Understanding Power System Behavior Through Mining Archived...
6
Understanding Power System Behavior Through Mining Archived Operational Data Sarasij Das, P S Nagendra Rao Abstract—This paper is the outcome of an attempt in mining recorded power system operational data in order to get new insight to practical power system behavior. Data mining, in general, is essentially finding new relations between data sets by analyzing well known or recorded data. In this effort we make use of the recorded data of the Southern regional grid of India. Some interesting relations at the total system level between frequency, total MW/MVAr generation and average system voltage have been obtained. The aim of this work is to highlight the potential of data mining for power system applications and also some of the concerns that need to be addressed to make such efforts more useful. I. I NTRODUCTION Advances in electronics, computer and information tech- nology are fueling major changes in the area of power sys- tem instrumentation. More and more microprocessor based digital instruments are replacing analog meters. Data logging is becoming automatic and frequent. Vast quantities of data generated by extensive deployments of digital instruments are creating information pressure on Utilities. The legacy SCADA based data management systems do not support management of such huge data. The present practice is to store the acquired data in SCADA for only a few months and then delete. In few cases after removing from the SCADA system, these data are stored in compact discs. At present the usefulness of historical data is not fully explored. So, utilities do not give importance to store such data efficiently. The traditional integrated power industry is going through a deregulation process. The market principle is bound to force competition between power utilities, which in turn demands a higher focus on profit. To optimize system operation and planning utilities need better decision-making processes that depend on the availability of reliable system information. It is expected that in this context historical data is going to be a vital asset. In [1] some possible applications of historical power system data is presented. Apart from a business perspective, historical data is important from another point of view also. Electric power system is a very complex system. Most of the mathematical models used for analyzing/predicting system behaviors are based on several assumptions. The availability of detailed measurements of power system parameters could provide an opportunity to Sarasij Das obtained M.Sc(Engg) in Electrical Engineering from Indian Institute of Science, Bangalore. He is currently with Power Research Devel- opment Consultant Pvt. Ltd. (e-mail: [email protected]). P S Nagendra Rao is with the Electrical Engineering Department, Indian Institute of Science, Bangalore.(e-mail: [email protected]). validate many of such models. The importance of data is being recognized widely in the recent times. Power system data management has been discussed in several works[1][2][3]. Data warehousing technology is being pro- posed to meet the future requirement of power systems. In [4] data mining as a feature of power system data warehouse is mentioned. In [5] it has been mentioned that power systems operation can be greatly improved through data analysis and/or assimilation In our work real system data is analyzed to find interrelation between several system parameters. This analysis can be viewed as a small attempt of data mining. Data mining is essentially an analysis of data sets in order to discover new relations between various quantities which is not obvious from the recorded data in its normal form. For our investigation, data of fives system parameters- voltage, frequency, MW and VAr generation, system demand - of the southern regional grid of India, collected from Southern Regional Load Despatch Center has been used. This paper is organized as follows. Section II outlines some features of the Southern Regional Grid. In Section III a brief description of the data set used is given. Section IV presents the results of data analysis. The paper is concluded in Section V. II. SOUTHERN REGIONAL GRID FEATURES The southern regional grid of India covers an area of 6,51,000 Sq. km encompasses four states namely Andhra Pradesh, Karnataka, Kerala, Tamilnadu and one Union Ter- ritory of Pondicherry. This region comprises of several central and state owned generating stations, independent power pro- ducers, distribution companies and state transmission utilities. In India, including SRLDC, there are five major regional grids. After August 2006 four of the five regions excluding the southern region are (synchronously) interconnected. The Southern region is connected with the other regional grids only in an asynchronous manner. Southern region is connected to Western region through HVDC back to back at Ramagundam- Chandrapur and to Eastern region at Jepore-Gazuwaka back- to-back and point to point HVDC line between KolarTalcher. The total installed capacity of southern region as in the beginning of 2007 is about 37370 MW. Some other salient features of the southern regional grid are [6] : • Covers approximately 19% of the geographical area, 22% of population and 29% of the installed capacity of the country. Fifteenth National Power Systems Conference (NPSC), IIT Bombay, December 2008 248
Transcript of Understanding Power System Behavior Through Mining Archived...
Understanding Power System Behavior ThroughMining Archived Operational Data
Sarasij Das, P S Nagendra Rao
Abstract—This paper is the outcome of an attempt in miningrecorded power system operational data in order to get newinsight to practical power system behavior. Data mining, ingeneral, is essentially finding new relations between data sets byanalyzing well known or recorded data. In this effort we make useof the recorded data of the Southern regional grid of India. Someinteresting relations at the total system level between frequency,total MW/MVAr generation and average system voltage havebeen obtained. The aim of this work is to highlight the potentialof data mining for power system applications and also some ofthe concerns that need to be addressed to make such efforts moreuseful.
I. INTRODUCTION
Advances in electronics, computer and information tech-nology are fueling major changes in the area of power sys-tem instrumentation. More and more microprocessor baseddigital instruments are replacing analog meters. Data loggingis becoming automatic and frequent. Vast quantities of datagenerated by extensive deployments of digital instruments arecreating information pressure on Utilities. The legacy SCADAbased data management systems do not support managementof such huge data. The present practice is to store the acquireddata in SCADA for only a few months and then delete. In fewcases after removing from the SCADA system, these data arestored in compact discs. At present the usefulness of historicaldata is not fully explored. So, utilities do not give importanceto store such data efficiently.The traditional integrated power industry is going through aderegulation process. The market principle is bound to forcecompetition between power utilities, which in turn demandsa higher focus on profit. To optimize system operation andplanning utilities need better decision-making processes thatdepend on the availability of reliable system information. Itis expected that in this context historical data is going to bea vital asset. In [1] some possible applications of historicalpower system data is presented.Apart from a business perspective, historical data is importantfrom another point of view also. Electric power system isa very complex system. Most of the mathematical modelsused for analyzing/predicting system behaviors are based onseveral assumptions. The availability of detailed measurementsof power system parameters could provide an opportunity to
Sarasij Das obtained M.Sc(Engg) in Electrical Engineering from IndianInstitute of Science, Bangalore. He is currently with Power Research Devel-opment Consultant Pvt. Ltd. (e-mail: [email protected]).
P S Nagendra Rao is with the Electrical Engineering Department, IndianInstitute of Science, Bangalore.(e-mail: [email protected]).
validate many of such models. The importance of data is beingrecognized widely in the recent times.Power system data management has been discussed in severalworks[1][2][3]. Data warehousing technology is being pro-posed to meet the future requirement of power systems. In[4] data mining as a feature of power system data warehouseis mentioned. In [5] it has been mentioned that power systemsoperation can be greatly improved through data analysis and/orassimilationIn our work real system data is analyzed to find interrelationbetween several system parameters. This analysis can beviewed as a small attempt of data mining. Data mining isessentially an analysis of data sets in order to discover newrelations between various quantities which is not obvious fromthe recorded data in its normal form.For our investigation, data of fives system parameters- voltage,frequency, MW and VAr generation, system demand - ofthe southern regional grid of India, collected from SouthernRegional Load Despatch Center has been used.This paper is organized as follows. Section II outlines somefeatures of the Southern Regional Grid. In Section III a briefdescription of the data set used is given. Section IV presentsthe results of data analysis. The paper is concluded in SectionV.
II. SOUTHERN REGIONAL GRID FEATURES
The southern regional grid of India covers an area of6,51,000 Sq. km encompasses four states namely AndhraPradesh, Karnataka, Kerala, Tamilnadu and one Union Ter-ritory of Pondicherry. This region comprises of several centraland state owned generating stations, independent power pro-ducers, distribution companies and state transmission utilities.In India, including SRLDC, there are five major regionalgrids. After August 2006 four of the five regions excludingthe southern region are (synchronously) interconnected. TheSouthern region is connected with the other regional grids onlyin an asynchronous manner. Southern region is connected toWestern region through HVDC back to back at Ramagundam-Chandrapur and to Eastern region at Jepore-Gazuwaka back-to-back and point to point HVDC line between KolarTalcher.The total installed capacity of southern region as in thebeginning of 2007 is about 37370 MW. Some other salientfeatures of the southern regional grid are [6] :
• Covers approximately 19% of the geographical area, 22%of population and 29% of the installed capacity of thecountry.
Fifteenth National Power Systems Conference (NPSC), IIT Bombay, December 2008
248
• 30-70% hydro-thermal mix• 3300 MW wind generating plants• 8000 MW capacity independent power producers.• 2000 MW capacity HVDC Talcher-Kolar double crcuit
interconnection with the Eastern Region.• 400/220 KV transmission system
III. DATA DESCRIPTION
The control center at Bangalore of SRLDC is equippedwith a computerized load despatch and communication facil-ities. Around 320 Remote Terminal Units(RTU) are used forreal time system monitoring and grid management. ThroughSCADA these RTUs communicate with the control center.From the collected data only five system parameters- voltage,MW generation, MVAr generation, frequency, system demand- are made available to us for use in this work. These datawere in the form of Microsoft Excel files stored in CompactDiscs. Data, starting from Jan 2004 to June 2006, is collected.The data logging interval is 1 minute for all parameters.Following are some salient features of the data of the fiveparameters are chosen.
A. Voltage
Voltage data consists of measurements at the 26 buses ofthe 400 KV grid. All voltages have been stored (in EXCELfile) as integers with a least count of 1 KV. Among the 26 busvoltages, the Kolar bus voltage remains constant at 400 KVall the time.
B. Frequency
Frequency data consists of frequency of the region. Preci-sion of frequency data is 0.01216 Hz.
C. MW Generation
MW generation data consists of information from 60 gener-ation buses. The precision of stored (in EXCEL file) data is 1MW. The 60 outputs at generation buses represent either unitoutputs or the total station output. Types of plants are hydro,thermal or nuclear.
D. MVAr generation
MVAr generation data corresponds to 88 generation units.Precision of stored(in EXCEL file) data is 1 MVAr. The 88generation units include hydro, thermal and nuclear powerunits. In this case the data corresponds to individual units inall cases.
E. System demand
System demand data consists of system demand of fourstates and the total demand of the region. The precision ofstored(in EXCEL file) data is 1 MW.One of the major challenges in handling real data is the issueof the outliers. Outliers are basically records whose featuresare distinct from the other records of the group. Outliers areclassified into four classes. The first class arises from dataentry error. For power systems data this type of error isgenerated due to malfunction of SCADA or communication
channels, etc. The second class of outlier is the outcome ofsome extraordinary events. In the context of power systemsfaults, switching operations etc could be the cause for suchevents. This type of outliers should be retained in the dataset. The third class of outlier comprises of extraordinaryobservations for which there is no explanation. These outliersmust be retained to capture some characteristics of the system(not known/explained). The fourth and final class of outlierare observations that fall within the range of each of thevariables but are unique in their combination of values acrossthe variables. In the data made available to us, it appears thatno attempt had been made to identify and replace outliers.Investigated data set contains all the four types of outliers.The outliers identified in the data set are:
- Considerable change in value for in one or two consecu-tive instances
- Occurrence of 0 values for a considerable time period- Occurrence of values not possible from system point of
view
Identifiable outliers are substituted with average value ofprevious and next non-outlier values.
IV. ANALYSIS OF PARAMETER INTERRELATIONS
An overview of some features of the collected data set hasbeen given in the last section. In this section we investigatethe interrelation of some of the measured system variables.In Figures 1 and 2 system frequency vs. average systemvoltage is plotted for 06/06/2006 and 16/4/2006. By averagesystem voltage we mean average voltage of all 400 KV buses.It is seen that the points are clustered around one of thediagonals. It can be seen that to some extent higher voltagescorrespond to higher frequency and lower voltage correspondsto lower frequency. This correlation is relatively well definedin Figure 2.In Figures 3 and 4 total system demand vs. system frequencyis presented for 6/6/2006 and 16/4/2006. In Figures 3 and 4by and large higher system demand corresponds to low systemfrequency and at lower demands system frequency is high.Figure 5 presents the total system demand vs. average system
405 410 415 42048.6
48.8
49
49.2
49.4
49.6
49.8
50
50.2
50.4
System average voltage on 06/06/2006
Syste
m f
requency
Fig. 1. System frequency vs. Average system voltage on 06/06/2006
voltage plot for 19/1/2006. Time instants corresponding to
Fifteenth National Power Systems Conference (NPSC), IIT Bombay, December 2008
249
408 410 412 414 416 418 42048.8
49
49.2
49.4
49.6
49.8
50
50.2
50.4
50.6
Average system voltage on 16/04/2006
Syste
m f
req
ue
ncy
Fig. 2. System frequency vs. Average system voltage on 16/04/2006
48.6 48.8 49 49.2 49.4 49.6 49.8 50 50.2 50.41.5
1.6
1.7
1.8
1.9
2
2.1x 10
4
System frequency on 06−06−2006
Tota
l syste
m d
em
and
Fig. 3. Total system demand vs. System frequency on 06/06/2006
some data points are also indicated in the figure. It can beseen that from 02:00 a.m, the average system voltage graduallydecreases as the system demand increases with time. After6:20 a.m the plot shows random changes. But, after 7:00 p.mtill the end of the day the average system voltage increaseswith decrease in total system demand. In Figure 6 the sameplot of Figure 5 is shown but without the random portion ofthe graph. It can be seen that the plot takes two distinct pathsat the start and end of the day. For the same system demandaverage system voltage is lower for the morning portion ofthe day and higher during the night. This corresponds to a′Hysteresis′ type of variation.In Figure 7 total system demand vs. total MVAr generation ispresented for the same day. It is interesting to see that this plotalso shows a ′Hysteresis′ loop when the random portions areexcluded. The Figure 7 is similar to the Figure 5 with the onlydifference being that in Figure 7, with time the plot moves anti-clockwise where as in Figure 5 it moves clockwise. In Figure8 the same Figure 7 is shown but with the random variationregion excluded.In Figure 9 total MVAr generation vs. average system voltageis plotted. From the Figure it can be seen that almost linearrelationship exists between total MVAr generation and averagesystem voltage. As average system voltage increases MVArinjection drops and generators absorb MVAr at high average
system voltages. In Figure 10 average generator power factorvs. total system demand is plotted for 19/1/2006. It can be seenthat as system demand increases in the morning the powerfactor starts improving. After 6 a.m the power factor enters arandom fluctuation zone but remains high in value. At night,as system demand starts decreasing the power factor starts todecrease.In Figure 11 total system demand vs. average system voltage
48.8 49 49.2 49.4 49.6 49.8 50 50.2 50.4 50.615500
16,000
16,500
17,000
17,500
18,000
18,500
19,000
19,500
System frequency on 16−04−2006
Tota
l syste
m d
em
and
Fig. 4. Total system demand vs. System frequency on 16/04/2006
402 404 406 408 410 412 414 416 41814000
15,000
16,000
17,000
18,000
19,000
20,000
21,000
Average system voltage
Tota
l syste
m d
em
and
00:00
02:0004:00
06:00
08:00
10:00
12:0014:00
16:00
18:00
20:0019:00
22:00
23:59
18:10
18:20
05:00
Fig. 5. Total system demand vs. Average system voltage on 19/01/2006
is plotted for 6/6/2006. In this case also random movementis seen during the middle of the day while the remainingpart exhibits hysteresis type of behavior. We have seen thishysteresis across several days taken from several months.More careful investigations are necessary to identify specificperformance patterns. What is evident is that there is someinteresting behavior seen in these plots. Further study couldhelp to understand this in a better way.In Figure 12 total system MVAr generation vs. average systemvoltage is presented for 6/6/2006. The relationship betweenMVAr and average system voltage is almost linear (in anaverage sense). As average system voltage increases large
Fifteenth National Power Systems Conference (NPSC), IIT Bombay, December 2008
250
404 406 408 410 412 414 416 41814000
15,000
16,000
17,000
18,000
19,000
20,000
21,000
Average system voltage
Tota
l syste
m d
em
and
Morning data pointsNigth data points
Fig. 6. Total system demand vs. Average system voltage plot (after removingrandom portion) of on 19/01/2006
−800 −600 −400 −200 0 200 400 600 80014000
15,000
16,000
17,000
18,000
19,000
20,000
21,000
Total MVAR generation
Tota
l syste
m d
em
and
(MW
)
00:0002:00
04:00
06:00
08:00
10:00
12:00
14:0016:00
18:00
20:00
22:00
23:59
Fig. 7. Total system demand vs. total MVAr generation plot on 19/01/2006
MVAr is consumed by generators and as average systemvoltage becomes low generators inject large MVAr into thegrid. Near linear relationship between the parameters is alsoevident for other days also.Till now we have discussed the inter-parameter relationshipconsidering the whole system. Parameter relationships arealso investigated for individual generation buses. In Figure13 scatter plot of MVAr and voltage at different generatorbuses are presented for the day 6/6/2006. It can be seen thatthe MVAr vs. voltage scatter plot at RGM generator bus isdifferent from other plots. For each voltage at the bus twodistinct MVAr values are seen. Scatter plots (for all the 8generating stations) indicate a near linear relationship betweenbus voltage and MVAr output of the units. Figure 14 showsthe plot of average of MVAr generation at each voltage valuevs. bus voltage value of generating stations (corresponding toFigure 13). The slope of the MVAr-voltage plot is differentfor different generating stations in Figure 14. The nominalvoltage at VTS bus is 220 KV. As the voltage is increasingthe MVAr injection at VTS bus is also increasing. On the other
−800 −600 −400 −200 0 200 40014000
15,000
16,000
17,000
18,000
19,000
20,000
21,000
Total MVAR generation
Tota
l syste
m d
em
and
Night data pointsMorning data points
Fig. 8. Total system demand vs. total MVAr generation plot (after removingrandom portion)on 19/01/2006
402 404 406 408 410 412 414 416 418−800
−600
−400
−200
0
200
400
600
800
Average system voltage
Tota
l M
VA
R g
enera
tion
Fig. 9. Total MVAr generation vs. average system voltage plot on 19/01/2006
hand MAP and MTPS are injecting less MVAr with increasingvoltage. KAI, SHVT and SIM are absorbing large MVAr at thehigher voltage levels while injecting a small amount at lowervoltages. RTPS is absorbing smaller MVAr at higher voltageas compared to the absorption at lower voltages. In Figure 15plot of voltage vs. MVAr (averaged for each voltage) is shownfor the day 13/3/2006. In this case RGM is absorbing largeMVAr at the higher side of the voltage range while injectingsmall MVAr at lower side of the voltage range. The natureof variations at other generating stations except RTPS in thefigure remain same as on 6/6/2006. In Figure 15 RTPS isinjecting a small MVAr at high voltage while injecting largeMVAr at low voltage. In Figure 16 Voltage vs. VAr injection(averaged for each voltage) is shown for the whole month ofMarch 2006. In this case also a near linear relationship can beseen between VAr and voltage. For SHVT and MAP the plotsare not linear at the extremities. Actually the number of samplepoints are very small in this range. In Figure 17 total systemdemand (averaged for each frequency) vs. system frequencyis plotted for the month of December 2005. Except at higher
Fifteenth National Power Systems Conference (NPSC), IIT Bombay, December 2008
251
14000 15,000 16,000 17,000 18,000 19,000 20,000 21,000−4
−3
−2
−1
0
1
2
Total system demand
Pow
er
facto
r angle
in d
egre
e
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
22:0023:59
Fig. 10. Total system demand vs. power factor plot on 19/01/2006
405 410 415 42015000
16,000
17,000
18,000
19,000
20,000
21,000
Average system voltage
Tota
l syste
m d
em
and
00:00
02:00
04:0006:00
08:00 10:0012:00
14:0016:00
18:00
20:00
22:00
23:59
Fig. 11. Total system demand vs. Average system voltage on 06/06/2006
405 410 415 420−800
−600
−400
−200
0
200
400
600
800
1000
1200
System average voltage on 06/06/2006
Syste
m V
ar
genera
tion
Fig. 12. Total system MVAr generation vs. Average system voltage on06/06/2006
frequencies the graph appears to be nearly a quadratic. Assystem demand increases system frequency decreases and viceversa. At the higher end of frequencies the graph is irregular
406 407 408 409 410 411 412 413 414 415−1000
−500
0
RG
M
400 402 404 406 408 410 412−500
0
500
SIM
227 228 229 230 231 232100
200
300
VT
S
404 406 408 410 412 414 416−80
−60
−40
RT
PS
212 213 214 215 216 217 218 219 220 221−200
0
200
SH
VT
229 230 231 232 233 234 235 236 2370
200
400
MT
PS
233 234 235 236 237 238 239 240 241 242−200
0
200
Voltage (KV)
KA
I
219 220 221 222 223 224 225 226 227 228 2290
50
100
Voltage (KV)
MA
P
Fig. 13. Scatter plot of Voltage vs. MVAr injection at different bus on06/06/2006
406 407 408 409 410 411 412 413 414 415−600
−400
−200
RG
M
400 402 404 406 408 410 412
−200
0
200
SIM
227 228 229 230 231 232100
150
200
VT
S
404 406 408 410 412 414 416−80
−60
−40
RT
PS
212 213 214 215 216 217 218 219 220 221−200
−100
0
SH
VT
229 230 231 232 233 234 235 236 2370
100
200
MT
PS
233 234 235 236 237 238 239 240 241 242
−100
−50
0
50
Voltage (KV)
KA
I
219 220 221 222 223 224 225 226 227 228 2290
50
100
Voltage (KV)
MA
PFig. 14. Plot of Voltage vs. VAr injection (Averaged for each voltage) atdifferent bus on 06/06/2006
402 403 404 405 406 407 408 409 410 411−500
0
500
RG
M
403 404 405 406 407 408 409 410 411 412 413−300
−200
−100
0
100
SIM
225 226 227 228 229 230 231
0
50
100
VT
S
402 403 404 405 406 407 408 409 4105
10152025
RT
PS
210 211 212 213 214 215 216 217 218−200
0
200
SH
VT
227 228 229 230 231 232 233 234 2350
500
1000
MT
PS
230 231 232 233 234 235 236 237−100
0
100
Voltage ( KV )
KA
I
220 222 224 226 228 230 2320
100
200
Voltage ( KV )
MA
P
Fig. 15. Plot of Voltage vs. VAr injection (Averaged for each voltage) atdifferent bus on 13/03/2006
due to insufficient number of sample points. In Figure 18 thesame plot is shown for the month of April 2006. Here also
Fifteenth National Power Systems Conference (NPSC), IIT Bombay, December 2008
252
228 230 232 234 236 238 240−100
0
100K
AI
216 218 220 222 224 226 228 230 232 234 2360
50
100
150
MA
P
224 226 228 230 232 234 236 2380
200
400
MT
PS
208 210 212 214 216 218 220−200
0
200
SH
VT
395 400 405 410 415−400
−200
0
200
Bus voltage for the month of March 2006
SIM
H
Fig. 16. Plot of Voltage vs. VAr injection (Averaged for each voltage) forthe month of March 2006
48.5 49 49.5 50 50.5 5112000
12,800
13,600
14,400
15,200
16,000
16,800
17,600
18,400
System frequency
Syste
m d
em
and a
vera
ged o
ver
each f
requency f
or
Dec 0
5
Fig. 17. Plot of System demand averaged at each frequency vs.systemfrequency for the month of December 2005
48.5 48.6 48.7 48.8 48.9 49 49.1 49.2 49.3 49.4 49.5 49.6 49.7 49.8 49.9 50 50.1 50.2 50.3 50.4 50.5 50.6 50.7 50.8 50.9 5115,000
16,000
17,000
18,000
19,000
20,000
21,000
22,000
System frequency
Syste
m d
em
and a
vera
ged f
or
each f
requency o
ver
month
of
April 06
Fig. 18. Plot of System demand averaged at each frequency vs.systemfrequency for the month of April 2006
the graph tends to show a similar nature except at the higherfrequency range.
V. CONCLUSION
The simple analysis attempted in the work brings out severalinteresting characteristics of the overall system behavior thatare not readily available anywhere for the souther regional grid
of India. By performing similar analysis on other systems thesimilarities/differences in their behavior can be found. It mustalso be pointed out that the primary aim of the present workis to emphasize that new relations/insights can be obtained byanalyzing operational data.The results presented are incidental.The present investigation had some constraints beyond ourcontrol. For example, all the parameters contained outliers.Erroneous outliers are to be identified and eliminated for thesake of meaningful analysis at the time of archiving. it isdifficult to do it later. We have identified some of the erroneousvalues and substituted them with reasonable values. With moresystem information it would have been possible that moreoutliers are identified. Much work is needed to find suitableways to identify and replace erroneous values at the timeof recording/archiving. Application of statistical methods foroutlier identification and replacement can be an interestingissue for further research.To get the real benefit from data analysis, utilities have tofocus on eliminating the limitations of the present data storagepractices. The limitations observed are :
1) The data set has many errors2) The data is not complete3) the data set acquisition/storage was not motivated by
data mining consideration
The aim of this investigation is to argue that if some ofthese limitations are overcome (it can be in fact done fairlyeasily), then mining such data could be extremely profitablefrom the point of view of efficient planning and operation ofpower systems.
ACKNOWLEDGMENT
The authors would like to thank Southern Regional LoadDespatch Center, Bangalore for providing system data for thiswork.
REFERENCES
[1] M.Werner and U.Hermansson, ”Integrated Utility Data Warehousing -A Prerequisite To Keep Up With Competetion On Electricity Markets”,Power Systems Management and Control.2002.Fifth International Con-ference on(Conf.Publ.No.488), 17-19 April 2002, pp 130-135
[2] D Shi, Y Lee, X Duan, Q.H.Wu, ”Power systems data warehouse”, IEEEComputer Applications in Power, July 2001, Vol. 14, No. 3, pp 49-55
[3] Lin Xu, ”Data modelling and processing in deregulated power systems”,Ph.D Theses, Washington State University, May 2005
[4] Xiaofeng He, Gang Wang and Jiancang Zhao, ”Research on the SCADA/EMS System Data Warehouse Technology”, IEEE/PES Transmission andDistribution Conference and Exhibition: Asia and Pacific, Dalian, China,2005, pp 1-6
[5] E.H.Abed, N.S.Namachchivaya, T.J.Overbye, M.A.Pai, P.W.Sauer andA.Sussman, ”Data-Driven power systems Operations”, Lecture Notes InComputer Science, 2006, NUMB 3993, pp 448-455
[6] http://www.srldc.org/Downloads/Srldc
Fifteenth National Power Systems Conference (NPSC), IIT Bombay, December 2008
253
