Comparison of Methods for Wind TurbineCondition Monitoring with SCADA Data

Michael WilkinsonGL Garrad Hassan UK†

Michael.Wilkinson@gl-garradhassan.com

Brian DarnellGL Garrad Hassan, UK†

Brian.Darnell@gl-garradhassan.com

Thomas van DelftGL Garrad Hassan, UK†

Thomas.VanDelft@gl-garradhassan.com

Keir HarmanGL Garrad Hassan, UK†

Keir.Harman@gl-garradhassan.com

†GL Garrad Hassan, St Vincent’s Works, Silverthorne Lane, Bristol BS2 0QD, UK+44 117 972 9900

AbstractWind turbine operational costs can be reducedby monitoring the condition of majorcomponents in the drivetrain. SCADA-basedcondition monitoring is attractive because thedata are already collected, resulting in rapiddeployment and modest set-up cost.

Three SCADA-based monitoring methods werereviewed. The Physical Model was identified asbeing the most reliable at predicting impendingcomponent failures.

A validation study on this method using 5operational wind farms showed that it ispossible to achieve a high detection rate andgood detection accuracy. An advance detectionperiod of between 1 month and 2 years wasachieved by the method.

The study has also highlighted limitations andareas for further development.

1 IntroductionWind turbine reliability remains an importantfocus for wind turbine owners, operators andmanufacturers. The effect of low reliability onwind turbine and wind farm availability isparticularly acute in the offshore environment,where a failure can lead to prolonged downtime.

Figure 1 presents availability versus the yearsof wind farm operation, with zero being thecommissioning date [1]. The figure illustrates agap between highest and lowest availability,which indicates there are a number of windfarms with an opportunity to makeimprovements in this area. A key factor forgood wind farm availability is high turbinereliability.

Reliability can be improved and downtimereduced through the use of conditionmonitoring. The use of SCADA data forcondition monitoring has a number ofadvantages; in particular the data are already

Figure 1: Availability versus time trend, reproduced from [1]

Presented at EWEA 2013 AnnualEvent, Vienna, 4-7 February 2013

collected, requiring no additional hardware.

Different approaches are available to utiliseSCADA data to predict the health of operationalwind turbines. Generally, they can be groupedinto three categories: Signal Trending, ArtificialNeural Networks (ANN) and Physical Models.This paper describes the results of aninvestigation into these approaches: examplesof application to real data sets are given andthe relative merits are compared. The methodwhich was found to be most effective was thensubjected to a large validation study, whichtested the effectiveness of the techniques indetecting a range of different componentfailures on 472 wind farm years of SCADA datafrom multiple turbine manufacturers.

2 Review of MethodsAn investigation has been undertaken intothree different approaches available forcondition monitoring with SCADA data.

2.1 Signal TrendingThe approach of Signal Trending generallyinvolves comparing one turbine against anotheror one period of time against another. Forexample, when the gearbox temperature onone turbine rises compared to a neighbour thismay indicate an impending failure. Thisapproach can be further refined through theapplication of appropriate filtering such as onlycomparing when the turbines are operational or

normalising a component temperature with anexternal ambient temperature.

An example of this technique is provided inFigure 2, which shows a time series ofnormalised temperature of a drivetraincomponent in a test turbine compared to fourcontrol turbines. The time series was derivedfrom 10-minute SCADA data by normalising thetemperature of the component to the wind farmaverage component temperature, calculated foreach 10-minute record. The averagecomponent temperature in the wind farm isassigned a value of 100%. The increasingdeviation of the test turbine compared to thecontrol turbines corresponds to increasing wearon the drivetrain component, and indicates anelevated risk of failure.

2.2 Artificial Neural NetworkMethods

An Artificial Neural Network (ANN) is a learningalgorithm that is used to reveal patterns in dataor to model complex relationships betweenvariables. In terms of wind turbine conditionmonitoring, ANNs have the potential to identifychanges in the relationships between SCADAsignals that indicate the development of afailure [2].

Multiple ANN methods have been investigatedand the Self-Organising Map (SOM) [3] hasbeen identified as the most promising in thiscontext. The application of SOMs for the

Figure 2: Time series of normalised component temperature of a test turbinecompared to four control turbines

purpose of wind turbine condition monitoringhas previously been explored, with somesuccess, by Catmull in [4].

SOMs are used to group similar phenomena ina dataset, with their topographicalrepresentations visualising high dimensionaldata.

Map Generation AlgorithmThe algorithm used to produce a SOM map forcondition monitoring analysis is as follows:

1. Choose the most appropriate data signalsfor the analysis (pre-processing ifnecessary), selecting a period of data thatrepresents the full range of normaloperation.

2. Clean the data of spurious values.3. Normalise the data.4. Randomise the map's nodes' weight

vectors.5. Randomly select an input data vector

(i.e.data for one timestamp).6. Traverse every node in the map.

i. Find the Euclidean distance betweenthe input vector and the node's weightvector.

ii. Determine the closest node (the ‘bestmatching unit’, BMU).

7. Update the nodes in the neighbourhood ofthe BMU by pulling them closer to the inputvector according to:

where:current iterationnode vectorinput vectorlearning rateneighbourhood function

8. Increment t and repeat from step 5 untilevery input data vector has been applied tothe map.

9. Repeat steps 5 to 8 a number of times torefine the map.

Example – Turbine Production CurtailmentTo illustrate how a SOM groups togethersimilar phenomenon, Figure 3 shows a SOMgenerated from wind speed and power signalsduring a period in which some power de-ratingoccurred. The unified distance matrix (U-matrix) map shows where similarities exist inthe relationship between inputs. The lightcolours on the U-Matrix show how the databranches into to two forks. The correspondingmap nodes on the wind speed layer and powerlayer show that one fork consists of high windspeed and high power and the other consists ofhigh wind speed and low power, the latterbeing de-rating.

Comparing DatasetsSOMs also allow one dataset to be measuredagainst another, thus allowing a dataset thatpotentially contains a developing failure, to becompared with a benchmark dataset of normal

Figure 3: The application of SOM methods to identify a power performance effect

operation. This is achieved by the followingmethod:

1. Generate a SOM in the method describedabove using a benchmark dataset.

2. With the test dataset, take the same signalsand apply the same pre-processing andnormalisation factors that were used ingenerating the map.

3. Select the first data point in the test dataset.

4. Traverse every node in the map.

I. Find the Euclidean distance betweenthe input vector and the node's weightvector.

II. Determine the BMU in the map.

III. Calculate the distance between theinput vector and the BMU's weightvector – this is known as the‘quantisation error’.

5. Select the next data point in the newdataset and repeat step 4 until each onehas an associated quantisation error.

Applying this method to wind turbine SCADAdata produces a time series of quantisationerrors which describe the deviation of the testdataset from the benchmark dataset. Figure 4shows increasing quantisation errors in themonths prior to gearbox failures.

2.3 Physical ModelsA description of the Physical Model generalapproach is given by Gray & Watson [5] and

Feng, et al [6] report on using the relationshipbetween SCADA signals for failure detection ingearboxes.

The energy balance for a generic component inthe wind turbine drivetrain is presented inFigure 5. It shows that the componenttemperature, T, depends on the mechanicalpower delivered, the component rotationalspeed, the cooling system duty (if applicable)and the surrounding temperature. Therefore, amodel that predicts the component temperatureshould include these properties.

The configuration of the wind turbine drivetrains were used to inform the choice of inputvariables used in the model. In some cases, itwas necessary to know the setup of the coolingsystem and signals such as oil/cooling watertemperature, pump/fan status, etc.

In order to predict the component temperature,an algorithm was developed using a number oftest datasets. The correlation was of the form:

Where:

= Coefficient of determination forthe given input

= Gradient of correlation

= Offset of correlation

Higher order polynomials were applied in somecases where they resulted in a higher overall

Figure 4: Time series of quantisation errors of gearbox temperatures

Gearbox failureand subsequent

replacement

Gearboxfailure

coefficient of determination, in order to improvemodel accuracy.

The model parameters were determined over achosen ‘training period’, which broadlyrepresented ‘normal’ operation. By plotting therolling average or cumulative model deviation(Actual – Predicted) over time, it was possibleto discern trends that imply deviation from therelationships established during the trainingperiod. Often these deviations result fromadditional frictional heat generated due toaccelerated wear of a drivetrain component.

In addition to temperatures, the method hasbeen applied to other low-frequency signalssuch as coolant pressure, cooling system duty,pitch motor duty cycle, etc, depending on thesignals available in the turbine SCADA system.

Figure 6 shows an example of the outputavailable from the physical model approach,showing a trend in the rolling average of thetemperature sensor of the main bearing. The

upward trend indicates excessive wear,requiring follow-up action such as inspection ofthe component.

2.4 Conclusions from theComparison Study

All three approaches studied demonstrated theability to detect component failures in advance.In order to select a method for a detailedvalidation study, each was applied to a singledataset containing several known componentfailures. The purpose was to understand whichmodel would be most suitable to developfurther as a condition monitoring technique.Therefore the models were ranked according toa set of criteria as listed in Table 1. The bestperforming method for each criterion wasassigned a score of 3, and the worst wasassigned a score of 1.

The Signal Trending approach benefits frombeing simple and readily applied to manydatasets. However, the comparison study

Figure 5: Energy balance for a wind turbine drivetrain component

Figure 6: Deviation from model for temperature sensor of a main bearing (grey line)overlaid with rolling average (black line)

found that it was not sufficiently accurate orreliable to account for intermittent changes intemperature caused by differing operationalmodes or external environmental conditions.

The SOM method was observed to be moresensitive to faults than the other approachesand so has some value in the investigation ofwind turbine health. The main drawback of theSOM method, as well as other ANN methods,is its inability to identify the nature of abnormaloperation. This makes identification ofimpending failures more difficult.

The Physical Model approach required themost effort in the initial set-up and tuning of themodels, and in the definition of suitable trainingperiods. However, once the model had beenestablished, it was found to have the mostsuccess at identifying abnormal conditions. Itproduced results which were concise and easyto interpret.

In summary, the Physical Model method wasjudged to be the most successful of theapproaches. Therefore this technique wasinvestigated further in order to measure itssuccess when applied to a range of datasets.

3 Validation StudyA large validation study was performed todetermine the effectiveness of the PhysicalModel method in detecting impending failures.

3.1 Description of the StudyBlind tests were conducted on a number ofwind farms: A total of 472 turbine-years of data

were considered including turbines frommultiple manufacturers, ranging between 2.5and 7 operational years, with differentcomponent signals available from each turbinetype. The sites are listed in Table 2. Theengineer conducting the blind test was given nodetails of the component failures, and wasasked to use the physical model toretrospectively determine the approximatefailure date and also the approximate date atwhich the deviation from the model indicatesunusual component behaviour.

The actual failure details were then determinedby studying additional data such as operationand maintenance reports.

Events where the suspected and actual failuresagree were deemed ‘True Detections’. Where afailure was suspected, but could not beconfirmed in the operation or maintenancereports, it was deemed a ‘False Detection’.

3.2 Example DetectionsFigure 7 shows examples of output from thePhysical Model, where a component failure hasbeen retrospectively detected. The figureprovides information on the component thatwas monitored, the inputs to the model, andhow far in advance the failure was detected.

For each time series in Figure 7, the firstvertical line corresponds with the point wherethe model deviation exceeds a chosenthreshold value, and the second vertical lineindicates where the component was reported tohave actually failed.

Table 2: Results of the validation study carried out on the Physical Model technique

Table 1: Scoring of methods

(a) Different signals on the same turbine

(b) Two different turbines at the same site

(c) Two different turbines at different sites

Figure 7: Example results from the validation study carried out on the PhysicalModel condition monitoring technique

3.3 ResultsThe overall results of the study aresummarised in Table 2. A high detectionrate and a good True:False ratio wasachieved for sites A, B and D. These sitesdemonstrate that the method has goodpotential for advance detection ofcomponent failures.

The detection rate was poor at C andmoderate at E. This indicates that there aresome classes of component failures whichdo not lend themselves to reliable advanceddetection using this method. This is asource of further investigation as it may bepossible to improve detection by includingalternative signals from the turbine into themodel.

A poor True:False ratio was achieved at SiteE. This site was also found to have poorerquality correlations. It was concluded thatthis is due to two reasons: 1) the duration ofoperational data available for the validationswas relatively short at 2.5 years; 2) thetiming of component failures was uncertain,resulting in challenges for definition ofsuitable training periods

3.4 Advance DetectionThe validation study showed failure ofcomponents can be detected in advance ofthe event. The advance notice period wasfound to range from 1 month to 2 years, asshown in Figure 8.

4 ConclusionsThe main findings of this study are:

The review of SCADA data conditionmonitoring methods showed that thePhysical Model is identified as being themost accurate at predicting impendingfailures.

The validation study showed that it ispossible to achieve a high detection rateand a favourable True:False detectionratio. The study has also highlightedlimitations and areas for furtherdevelopment.

An advance detection period of between1 month and 2 years was achieved bythe method.

When combined with a targetedinspection programme, the SCADACondition Monitoring method developedhere enables optimisation of operationalcosts by making early intervention torepair or replace a component showingaccelerated wear.

References[1] Harman, K., Walker, R, Wilkinson, M.,

‘Availability Trends Observed AtOperational Wind Farms’, EWEC 2008,Brussels.

[2] Garcia et al, M. C. (2006). SIMAP:Intelligent System for PredictiveMaintenance Application to the healthcondition monitoring of a wind turbinegearbox. Computers in Industry, Vol 57pp 552-568.

[3] T. Kohonen, ‘Self-Organising Maps’,Springer, Third Edition, 2001

Figure 8: Advance detection period of failures identified in the validation study

[4] S. Catmul, ‘Self-Organising Map BasedCondition Monitoring of Wind Turbines’,EWEA Conference, Brussels, 2011

[5] C.S.Gray, S.J.Watson, ‘Physics ofFailure approach to wind turbinecondition based maintenance’, WindEnergy, Volume 13, Issue 5, pp. 395-405, July 2010.

[6] Y. Feng, et al., ‘Use of SCADA andCMS Signals for Failure Detection andDiagnosis of a Wind Turbine Gearbox’,Proc. EWEA 2011, Brussels.