Geo-Lightning grid based on a Geographical Information System, to Improve polesdistribution network designs, prioritize maintenance and boost the power system

reliability.

Luis Alejandro ZorrillaWorldwide Engineering. Major Projects Dept.

Occidental Oil & Gas Corp.Houston, Texas

Alejandro_zorrilla@oxy.com

Abstract: This paper presents a grid built based on thecorrelation of lightning strikes location and amplitude in kiloamperes, to understand and improve the designs of theelectrical poles located in critical areas of significant lightningactivity. The geo-lightning grid is colored blue to red, being redcritical, to prioritize the areas. Overlapped with the electricalnetwork geographical topology, displays the sections of thenetwork that must be strengthened to meet the lightningdensity with such characteristics. The geo-lightning grid andthe electrical network are based on a GeographicalInformation System. A case study is presented to illustrate theimpact of this approach, studying a power system which feedsan Oil field, where the power supply is tied with the barrels ofoil production. Being the power reliability a milestone as aconsequence of the tremendous economic impact that a failuredue to lightning may cause.

Keywords: Power System, Reliability, Lightning,Maintenance, Pole, Grid, Electrical Network, Lightning LocationSystem (LLS), Geographical Information System (GIS).

I. INTRODUCTION

Power quality concerns have created more interest inlightning, and improved lightning protection of overheaddistribution lines against faults is being considered as a wayof reducing the number of momentary interruptions andvoltage sags.[1]

In the industry, power supply and time response are tiedto the profit, due to the tremendous economic impact that afailure in the power system may cause, and the time that actsas a multiplier increasing the severity of the losses. The Oil& Gas Industry presents a particular geographical challengein the field, that may differ from other Industries, where youhave a plant with all your equipment confined into areasonable reachable area. The main reason is because in anOil field, you need to feed big electrical loads, most of themspread in a big non residential geographical area. These loadsrequire complex dynamic power system to run, includingdifferent voltage levels, protective devices, generation, andsingle node connection to the transmission system, dynamictopology distribution networks, etc. The dynamiccharacteristic as a consequence of the new wells that need

power to operate, adds new equipment to the systemconstantly, even daily. Thus, this set of variables and due tothe amount of nodes that are susceptible to fail, make a verytough labor to understand which of the designs are failing,causing greater impact in the whole system. Lightning strikesare the physical phenomena which have adverse effects onpower delivery systems lines. Predicting the geographicaland temporal distribution of the lightning strike densitiesthrough modeling can help power system designers toimprove the protection of the existing and new powersystems and transmission lines. [2] Each distribution pole isa node that depending on its location on the system affectsthe power supply to one or multiple loads, causing severedamage. Even if all the equipment downstream a node is inperfect conditions a failure stops the production, impactingthe whole business cycle. Lightning is one of the mostcommon failure factors in distribution networks and being inthe capability of strengthen the right area of your network,determines a huge impact on the reliability of your system.Hence, understanding and boosting the critical areas exposedto lightning, allows the electrical network to overcome theparticular lightning activity of its location.

To understand and improve the designs of the electricalpoles located in critical areas of significant lightning activity,a geo-lightning grid built based on the correlation oflightning strikes location and current amplitude in kiloamperes is described in Section 2, a prioritized maintenanceto strengthen the right areas is discussed in Section 3, a casestudy to show the applicability and the impact of thisapproach is presented in Section 4. Finally, in Section 5 thework is concluded.

II. GEO-LIGHTNING GRID

Lightning is the transfer of significant charge betweentwo charged objects. Lightning discharges can occur ascloud-to-cloud, (C-C, inter-cloud and intracloud), cloud-to-air, and cloud-to-ground (CG). Since C-G lightning strikesare among the most frequent yet least understood causes ofservice interruption, [2] C-G data are the input of the geo-lightning grid. A C-G flash is typically composed of asequence of individual C-G return strikes which transfersignificant charge from the cloud to the ground, each strike

Fig. 1

(a) Geo-Lightning Grid

40 kA

20 kA

60 kA

B2

40 kA

20 kA

60 kA

B2

B2

Density: 3 Strikes

30 kA average

B2

Density: 3 Strikes

30 kA average

(b) e.g. of a cell. (c)Fig. 2

exhibiting peak current in the range of a few kilo amperes(kA) to 300 kA [3]. These flashes have a nominal duration of20 to 50 microseconds (μs), and are usually comprised of 2 to 3 strikes, but may vary between one and twenty. For mostflashes, the strike which occur after the first strike of a flashcontacts the earth at the same strike point as the first strike,because they travel through the channel established by thefirst strike. However, approximately one-third of all flashescan contain strikes with different ground strike points. Forpractical purposes, researchers have defined a typical flash asa set of all C-G discharges which occur within 10 km of oneanother within a one second interval.[2]

Lightning locating system is a powerful tool for lightninglocating and lightning disaster forecasting.[4] The lightninglocating techniques are described by V. A. Rakov and M. A.Uman in “Lightning: physics and effects.” [5]. A lightninglocating system provides both real-time data and archivedlightning information. Latitude, longitude, date, time, peakcurrent, and polarity of each lightning strike is stored in adata file.

In a geographical information system any element in theworld can be represented by a polygon, a line or a point. In aGeo Model that represents a power system, you may havepoles represented as points, cables as lines and substations aspolygons, etcetera. This Geo Model, gathers the differentattributes defined for each element of the power system.

To be able to analyze the lightning strikes data youshould:

1) Define an extent:Define an extent to study, in which you have the electricaldistribution system based on a Geographical InformationSystem (e.g. every pole has its geographical attributes: Isdefined as a point and has Latitude and longitude amongother attributes). Fig.1. (a)

2) Get a significant amount of years lightning data fromthe extent definedGet a significant amount of years lightning data from theextent defined, organized in a data base, being the attributesof each lightning strike: Latitude, longitude, date, time, peakcurrent and polarity. Fig.1. (b)

With the extent defined and the archived data, a new layer ofspatiotemporal distribution of lightning strikes defined aspoints in the Electrical Geo model can be deployed andoverlapped with the topology of the network. Fig.1. (c)

B. Definition of the Geo-lightning grid

Due to the complexity of real electrical networks and theamount of data to be analyzed, a geo-lightning grid for thestrikes is defined as follows:

The geo-lightning grid: Fig. 2. (a) Is based on the same geographical information

system of the power system network to bestudied.

Covers the extent to be studied. Is made up from cells with a defined edge, each

cell has the characteristic of a polygon. Each cell has name:

To create valuable information based on the lightningstrikes data, the geo-lightning grid is used as a tool, gatheringthe information of a number of strikes in the area of each celland the average current of these strikes.

To perform this task, a join between each cell (polygon)and the location of every strike (point) need to be done. Fig.2. (b)

Thus, after this join each cell has, but is not limited to:Fig. 2. (c)

Name Number of strikes in its area Average of peak current of the strikes in its area.

Fig. 4 Geo lightning Grid Display with Table 1. data.

Fig. 3 Geo lightning Grid Display with Table 1 data.

Fig. 5 Geo lightning Grid Display with Table 1.data.

C. Displays of the Geo-lightning grid

One of the powerful features of a GeographicalInformation system, is its capability to display any numericalattribute in a particular set of elements, colored according toa defined order in a range of the Color Palette.

Based on the theory of color, it has been provencontinuously by scientists; even from very young ageindividuals not only show specific color preferences but alsoexperience affective attraction towards color. [6]

The geo-lightning grid for all its possible displays, iscolored blue to red, being red critical, and the main reason isbecause blue is associated with happiness and pleasantemotions, and red with anger and danger [7], therefore thecritical areas will be easily recognized.

Once defined, the geo-lightning grid can easily beupdated with real time data and will dynamically update itsstate per cell.

Among many different possible displays for the geo-lightning grid, the focus lays in three of them.

Cell Strikes in cell Average kAA1 5 20A2 35 25A3 15 20B1 5 30B2 40 50B3 35 45C1 15 30C2 50 40C3 15 35

Table 1. Sample of lightning data.

1) Density of strikes, per cell, per year display.

In this display the geo-lightning grid, present de number ofstrikes inside the area of each cell (polygon) in a definedrange of time. Fig. 3

2) KA average of peak current, per strikes inside a cell,per year display.

In this display the geo-lightning grid, present de average ofthe peak current of strikes inside the area of each cell(polygon) in a defined range of time. Fig. 4

3) Correlation of KA average of peak current strikes percell per year display.

In this display the geo-lightning grid, present thecorrelation between the first two displays, and is the perfecttool to prioritize the maintenance in the power system. Fig. 5

III. STRENGHTEN THE RIGHT AREA, PRIORITIZING THE

MAINTENANCE.

Once the critical areas are identified with the geo-lightning grid, you need to prioritize the maintenance tostrengthen the right area because, with the grid you are nowable to understand the local behavior of the lightning, butthis order of critical areas deployed by the grid, might be ornot be consistent with the critical nodes in your system andor density of equipment susceptible to fail.

Because the final focus is the power system reliability, inthe critical areas, the grid must also count the number of

poles in the area, giving a significant value to the criticalnodes (e.g. To count the number of poles within a cell, anormal node value is one and each of the critical node varyin a range between two to ten).

The critical nodes are: The poles with a cascade effect, which in a

failure will affect other nodes in the system,causing unexpected losses of production innormal operation equipment.

Poles that feed part of the network with highproduction.

To perform this task: The poles must have the attribute of value per

criticality A join between each critical cell (polygon) and

the location of every pole (point) need to bedone.

Thus, after this join each critical cell has, but is notlimited to:

Name Number of strikes in its area Average of peak current of the strikes in its area Number of poles Given values per criticality

With this information, the lighting performance of thepower system, can be dramatically improved based on thealternatives for reducing lightning-cause faults presented inthe IEEE Standards [1] [8] and [9], having a significantpositive impact in the reliability of the system and indirectlyin the business cycle.

IV. CASE STUDY

The case study was focus, in an Oil field located inColombia, at the North of South America. Colombia rankssecond, after the Democratic Republic of Congo in annuallightning strikes, according to NASA’s Global Hydrologyand Climate Center[10].

Following the approach, and to be able to analyze thelightning data, an area with the information of the powersystem based on a GIS must be defined:

1) Define an extent:

The extent defined for the case study has 160 km2,located right in the center of Colombia, in the past two years,this area alone attracted 30,000 lightning strikes.

With this atmospheric activity, lightning is a majorproblem, affecting the electrical distribution network whichfeeds the engines that operate the wells, causing terribleeffects: production looses, equipment damage, uncertainty inthe designs, etc.

The electrical distribution network is made up to 3000poles, about 600 km of line length and 1000 engines. Thiselectrical infrastructure is constantly, even daily changingadding lines, poles, electrical and communication equipmentdue to the connection of new wells (engines) . This process

demands planning, designs, connection and feedback ofwork done in situ.

The pole designs that built the distribution network, weredone with the information of average lightning density andcurrent amplitude of the extent, having 30 lightning strikesper year. Albeit all the designs were previously studied,faults keep occurring in every storm day, having about 600faults in the distribution network circuits that feed the field inonly one year.

Thus, the questions were obvious: why are the designsfailing? And, how can we improve the designs? Studying theelectrical last years faults data a clue emerged, about 90% ofthe failures occurred by lightning discharges ( the shieldingangle did not protect the Lines, grounding issues) andflashovers.

A more specific and accurate measurement of thelightning density and current amplitude was required.

2) Get a significant amount of years lightning data fromthe extent defined.

Colombia’s National lightning data system has 50sensors in the extent that let one know the location and theelectrical characteristics of a lightning.

The data from 30000 strikes and its attributes: Latitude,longitude, date, time, peak current and polarity from twoyears was acquired. Around 21000 data to create valuableinformation.

Importing this data as points into the GIS, a new layer ofspatiotemporal distribution of lightning strikes can bedeployed and overlapped with the topology of the network inthe Electrical Geo model.

B. Geo-lightning grid

Due to the complexity of this electrical network and theamount of data to be analyzed, with the approach describedthrough the theory:

The geo-lightning grid: Is based on the same GIS of the power system

network to be studied. Covers the 160km2. Is made up from cells with edge 1km. Each cell has name

This grid covers all the extent, counts the lightningstrikes and gives the average current amplitude per cell.

C. Displays of the Geo-lightning grid

1) Density of strikes, per cell, per year display.Fig. 6 (a)

2) KA average of peak current, per strikes inside a cell,per year display. Fig. 6. (b)

3) Correlation of KA average of peak current strikes percell per year display. Fig. 6. (c)

(a) Strikes per cell per year. (b) Average of peak current per cell, per year (c) Correlation (a) and (b).

Fig 6. Displays of the Geo-lightning grid.

Overlapping the electrical power system with the threedifferent displays of the grid, amazing relationship betweenthe failures and the critical areas appeared.

The lightning data visualized as a grid with thecorrelation of the two variables (Density and currentamplitude kA) helps to locally understand why the polesdesigns failed.

Analyzing the geo lightning grid made up from cells of 1km2 average in lieu of 160 km2, deployed new informationwith areas with more than 60 strikes and 60 kA (more thantwice the original value) that needed to be strengthen

After the join between the poles and each critical area,the grid has, but is not limited to:

Name Number of strikes in its area ranged from 2 to

60. Average of peak current of the strikes in its area,

between 20 kA and 65 kA Number of poles from 0 to 80 Given values per criticality from 0 to 100

With this grid created based on a GIS, every pole designis being done according to its location and the alternativesfor reducing lightning-cause faults presented in the IEEEStandards [1].

V. SIGNIFICANCE OF MAJOR RESULTS, DISCUSSION, AND

CONCLUSIONS

Giving the criticality of the cells the maintenance wasprioritized. With the maintenance performed in 600 poles,corresponding to 20 percent of the network, but covering themost critical areas of the extent, the failures were reduced in80 percent, from 600 faults in 2009 to 120 faults in 2010having a tremendous positive impact in the reduction oflosses due to lightning faults.

A deep understanding of the lightning activity is nowavailable. Better decisions are done and the designsdepending on the location with similar features in lieu of aunique design for all the poles are standardized.

The benefits that result from the development of the geolightning grid are greater than anticipated. Constantly newgeo-applications have been found to boost the performanceof the Energy Management Department daily work.

REFERENCES

[1] IEEE Std 1410 ™-2010 (Revision of IEEE 1410-2004), NY January28 2011.

[2] Aram Faghfouri, Witold Kinsner, and David Swatek, “A NewApproach to the Study of Lightning-Strike Density Distributions,”IEEE International Conference on Cognitive Informatics 2010,Page(s): 401 – 408.

[3] Global Atmospherics, Fault Analysis and Lightning Location System,User’s Guide. Tucson, Arizona, USA: Document No. 40144, Rev.9910; Product Version 3.0, 1999

[4] A Zhong Chen, “The characteristic of the lightning locating systemand its application in lightning risk assessment,” in The 1stInternational Conference on Information Science and Engineering(ICISE2009).

[5] V. A. Rakov and M. A. Uman, Lightning: physics and effects.Cambridge, United Kingdom: Cambridge University Press, 2003.

[6] Karp, E. M. and H. B. Karp, Color Associations of Male and FemaleFourth-Grade School Children. The Journal of Psychology, 122 (4),383-388 (1988)

[7] K G D Tharangie, Ashu Marasinghe and Koichi Yamada, “WhenChildren Sense in Colours: Determinants of Colour – EmotionAssociations” International Conference on Biometrics and KanseiEngineering, IEEE Computer Society, 2009.

[8] IEEE Std C62.22™-2009, IEEE Guide for the Application of Metal-Oxide Surge Arresters for Alternating-Current Systems (ANSI).

[9] IEEE Std 1243™-1997, IEEE Guide for Improving the LightningPerformance of Transmission Lines.

[10] Lightning Imaging Sensor Instrument Team and the GlobalHydrology Resource Center (GHRC), NASA. USA Government.