom Lambert
Contact: [email protected]
Last modified: May 5, 2008
Wind Speed Sensor Summary Table
Variable Description
Height above ground You specify the height of each wind speed sensor on the Configure Data Set window.
Mean wind speed The mean (average) value of the wind speeds at this height.
Median wind speed The median value, below which 50% of the values fall.
Min wind speed The lowest recorded value of wind speed at this height.
Max wind speed The highest recorded value of wind speed at this height.
Mean power density The mean wind power density at this height.
Mean energy content The mean wind energy content at this height.
Energy pattern factor A factor related to the distribution of wind speeds.
Weibull k The Weibull shape factor at this height.
Weibull c The Weibull scale factor at this height.
1-hr autocorrelation coefficient
A measure of the autocorrelation of the wind speeds at this height.
Diurnal pattern
strengthA measure of how strongly the wind speed depends on the time of day at this height.
Hour of peak wind speed
The hour of the day with the highest average wind speed at this height.
Mean turbulence intensity The mean value of the turbulence intensity at this height. See the Turbulence Analysis window for more details.
Standard deviation The standard deviation of the wind speed values at this height.
Coefficient of variation The standard deviation divided by the mean wind speed times 100%.
Frequency of calms The frequency with which the wind speed is at or below the calm threshold.
Possible records The total number of time steps in this data column.
Valid records The number of time steps that contain data for this data column.
Missing recordsThe number of time steps that do not contain data for this data column. A data value may be missing because it was missing in the original raw data file, or because it was filtered out or removed in the Quality Control window.
Data recovery rate The ratio of valid records to possible records.
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This table shows for each year the number of possible records, number of valid records, data recovery rate, mean, median, min, max, and standard deviation of a data column. For wind speed data columns, it
also shows the best-fit Weibull parameters.
Tip: Mean wind directions refer to vector means.
Right click on this or any table to export it to a text file or to copy it to the clipboard:
You can create this table and many others on the Tables tab of Windographer's main window.
See also
Data recovery rate
Weibull k parameter
Weibull c parameter
Vector mean
Tables tab
Monthly Statistics table
Directional Statistics table
Written by: Tom Lambert Contact: [email protected]
Last modified: April 29, 2008
Annual Statistics Table
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This table shows for each month the number of possible records, number of valid records, data recovery rate, mean, median, min, max, and standard deviation of a data column. For wind speed data columns, it
also shows the best-fit Weibull parameters. It also shows the mean for all data and the mean of monthly
means.
Tip: Mean wind directions refer to vector means.
If you check the checkbox labeled Combine years together, the table will contain 13 rows, one for each month of the year and one for the entire data set. In that case, the January row will represent all January
data in the data set, regardless of year. If you do not check that checkbox, the table will contain as many
rows as there are months in the data set, plus one for the entire data set. In that case, Windographer calculates and displays the January 2004 statistics separately from the January 2005 statistics.
Right click on this or any table to export it to a text file or to copy it to the clipboard:
You can create this table and many others on the Tables tab of Windographer's main window.
See also
Data recovery rate
Vector mean
Mean of monthly means
Weibull k parameter
Weibull c parameter
Tables tab
Annual Statistics table
Directional Statistics table
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 29, 2008
Monthly Statistics Table
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This table shows the number of possible records, number of valid records, mean, median, min, max, and standard deviation of a data column for each wind direction sector. For wind speed data columns, it also
shows the best-fit Weibull parameters. Use the controls on the left to select a data column, to filter the
data by year and/or month, and to choose the number of direction sectors.
Note: The number of possible records refers to the number of time steps in which
the wind direction sensor reports data. (Any time steps in which the wind direction sensor is missing data will not be included in the number of possible records.) The
number of valid records is the number of those possible time steps in which the
selected data column also reports data.
Right click on this or any table to export it to a text file or to copy it to the clipboard:
You can create this table and many others on the Tables tab of Windographer's main window.
See also
Weibull k parameter
Weibull c parameter
Tables tab
Annual Statistics table
Monthly Statistics table
Written by: Tom Lambert
Contact: [email protected] Last modified: April 29, 2008
Directional Statistics Table
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This table shows the mean value of a data column for each hour of the day and each month of the year. You can filter for a particular year using the drop-down box on the left hand side of the tab.
Tip: You can create daily profile graphs on the Daily Profile tab of the main window.
Right click on this or any table to export it to a text file or to copy it to the clipboard:
You can create this table and many others on the Tables tab of Windographer's main window.
See also
Daily Profile tab
Tables tab
Seasonal Profile table
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 24, 2008
Daily Profile Table
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This table shows the mean value of a data column for each month of the year and each year of the data set. A cell appears blank if the selected data column contains no data for that year and month.
Right click on this or any table to export it to a text file or to copy it to the clipboard:
You can create this table and many others on the Tables tab of Windographer's main window.
See also
Tables tab
Daily Profile table
Written by: Tom Lambert
Contact: [email protected] Last modified: April 24, 2008
Seasonal Profile Table
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This table shows the number of occurrences and the frequency with which the values in a particular data column fall within various bins. You can set the bin size and filter by year, month, and direction sector
using the controls on the left hand side of the tab.
Tip: You can create frequency histogram graphs on the PDF tab of the main
window.
Right click on this or any table to export it to a text file or to copy it to the clipboard:
You can create this table and many others on the Tables tab of Windographer's main window.
See also
PDF tab
Tables tab
Statistics by Bin table
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 24, 2008
Frequency by Bin Table
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In the Statistics by Bin table, Windographer shows the statistical characteristics of one data column versus bins defined by
a second data column. The example below shows the statistical characteristics of the 'Power Law Exponent' data column versus bins defined by the 'Speed 45m' data column.
The highlighted row shows that the data set contains 2,985 time steps in which the 'Speed 45m' column falls between 12 m/s and 14 m/s, and that within those 2,985 time steps, the 'Power Law Exponent' column has a mean value of 0.108, a
median value of 0.078, a minimum value of -0.143, a maximum value of 1.905, and a standard deviation of 0.10.
You can adjust the bin size and filter for year, month, and direction sector using the controls on the left hand side of the
tab.
Right click on this or any table to export it to a text file or to copy it to the clipboard:
You can create this table and many others on the Tables tab of Windographer's main window.
See also
PDF tab
Tables tab
Frequency by Bin table
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 24, 2008
Statistics by Bin Table
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The Summary Report displays the statistical characteristics of the data set that wind energy analysts typically consider most important.
Data Set Properties
This section of the report provides an overview of the data set. The information shown in the properties
table also appears on the Summary tab, and in the Data Set Summary table on the Tables tab. The monthly statistics for temperature appear as a boxplot. This graph also appears on the Boxplot tab.
Wind Speed and Direction
This section of the report includes two graphs from the Summary tab, one from the PDF tab, and three
wind roses from the Wind Rose tab. Please refer to the articles on those windows for further information
about any particular graph. Each graph in this section displays data from the uppermost wind speed sensor and/or the uppermost wind direction sensor.
Wind Shear
This section of the report displays four graphs from the Wind Shear Analysis window. For further
information, please refer to the help file article on that window. In these graphs, Windographer displays either the power law exponent or the surface roughness, depending on the preference you have specified
in the Options window.
Turbulence Intensity
This section of the report includes four graphs from the Turbulence Analysis window. Each graph displays data from the uppermost wind speed sensor that has an associated standard deviation data column. The
graphs display either the characteristic turbulence intensity or the representative turbulence intensity,
depending on which IEC standard you have chosen as your preference on the Options window.
Data Column Properties
This section of the report contains a table showing the label, units, and measurement height, of each data column, as well as the number of records and some basic statistics (mean, minimum, maximum, standard
deviation). This table also appears on the Tables tab as the Data Columns table.
See also
Uppermost wind speed sensor
Summary tab
Wind Rose tab
Boxplot tab
Tables tab
Reports tab
Summary Report
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Wind Shear Analysis window
Turbulence Analysis window
Options window
Written by: Linda Sloka
Contact: [email protected]
Last modified: February 24, 2009
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The Monthly Report displays detailed data for any calendar month that the data set covers.
Report Settings
This section of the report shows the name of the data set, the month covered, and the time the report
was generated.
Wind Speed Data
This graph shows the wind speed through the specified month for all wind speed sensors.
Wind Direction Data
This graph shows the wind direction through the specified month for all wind direction sensors.
Temperature Data
This graph shows the temperature through the specified month.
Tip: You can create each of these time series graphs on the Time Series tab.
See also
Reports tab
Time Series tab
Written by: Linda Sloka
Contact: [email protected]
Last modified: February 24, 2009
Monthly Report
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In the Configure Data Set window you specify the properties of each data column and the properties of the entire data set. This window appears when you import a raw data file or when you choose Configure
Data Set from the Data menu or when you select it from the toolbar:
This window consists of two tabs. The Data Columns tab lists the properties of each data column, while the Data Set tab gives access to the properties of the entire data set.
Data Columns Tab
On this tab, each data column appears as an item in the list control. Click an item to select a data column,
then use the controls in the Data column properties section of the window to specify the type, label, units,
and color of that data column. The data column type identifies whether it contains wind speed, wind direction, temperature, pressure, or some other type of data. Set the type to Hidden if you do not want
the data column to appear in any graph or in any list of data columns.
You can identify any number of wind speed and wind direction columns, but only one temperature column
and one pressure column. Windographer uses the temperature and pressure data to calculate air density.
Some data loggers record very high or very low numbers to indicate errors or missing data elements. Click Apply filter to filter out these and other extreme values. Windographer will treat any values outside the
acceptable range as gaps or missing elements. If you want to, you can fill the resulting gaps with
Windographer's gap filling module or in the Quality Control window.
When you select a wind speed data column, the Associated data columns section appears. From the Std. dev. drop-down box, choose which data column, if any, contains the standard deviation of the wind speed within each time step. Windographer uses this to calculate turbulence intensity. From the Gust drop-down
box, choose which data column, if any, contains the peak wind gust within each time step. Windographer
uses this in the Extreme Wind Analysis window.
Tip: You can save the properties for all data columns to a template file. Then in
the future if you open a raw data file with the same data structure, you can simply
Configure Data Set Window
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apply the template rather than re-entering the information for each data column
individually. See the article on templates for details.
Click the Assign Default Colors button if you want Windographer to automatically assign a color to each
data column. To wind speed data columns, Windographer assigns a sequence of blue colors ranging from
dark blue for the highest wind speed sensor to light blue for the lowest. Similarly, it assigns an orange color sequence to the wind direction columns and a blue-green color sequence to the wind speed standard
deviation columns. Temperature and pressure columns also receive specific colors.
You can select more than one data column at a time using by using SHIFT and click for adjacent data, or
CRTL and click for nonadjacent data. This could be used to change the units of many columns at once, or
to apply the same filter to all the wind speed columns. Note that some data column properties cannot be specified when more than one column is selected (for example, color) and will not be available when
multiple data columns are selected.
Data Set Tab
On this tab, you can enter a text description of the data set, specify the latitude, longitude, elevation, and calm threshold, and indicate whether the time values in the data file refer to the start, middle, or end of
the time steps.
See also
Importing raw data files
Appending data
Templates
Air density
Filling gaps
Quality Control window
Turbulence intensity
Extreme Wind Analysis window
Elevation
Calm threshold
Probability distribution function
Written by: Tom Lambert
Contact: [email protected]
Last modified: January 23, 2009
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This window displays a record of all the modifications made to the current data set. Events that may appear in the list include:
� the importing of a data file
� a change in the configuration of the data set
� the addition of a data column such as a virtual anemometer column or a wind turbine output
column
� the filling of gaps in the data set
� the removal or replacement of a data segment
You can type and store text in the Notes box.
Tip: the Quality Control window also lists all of the data removal or
replacement events. It provides additional details on these events, and gives you
a chance to restore the original data.
Written by: Tom Lambert
Contact: [email protected]
Last modified: March 7, 2007
Document History Window
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This window lets you modify the contents of one or more data columns, for example to convert units or to apply an offset to a wind vane. Choose the data column(s) you want to modify, the time interval, the
multiplier value and the offset value. When you click OK, the window will close and Windographer will
modify the columns you have chosen.
Note: Windographer keeps a record of any modifications you make to the data
set using this window. The record appears in the Document History window.
See also
Document History window
Delete Data window
Written by: Tom Lambert Contact: [email protected]
Last modified: September 16, 2008
Modify Data Columns Window
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This window lets you shift an entire data set in time, or a portion of your data. To access this window, choose Time Shift from the Data menu:
Shift the entire data in time
If you choose to Shift the entire data in time, Windographer shows you the date and time of the first data
item in the data set. Enter the new start time and click OK to shift your data. Shifting the data set in this way will not affect the data itself; it will only affect the time stamps.
You could use this time shift capability to change the time zone of the data, or correct a problem related
to daylight savings time or the leap year. Or you could use it to shift the time stamps to a standard
pattern, as in the example shown below. In this example, the original data set's time stamps are offset by
three minutes from the top of the hour. So the time stamps have the pattern :03, :13, :23, and so on. The user wants to shift these time stamps so that they fall on the hour, ten minutes after the hour, twenty
minutes after the hour, and so on. She may need to do this to append this data set to another data set
that has conventional time stamps, for example.
When the user clicks OK, all time stamps in her data set will shift by three minutes, like so:
Time Shift Window
Date/Time Spd Dir
3/31/2007 21:03 9.34 180
3/31/2007 21:13 9.96 203
3/31/2007 21:23 10.86 203
3/31/2007 21:33 10.95 203
Date/Time Spd Dir
3/31/2007 21:00 9.34 180
3/31/2007 21:10 9.96 203
3/31/2007 21:20 10.86 203
3/31/2007 21:30 10.95 203
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These changes become permanent when she saves the .windog file.
Shift a particular data segment in time
If you choose to shift a portion of the data in time, Windographer will show you a list of all the data columns in your data set. Select the data columns you wish to shift. In the screen shot below, data from
the Dir column will be shifted in time, but data in the Spd and Temp columns will not.
Next, specify the time interval of the data to be shifted. It could be the entire column of data, or only a
portion. In the example below, just one hour of data from May 2007 will be shifted in time.
Specify how many time steps to shift the data. In the example below, a shift of 2 time steps will move the
data 20 minutes because the data has 10 minute time steps. Next, specify if the data should be shifted forward or backward in time.
When the user clicks OK, that one-hour segment of the Dir column will be shifted two times steps, like so:
3/31/2007 21:43 10.72 225
3/31/2007 21:53 11.35 225
3/31/2007 22:03 12.29 203
3/31/2007 22:13 12.20 180
3/31/2007 22:23 10.59 225
3/31/2007 22:33 10.90 270
3/31/2007 22:43 11.39 248
3/31/2007 22:53 12.07 315
3/31/2007 23:03 10.63 203
shift
3/31/2007 21:40 10.72 225
3/31/2007 21:50 11.35 225
3/31/2007 22:00 12.29 203
3/31/2007 22:10 12.20 180
3/31/2007 22:20 10.59 225
3/31/2007 22:30 10.90 270
3/31/2007 22:40 11.39 248
3/31/2007 22:50 12.07 315
3/31/2007 23:00 10.63 203
Date/Time Spd Dir
5/30/2007 23:10 9.34 180
5/30/2007 23:20 9.96 203
5/30/2007 23:30 10.86 203
5/30/2007 23:40 10.95 203
5/30/2007 23:50 10.72 225
5/31/2007 00:00 11.35 225
5/31/2007 00:10 12.29 203
5/31/2007 00:20 12.20 180
5/31/2007 00:30 10.59 225
5/31/2007 00:40 10.90 270
shift
Date/Time Spd Dir
5/30/2007 23:10 9.34 180
5/30/2007 23:20 9.96 203
5/30/2007 23:30 10.86 203
5/30/2007 23:40 10.95
5/30/2007 23:50 10.72
5/31/2007 00:00 11.35 203
5/31/2007 00:10 12.29 225
5/31/2007 00:20 12.20 225
5/31/2007 00:30 10.59 203
5/31/2007 00:40 10.90 180
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This could result in data being overwritten as in the example above. Be careful! These changes become
permanent once you save the .windog file.
Tip: You cannot undo the changes you make with the Time Shift window.
See also
Delete Data window
Modify Data Columns window
Written by: Linda Sloka
Contact: [email protected] Last modified: February 26, 2009
5/31/2007 00:50 11.39 248
5/31/2007 01:00 12.07 315
5/31/2007 01:10 10.63 203
5/31/2007 01:20 10.90 180
5/31/2007 00:50 11.39 225
5/31/2007 01:00 12.07 315
5/31/2007 01:10 10.63 203
5/31/2007 01:20 10.90 180
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This window lets you remove columns from the data set, or delete data within a certain date range. If you choose to delete a column, Windographer will remove that column from the data set entirely, and it will no
longer appear in any list of data columns. The diagram below illustrates this situation:
If you choose to delete from a column the data within a certain data range, Windographer will remove
those data elements but the column itself will remain. The diagram below illustrates this situation:
You can use this window to change the start or end date of the data set. If you delete data from all
columns starting from the first time step, the data set start time will change accordingly. The diagram
below illustrates this situation:
Delete Data Window
Date/Time Spd Dir
2004-05-30 23:10 9.34 180
2004-05-30 23:20 9.96 203
2004-05-30 23:30 10.86 203
2004-05-30 23:40 10.95 203
2004-05-30 23:50 10.72 225
2004-05-31 00:00 11.35 225
2004-05-31 00:10 12.29 203
2004-05-31 00:20 12.20 180
2004-05-31 00:30 10.59 225
2004-05-31 00:40 10.90 270
2004-05-31 00:50 11.39 248
2004-05-31 01:00 12.07 315
2004-05-31 01:10 10.63 203
2004-05-31 01:20 10.90 180
delete
Date/Time Dir
2004-05-30 23:10 180
2004-05-30 23:20 203
2004-05-30 23:30 203
2004-05-30 23:40 203
2004-05-30 23:50 225
2004-05-31 00:00 225
2004-05-31 00:10 203
2004-05-31 00:20 180
2004-05-31 00:30 225
2004-05-31 00:40 270
2004-05-31 00:50 248
2004-05-31 01:00 315
2004-05-31 01:10 203
2004-05-31 01:20 180
Date/Time Spd Dir
2004-05-30 23:10 9.34 180
2004-05-30 23:20 9.96 203
2004-05-30 23:30 10.86 203
2004-05-30 23:40 10.95 203
2004-05-30 23:50 10.72 225
2004-05-31 00:00 11.35 225
2004-05-31 00:10 12.29 203
2004-05-31 00:20 12.20 180
2004-05-31 00:30 10.59 225
2004-05-31 00:40 10.90 270
2004-05-31 00:50 11.39 248
2004-05-31 01:00 12.07 315
2004-05-31 01:10 10.63 203
2004-05-31 01:20 10.90 180
delete
Date/Time Spd Dir
2004-05-30 23:10 9.34 180
2004-05-30 23:20 9.96 203
2004-05-30 23:30 10.86 203
2004-05-30 23:40 10.95
2004-05-30 23:50 10.72
2004-05-31 00:00 11.35
2004-05-31 00:10 12.29
2004-05-31 00:20 12.20
2004-05-31 00:30 10.59
2004-05-31 00:40 10.90 270
2004-05-31 00:50 11.39 248
2004-05-31 01:00 12.07 315
2004-05-31 01:10 10.63 203
2004-05-31 01:20 10.90 180
Date/Time Spd Dir
2004-05-30 23:10 9.34 180
2004-05-30 23:20 9.96 203
2004-05-30 23:30 10.86 203
2004-05-30 23:40 10.95 203
Date/Time Spd Dir
2004-05-31 00:00 11.35 225
2004-05-31 00:10 12.29 203
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Similarly, if you delete data from all columns ending at the last time step, the data set end time will
change accordingly. The diagram below illustrates this situation:
Tip: You cannot restore data that you delete with the Delete Data window. If you want to delete a segment of data but keep the option of restoring the original
data, you must use the Quality Control window instead.
See also
Quality Control window
Hiding data columns
Written by: Tom Lambert
Contact: [email protected] Last modified: April 25, 2008
2004-05-30 23:50 10.72 225
2004-05-31 00:00 11.35 225
2004-05-31 00:10 12.29 203
2004-05-31 00:20 12.20 180
2004-05-31 00:30 10.59 225
2004-05-31 00:40 10.90 270
2004-05-31 00:50 11.39 248
2004-05-31 01:00 12.07 315
2004-05-31 01:10 10.63 203
2004-05-31 01:20 10.90 180
delete
2004-05-31 00:20 12.20 180
2004-05-31 00:30 10.59 225
2004-05-31 00:40 10.90 270
2004-05-31 00:50 11.39 248
2004-05-31 01:00 12.07 315
2004-05-31 01:10 10.63 203
2004-05-31 01:20 10.90 180
Date/Time Spd Dir
2004-05-30 23:10 9.34 180
2004-05-30 23:20 9.96 203
2004-05-30 23:30 10.86 203
2004-05-30 23:40 10.95 203
2004-05-30 23:50 10.72 225
2004-05-31 00:00 11.35 225
2004-05-31 00:10 12.29 203
2004-05-31 00:20 12.20 180
2004-05-31 00:30 10.59 225
2004-05-31 00:40 10.90 270
2004-05-31 00:50 11.39 248
2004-05-31 01:00 12.07 315
2004-05-31 01:10 10.63 203
2004-05-31 01:20 10.90 180
delete
Date/Time Spd Dir
2004-05-30 23:10 9.34 180
2004-05-30 23:20 9.96 203
2004-05-30 23:30 10.86 203
2004-05-30 23:40 10.95 203
2004-05-30 23:50 10.72 225
2004-05-31 00:00 11.35 225
2004-05-31 00:10 12.29 203
2004-05-31 00:20 12.20 180
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Contents
� Overview
� Creating and editing search rules
� Finding and displaying data in the time series graph
� Revising data
� Restoring data
� Quality control history
� Examples
Overview
The Quality Control window allows you search the data set for abnormalities, gaps, or patterns, and, as appropriate, to delete or
replace those data. For example, you can find anomalous data that might have been caused by an icing event, and if you want, delete
the anomalous data segment or even replace it with synthesized data.
There are two ways to search for data in the Quality Control window: the first is to use the scroll bar and zoom buttons at the bottom
Quality Control Window
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of the time series graph to manually navigate through the data and look for patterns in the line graphs. Another approach is to create
one or more search rules and have Windographer search the data set for you. A search rule is a set of criteria that describe the data
you are looking for. Each criterion is a comparison of a data column with either a fixed value or with another data column. Each search
rule includes a time span specification. For example, a search rule to find a potential wind vane malfunction caused by ice might
consist of the following search criteria: wind direction changes less than ten degrees and temperature is less than zero degrees Celsius
over thirty minutes.
For each search rule that you create, Windographer searches the data set for items, or segments of contiguous data, that meet the
rule's search criteria and displays a list of the items in the Search results table. You can display an item in the time series graph by
clicking its name in the table. Windographer highlights each item in the time series graph using a different color for each search rule.
In the graph, you can choose data columns to display and select segments of data to either delete or replace with synthesized data.
Creating and editing search rules
To create a new search rule, click Add to open the Create New Search Rule window.
Assign a name and color to the rule (or use the default values), and make selections in the drop down boxes that describe each search
criterion that you want to include in the rule. At the bottom of the window, define the length of time over which the rule applies. You
can specify the time interval in time steps, minutes, or hours.
You can create a rule with more than one search criterion by checking And to display a new criterion. You can assign up to four criteria
to each search rule. Remember to specify the time span for each rule that you create.
When you finish creating the rule, click OK to return to the Quality Control window. Windographer will display the new rule in the
Search rules table.
You can edit and delete search rules, and create additional rules by selecting rules in the Search rules table and using the Edit, Add,
and Delete buttons. When you create an additional search rule, you can either create the rule based on a copy of an existing rule, or
create the rule from scratch. To create a new search rule based on a copy of an existing one, in the Search rules table, click the search
rule on which to base the copy and click Add. To create the rule from scratch, make sure that none of the existing search rules is
selected in the Search rules table before clicking Add.
Finding and displaying data in the time series graph
Windographer displays a list of time steps in the Search results table that meet the search criteria for each rule. Contiguous time steps
appear as a single item in the table. When you click an item in the table, Windographer highlights and displays the item in the time
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series graph. The color of the highlight matches the color shown in the Search rules table.
You can click Export to create a tab delimited text file containing the search results list.
Windographer allows you to display any data column in the time series graph. To display a data column, check one of the boxes in the
list of data column names. The two check boxes for each data column allow you to split the time series graph into two graphs: check
the leftmost box to display the data column in the top graph, check the rightmost box to display the data column in the bottom graph.
Note that the color of the lines in the time series graph is determined by the settings in the Configure Data Set window.
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You can change the time scale of the graph using the zoom buttons at the bottom right corner, or simply by selecting a segment in the
graph. But to zoom to the selected segment, you must first choose Zoom to the segment under Upon selecting a segment on the graph.
For more about zooming, scrolling, changing properties of the graph, and exporting images of the graph, please see the article on the
Time Series tab.
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Deleting and replacing data
A data revision is a change to a data segment. A segment is a single series of contiguous time steps in the data set. Windographer
allows three types of revisions: deletion, gap fill, and replacement. Windographer also allows you to restore original data to a deleted
or replaced segment.
� Deleting a segment removes data from columns in the data set. When you delete a segment, Windographer only removes data
from those columns that are visible in the time series graph. Deleting data leaves a gap in the data set. There is no limit to the
number of gaps that can exist in a data set: Windographer simply ignores data gaps in calculations and displays them as blank
spaces in graphs.
� Filling gaps in a segment replaces any missing data in the visible columns with synthesized data. The Quality Control window
allows you to fill gaps in individual segments of one or more data columns. See Filling Gaps for a description of how
Windographer fills gaps. If you want to fill all gaps in all columns in the data set, you can use the Gap Fill Setup window.
� Replacing a segment is equivalent to deleting a segment and then filling the resulting gap: Windographer replaces the existing
data for the columns visible in the graph with synthesized data.
Tip: When you make a data revision, Windographer keeps a permanent copy of the original data. You
can always restore the original data, even after accepting changes in the Quality Control window or in
future Windographer sessions. Windographer keeps a record of all revisions, which you can view both on
the History tab of the Quality Control window and in the Document History window.
To revise data, display the data columns that you want to revise by checking their names in the time series graph. Under Upon selecting a segment in the graph, click Select the segment for revision. To find the time steps to include in the segment, either use the
scroll bar and zoom buttons at the bottom of the graph to visually locate the time steps, or click an item in the Search results table to
jump to the item's location in the graph. In the graph, select the time steps to include in the revision: Point to one end of the segment
and drag the pointer to the right or left to select the segment. You can use the arrow buttons to set a segment's exact start and end
points.
Type a few words describing the cause for revision, or select a cause from the drop-down box. If the segment you selected includes a
search result item, Windographer will suggest the item's search rule name as the cause for revision. Click the appropriate revision
button to either delete, replace, or fill gaps in the selected segment.
In the image below, we selected the Speed 10m and Speed 30m data columns for revision. Note the segment start and end points
under Revision, indicating that Windographer will revise data in those two columns starting at 1/4/2003 09:00 and ending at 1/7/2003
10:00. Windographer will not revise the Direction 10m or Direction 45m data columns because they appear in the top graph and are
not highlighted. Windographer has suggested 'Speed sensor icing' as the cause for revision because that is the name of the search rule
that found search result item 15, which falls within the selected segment.
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Clicking the button labeled Delete Segment causes Windographer to delete the segments from the two speed data columns. The deleted
segment then appears as a gap in the lower graph:
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Note that Windographer will accept any number of gaps in the data set. The presence of gaps does not prevent Windographer from
performing a complete statistical analysis of the data set, nor from estimating the output of a wind turbine in the wind regime. You
can fill a gap with synthetic data using the button labeled Fill Gaps in Segment, or you can fill all gaps in the data set at once using the
Fill Gaps window.
Tip: For higher quality gap fills, remove all the problem data segments from a data column before you
fill any gaps in that data column. That way, the erroneous data segments will not corrupt the statistical
characteristics that Windographer aims to replicate in the synthetic data.
Restoring data
If you want to discard a revision and revert to the state of the data set before you opened the Quality Control window, you can click
Cancel to close the window. Windographer will discard all of your work in the window and return to the main window.
A better way to restore revisions that allows you to keep a record of the revision and restoration is to use the Restore Original Data
button on the Quality Control window's History tab. In the tab's Revision table, select the revision that you want to restore.
Windographer will highlight the segment in light grey and display the names of the affected columns in the Column table under Details.
To restore the original data, type a reason for the restoration in the Cause for restoration box, and click the button labeled Restore Original Data. A record of the restoration will appear in the revisions table, and will also be visible in the Document History window.
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Quality Control history
The History tab on the Quality Control window serves two purposes: it displays a list of all data revisions and restorations that have
been made to the data set, and allows you to make restorations to revised data. You can click Export to create a tab-delimited text
file with a list of the revision history.
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Tip: A similar but less detailed history of the quality control revisions appears in the Document History
window.
See also
Quality Control Examples
Configure Data Set window
Fill Gaps window
Time Series tab
Document History window
Tower Shading Analysis window
Written by: Paul Gilman
Contact: [email protected]
Last modified: March 7, 2007
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Measured data sets often contain gaps or missing values. Windographer can fill these gaps with synthetic data that have statistical properties similar to the measured data. To fill gaps in a data set that you have
open in Windographer, choose Fill Gaps... from the Data menu:
The Fill Gaps window will appear with some information as to the number of gaps in the data set and some settings you can choose relating to how Windographer should fill gaps in data sets containing
multiple anemometers. Once you click the Fill Gaps... button on that window, Windographer will start the
process of filling the gaps in each data column. In large data sets the gap filling process may take several
seconds.
When the gap filling process is complete, another window will appear allowing you to view the data set with the gaps filled. The synthesized data appears in the graph as thin dashed lines. In the example
below, the original data set contained no data between 10:00am on January 29 and 7:00pm on January
30. Windographer synthesized the data shown by the thin dashed line.
Windographer ensures that the synthesized data splice together well the measured values before and
after the gap, and that the statistical properties of the synthesized data (including the diurnal pattern and
the random variability from one time step to the next) closely match those of the measured data. Windographer's gap filling algorithm is based on a Markov transition matrix approach.
Filling Gaps
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Windographer fills gaps in every column of a data set. The procedure works well with most types of data,
including wind speed, wind direction, temperature, and many other types of meteorological data. (It does not work as well on solar radiation data, which are better synthesized by treating the cloudiness
separately from the extraterrestrial radiation.) The example below shows wind speed and temperature
data synthesized to fill a three-day gap.
Windographer treats wind direction data in a special way, recognizing that 359° is very close to 1°. In the example below, the synthesized wind direction data makes a realistic transition from northwest (near
300°) to northeast (near 30°), passing through due north on its way. Had Windographer treated the wind
direction as a scalar variable, that transition would have likely taken more of a straight-line trajectory that
would not have passed through north.
Because it imposes the diurnal pattern on the synthesized data, Windographer can fill even lengthy gaps. The example below shows three weeks of electric load data in which Windographer has filled a five day
gap. The synthesized data display the same strong diurnal pattern as the measured data.
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Data sets containing data from two or more wind speed sensors require special treatment when filling
gaps. In the graph above showing wind speed and temperature, Windographer synthesized two
independent data segments. But that approach is inappropriate when synthesizing data for multiple wind speed sensors because the data from those sensors tend to be strongly correlated. In time steps that
contain data for some but not all wind speed sensors, Windographer synthesizes the missing data by
scaling the existing data according to the wind shear profile.
In the example below, for the first half of January 18 the data set contained wind speed data at 1m, 2m, and 10m above ground, but not at 20m and 49m above ground. For those time steps Windographer
synthesized the wind speeds at 20m and 49m by scaling the 10m data to the appropriate height using the
measured wind shear profile. For the remainder of January 18, the data set contained data only for the 1m above ground, so Windographer synthesized data for all other heights based on the measured data
from 1m. For January 19, the data set contained no wind speed data, so Windographer filled the gap in
the 49m data and then synthesized data for all other heights by scaling that 49m data according to the
measured wind shear profile. The result is that all synthetic wind speed data are perfectly correlated.
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See also
Fill Gaps window
Wind shear
Written by: Tom Lambert
Contact: [email protected] Last modified: February 19, 2007
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The Virtual Anemometer window allows you to synthesize wind speed data for any height above ground. You may want to use this capability to estimate the wind speed at the hub height of a wind turbine. Most
meteorological towers are 50m or 60m in height, but many of today's largest wind turbines have hub
heights of between 70m and 120m. Windographer's virtual anemometer facility allows you to extrapolate
upwards from the heights at which you have measured wind speed data, to estimate the wind speeds at higher heights.
Tip: The procedure of extrapolating wind speeds upwards in height is inherently
uncertain, and in some cases what happens above 50m or 60m may be quite
different from what happens below. We designed Windographer's virtual
anemometer feature to do the best it can based on the measured data, but if the data measured below 50m poorly represent the wind speeds above 50m, no
algorithm will be able to accurately extrapolate. Please use this feature with
caution.
In the table at the top left of the window, enter the heights above ground for which you would like
Windographer to synthesize wind speed data. For each height you enter, you must also enter a label for
the data column that Windographer will create to store the synthesized wind speed data for that height.
Use the checkboxes in the top right to select the wind speed sensors you would like Windographer to use
in synthesizing wind speed data. By default, Windographer will use all wind speed sensors, but you can unselect any sensor you would like Windographer to ignore.
Use the radio buttons in the bottom left to select the functional form of the wind shear profile you would
like Windographer to use when synthesizing wind speed data. For details, please see the article on wind
shear.
In the bottom right of the window appear radio buttons for the two available methods for extrapolating or
interpolating to the requested heights. If you have chosen to use multiple wind sensors, Windographer will calculate the best-fit wind shear profile in every time step, and use that profile to estimate the wind speed
at the requested heights. If you have chosen to use only one wind sensor, Windographer will assume a
constant wind shear profile. In that case, you must enter either the surface roughness or the power law exponent that you want Windographer to use in extrapolating to the requested heights.
When you click the button labeled Synthesize Data & Append To Data Set, for each height you requested
Windographer will synthesize wind speed data and adds those data as a new column to your data set.
Virtual Anemometer Window
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From then on, you can graph and analyze the synthesized wind speed just like you could any other data
column. Windographer will automatically assign a color to each synthesized data column, but you can make changes with the Configure Data Set window.
See also
Wind shear
Configure Data Set window
Written by: Tom Lambert
Contact: [email protected]
Last modified: February 16, 2007
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The Wind Turbine Output window allows you to estimate the energy production of a wind turbine in the measured wind regime. In its calculations, Windographer takes into account the effects of varying air
density and wind shear, and it lets you enter various loss factors to account for losses due to downtime,
icing, interference from other wind turbines, and so on.
Wind Turbine Library
The drop-down box contains a list of wind turbine models. Use the buttons to the right of this drop-down
box to see details about a particular wind turbine, to add to or remove from the list, or to compare wind
turbines. The table below describes the buttons.
When you choose a wind turbine model, Windographer displays its power curve, which is a graph of power
output versus wind speed at hub height . A radio button to the right of the power curve lets you choose
from the standard hub heights of the selected wind turbine. (These standard hub heights are properties of
the wind turbine.) Choose one of the standard hub heights, or choose the last radio button and enter a custom hub height.
Losses
You can enter four separate loss terms, which Windographer combines in a multiplicative fashion into a
single number, the overall loss factor. The table below describes the four loss terms.
Wind Turbine Output Window
Button Action
DetailsOpens the Wind Turbine Details window, where you can view the detailed properties of the selected wind turbine model.
EditOpens the Edit Wind Turbine window, where you can modify
the properties of the selected wind turbine model.
NewCreates a new wind turbine model and opens the Create New Wind Turbine window, where you can specify its properties.
DeleteRemoves the currently-selected wind turbine model from the
library.
CompareOpens the Compare Wind Turbines window, where you can
compare the power curves of different wind turbines.
Loss Term Description
Downtime
losses
The energy production lost when the turbine is offline due
to scheduled maintenance or repair. Because of the high availability of modern wind turbines and because operators
can often schedule maintenance for low-wind times of the year, downtime losses rarely exceed a few percent.
Array losses
Losses resulting from aerodynamic interference between
wind turbines in a wind farm. For a single wind turbine, the array losses would be zero since no nearby wind turbines
would interfere with the airflow. For a wind farm, the array losses could range from a few percent (in the case of a
very sparse arrangement or one designed to avoid such interference) to more than 10% (in the case of a dense or
poorly-designed arrangement).
Icing/soiling losses
Losses caused by the accumulation of dirt or ice on the turbine blades, which can harm their aerodynamic
performance. Such losses could range from near zero to several percent, depending principally on the frequency of
icing events.
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Outputs
When you click Calculate Output, Windographer steps through the data set to calculate the gross power
output of the wind turbine in each time step. It then calculates several monthly and annual statistics,
including the mean wind speed at hub height, time spent at zero power output, time spent at or above rated power output, average net power output, average net energy output, and average net capacity
factor. (Net means after accounting for losses.) For a full description of this process, please see the article
on calculating the energy output of a wind turbine.
Choose Monthly details to see the detailed monthly results, or choose Turbine comparision to compare the
annual statistics for many different wind turbines. Windographer adds another row to this turbine comparison table each time you click Calculate Output. Right-click the table to export the data to a text
file:
If you would like to analyze the time series data of the wind turbine power output in each time step, you can add those data to your data set using the button in the bottom right corner of the window:
When you click this button, Windographer adds to your data set a new data column containing the gross
power output of the wind turbine in each time step. (Gross means before accounting for losses.) You can then graph and analyze those data just like any other data column in Windographer. You could, for
example, generate a probability distribution function for the wind turbine power output in a particular
month, or graph the average daily profile of the turbine power output, or create a scatterplot of wind turbine output versus wind speed.
Note that if, in a single use of the Wind Turbine Output window, you calculate the output of more than one wind turbine, clicking this button will add the time series data for only the most recently-calculated wind
turbine. If you want to add data columns corresponding to two different wind turbines, you will have to
Other lossesAny other factor that might reduce energy output, including wiring losses, transformer losses, and production
losses due to cut-out at high wind speeds.
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open the Wind Turbine Output window twice.
Tip: For a more accurate estimate of wind turbine output, clean up any problems
in your data set with the Quality Control window before using the Wind Turbine
Output window.
Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For
information about exporting graphs from Windographer, please refer to the article on exporting graphs.
See also
Calculating the energy output of a wind turbine
Wind Turbine Details window
Create New Wind Turbine window
Edit Wind Turbine window
Compare Wind Turbines window
Quality Control window
Exporting data
Written by: Tom Lambert Contact: [email protected]
Last modified: April 25, 2008
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The Turbulence Analysis window allows you to observe how the turbulence in the wind varies with wind speed, wind direction, year, or month.
Display Settings
The first radio button in the top left corner of the window allows you to display turbulence versus wind
speed, wind direction, month, or time of day. The second radio button allows you to display the resulting data in graphical or tabular format.
IEC Standards
Standard 61400-1 of the International Electrotechnical Commission forms the basis of Windographer's
treatment of turbulence intensity data. The 3rd edition of that standard, released in 2005, differs in
approach from the earlier 2nd edition, released in 1999. You can choose whether you want Windographer to present results consistent with the second edition or the third edition of the standard.
The 2nd edition of IEC 61400-1 defines three turbulence categories based on the characteristic
turbulence intensity at a wind speed of 15 m/s:
The 3rd edition of IEC 61400-1 defines four turbulence categories based on mean turbulence intensity at a wind speed of 15 m/s:
If you choose the 2nd edition IEC standards, Windographer will display the mean turbulence intensity, the
characteristic turbulence intensity, and the peak turbulence intensity versus whatever you have chosen as the independent variable. If you choose the 3nd edition standards, Windographer will display
the mean turbulence intensity, the representative turbulence intensity, and the peak turbulence
intensity versus the independent variable.
If you have chosen to display turbulence versus wind speed, you can select Show IEC turbulence categories in graph to see a graph of either the characteristic turbulence intensity or the representative turbulence intensity compared to the IEC turbulence categories.
Turbulence Analysis Window
Turbulence Categories defined in IEC 61400-1 2nd Edition
CategoryCharacteristic TI
at 15 m/s
S > 0.18
A 0.16-0.18
B 0-0.16
Turbulence Categories defined in IEC 61400-1 3rd Edition
CategoryMean TI at
15 m/s
S > 0.16
A 0.14-0.16
B 0.12-0.14
C 0-0.12
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Speed and Direction Sensors
In the Wind speed sensor drop-down box, Windographer lists each wind speed data column that has a
corresponding standard deviation column:
If the wind speed data column you are looking for does not appear in this drop-down box, you must return to the Configure Data Set window and associate a standard deviation column with that wind speed data
column. For more information please refer to the article on the Configure Data Set window.
Filters
In the Wind direction sector drop-down box, choose the direction sensor to which you want Windographer to refer when plotting versus direction or filtering by direction sector.
The Wind direction sector drop-down box allows you to filter for a particular wind direction sector.
Windographer disables this drop-down box when you plot turbulence versus direction. The Sectors drop-
down box lets you choose the number of direction sectors.
The Year and Month drop-down boxes allow you to filter the data for a particular year and/or a particular
month.
Check Filter by wind speed if you want to restrict the analysis only to wind speeds within a specific range.
Windographer disables this capability when you plot turbulence versus wind speed.
Calculations
To create the turbulence table, Windographer performs the following steps:
1. It searches the data set for all the time steps that meet your filter criteria. For example, if you
select the 45° - 75° wind direction sector and the month of April, it will search for time steps in
April in which the wind direction sensor that you have selected reports wind directions between 45° and 75°. For Windographer to include a time step in the analysis, both the wind speed column that
you have selected and its associated standard deviation column must report valid values.
2. It takes the resulting subset of the data and further subdivides it into bins depending on what you have chosen for the independent variable. For example, if you choose to display turbulence versus
wind speed, it subdivides the results into wind speed bins of 1 m/s width. If you choose to display
turbulence versus time of day, it subdivides the results into 24 bins, one for each hour of the day. Each bin therefore contains a set of turbulence intensity values.
3. For each bin, Windographer takes the set of turbulence intensity values and calculates its mean, standard deviation, and peak value. It then calculates either the characteristic turbulence intensity
or the representative turbulence intensity, depending on whether you have chosen the 2nd or 3rd
edition IEC standard.
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The example below shows that the data set contained 856 time steps that satisfied the filter criteria, that
contained valid data in both the selected wind speed data column and its associated standard deviation data column, and in which the wind speed fell within the 14.5 - 15.5 m/s range. The resulting 856 values
of turbulence intensity exhibited a mean value of 0.076 and a standard deviation of 0.025, giving a
representative turbulence intensity of 0.076 + 1.28 * 0.025 = 0.108. The peak value from those 856
values of turbulence intensity was 0.169.
Summary Results
The summary results appear in a table in the top right corner of the window. This table repeats three
numbers that appear in the 15 m/s bin in the table of turbulence versus wind speed: the number of records in that bin, the mean turbulence intensity in the bin, and either the characteristic turbulence
intensity or the representative turbulence intensity in the bin, depending on which edition of the IEC
standard you choose. The table also reports the IEC turbulence category that results from these 15 m/s
statistics.
See also
Turbulence intensity
Characteristic turbulence intensity
Representative turbulence intensity
Configure Data Set window
Exporting graphs
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With this window you can see how the wind shear varies with month, time of day, wind direction and wind
speed. To access this window, choose Wind Shear from the Analyze menu or click the wind shear icon in the tool bar. For more information on the calculations, please refer to the article on wind shear.
Display Format
Choose whether you want Windographer to report the results in graphical or tabular form. Windographer
disables this radio button if you display the wind shear versus month and wind direction, or versus time of day and wind direction, as it can display these results only in tabular form.
Tip: In many cases, the tabular results report more information than do the
graphical results. Some of the tabular results report the mean wind speed for each wind speed sensor, and the number of time steps upon which the mean is based.
Wind Shear Profile
Use the radio button to choose between the logarithmic law or the power law. Windographer will report
the surface roughness if you choose the logarithmic law, or the power law exponent if you choose the
power law. Windographer disables this radio button when you view the results in a format that can display
both power law and log law results simultaneously.
Filter Settings
The filter settings allow you to confine the wind shear calculations only to a certain range of wind speeds,
for example, or only to a certain wind direction sector. Windographer may disable some of these filter
options depending on how you have chosen to display the wind shear. For example, you cannot filter by wind speed when Windographer displays the wind shear versus wind speed. Similarly, you cannot filter by
month when Windographer displays the wind shear versus month.
To confine the calculations to a certain wind speed range, check the checkbox labeled Filter by wind speed.
You will need to specify the wind speed sensor on which to base this filter. In the example below, Windographer will base its wind shear calculations only on time steps in which the Speed 45m sensor
reports wind speeds between 5 m/s and 30 m/s inclusive.
If your data set contains data from more than one wind direction sensor, Windographer will display a list
of available sensors in the drop-down box labeled Wind direction sensor. To show data for particular wind direction sector, select a range of directions from the drop-down box labeled Wind direction sector. By
Wind Shear Analysis Window
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default, Windographer will display the number of direction sectors that you have specified in your
preferred wind rose settings in the Options window, but you can change the number of sectors with the drop-down box labeled Sectors.
Use the drop-down boxes labeled Year and Month to filter for a particular year and/or month.
Wind Speed Sensors
At the lower left of the window appears a list of checkboxes, one for each wind speed sensor in the data set:
Windographer will base its wind shear calculations on the wind speed sensors that you choose with these
checkboxes. You may wish to exclude a sensor if you think it is less accurate than the others, if it has a
low data recovery rate, or if you want Windographer to focus on a particular range of heights to the exclusion of others.
Tip: When determining the mean wind speed from each wind speed sensor for the purpose of calculating wind shear, Windographer considers only those time steps
that contain data from all selected wind speed sensors. If even one of the selected
wind speed sensors is missing data in a particular time step, Windographer will not use that time step to calculate the mean wind speeds.
Wind shear versus height
In this mode, Windographer displays the vertical wind shear profile, which is the mean wind speed at each
measurement height, along with the logarithmic law and power law profiles that best fit those mean wind
speeds.
Note: These mean wind speeds are based only on those time steps that contain data for all the selected wind speed sensors. They may also be filtered in one or more of the ways that this window allows.
Therefore, they may differ significantly from the overall mean wind speeds that Windographer may report
elsewhere.
Wind shear versus month
In this mode, Windographer displays the best-fit wind shear parameter for each month of the year, for the
filter settings you have specified. Note that the wind shear versus year and month mode displays the
same data broken out for individual years.
The example below shows a typical pattern for a cold climate in the northern hemisphere, where snow
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cover and decreased foliage tend to reduce the surface roughness during the winter months:
Wind shear versus time of day
In this mode, Windographer displays the best-fit wind shear parameter for each hour of the day, for the
filter settings you have specified. Note that the wind shear versus month and time of day mode displays similar data broken out by month.
Wind shear versus wind direction
In this mode, Windographer displays the best-fit surface roughness or power law exponent for each
direction sector, for the filter settings you have specified. As described above, you can choose the number of direction sectors and the wind direction sensor that Windographer uses to create these results.
Wind shear versus wind speed
In this mode, Windographer displays the best-fit wind shear parameter for each wind speed bin. Note that
these wind speed bins correspond to the wind speed sensor selected in the filter section. From the tabular display, you can see the wind speed bins and number of valid data points in each one.
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Wind shear versus month and time of day
In this mode, Windographer displays the best-fit wind shear parameter for each hour of the day, for each
month, for the filter settings you have specified. In graphical mode, use the checkboxes to show or hide the line for any particular month.
The example below shows data from a northern hemisphere location. The annual line shows a very typical
daily pattern, where the wind shear drops dramatically during the warm part of the day when solar
heating leads to greater vertical mixing. The colored monthly lines indicate that this daytime drop in wind
shear begins earlier and ends later in the day during the summer months. They also indicate that the summer months exhibit stronger night-time wind shear than the winter months, possibly due to increased
foliage-related roughness during the summer, or snow cover during the winter.
Wind shear versus year and month
In this mode, Windographer displays the best-fit wind shear parameter for each month of the year, for the
filter settings you have specified. In graphical mode, use the checkboxes to show or hide the line for any individual year.
Wind shear versus month and wind direction
In this mode, Windographer displays the best-fit wind shear parameter for each month of the year, for
each wind direction bin, for the filter settings you have specified. Windographer can only display this information in tabular form.
Wind shear versus time of day and wind direction
In this mode, Windographer displays the best-fit wind shear parameter for each hour of the day, for each
wind direction bin, for the filter settings you have specified. Windographer can only display this information in tabular form.
Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For
information about exporting graphs from Windographer, please refer to the article on exporting graphs.
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See also
Wind shear
Wind Shear Tool window
Surface roughness
Power law exponent
Exporting graphs
Wind Speed Sensor Summary report
Written by: Tom Lambert
Contact: [email protected]
Last modified: February 24, 2009
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The Tower Shading Analysis window displays graphs of differences in measurements between two wind speed sensors as a function of wind direction. Tower shading analysis requires at least two wind speed
measurements and one wind direction measurement, ideally all taken at the same height.
The controls at the top of the Tower Shading Analysis window allow you to choose the wind speed sensors
and wind direction sensors that Windographer uses to create the graphs. You can choose to display polar
or cartesian graphs of either the quotient of two speed sensors or their difference versus the wind direction. You can also choose a time period to plot, ranging from the entire data set to a single month.
The Threshold wind speed is the minimum wind speed that will appear on the graphs. Each time you open
the Tower Shading Analysis window, Windographer sets the threshold value to the Calm threshold from
the Other tab of the Configure Data Set window.
The tower shading graphs help you explore the effects of tower shading on wind speed and direction
sensors. The graphs can also help you locate icing events, although the Quality Control window has
more effective tools for that purpose. In the above example, the bumps between 90 and 120 degrees and between 180 and 210 degrees indicate possible tower shading for the year 2005. In this case, the
Tower Shading Analysis Window
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threshold wind speed of 5 metres per second corresponds to the minimum average wind speed for this
data set. The graph indicates that the speed sensor B measurements between 90 and 120 degrees may be artifacts of tower shading. Similarly, sensor A may be shaded between about 190 and 220 degrees.
The radial lines between 180 and 210 degrees on the polar plot may indicate that the wind direction
sensor froze in position while the speed sensors continued taking measurements. Or, perhaps of one the
speed sensors froze during a period of consistent winds from a prevailing direction. By displaying a single
month of data at a time, you can find the month that contains the data for the icing event. For example, in the above graph, two possible icing events appear during 2005 between 180 and 210 degrees. The two
graphs below show that the events were in January 2005 and December 2005. You can explore these
possible icing events in more detail and make data revisions on the Quality Control window.
Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For
information about exporting graphs from Windographer, please refer to the article on exporting graphs.
See also
Quality Control window
Configure Data Set window
Written by: Paul Gilman
Contact: [email protected]
Last modified: January 30, 2006
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This window displays a data coverage chart that shows the location of gaps in each column of the data set.
Note that you can fill gaps in the Fill Gaps window and the Quality Control window.
See also
Data coverage chart
Fill Gaps window
Quality Control window
Written by: Tom Lambert
Contact: [email protected] Last modified: September 16, 2008
Data Coverage Window
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This window helps to clarify how Windographer determines the wind power class. The wind power classes are defined by ranges of mean wind power density at 50m above ground. The prerequisite to determining
the wind power class is therefore the calculation of the mean wind power density at 50m.
For a data set that contains wind speed measurements at multiple heights above ground, Windographer
calculates the mean wind power density at each height. If two or more wind speed sensors have the same
height, Windographer will average them to find the mean wind power density at that height. The resulting mean wind power densities appear in the table in the top right corner of the window and as diamonds in
the graph.
Windographer uses linear least squares regression to find the straight line that best fits the graph of the
natural logarithm of mean wind power density versus the natural logarithm of height. The graph shows this line of best fit.
To find the best estimate of the mean wind power density at 50m, Windographer calculates the value of
mean power density at which the line of best fit crosses 50m above ground. Please see the article on
calculating the mean wind power density at 50m for details.
Note that Windographer calculates the mean wind power density at 50m from the
line of best fit even if the data set contains one or more wind speed sensors at 50m.
See also
Calculating the mean wind power density at 50m
Wind power density
Wind power class
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 25, 2008
Wind Power Class Analysis Window
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The Extreme Wind Analysis window allows you to calculate the expected extreme wind speeds over periods of time up
to 100 years. It does this by finding the maximum wind speeds in each year of the data set, fitting a Gumbel
distribution to those values, and then calculating 50-year or 100-year return values from that best-fit Gumbel
distribution.
You can access this window by choosing Extreme Winds from the Analyze menu:
Wind Data
The drop-down box at the top left of the window lists the wind speed data columns in your data set. Windographer
searches the selected data column to find the maximum values that occur in each year of the data set. If the selected
wind speed data column has an associated maximum value column, Windographer will also search this maximum
value column, and in the table and graphs, it will identify this data as gust data.
Tip: You can associate a maximum value column with a wind speed data column in the
Configure Data Set window.
In the example below, the data column called 'Speed 40m' contains the 10-minute average wind speed at 40m and
another data column called '40m peak gust' contains the maximum 3-second gust within each time step.
The table on the right summarizes the annual data available for the chosen wind speed sensor for each year in the
data set. If the data set contains more than three years of data, the scroll bar on the right of the table will allow you
to scroll through all the years. The checkboxes allow you to exclude years that contain insufficient data.
To remove years with a low data recovery rate, check the checkbox labeled Whose data recovery rate is less than and
enter the acceptable rate. In the example above, setting a minimum data recover rate of 75% excludes both the 10-
minute data and the gust data from 2006. In the table, Windographer shades excluded data in pink.
To remove years with partial data, check the checkbox labeled Whose number of valid records is less than and enter the
minimum number of valid records. In the example above, the requirement of at least 2600 valid records excludes the
10-minute data from 2004.
Extreme Wind Analysis Window
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Curve Fit
Use the radio buttons to choose whether to plot the 10-minute data or the gust data. This choice applies to the two
curve fit graphs only. Note that if the selected wind speed sensor has no associated maximum value column,
Windographer disables the radio buttons.
Two closely related graphs appear in the Curve Fit section of the window. The first graph plots the cumulative
distribution function of the measured data along with the best-fit Gumbel distribution. In this graph the y-axis
indicates the probability that the maximum annual 10-minute wind speed (or maximum annual gust) will be less than
or equal to a particular value.
The example below shows data from a 16-year data set of 10-minute mean wind speeds at 75m above ground. The
measured data points fall close to the best-fit Gumbel distribution, meaning the Gumbel distribution is a reasonable fit
to the data in this case. (It may not always fit the data this well.) The graph shows that according to the best-fit
Gumbel distribution, there is a 60% chance that in any given year, the maximum 10-minute wind speed will be less
than or equal to 26 m/s. Similarly, there is an 85% chance that it will be less than or equal to 28 m/s.
The second graph shows the same information in a slightly different way. In this graph the y-axis indicates the
probability that the annual peak wind speed will exceed a particular value. This probability of exceedence is equal to
one minus the value returned by the cumulative distribution function. The y-axis of this graph uses a logarithmic
scale. The example below shows that for any given year, the probability that the maximum wind speed will exceed 31
m/s is about 3%.
Below the probability of exceedence graph, Windographer displays the parameters of the best-fit Gumbel distribution,
along with the r² value, which indicates the goodness of fit. An r² value close to 1 indicates a good fit.
Results
The graph in the Results section shows the expected extreme wind speed versus the return period. The return period
is the reciprocal of the probability of exceedence, so a probability of exceedence of 2% corresponds to a return period
of 1 / 0.02 = 50 years.
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The graph above shows that for a mean wind speed of 30 m/s, the probability of exceedence is 4.8%. Therefore the
return period for a wind speed of 30 m/s is 1 / 0.048 = 20.8 years. In other words, we would expect a 10-min mean
wind speed of 30 m/s or greater to happen once every 20.8 years. The graph below confirms that an extreme wind
speed of 30 m/s corresponds to a return period of about 21 years. The graph and table below show that that the 50-
year extreme 10-minute mean wind speed is 31.6 m/s , and the 50-year extreme gust is 36.7 m/s.
Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For information
about exporting graphs from Windographer, please refer to the article on exporting graphs.
See also
Gumbel distribution
Configure Data Set window
Data recovery rate
Cumulative distribution function
Written by: Tom Lambert
Contact: [email protected]
Last modified: February 24, 2009
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This window uses three different algorithms to fit the Weibull distribution to wind speed data, and plots them all on the same graph, along with the frequency histogram of the measured wind speed data. To
access this window, choose Frequency Distribution from the Analyze menu.
Use the drop-down box labeled Wind speed sensor to choose the wind speed data to analyze. Use the filter
capabilities to analyze the frequency distribution for a particular wind direction or specific year and/or
month. Windographer displays the results in tabular and graphical format.
The following table briefly describes the three Weibull fitting algorithms. Click on one of the links for detailed information on any of the algorithms.
Filter Settings
Windographer allows you to perform a frequency distribution analysis on a portion of the data set - filtered
by wind direction, or date.
If your data set contains multiple wind direction sensors, Windographer will list them in the drop-down
box labeled Wind direction sensor. To filter for a particular wind direction sector, select a range of
directions from the drop-down box labeled Wind direction sector. By default, Windographer will display the number of direction that you specify in the preferred wind rose settings in the Options window, but you
can change the number of direction sectors with the drop-down box labeled Sectors.
With the Year and Month drop-down boxes, you can choose to plot data for a specific year and/or a
specific month. If you select all years and all months, Windographer will not filter by date. If you select all
years and one particular month, Windographer will plot data from that month for all years in the data set. For example, if you choose April in a four-year data set, the graph will include data from all four Aprils. If
you select one particular year and all months, Windographer will plot data from only that year. If you
select a particular year and a particular month, Windographer will plot data for that single month.
Windographer automatically chooses a reasonable bin size, but you can enter a different bin size with the input box below the filters.
Frequency Distribution Analysis Window
Algorithm Description
Maximum
likelihood
algorithm
This iterative algorithm is the one that Windographer uses to calculate the Weibull parameters everywhere else in the
software.
Least squares
algorithm
Sometimes called the 'graphical method', this algorithm works by transforming the axes of the cumulative distribution
function so that a Weibull distribution would appear as a straight line, then finding the straight line that best fits the actual data.
WAsP algorithmThis algorithm, used by the WAsP wind flow model, finds the Weibull distribution which matches that actual distribution in
terms of two parameters: the mean wind power density and the proportion of values that exceed the mean.
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Results
In the graph, Windographer plots the three Weibull distribution curves overlaid on the frequency
histogram of the actual measured data. In the table, it displays the following statistical parameters for each fitted distribution and for the actual measured data:
Discussion
The maximum likelihood algorithm and the least squares algorithm both attempt to fit a Weibull
distribution directly to the measured wind speed distribution, and they tend to produce very similar
results. The WAsP algorithm, however, takes a different approach and therefore sometimes produces significantly different results. The WAsP algorithm does not consider the shape of the actual wind speed
distribution, but rather considers only its mean wind power density and its proportion of values above the
mean. The Weibull distribution that the WAsP algorithm produces always matches these parameters
perfectly, but sometimes it does not closely match the actual wind speed distribution.
The graph below shows an example where the WAsP algorithm has produced a Weibull distribution that fits the measured distribution very poorly:
But as the table below shows, that Weibull distribution perfectly matches the measured data in terms of
mean wind power density and proportion above the mean:
Variable Description
Weibull k The shape factor of the two-parameter Weibull distribution.
Weibull c The scale factor of the two-parameter Weibull distribution.
Mean The arithmetic mean (average) value of the data or the fitted distribution.
Proportion Above
MeanThe fraction of values in the data set or the fitted distribution that exceed the mean value.
Power Density The mean wind power density of the data set or the fitted distribution.
R SquaredThe goodness-of-fit parameter that indicates how closely the fitted Weibull distribution matches the frequency histogram of
the measured data. Values approaching one indicate a good fit.
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As this example suggests, your choice of Weibull fitting algorithm will depend on your objective. If you wish to find the Weibull distribution that best matches the measured wind speed distribution, you should
choose either the maximum likelihood algorithm or the least squares algorithm. If you wish to find the
Weibull distribution that best matches the measured wind power density and proportion above the mean,
you should choose the WAsP algorithm.
See also
Weibull distribution
Wind power density
Maximum likelihood algorithm
Least squares algorithm
WAsP algorithm
Written by: Linda Sloka
Contact: [email protected]
Last modified: February 24, 2009
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The Probability of Exceedence window analyzes the distribution of annual mean values for a particular data column, and calculates the probability that the annual mean will exceed certain values. It can use 5
or 10 years of data to estimate the probability that over the long term, the mean value for any given year
will exceed a certain value. This estimate becomes more accurate with more years of data.
You can access this window by choosing Probability of Exceedence from the Analyze menu.
When you choose a data column from the drop-down box, Windographer finds the mean value of that
data column in each year of the data set. It puts those annual mean values in the table at the left side of the window, along with the number of time steps that went into that calculation.
With the Month drop-down box, you can choose to analyze the data for a specific month. If you select All, Windographer will consider all the data from the entire data set. If you select one particular month,
Windographer will use data from that month for all years in the data set.
With the edit box labeled Minimum number of time steps, you can choose not to use data years with fewer
data points (time steps). In the example above, by setting the minimum number of time steps to 3600, we have excluded the year 2001 because it contains only 3,516 time steps. Above the table,
Windographer reports that five years contain sufficient time steps, which confirms that one of the six
years is not included in the analysis.
The Frequency Histogram graphically displays the frequency with which the annual mean values fall within
various bins. The example below shows that the mean wind speed fell between 8.8 m/s and 8.9 m/s in almost 30% of the years in the data set.
Probability of Exceedence Window
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The Probability of Exceedence graph shows both the measured values and the normal distribution that
best fits those values. The y-axis indicates the probability that the annual mean value will exceed a
particular value. The example below shows data from a 16-year data set . The measured data points fall
close to the best-fit normal distribution, meaning the normal distribution is a good fit to the data in this case. (It may not always fit the data this well.) This graph shows that for any particular year, the
probability that the mean annual wind speed will exceed 8.8 m/s is about 50%, and the probability that it
will exceed 9.0 m/s is about 12%.
The table to the left of the graph shows the key percentile values for both the actual distribution and the
best-fit normal distribution. The table below shows that, according to the best-fit normal distribution, the P95 value is 8.519 m/s, which means that we would expect the mean annual wind speed to exceed 8.519
m/s in 95 years out of 100.
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Note that Windographer requires many years of data to calculate all the percentiles for the actual distribution. It requires only a few years to fit the normal distrbution though, and from the normal
distribution it can calculate any percentile. To calculate the percentile values for the actual distribution
column, Windographer interpolates linearly between the values of the actual distribution, as shown in the
following explanation.
To find the P90 value, draw a horizontal line at 90% POE. The x-value at which that line intersects the actual distribution is the P90 value shown in the column labeled Actual Distribution. The x-value at which
that line intersects the normal distribution is the P90 value shown in the column labeled Normal
Distribution. The graph below shows that the P90 value from the actual distribution is about 8.55 m/s, and P90 value from the normal distribution is about 8.58 m/s. Those numbers confirm the P90 values shown
in the table above.
Note that this window differs from the Extreme Wind Analysis window. This window analyzes the annual mean values, which tend to conform to a normal
distribution. The Extreme Wind Analyis window analyzes the annual maximum
values, which tend to conform to a Gumbel distribution.
See also
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Extreme Wind Analysis window
Written by: Linda Sloka
Contact: [email protected]
Last modified: February 24, 2009
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The Standard Atmosphere Tool window plots temperature and pressure data given by the equations of the
International Standard Atmoshere, as well as the resulting air density given by the ideal gas law. The graph shows values over a range of elevations, but you can also see the specific values at a particular elevation by
typing an elevation into the Elevation box.
The following equation closely approximates the pressure of the International Standard Atmosphere up to an
elevation of 5,000m:
The following elevation closely approximates the temperature of the International Standard Atmosphere up to
an elevation of 11,000m:
Source for the above equations: Manwell et. al (2002). Windographer uses the above equations to estimate air temperature and/or air pressure for any time step in which the data set does not contain measured temperature or pressure data. (Temperature or pressure data
may be missing either because the data set does not include temperature or pressure data.) It does so to calculate the air density in each time step, which it uses to calculate wind power density and wind turbine
power output.
Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For
information about exporting graphs from Windographer, please refer to the article on exporting graphs.
See also
Quality Control window
Configure Data Set window
Air density
Air pressure
Air temperature
Calculating the energy output of a wind turbine
Written by: Tom Lambert Contact: [email protected] Last modified: April 25, 2008
Standard Atmosphere Window
where:
p is the air pressure [kPa]
z is the elevation [m]
where:
T is the air temperature [K]
T0 is the standard sea-level temperature [288.16 K]
B is the standard lapse rate [0.00650 K/m]
z is the elevation [m]
![Page 108: Tutorial Windografher](https://reader035.fdocuments.in/reader035/viewer/2022082201/563dbb1c550346aa9aaa5c82/html5/thumbnails/108.jpg)
This tool window allows you to generate one year of synthetic hourly wind speed data with the statistical properties you prescribe. Using the drop-down box at the top of the window you can choose between
three levels of detail. If you choose Simple you will need to specify the mean wind speed for each month
of the year, and the annual values of the following four parameters:
� Weibull k
� Autocorrelation coefficient
� Diurnal pattern strength
� Hour of peak wind speed
If you set the level of detail to Intermediate you must specify monthly values of mean wind speed and each
of the four parameters listed above. If you set the level of detail to Advanced, then for each month you
must specify the Weibull k, the autocorrelation coefficient, and the mean 24-hour wind speed profile.
Once you have chosen a level of detail and specified all the parameters, click the button labeled Generate and Export to create the hourly data set. Windographer will ask you to specify a file name to which to save the data set. The exported file is a text file with one column and 8,760 lines, each line containing the
hourly mean wind speed. The first line of data corresponds to the first hour of the year, meanig the one
starting at midnight, January 1st. The exported file is suitable for import into the HOMER micropower optimization model.
For a description of the process by which Windographer synthesizes wind speed, please see the article on synthesizing wind speed data.
See also
Weibull k
Autocorrelation coefficient
Diurnal pattern strength
Hour of peak wind speed
Synthesizing wind speed data
Written by: Tom Lambert
Contact: [email protected] Last modified: February 23, 2009
Synthesize Wind Speed Data Tool
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The wind shear tool window allows you to enter the wind speeds at different heights above ground and see the logarithmic and power law profiles that best fit the data you enter. The window displays the best-
fit values of surface roughness and power law exponent, and lets you enter another height at which it
estimates the wind speed based on the best-fit profiles.
If you have a data file open when you open the Wind Shear Tool window, Windographer will enter the
measured wind speed profile from the current document into the table of known data points.
For information about exporting graphs from Windographer, please refer to the article on exporting graphs.
See also
Wind shear
Wind Shear Analysis window
Surface roughness
Power law exponent
Exporting graphs
Written by: Tom Lambert
Contact: [email protected] Last modified: September 14, 2005
Wind Shear Tool Window
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This window lets you estimate the annual energy production of a wind turbine in a wind regime that you describe with a few statistical parameters rather than with time series data.
Use the radio button in the top left corner to choose whether to specify the wind resource data using
annual mean values, monthly mean values, or a frequency histogram. You will need to enter the height at
which the wind speed data apply, and the power law exponent to define the wind shear between the
anemometer height and the wind turbine hub height.
Once you have described the wind resource, choose a wind turbine model and a hub height. Enter the various loss terms (described in the article on the Wind Turbine Output window) and click the Calculate Output button. The tables in the bottom right corner will display the mean power output, the annual
energy output, and the capacity factor before and after losses.
To estimate the energy output of a wind turbine in a wind regime for which you
have measured time series data, open the data set using File > Open and open
the Wind Turbine Output window.
Right-click any graph or table to copy it to the clipboard or export it to a file.
See also
Weibull k
Wind Turbine Output window
Calculating the energy output of a wind turbine
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 25, 2008
Wind Turbine Output Tool
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The Options window allows you to specify a few settings that affect Windographer. To access this window, choose Options from the Tools menu:
General Settings
The Preferred wind shear profile selection determines the initial (or default) setting for wind shear graphs
throughout Windographer. Windographer will report the surface roughness if you choose the Logarithmic law, or the power law exponent if you choose the Power law. This selection will impact graphs on the Reports tab.
The Preferred edition of IEC standard 61400-1 for turbulence selection determines the initial setting for
turbulence analysis throughout Windographer. If you choose the 2nd edition IEC standards, Windographer
will display the characteristic turbulence intensity on turbulence graphs. If you choose the 3nd edition
standards, Windographer will display the representative turbulence intensity on turbulence graphs.
This selection will affect graphs on the Reports tab, and will affect the initial setting in the Turbulence Analysis window. You can also change the preferred setting for turbulence analysis from the Turbulence
Analysis window. If you have a different IEC selection when you close the Turbulence Analysis window,
Windographer will ask you whether you wish to change your preference.
Default Wind Rose Settings
These settings determine the initial (or default) appearance of the wind roses that Windographer displays
on the Wind Rose tab, the Summary tab, and the Reports tab, and some of the Analysis windows. On
Wind Rose tab you can override these default settings, but if you typically prefer a particular drawing style or number of sectors, you can specify those settings here to avoid having to specify them each time you
plot a wind rose.
Location of SDR Software
Windographer can import .RWD files written by the Symphonie Data Logger by NRG Systems. You must
have the SDR software installed on you computer to do so. This edit box allows you to specify the location of that software.
Options Window
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Append Settings
These settings allow you to specify how Windographer should handle the scenario in which you append
data that overlap the existing data in time. You can choose among four possible alternatives:
1. The appended data never overwrites the existing data.
2. The appended data only overwrites existing data in time steps in which the existing data is
missing. In this case, the appended data can fill gaps in the existing data, but it cannot overwrite
any values.
3. The appended data overwrites existing data in all time steps except those in which the appended
data is missing.
4. The appended data always overwrites the existing data.
See also
Reports tab
Surface roughness
Power law exponent
Turbulence Analysis window
Characteristic turbulence intensity
Representative turbulence intensity
Wind Rose tab
Importing .RWD files
Appending data
Written by: Linda Sloka
Contact: [email protected]
Last modified: February 23, 2009
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The air density is defined as the mass of a quantity of air divided by its volume. To calculate the air density in each time step, Windographer uses the ideal gas law, one form of which is:
Solving for density gives:
For air, which has a molar mass of 28.9664 kg/kmol, we can write the equation as follows:
Windographer uses that equation to calculate the air density in each time step. If the data set includes a
temperature column, Windographer will use that temperature data in the above equation. If the data set does not include a temperature column, or in time steps where the temperature column does not contain
data, Windographer estimates the temperature based on the site elevation, as described in the article on
air temperature. Similarly, if the data set does not include an air pressure column, or in time steps
where the air pressure column does not contain data, Windographer estimates the pressure based on the site elevation, as described in the article on air pressure.
Windographer adds the air density column to the data set as a calculated column. Windographer uses
the air density to calculate the wind power density and the wind energy content. You can view the air
density in any of the graphic or tabular formats that Windographer provides for any other data column. The example below shows a DMap of the air density over one year. The yellows and reds indicating high
air density correspond to the coldest parts of the year, while the blues indicating low air density
correspond to the warmest times of the year. You can create DMaps on the DMap tab.
Air Density
where:
p is pressure [kPa]
ρ is density [kg/m3]
R is the universal gas constant [8.314472 m3·kPa·K-1·kmol-1]
M is the molar mass [kg/kmol]
T is temperature [K]
where:
p is the air pressure [kPa]
T is the air temperature [K]
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See also
Air pressure
Air temperature
Wind power density
Wind energy content
Written by: Tom Lambert Contact: [email protected]
Last modified: October 31, 2007
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The air pressure (also called atmospheric pressure, barometric pressure, or just pressure) is the force per unit area exerted against a surface by the weight of the air molecules above that surface. Windographer
uses the air pressure to calculate the air density in each time step.
If one of the data columns in the data set contains measured air pressure data, Windographer uses that
measured air pressure data when calculating the air density in each time step.
Tip: Measured air pressure data should not be adjusted to sea level.
If the data set does not contain measured air pressure data, or the air pressure column does not contain data for the time step in question, Windographer calculates the air pressure from the elevation according
to the following equation, which closely approximates the pressure of the International Standard
Atmosphere up to an elevation of 5,000m:
where z is the elevation in metres. Source: Manwell et. al (2002).
The graph below plots this equation versus elevation:
Barometric pressure does not vary drastically with weather conditions, but depends mainly on the elevation. So the International Standard Atmosphere fairly accurately predicts the true barometric
pressure.
See also
Air temperature
Air density
Elevation
Air Pressure
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Windographer uses the air temperature (also called ambient temperature) to calculate the air density in each time step.
If one of the columns in the data set contains the air temperature, Windographer uses the appropriate
value from that data column when performing calculations. (If the temperature units are not Kelvin,
Windographer first converts the temperature to K.)
If the data set does not contain air temperature data, or the air temperature data column does not
contain data for the time step in question, Windographer calculates the air temperature from the elevation according to the following equation, which closely approximates the temperature of the International
Standard Atmosphere up to an elevation of 11,000m:
Source: Manwell et. al (2002).
The graph below plots this equation versus elevation:
Note: Temperature varies strongly with seasonal weather patterns. The constant
temperature given by the International Standard Atmosphere only roughly approximates the true temperature. If your data set contains measured
temperature data, but values are missing for some time steps, you can use the
Quality Control window to fill the gaps with data that is statistically similar to the measured data. That technique should produce more accurate temperature data
than does the International Standard Atmosphere equation.
Air Temperature
where:
z is the elevation [m]
T0 is the standard sea-level temperature [288.16 K]
B is the standard lapse rate [0.00650 K/m]
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See also
Air pressure
Air density
Elevation
Quality Control window
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 25, 2008
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Wind speed data typically exhibit some amount of autocorrelation, which can be defined as the degree of dependence on preceding values. For a time series z
1, z2, z3, ..., z
N, we can define an autocorrelation
coefficient rk as follows:
The autocorrelation coefficient rk represents the degree to which the value in one time step tends to
depend on the value k time steps in the past. For example, if r3 were equal to 0.8, that would indicate that
the value in one time step is 80% determined by the value three time steps in the past, and 20%
determined by randomness and other factors. By definition, r0 = 1.
For each anemometer in the data set, Windographer calculates the autocorrelation coefficient for one hour of lag, meaning it calculates the value of r
k for a value of k equal to the number of time steps in one hour.
For a data set with a time step of 10 minutes, k = 6. For 30-minute data, k = 2, and for 60-minute data k
= 1. The one-hour autocorrelation coefficient is undefined if the time step is greater than one hour.
Windographer displays the one-hour autocorrelation coefficient in the Wind Speed Sensor Summary table.
The graph below illustrates the effect of autocorrelation on wind speed data. Both data sequences shown
in the graph are hourly wind speed sequences synthesized using Windographer's Synthesize Data window. The two sequences have the same Weibull distribution, and are different only in the degree of
autocorrelation. The one-hour autocorrelation coefficient is a very low 0.50 for the sequence plotted in
orange, and a very high value of 0.99 for the sequence plotted in blue. Periods of high and low winds tend to persist much longer in the the strongly autocorrelated sequence.
One-Hour Autocorrelation Coefficient
where:
rk is the autocorrelation factor for a time lag of k time steps
N is the number of time steps
zi is the value in time step i
is the average value over the N time steps
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Real measured wind speed sequences typically exhibit one-hour autocorrelation coefficients between 0.65
and 0.98.
See also
Wind Speed Sensor Summary table
Synthesize Data window
Weibull distribution
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 25, 2008
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In Windographer, a data set consists of one or more original data columns plus one or more calculated columns. An original data column is a column of recorded data that comes from the data file. An original
data column might contain, for example, the air temperature at 2m above ground, or the wind speed at
30m above ground. A calculated column is one that Windographer creates from calculations on the
original data columns. A calculated column might contain, for example, the air density, the wind power density at 30m, or the turbulence intensity at 50m. You can view a calculated column's data in any of the
graphic and tabular formats available in Windographer.
Windographer adds the following calculated columns to the data set:
� The air density.
� The surface roughness (if the data set contains wind speed data from two or more heights above
ground).
� The power law exponent (if the data set contains wind speed data from two or more heights above ground).
� The wind power density for each wind speed data column.
� The turbulence intensity for each wind speed data column that has an associated standard
deviation column.
Written by: Tom Lambert
Contact: [email protected] Last modified: April 23, 2008
Calculated Columns
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The calm threshold is a wind speed value below which Windographer considers the wind to be 'calm'. You specify the calm threshold on the Configure Data Set window.
The value of the calm threshold affects wind rose diagrams. When drawing wind frequency roses, which
show the frequency with which the wind blows from each direction sector, Windographer includes only
those direction observations that correspond to wind speeds above the calm threshold. (In data sets that
contain data from more than one anemometer, Windographer will refer to the anemometer closest in height to the plotted wind direction sensor when checking whether the wind speed exceeds the calm
threshold.) The calm frequency, or the percentage of time that the wind speed is at or below the calm
threshold, appears at the top right of all wind frequency rose diagrams, as highlighted in the example below:
The calm threshold also affects the 'frequency of calms' variable that appears in the Wind Speed Sensor Summary table.
See also
Wind Speed Sensor Summary table
Configure Data Set window
Written by: Tom Lambert
Contact: [email protected] Last modified: April 25, 2005
Calm Threshold
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The capacity factor of a wind turbine is equal to its average power output divided by its rated power. The average power output is often calculated over one year or one month, but it could be calculated over any
period.
On the Wind Turbine Output window Windographer displays overall average capacity factor (averaged
over the entire data set) and the average capacity factor for each month of the year.
See also
Rated power
Wind Turbine Output window
Written by: Tom Lambert
Contact: [email protected]
Last modified: September 6, 2006
Capacity Factor
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The cumulative distribution function F(x) gives the probability that a variable will take on a value less than
or equal to a number x.
The cumulative distribution function is also called the cumulative density function or simply the
distribution function. Within Windographer, we sometimes abbreviate cumulative distribution function as CDF.
See also
Probability distribution function
Written by: Tom Lambert Contact: [email protected]
Last modified: February 23, 2009
Cumulative Distribution Function
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For a set of 10-minute time steps, each of which contains a value of turbulence intensity, the characteristic turbulence intensity is equal to the 84th percentile of the turbulence intensity values,
meaning the value below which 84% of the values fall. If the turbulence intensity values conform to a
normal distribution, the 84% percentile is equal to the mean plus one standard deviation.
Windographer does assume that the turbulence intensity values are normally distributed, so it calculates
the characteristic turbulence intensity as the mean plus one standard deviation.
For example, the following table contains 30 time steps of data on mean wind speed, standard deviation of wind speed, and turbulence intensity, which is the ratio of the two:
Characteristic Turbulence Intensity
Date Time
Mean
Wind
Speed
Std. Dev. of
Wind Speed
Turbulence
Intensity
12/22/2007 15:30:00 14.5 1.2 0.0828
12/22/2007 15:40:00 14.2 1.3 0.0915
12/22/2007 15:50:00 12.6 1.2 0.0952
12/22/2007 16:00:00 12.3 1.0 0.0813
12/22/2007 16:10:00 13.3 0.8 0.0602
12/22/2007 16:20:00 13.0 1.0 0.0769
12/22/2007 16:30:00 13.4 1.0 0.0746
12/22/2007 16:40:00 13.7 1.0 0.0730
12/22/2007 16:50:00 12.8 1.0 0.0781
12/22/2007 17:00:00 13.7 1.0 0.0730
12/22/2007 17:10:00 14.2 0.9 0.0634
12/22/2007 17:20:00 13.9 0.7 0.0504
12/22/2007 17:30:00 13.8 0.9 0.0652
12/22/2007 17:40:00 13.6 1.1 0.0809
12/22/2007 17:50:00 13.1 1.0 0.0763
12/22/2007 18:00:00 12.2 1.1 0.0902
12/22/2007 18:10:00 12.5 1.0 0.0800
12/22/2007 18:20:00 12.4 0.8 0.0645
12/22/2007 18:30:00 12.7 0.8 0.0630
12/22/2007 18:40:00 12.3 1.1 0.0894
12/22/2007 18:50:00 12.1 0.7 0.0579
12/22/2007 19:00:00 12.7 0.7 0.0551
12/22/2007 19:10:00 11.4 1.0 0.0877
12/22/2007 19:20:00 12.1 1.0 0.0826
12/22/2007 19:30:00 11.6 0.8 0.0690
12/22/2007 19:40:00 11.4 0.9 0.0789
12/22/2007 19:50:00 10.7 0.7 0.0654
12/22/2007 20:00:00 11.0 0.8 0.0727
12/22/2007 20:10:00 10.8 0.8 0.0741
12/22/2007 20:20:00 11.7 0.7 0.0598
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Those 30 turbulence intensity values (highlighted in the table) have a mean of 0.0738 and a standard
deviation of 0.0113. Windographer would therefore calculate the characteristic turbulence intensity over that period as 0.0738 + 0.0113 = 0.0851.
The 2nd edition of IEC standard 61400-1 defined standard turbulence categories based on the value of the
characteristic turbulence intensity at 15 m/s. For more information, please see the article on the
Turbulence Analysis window.
See also
Turbulence Analysis window
Turbulence intensity
Representative turbulence intensity
Written by: Tom Lambert
Contact: [email protected]
Last modified: September 11, 2008
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For each wind speed column in the data set, Windographer calculates the coefficient of variation (sometimes called the relative standard deviation) using the following equation:
Windographer reports the coefficient of variation for each wind speed data column in the Wind Speed
Sensor Summary table, which you can generate on the Tables tab of the main window.
See also
Standard deviation
Tables tab
Written by: Tom Lambert
Contact: [email protected]
Last modified: February 16, 2007
Coefficient of Variation
where:
σ is the standard deviation of the wind speed distribution
is the long-term average wind energy speed [same units as σ]
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A data coverage chart shows the time segments in which data columns do or do not contain data. The example below shows that:
� all twelve data columns contain data from mid-2001 to the end of 2004
� all twelve data columns contain a gap of about one month in mid-2003, and two shorter gaps in
2004
� most data columns contain four more short gaps, two in 2003 and two in 2004
� the 'Direction 45m' data columns contain a three-month gap in early 2002
Windographer displays a data coverage chart in the Fill Gaps window and in the Data Coverage window.
See also
Fills Gaps window
Data Coverage window
Written by: Tom Lambert
Contact: [email protected] Last modified: January 20, 2009
Data Coverage Chart
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Windographer uses the following equation to calculate the data recovery rate over a particular time interval:
In this context, a 'valid record' is a time step which contains a data value, as opposed to a time step in
which the data value is missing for one reason or another. The possible number of records is equal to the
number of valid records plus the number of missing records.
Note that when calculating the data recovery rate for a month or a year, the
possible number of records refers to the number of time steps that the data set
covers within that month or year, which may be fewer than the total number of time steps within the month or year. For example, if the data set begins halfway
through April, then the possible number of records in April will be half of the total
number of time steps in April.
Windographer reports the data recovery rate in the annual statistics table, the monthly statistics table,
and the wind speed sensor summary table. The links below give additional information about these tables.
See also
Tables tab
Annual Statistics table
Monthly Statistics table
Wind Speed Sensor Summary table
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 23, 2008
Data Recovery Rate
where:
Nvalid is the number of valid records in the time interval
Npossible is the possible number of records in the time interval (the number of time steps in the time interval)
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The diurnal pattern strength is a measure of how strongly the wind speed depends on the time of day. To calculate this factor Windographer fits a cosine wave to the daily wind speed profile, as the graph below
illustrates:
The diurnal pattern strength is equal to the amplitude of this cosine wave divided by its average value. The hour of peak wind speed is the hour in which this best-fit cosine wave peaks.
In the above example, the amplitude of the cosine wave is 2.70 km/hr and its average is 19.02 km/hr, so
the diurnal pattern strength is 0.142. The cosine wave peaks in the 15th hour of the day, so the hour of
peak wind speed is 15.
Windographer calculates the diurnal pattern strength and the hour of peak wind speed for each wind speed data column in the data set, and displays that information in the Wind Speed Sensor Summary
table.
See also
Hour of peak wind speed
Wind Speed Sensor Summary table
Written by: Tom Lambert Contact: [email protected]
Last modified: April 25, 2005
Diurnal Pattern Strength
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The elevation (also called altitude) is the height above sea level. You can specify the elevation in metres or feet on the Configure Data Set window. If the data set does not contain data on air pressure,
Windographer uses the elevation to estimate it based on the international standard atmosphere. Similarly,
if the data set does not contain data on air temperature, Windographer estimates it from the elevation
using the international standard atmosphere.
In a data set that does contain an air temperature column, Windographer will use the elevation to estimate the temperature only in time steps for which the air temperature column contains no data.
Likewise, in a data set that contains an air pressure column, Windographer will use the elevation to
estimate the pressure only in time steps for which the air pressure column contains no data.
Windographer uses the temperature and pressure to calculate the air density.
See also
Air pressure
Air temperature
Air density
Configure Data Set window
Written by: Tom Lambert
Contact: [email protected]
Last modified: September 14, 2005
Elevation
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For each wind speed data column, Windographer calculates the energy pattern factor (also called the cube factor) using the following equation:
If one were to assume constant air density, the energy pattern factor would be the ratio of the actual
mean wind power density to the wind power density one would calculate using only the mean wind speed:
Windographer displays the energy pattern factor for each wind speed sensor in the Wind Speed Sensor
summary table.
See also
Wind Speed Sensor Summary table
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 25, 2008
Energy Pattern Factor
where:
N is the number of time steps
Ui is the wind speed at time step i
is the mean wind speed [same units as vi]
where:
is the mean wind power density [W/m3]
ρ is the air density [kg/m3]
is the mean wind speed [m/s]
Ke is the energy pattern factor
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A gap is a set of one or more time steps that contain no data. A time step might contain no data because:
� the raw data file did not specify a value for that time step
� the value was removed by a filter specified on the Configure Data Set window, or
� the value was removed by a deletion in the Quality Control window
Windographer can accept any number of gaps in a data set. Gaps do not prevent
Windographer from displaying data in graphical or tabular format, nor do they prevent it from performing any calculations.
In time series graphs, gaps appear as missing line segments. The example below shows a four-hour gap late in the day on July 14:
In a data coverage chart, gaps appear as blank segments. The example below shows a large gap in the
'Direction 45m' data column in early 2002, a large gap in all data columns in mid-2003, and several smaller gaps:
You may choose to fill gaps with synthetic data. In Windographer, you can do that in the Fill Gaps window
or in the Quality Control window.
See also
Fill Gaps window
Quality Control window
Configure Data Set window
Data coverage chart
Gap
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The Gumbel distribution is useful for modeling the probability of extreme wind speeds. The following equations give the probability distribution function and the cumulative probability distribution function of
the Gumbel distribution:
where x is the extreme value, β is a scale parameter, and µ is a mode parameter. Both parameters have
the same units as x.
As its name implies, the mode parameter specifies the most probable value of x. The graph below shows
the probability distribution function for three Gumbel distributions, all with a scale parameter of 8, but
with the mode parameter varying from 15 to 45:
The scale parameter determines the breadth of the distribution. The graph below shows the probability distribution function for five Gumbel distributions, all with a mode parameter of 30 but with the scale
parameter varying from 4 to 12:
Gumbel Distribution
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Return Period
The return period is a useful concept in the field of extreme value analysis. The return period is defined as
the reciprocal of the probability of exceedence. For example, say that a given Gumbel distribution
represents the distribution of annual extreme wind speeds, and that when x takes a value of 35 m/s, the
cumulative distribution function F(x) gives a value of 0.9875. That means that there is a 98.75%
probability that in any one year, the annual extreme wind speed x will be equal to or less than 35 m/s.
The probability of exceedence of 35 m/s is therefore 100% - 98.75% = 1.25%. The resulting return
period is 1 / 0.0125 = 80 years. That means we would expect an extreme wind speed of 35 m/s once in
80 years.
Analysts typically want to know the annual extreme value corresponding to a particular return period,
such as 50 years or 100 years. The following equation gives the annual extreme value for a specified Gumbel distribution and a specified return period:
where R is the return period in years.
In the Extreme Wind Analysis window, Windographer fits a Gumbel distribution to the annual extreme wind speed data, and calculates the extreme wind speeds expected for a range of return periods.
See also
Extreme Wind Analysis window
Written by: Tom Lambert Contact: [email protected]
Last modified: February 16, 2007
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The gust factor is the ratio of the peak gust within a time step divided by the mean wind speed for that time step.
For each wind speed sensor for which you have indentified an associated gust column, Windographer
creates a calculated column containing the gust factor in every time step. It names this calculated column
the same as the wind speed sensor name, plus the letters "GF". For example, if the wind speed column
name is "Speed 60m", its associated gust factor column will have the name "Speed 60m GF". You can view the gust factor column in all the graphic and tabular formats that Windographer provides, as you can
for any other data column.
Tip: The Configure Data Set window allows you to indicate whether a wind speed
column has an associated gust column.
See also
Calculated column
Configure Data Set window
Written by: Tom Lambert Contact: [email protected]
Last modified: February 26, 2009
Gust Factor
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Windographer fits a cosine wave to the daily wind speed profile, as the graph below illustrates:
The hour of peak wind speed is the hour in which this best-fit cosine wave peaks. The diurnal pattern
strength is equal to the amplitude of this cosine wave divided by its average value.
In the above example, the amplitude of the cosine wave is 2.70 km/hr and its average is 19.02 km/hr, so
the diurnal pattern strength is 0.142. The cosine wave peaks in the 15th hour of the day, so the hour of peak wind speed is 15.
Windographer calculates the diurnal pattern strength and the hour of peak wind speed for each wind
speed data column in the data set, and displays that information in the Wind Speed Sensor Summary
table.
See also
Diurnal pattern strength
Wind Speed Sensor Summary table
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 25, 2008
Hour Of Peak Wind Speed
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The mean of monthly means (MMM) is an average of the twelve monthly averages. For seasonally-biased data sets, in which some months are over-represented and some are under-represented, the mean of
monthy means provides a more accurate estimate of the long-term mean than does a simple arithmetic
mean.
To calculate the mean of monthly means, Windographer first calculates the mean of all the January data
in the data set, then the mean of all the February data, then the mean of all the March data, and so on. It then multiplies each monthly mean by the appropriate weighting factor, shown below, then divides by
365.24. The monthly weighting factors account for the unequal sizes of the months. (Note that February
contains 28.24 days on average.)
The mean of monthly means is useful because it removes any bias in the data set resulting from unequal
representation of months. Imagine, for example, a 13-month set of temperature data that includes two
Augusts but only one of every other month. A simple arithmetric mean over the entire data set would therefore include twice as much data from August as it would from the other eleven months of the year. If
the mean August temperature is higher than the mean annual temperature, that simple arithmetic mean
would be erroneously high due to the over-representation of August. By contrast, the mean of monthly means gives the August mean the same weight as it does the July mean or the October mean. As a result,
the mean of monthly means should reflect the true long-term mean more accurately than does the simple
mean.
Windographer displays the mean of monthly means in the Monthly Statistics table.
See also
Monthly Statistics table
Written by: Tom Lambert
Contact: [email protected] Last modified: February 26, 2009
Mean of Monthly Means
MonthWeighting
Factor
January 31
February 28.24
March 31
April 30
May 31
June 30
July 31
August 31
September 30
October 31
November 30
December 31
365.24
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The term negative wind shear refers to an atmospheric condition in which the wind speed decreases with height above ground. This is a reversal of the typical situation, in which the wind speed increases with
height. Negative wind shear can occur during calm conditions in hilly terrain as a result of cold air
drainage, a phenomenon whereby cold air flows down a slope because of its higher density relative to the
surrounding air. But in the flatter, windier locations that typically interest wind energy developers, negative wind shear is a very rare phenomenon, and apparent negative wind shear often turns out to be
an artifact of anemometer icing. For example, if the highest anemometer is the one most severely
affected by icing, it may as a result record a wind speed that is artificially low, perhaps lower than the other anemometers record. This apparent negative wind shear can serve as evidence for icing.
You can search for negative wind shear and remove iced data segments using the Quality Control window.
See also
Wind shear
Quality Control example
Quality Control window
Written by: Tom Lambert
Contact: [email protected] Last modified: February 19, 2007
Negative Wind Shear
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On the Wind Turbine Output window and the Wind Turbine Output Tool window you can enter four loss factors that specify the amount of energy lost due to several factors:
Windographer combines these four loss factors into the overall loss factor using the following equation:
Windographer uses the overall loss factor to calculate the net power and energy output of a wind turbine. For details please see the article on calculating the energy output of a wind turbine.
See also
Wind Turbine Output window
Calculating the energy output of a wind turbine
Written by: Tom Lambert Contact: [email protected]
Last modified: April 25, 2008
Overall Loss Factor
where:
fdowntime is the downtime losses factor
farray is the array losses factor
ficing is the icing/soiling losses factor
fother is the other losses factor
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The probability distribution function f(x) gives the probability that a variable will take on the value x. It is
often expressed using a frequency histogram, which gives the frequency with which the variable falls within certain ranges or bins.
The probability distribution function is also called the probability density function or simply the density
function. Within Windographer, we sometimes abbreviate probability distribution function as PDF.
See also
Cumulative distribution function
Written by: Tom Lambert
Contact: [email protected]
Last modified: February 23, 2009
Probability Distribution Function
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The power law exponent (sometimes called the power law coefficient) is a number that characterizes the wind shear, which is the change in wind speed with height above ground. The power law uses the power
law exponent as a parameter. The graph below shows the effect of the power law exponent on the wind
shear profile predicted by the power law. Each line on the graph corresponds to a different power law
exponent, indicated in the graph with the symbol α. In all cases, the wind speed is 10 m/s at 100m above
ground. The wind speed at lower heights decreases with increasing power law exponent.
For data sets that contain wind speed data for two or more different heights above ground, Windographer calculates the power law exponent from the observed wind shear profile. To do so, Windographer solves
for the value of the power law exponent that causes the power law profile profile to most closely fit the
measured wind shear profile.
Note: Windographer can calculate the wind shear only if the data set contains two
or more wind speed sensors at different heights. For data sets that contain wind speed data from only one height above ground, Windographer simply sets the
power law exponent equal to the default value of 0.14.
The shape of the wind shear profile typically depends on several factors, most notably the roughness of the surrounding terrain and the stability of the atmosphere. Since the atmospheric stability changes with
season, time of day, and and meteorological conditions, the power law exponent also tends to change in
time.
The value of the power law exponent that Windographer displays on the Summary tab is based on the overall wind shear profile, meaning the wind shear profile that Windographer calculates from the entire
data set. But Windographer also calculates the wind shear profile and corresponding power law exponent
for each month of the year, each hour of the day, each wind direction sector, and each individual time step. The Wind Shear Analysis window displays the results of these calculations.
Power Law Exponent
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As with any calculated column, you can view the power law exponent data column in each time step using
all of the graphic and tabular formats that Windographer provides. You could, for example, plot the power law exponent versus the wind speed on the Scatterplot tab.
Windographer displays the overall best fit power law exponent on the Summary tab and the Data Set
Summary table on the Tables tab.
See also
Wind shear
Surface roughness
Summary tab
Tables tab
Wind Shear Analysis window
Calculated column
Written by: Tom Lambert
Contact: [email protected] Last modified: April 25, 2008
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A probability transformation is a statistical procedure by which one modifies a set of numbers to conform to a desired probability distribution function.
To perform a probability transformation, Windographer first calculates the cumulative distribution function
of the original set of data -- we will refer to this as the 'original CDF'. Then for each original data point, it
performs the following steps:
1. It refers to the original CDF to calculate the percentile value corresponding to that original data
point
2. It refers to the desired CDF to calculate the transformed value corresponding to that same
percentile value
Let's look at an example to illustrate this process. Imagine that we have a set of data that conform to a normal distribution, and we want to transform it so that it conforms to a Weibull distribution.
(Windographer does exactly this when synthesizing wind speed data.)
If our normally-distributed data had a mean of zero and a standard deviation of 1, its probability
distribution function would look like so:
And its cumulative distribution function -- the original CDF -- would look like so:
Probability Transformation
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Imagine that we wish to transform this data to fit a Weibull distribution with a mean value of 6 and a
Weibull k value of 2. Our desired probability distribution function would therefore look like so:
And our desired cumulative distribution function -- the desired CDF -- would look like so:
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To transform each value in the original data set, we would refer to the original CDF to find its
corresponding y-value, then we would take that same y-value to the desired CDF and find its corresponding x-value.
An original value of zero, for example, corresponds to a CDF value of 0.5 on the original CDF. Looking at
the desired CDF, we find that the value corresponding to a CDF value of 0.5 is approximately 5. That
means that any zero value in the original data set gets transformed into a value of 5 in the transformed
data set. Similarly, an original value of -1 would be transformed to value of approximately 2.5, and an original value of 1.5 would be transformed to a value of approximately 10.
This example looks at transforming data from a normal distribution to a Weibull distribution, but with this
same probability transformation approach, we could transform from any distribution to any other
distribution.
See also
Cumulative distribution function
Probability distribution function
Synthesize Wind Speed Data Tool
Written by: Tom Lambert
Contact: [email protected]
Last modified: February 23, 2009
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The rated power of a wind turbine is its nameplate capacity, meaning the amount of power it produces under the wind speed and air density conditions at which the manufacturer rates the turbine. Under
different conditions, the wind turbine may produce a different amount of power.
Written by: Tom Lambert
Contact: [email protected]
Last modified: September 6, 2006
Rated Power
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For a set of 10-minute time steps, each of which contains a value of turbulence intensity, the representative turbulence intensity is equal to the 90th percentile of the turbulence intensity values,
meaning the value below which 90% of the values fall. If the turbulence intensity values conform to a
normal distribution, the 90% percentile is equal to the mean plus 1.28 standard deviations.
Windographer does assume that the turbulence intensity values are normally distributed, so it calculates
the representative turbulence intensity as the mean plus 1.28 standard deviations.
For example, the following table contains 30 time steps of data on mean wind speed, standard deviation of wind speed, and turbulence intensity, which is the ratio of the two:
Representative Turbulence Intensity
Date Time
Mean
Wind
Speed
Std. Dev. of
Wind Speed
Turbulence
Intensity
12/22/2007 15:30:00 14.5 1.2 0.0828
12/22/2007 15:40:00 14.2 1.3 0.0915
12/22/2007 15:50:00 12.6 1.2 0.0952
12/22/2007 16:00:00 12.3 1.0 0.0813
12/22/2007 16:10:00 13.3 0.8 0.0602
12/22/2007 16:20:00 13.0 1.0 0.0769
12/22/2007 16:30:00 13.4 1.0 0.0746
12/22/2007 16:40:00 13.7 1.0 0.0730
12/22/2007 16:50:00 12.8 1.0 0.0781
12/22/2007 17:00:00 13.7 1.0 0.0730
12/22/2007 17:10:00 14.2 0.9 0.0634
12/22/2007 17:20:00 13.9 0.7 0.0504
12/22/2007 17:30:00 13.8 0.9 0.0652
12/22/2007 17:40:00 13.6 1.1 0.0809
12/22/2007 17:50:00 13.1 1.0 0.0763
12/22/2007 18:00:00 12.2 1.1 0.0902
12/22/2007 18:10:00 12.5 1.0 0.0800
12/22/2007 18:20:00 12.4 0.8 0.0645
12/22/2007 18:30:00 12.7 0.8 0.0630
12/22/2007 18:40:00 12.3 1.1 0.0894
12/22/2007 18:50:00 12.1 0.7 0.0579
12/22/2007 19:00:00 12.7 0.7 0.0551
12/22/2007 19:10:00 11.4 1.0 0.0877
12/22/2007 19:20:00 12.1 1.0 0.0826
12/22/2007 19:30:00 11.6 0.8 0.0690
12/22/2007 19:40:00 11.4 0.9 0.0789
12/22/2007 19:50:00 10.7 0.7 0.0654
12/22/2007 20:00:00 11.0 0.8 0.0727
12/22/2007 20:10:00 10.8 0.8 0.0741
12/22/2007 20:20:00 11.7 0.7 0.0598
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Those 30 turbulence intensity values (highlighted in the table) have a mean of 0.0738 and a standard
deviation of 0.0113. Windographer would therefore calculate the representative turbulence intensity over that period as 0.0738 + 1.28 * 0.0113 = 0.0883.
The 3nd edition of IEC standard 61400-1 defined standard turbulence categories based on the value of the
representative turbulence intensity at 15 m/s. For more information, please see the article on the
Turbulence Analysis window.
See also
Turbulence Analysis window
Turbulence intensity
Characteristic turbulence intensity
Written by: Tom Lambert
Contact: [email protected]
Last modified: September 11, 2008
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The roughness class is a dimensionless number based on the value of the surface roughness. The following equation defines the roughness class C:
where z0 is the surface roughness in m.
The following graph illustrates this relationship:
The following table lists the terrain types characteristic of various values of roughness class. Source:
Wind Energy Reference Manual at www.windpower.org.
Roughness Class
Roughness Class
Surface
Roughness (m)
Landscape Type
0 0.0002 Water surface
0.5 0.0024Completely open terrain with a smooth surface, e.g. concrete runways in airports, mowed grass, etc.
1 0.03Open agricultural area without fences and hedgerows and very scattered buildings. Only softly rounded hills.
1.5 0.055 Agricultural land with some houses and 8 metre tall
sheltering hedgerows with a distance of approx. 1250
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Windographer displays the roughness class on the Summary tab and the Data Set Summary table.
See also
Surface roughness
Summary tab
Data Set Summary table
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 25, 2005
metres.
2 0.1
Agricultural land with some houses and 8 metre tall
sheltering hedgerows with a distance of approx. 500 metres.
2.5 0.2
Agricultural land with many houses, shrubs and plants, or 8
metre tall sheltering hedgerows with a distance of approx. 250 metres.
3 0.4Villages, small towns, agricultural land with many or tall sheltering hedgerows, forests and very rough and uneven
terrain.
3.5 0.8 Larger cities with tall buildings.
4 1.6 Very large cities with tall buildings and skycrapers.
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For any data column in the data set, Windographer calculates the standard deviation using the following equation:
Windographer reports standard deviations in the Annual Statistics, Monthly Statistics, Directional Statistics, and Wind Speed Sensor Summary tables. You can generate these tables on the Tables tab of
the main window.
Tip: The overall standard deviation for a wind speed data column represents the
breadth of the distribution of wind speeds over a long period, such as a month, a
year, or the entire data set. It is different from the data column that contains the standard deviation of wind speeds within each time step, and the turbulence
intensity which Windographer calculates from that data column.
See also
Coefficient of variation
Turbulence intensity
Tables tab
Annual Statistics table
Monthly Statistics table
Directional Statistics table
Wind Speed Sensor Summary table
Written by: Tom Lambert
Contact: [email protected]
Last modified: March 7, 2007
Standard Deviation
where:
zi is the average value in time step i
is the long-term average value
N is the number of time steps
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The surface roughness (sometimes called surface roughness length or just roughness length) is a number that characterizes the wind shear, which is the change in wind speed with height above ground. The
logarithmic law uses the surface roughness as a parameter. The graph below shows the effect of the
surface roughness on the wind shear profile predicted by the logarithmic law. Each line on the graph
corresponds to a different value of surface roughness, indicated in the graph with the symbol z0. In all
cases, the wind speed is 10 m/s at 100m above ground. The wind speed at lower heights decreases with
increasing surface roughness.
Different types of terrain are characterized by different values of surface roughness. The following table
shows typical surface roughness values for several types of terrain. Source: Manwell et al. (2002).
Reproduced with permission.
For data sets that contain wind speed data for two or more different heights above ground, Windographer
calculates the surface roughness from the observed wind shear profile. To do so, Windographer solves for
Surface Roughness
Terrain Description Surface Roughness (m)
Very smooth, ice or mud 0.00001
Calm open sea 0.0002
Blown sea 0.0005
Snow surface 0.003
Lawn grass 0.008
Rough pasture 0.01
Fallow field 0.03
Crops 0.05
Few trees 0.10
Many trees, few buildings 0.25
Forest and woodlands 0.50
Suburbs 1.50
Centers of cities with tall buildings 3.00
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the value of surface roughness that causes the logarithmic wind shear profile to most closely fit the
measured wind shear profile. For data sets that contain wind speed data from only one height above ground, Windographer cannot calculate the surface roughness so it simply sets it equal to the default
value of 0.01m.
The wind shear profile is affected not only by the roughness of the surrounding terrain, but also by other
factors, most notably the stability of the atmosphere. Since the stability of the atmosphere changes with
season, time of day, and meteorological conditions, the shape of the wind shear profile (and hence the surface roughness) also changes in time.
The value of surface roughness that Windographer displays on the Summary tab is based on the overall
wind shear profile, meaning the wind shear profile that Windographer calculates from the entire data set. But Windographer also calculates the wind shear profile and corresponding surface roughness for each
month of the year, each hour of the day, each wind direction sector, and each individual time step. The
Wind Shear Analysis window displays the results of these calculations. These calculations are only possible for data sets that contain two or more anemometers at different heights.
As with any calculated column, you can view the data column containing the surface roughness in each time step using all of the graphic and tabular formats that Windographer provides. The example below
shows a scatterplot of the surface roughness versus the solar radiation. The graphs illustrates a common
phenomenon, which is that the highest surface roughness values typically coincide with the lowest values of solar radiation. That is because high levels of solar radiation tend to cause greater atmospheric
instability and mixing of layers, which causes the wind speeds in the different vertical layers to be
relatively consistent, resulting in low surface roughness values. During times of low or no solar radiation, the atmosphere tends to be much more stable so the layers do not mix, and the wind speed tends to
increase sharply with increasing height above ground, resulting in high surface roughness values. You can
create scatterplots on the Scatterplot tab.
Windographer displays the overall best-fit surface roughness on the Summary tab and in the Data Set
Summary table.
Note that Windographer always calculates the surface roughness in metres, regardless of the units used
for height above ground.
See also
Wind shear
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Power law exponent
Roughness class
Summary tab
Scatterplot tab
Data Set Summary table
Wind Shear Analysis window
Calculated column
Written by: Tom Lambert Contact: [email protected]
Last modified: April 25, 2008
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The turbulence intensity, or TI, is a dimensionless number defined as the standard deviation of the wind speed within a time step divided by the mean wind speed over that time step. Windographer calculates
the turbulence intensity in each time step using the following equation:
For each wind speed sensor for which you have identified an associated standard deviation column,
Windographer creates a calculated column containing the turbulence intensity in every time step. It
names this calculated column the same as the wind speed sensor name, plus the letters "TI". For example, if the wind speed column name is "Speed 60m", its associated turbulence intensity column will
have the name "Speed 60m TI".
Tip: The Configure Data Set window allows you to indicate whether a wind speed
column has an associated standard deviation column.
You can view turbulence intensity column in all the graphic and tabular formats that Windographer provides for any other data column. The example below shows a scatterplot of the turbulence intensity
versus the wind speed. The graph shows a typical phenomenon: the highest values of turbulence intensity
tend to correspond to the lowest wind speeds. You can create scatterplots on the Scatterplot tab.
For a detailed look at turbulence, see the Turbulence Analysis window.
See also
Turbulence Intensity
where:
Ui is the average wind speed in time step i
σι is the standard deviation of the wind speed within time step i [same units as Ui]
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Turbulence Analysis window
Configure Data Set window
Wind Speed Sensor Summary table
Calculated column
Scatterplot tab
Written by: Tom Lambert Contact: [email protected]
Last modified: February 26, 2009
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The uppermost wind speed sensor is the wind speed sensor with the highest measurement height. For instance, if your data set contains wind speed sensors at 10m, 30m and 60m above ground, the 60m
sensor is the uppermost.
By default, many of the graphs that Windographer produces show data from the uppermost wind speed
sensor.
Note that you can see and modify the measurement height of any wind speed or direction sensor in the
Configure Data Set window.
See also
Configure Data Set window
Reports tab
Written by: Linda Sloka
Contact: [email protected] Last modified: February 24, 2009
Uppermost Wind Speed Sensor
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To find the mean wind direction over some time interval, Windographer calculates a vector mean, also called a vector average. One calculates a vector mean by adding multiple vectors together tip to tail, and
calculating the direction of the resultant vector.
The example below shows the vector mean of three vectors: the first with a length of 38 and a direction of
320°, the second with a length of 90 and a direction of 45°, and the third with a length of 20 and a
direction of 340°. The resultant vector, which appears black in the diagram, has a vector mean direction of 16.2°.
Note: A simple calculation of the scalar mean direction often gives erroneous results. In the example above, the scalar mean direction is 235° (one third the
sum of 320°, 45°, and 340°). This is virtually the opposite direction of the true
vector mean.
To calculate a vector mean direction over several time steps for a particular wind direction sensor,
Windographer constructs a wind speed vector for each time step and adds these vectors tip to tail as
shown above. To calculate the wind speed vector for a particular time step, Windographer uses the value of wind speed from the wind speed sensor that is nearest in height to the wind director sensor of interest.
Windographer reports vector mean wind directions in the graph on the Time Series tab (when showing
daily, monthly, or annual averages) and in the Annual Statistics and Monthly Statistics reports on the
Tables tab.
See also
Tables tab
Time Series tab
Annual Statistics table
Vector Mean
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Monthly Statistics table
Wind Speed Sensor Summary table
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 23, 2008
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Wind analysts typically use the Weibull distribution to characterize the breadth of the distribution of wind speeds. The following equations give the probability distribution function and the cumulative
distribution function of the two-parameter Weibull distribution:
where U is the wind speed, k is a unitless shape factor, and c is a scale factor with the same units as U.
The following equation gives the relationship between the scale factor c and the long-term average wind
speed:
where is the long-term average wind speed and Γ is the gamma function.
The Weibull k value reflects the breadth of the distribution; the broader the distribution, the lower the
value of the Weibull k. The graph below shows several Weibull distributions, all with an average wind
speed of 7 m/s, but with the Weibull k value varying from 1.5 to 3.5.
Weibull Distribution
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The Weibull distribution often fits measured wind speed distributions well. The graph below shows a
measured wind speed distribution along with the best-fit Weibull distribution:
Windographer uses the maximum likelihood algorithm to fit a Weibull distribution to a measured wind
speed distribution.
See also
Weibull k
Weibull c
Maximum likelihood algorithm
Written by: Tom Lambert
Contact: [email protected] Last modified: February 24, 2008
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The Weibull c value is the scale factor from the Weibull distribution. This factor is related to the average wind speed by the following equation:
Windographer calculates the best-fit Weibull parameters using the maximum likelihood method (Stevens and Smulders, 1979). For details, please see the article on the Weibull distribution.
The Weibull parameters appear on the PDF tab and in numerous tables on the Tables tab.
See also
Weibull k
Weibull distribution
Tables tab
Written by: Tom Lambert Contact: [email protected]
Last modified: April 25, 2007
Weibull c
where:
is the average wind speed
Γ is the gamma function
k is the Weibull k factor
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The Weibull k value is the unitless shape factor from the Weibull distribution. This factor reflects the breadth of the distribution, with lower values corresponding to broader distributions. The graph below
shows five Weibull distributions, all with an average wind speed of 7 m/s. Lower k values correspond to
broad distributions where the wind speed tends to vary widely, whereas higher k values correspond to
tighter distributions where the wind speed tends to stay within a narrower range.
To calculate the best-fit Weibull parameters, Windographer uses the maximum likelihood method (Stevens and Smulders, 1979). For details, please see the article on the Weibull distribution.
Windographer displays the best-fit Weibull parameters on the PDF tab and in the Annual Statistics,
Monthly Statistics, Directional Statistics, and Wind Speed Sensor Summary tables. You can create these
tables on the Tables tab.
See also
Weibull distribution
Weibull c
PDF tab
Tables tab
Annual Statistics table
Monthly Statistics table
Directional Statistics table
Wind Speed Sensor Summary table
Written by: Tom Lambert Contact: [email protected]
Last modified: May 4, 2006
Weibull k
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For each wind speed column in the data set, Windographer calculates the average wind energy content using the following equation:
Windographer displays the mean wind energy content for each wind speed sensor in the Wind Speed
Sensor Summary table.
See also
Wind power density
Wind Speed Sensor Summary table
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 25, 2008
Wind Energy Content
where:
is the average wind energy content [kWh/m2/yr]
is the average wind power density [W/m2]
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The wind power class is a number indicating the mean energy content of the wind resource. Wind power classes are based on the mean wind power density at 50 metres above ground, according to the following
table:
Source: Wind Energy Resource Atlas of the United States
(http://rredc.nrel.gov/wind/pubs/atlas/tables/A-8T.html)
Windographer classifies any wind resource with an average wind power density above 2000 W/m2 as class 8.
Windographer displays the wind power class on the Summary tab and in the Data Set Summary table.
See also
Calculating the mean wind power density at 50m
Wind power density
Summary tab
Data Set Summary table
Written by: Tom Lambert
Contact: [email protected] Last modified: April 25, 2008
Wind Power Class
Wind Power Class Description Power Density at 50m (W/m2)
1 Poor 0-200
2 Marginal 200-300
3 Fair 300-400
4 Good 400-500
5 Excellent 500-600
6 Outstanding 600-800
7 Superb 800-2000
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For each wind speed column in the data set, Windographer calculates the wind power density at each time step using the following equation:
For each wind speed sensor, Windographer creates a calculated column containing the wind power density in every time step. It names this calculated column the same as the wind speed sensor name, plus the
letters "WPD". For example, if the wind speed column name is "Speed 60m", its associated wind power
density column will have the name "Speed 60m WPD". You can view the wind power density column in all the graphic and tabular formats that Windographer provides, as you can for any other data column.
Windographer calculates the overall mean wind power density for each wind speed sensor, and displays
this value in the Wind Speed Sensor Summary table. It also calculates the wind power density at 50m
above ground to determine the wind power class. For more information please refer to the article on the
wind power class.
See also
Air density
Wind power class
Wind energy content
Wind Speed Sensor Summary table
Calculated column
Written by: Tom Lambert
Contact: [email protected]
Last modified: February 26, 2009
Wind Power Density
where:
is the wind power density, or the power per unit area, within the time step [W/m2]
ρ is the air density within the time step [kg/m3]
U is the average wind speed within the time step [m/s]
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Wind shear refers to the change in wind speed with height above ground. The wind speed tends to increase with the height above ground, as in the following example where the wind speed was measured
at eight different heights above ground, from 0.5 m to 49 m:
The variation in the wind speed with height above ground can be called the wind shear profile. In the field of wind resource assessment, analysts typically use one of two mathematical relations to characterize the
measured wind shear profile: the logarithmic profile (log law) and the power law profile (power law). Brief
descriptions of each follow.
Logarithmic Profile
The logarithmic law (or log law) assumes that the wind speed varies logarithmically with the height above
ground according to the following equation:
If you know the average wind speed for two or more heights above ground, you can do a curve fit on the
Wind Shear
where:
U(z) is the wind speed [m/s] at some height above ground z [m]
U* is the friction velocity [m/s]
k is von Karman's constant (0.4)
z0 is the surface roughness [m]
ln is the natural logarithm
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above equation to find the surface roughness. Knowing the surface roughness and the wind speed at
some height z1, one can use the logarithmic law to calculate the wind speed at another height z
2 using the
following equation:
For data sets comprising two or more anemometers at different heights above ground, Windographer
solves for the surface roughness value that causes the logarithmic profile to best fit the measured wind shear profile. (To do so, Windographer uses a linear least squares algorithm to fit a straight line to the
graph of average wind speed versus the logarithm of the height above ground.) The following graph
compares the resulting best-fit logarithmic profile to the measured wind shear profile we saw earlier:
Power Law Profile
The power law assumes that the wind speed varies with the height above ground according to the following equation:
If you know the average wind speed for two or more heights above ground, you can do a curve fit on the above equation to find the power law exponent. Knowing the power law exponent and the wind speed at
some height z1, one can use the power law to calculate the wind speed at another height z
2 using the
following equation:
where:
U(z) is the average wind speed (in m/s) at some height above ground z (in m)
β is a constant [m/s]
α is the power law exponent
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For data sets comprising two or more anemometers at different heights above ground, Windographer solves for the power law exponent that causes the power law profile to best fit the measured wind shear
profile. (To do so, Windographer uses a linear least squares algorithm to fit a straight line to the graph of
the logarithm of the average wind speed versus the logarithm of the height above ground.) The following
graph compares the resulting best-fit power law profile to the measured wind shear profile we saw earlier:
Note that although the power law profile fits the data more precisely than does the logarithmic law profile
in this case, in other cases the logarithmic law profile fits the data more precisely.
See also
Wind Shear Analysis window
Surface roughness
Power law exponent
Negative wind shear
Written by: Tom Lambert
Contact: [email protected]
Last modified: February 16, 2007
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A Windographer document contains:
� the data you have imported from one or more raw data files
� the data column types, names, colors, and other information you have entered in the Configure
Data Set window
� any synthetic data you have generated to fill gaps using the Quality Control window or the Gap
Filling window
� any data columns you have added to the data set using the Virtual Anemometer window or the
Wind Turbine Output window
� a record of all the raw data files have imported and all the changes you have made to the data set
The Windographer document reflects all the changes you have made to the data set. So if you delete a section of data using the Quality Control window or the Delete Data window, that section will appear as a
gap when you view the Windographer document. Similarly, if you delete a data column using the Delete
Data window, that data column will no longer appear in the Windographer document. If you apply a slope or offset to a data column using the Modify Data Columns window, the Windographer document will
contain the modified, not the original, data.
Tip: You can see a record of all the changes you have made to the data set in the
Document History window. The History tab of the Quality Control window shows a
more detailed record of any changes you have made in the Quality Control
window.
When you save a Windographer document, the program creates a file with a .windog extension. These
files use a binary file format that is compact and fast to read and write, but only Windographer can read them.
You can export data from a Windographer document to a text file using the Export Data window. You can
also export data from individual tables and graphs to text files, image files, or the clipboard. See the
articles on exporting graphs and exporting tables for details.
Note that the Windographer help system uses the terms 'Windographer document' and 'data set' interchangeably.
See also
Importing raw data files
Appending data
Configure Data Set window
Document History window
Quality Control window
Wind Turbine Output window
Virtual Anemometer window
Exporting data
Exporting graphs
Exporting tables
Windographer Document
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The process by which Windographer estimates the energy output of a wind turbine in the measured wind regime consists of four major steps. First, it estimates the wind speed at the hub height of the wind
turbine in each time step. Second, it uses the hub height wind speed and air density in each time step to
estimate the gross power output of the wind turbine in each time step. Third, it finds the overall mean and
the mean in each month of the gross power output, and multiplies this by the overall loss factor to calculate the mean net power output for each month and for the entire data set. Fourth and finally, it
multiplies the mean net power output by the number of hours in a year (8,760) to find the annual mean
net energy production, and it similarly multiplies the monthly mean net power outputs by the number of hours in each month to find the monthly mean net energy production.
Determining the wind speed at hub height
If the data set contains wind speeds measured at the turbine hub height, Windographer will simply use
those wind speeds. Otherwise, it will synthesize wind speed data at the hub height for each time step in the data set. In doing so, Windographer solves for the best-fit power law profile in each time step and
uses that profile to extrapolate (or interpolate) to the hub height. For greater control of this process, use
the Virtual Anemometer window to synthesize the wind speed at hub height before calculating the output of the wind turbine.
Estimating turbine power output in each time step
Windographer assumes that the gross turbine power output (before accounting for losses) depends on
three factors: the turbine's power curve, the wind speed at hub height, and the air density. To calculate
the gross power output in a particular time step, Windographer first chooses the appropriate power curve. Often the wind turbine properties comprise only one power curve, in which case no choice must be made.
But if the turbine properties comprise multiple power curves, each measured at a different air density,
Windographer chooses the power curve whose corresponding air density is closest to the air density in the current time step. The article on air density explains how Windographer calculates the air density in each
time step. The article on the Create New Wind Turbine window explains how to specify multiple power
curves for different air densities.
Once it has chosen the appropriate power curve, Windographer calculates the power output by referring to
the power curve and performing an air density correction to account for any difference between the actual air density and the air density at which the power curve applies. Windographer performs this air density
correction according to the recommendations of IEC standard 61400-12-1 (2005). The nature of this
air density correction depends on the power regulation method that the wind turbine uses.
For a stall-controlled wind turbine, Windographer first calculates the power output predicted by the power curve for the measured wind speed, then adjusts the power output according to the following equation:
Calculating the Energy Output of a Wind Turbine
where:
P0 is the power output predicted by the power curve for the wind speed in the current time step [kW]
is the actual air density in the current time step [kg/m³]
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For a pitch-controlled wind turbine, Windographer first calculates the 'effective wind speed' resulting from
the current air density, then refers to the power curve to find the power output predicted at that effective
wind speed. The following equation gives the effective wind speed:
Calculating mean net power output
Once Windographer has calculated the gross power output of the wind turbine in each time step, it
calculates the mean gross power output for each month and for the data set as a whole, then calculates
the mean net power output using the following equation:
Calculating mean net energy output
Once Windographer has calculated the mean net power output for each month of the year and for the
entire data set, it multiplies these values by the appropriate number of hours to find the turbine's mean
net energy output annually and by month. For example, to calculate the mean annual net energy output, Windographer uses the following equation:
Similarly, Windographer calculates the average net energy output for each month using the following
equations:
ρρ0 is the air density at which the power curve applies [kg/m³]
where:
U0 is the actual wind speed recorded in the current time step [m/s]
ρ is the actual air density in the current time step [kg/m³]
ρ0 is the air density at which the power curve applies [kg/m³]
where:
is the mean gross power output, before losses [kW]
foverall is the overall loss factor
where:
is the mean net power output over the entire data set [kW]
8760 is the number of hours in a (non-leap) year
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and so on,
Tip: For a more accurate estimate of wind turbine output, clean up any problems
in your data set with Quality Control window before performing the calculations in the Wind Turbine Output window.
See also
Air density
Overall loss factor
Power regulation method
Create New Wind Turbine window
Quality Control window
Virtual Anemometer window
Wind Turbine Output window
Written by: Tom Lambert Contact: [email protected]
Last modified: April 30, 2008
where:
is the mean January net power output [kW]
744 is the number of hours in January
is the mean February net power output [kW]
672 is the number of hours in a non-leap February
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The following examples illustrate the use of the Quality Control window. The data sets that appear in the images below, one from
Napoleon, North Dakota and another from Raynesford, Montana in the United States, came from the University of North Dakota's
Energy and Environmental Research Center website www.undeerc.org/wind/winddb/default.asp in January 2007. Though the
data sets are real, our analyses are purely hypothetical, and serve only to illustrate the kind of analysis you could perform with the
Quality Control window.
In the first example, we created a search rule to find anomalies in the wind direction data that might have been the result of direction
sensor icing. The search rule applied when the temperature was below freezing and the 40 metre direction varied less than six degrees
over a four-hour period.
After searching the data set, Windographer displayed in the Search results table a numbered list of all items that met the search
criteria. Item 2 in the Search results table below reports a segment of 223 time steps that meets the search criteria:
Quality Control Examples
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In the legend of the time series graph, we checked Direction 40m and Temperature to display those columns in the graph.
Tip: Check one of the leftmost checkboxes to display the variable in the top graph, or one of the
rightmost checkboxes to display it in the bottom graph. The top graph does not appear if you check none
of the leftmost checkboxes.
The time series graph shows that the 40 metre direction sensor appears to have frozen in place just before midnight on October 16
when the temperature dropped below freezing and remained frozen until midday on October 18 when the temperature rose above
freezing.
To find out whether the wind speed sensors may also have frozen, we created a new search rule. This time we searched for times
when all three speed sensors showed measurements of less than one metre per second over a two-hour period. Because we had
already created a search rule to identify wind direction sensor icing, we based our new search rule on a copy of the first one.
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After changing the name of the rule to 'All speed sensor icing' and clicking OK, we clicked Search Data Set to display a list of all items
meeting the search criteria for both search rules in the Search results table.
Windographer displayed the two rules in the Search rules table along with their identifying colors. Item 3 in the Search results table
met the criteria for the All speed sensor icing search rule and appeared with a blue highlight in the time series graph. The time series
data show a period of negative wind shear before all three speed sensors appear to freeze before midnight on October 17. At
midday on October 18, all three speed sensors appear to become unstuck.
In the previous examples, we defined search criteria to compare values in data columns to single values, searching for wind direction
values varying less than six degrees, temperature values less than zero degrees Celsius, or wind speed values less than one metre per
second. In the final example we defined search criteria to compare data columns to each other to find occurrences of negative wind
shear. We only considered times when the wind speeds decreased with height across all three anemometers over four hours.
By examining the following time series graph for Item 5, we observed that negative wind shear events tend to coincide with winds
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from the west, between about 260 degrees and 275 degrees. We might believe this pattern to be legitimate if the terrain rose steeply
to the west of the measurement station, therefore occasionally causing negative wind shear due to cold air drainage. A quick look at
the topographic map, however, shows that the terrain surrounding the measurement station is quite flat and even slopes downward to
the west. That combined with the fact that many of the negative wind shear events, including this one, occur just before or after an
apparent icing event, suggest that the negative shear events are simply artifacts of wind speed sensor malfunctions due to icing.
Because this data segment appears to be unreliable, deleting it or replacing it with synthesized data would improve the quality of the
data set.
To delete a data segment, select it by clicking and dragging on the graph, then click the button labeled Delete. For a full description of
deleting, replacing, and restoring data segments, please see the article on the Quality Control window.
See also
Wind shear
Negative wind shear
Quality Control Window
Written by: Paul Gilman
Contact: [email protected]
Last modified: January 29, 2007
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� IEC Standard 61400-1 2nd Edition (1999) 'Wind turbine generator systems – Part 1: Safety
requirements'
� IEC Standard 61400-1 3rd Edition (2005) 'Wind turbine generator systems – Part 1: Design
requirements'
� IEC Standard 61400-12-1 1st Edition (2005) 'Wind turbines – Part 12-1: Power performance
measurements of electricity producing wind turbines'
� Manwell JF, McGowan JG, and Rogers AL (2002) Wind Energy Explained, John Wiley & Sons
Limited.
� Stevens MJM, Smulders PT (1979) 'The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes', Wind Engineering, 3, 132-145
� Wind Energy Resource Atlas of the United States at http://rredc.nrel.gov/wind/pubs/atlas/tables/A-8T.html
� Wind Energy Reference Manual at www.windpower.org (last accessed April 30, 2008)
Written by: Tom Lambert
Contact: [email protected]
Last modified: April 30, 2008
References