1. INTRODUCTION ARTIFICIAL INTELLIGENCE APPLICATIONS IN ON-LINE DYNAMIC SECURITY ASSESSMENT. ABS'fRACT

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1 ARTIFICIAL INTELLIGENCE APPLICATIONS IN ON-LINE DYNAMIC SECURITY ASSESSMENT G. Cauley Pwer Systems Planning and Operatins Prgram Electrical Systems Divisin Electric Pwer Research Institute 3412 Hillview Avenue Pal Alt, Califrnia 94303, USA ABS'fRACT On-line Dynamic Security Assessment (DSA) requires the ability t analy.ze hundreds f transient and dynamic stability cntingencies every 10 t 30 minutes using nline data frm the state estimatin. The gal is t present timely infrmatin t the system peratr n transfer limits and stability margins. On-line DSA implementatin in a cntrl center requires the applicatin f diverse technlgies, including artificial intelligence methds. In the curse f feasibility studies fr varius appraches t DSA, several significant new applicatins f artificial intelligence were identified and demnstrated. An expert system was successfully used t build a Relevant Cntingency Selectr. This mdule pstulates and selects ptentially severe cntingencies fr further cnsideratin. The input data are btained frm a static snapsht f a pwer system. The cntingencies selected in the tests clsely crrespnd t thse selected by a human expert. A artificial neural netwrk (ANN) was applied t distinguish between definitely stable and ptentially unstable cntingencies. This classificatin was based n the values f a few severity indices capturing the system state immediately after fault clearing. Tests n large scale pwer systems have prven a high level f reliability. The use f a fixed number f severity indices makes it pssible t apply the ANN apprach t large systems. The feasibility study has shwn that artificial intelligence methds hld significant prmise fr nline DSA. Selectin and screening f cntingencies, deciding which cases are t be simulated in detail, fr bw lng, are all pssible AI functins. AI is als useful fr managing input data and interpreting DSA results. AB.R. Kumar, V. Bnmdwajn. A Ipakchi ABB Systems Cntrl Cmpany Inc Walsh Avenue Santa Clara, Califrnia , USA 1. INTRODUCTION The cmputatinal requirements f DSA by cnventinal methds are apprximately three rders f magnitude higher than thse f static security assessment. 'I'he8e high requirements make it inf~ible t implement a cmprehensive n-line DSA in a present day Energy Management System. Nvel methds and appraches are needed t remve this cmputatinal barrier. Recgnizing the cmplexity f the prblem, the Electric Pwer Research Institute (EPRl) spnsred several research prjects t define the R&D needs, scpe and feasibility f the n-line DSA functin [1]. Subsequently, several feasibility studies f different techniques have been initiated by EPRI ver the last few years. This paper presents sme imprtant results f ne such feasibility study designed t evaluate the ptential rle f Artificial Intelligence (Al) technlgy in an n-line implementatin f the DSA functin. A number f papers have been published n the applicatin f AI technlgy t pwer system security analysis. Examples f such wrks can be fund in [3]. References [4 and 5] discuss the ptential rle f expert system techniques in security analysis. References [7, 20 and 21] illustrate the use f expert systems t slve prblems in pwer systems engineering. A gd general intrductin t the artificial neural netwrk (ANN) methdlgy can be fund in references [8 thrugh 12]. References [13 thrugh 16] are illustrative examples.f the applicatins f ANN methds t DSA and [18] and [19] are examples f applicatins t static security analysis. Previus appraches t explit AI techniques fr pwer system security analysis have a number f characteristics in cmmn: they are aimed at verificatin f cncepts they cnsider very small system mdels the methds reprted cannt easily be adapted t large-scale pwer systems. 881

2 This paper starts with an veiview f the functinal requirements f n-line DSA and identifies the areas in which AI techniques can be effective. New appraches fr an expert system fr autmatic cntingency selectin and a neural netwrlt fr cntingency screening are described. Results fr realistic test cases are presented t demnstrate the feasibility f these appraches. 2. C>VER,VIEW OF ON-LINE DSA On-line implementatin f the DSA functin is a very cmplex prblem which cannt presently be slved with a single apprach. The DSA functin cnsists f several diverse subprblems, each ne requiring a different specializ.ed apprach. T identify the varius subprblems and their requirements, a functinal mdel f DSA was develped. This mdel is shwn in Figure 1. Change Mnitr Cntingency Screens Simulatr Security Mnitr Figure 1: Dynamic Security Analysis A brief descriptin f each majr subprblem is given belw: Change Mnitr: Check whether the changes in system cnditins since the previus assessment are significant enugh t warrant a new assessment. Relevant Cntingency Selectr: Select cntingencies relevant t the current system state. Cntingency Screens: Evaluate whether the current system state is secure with respect t a given cntingency based n varius apprximatins. A prperly rchestrated set f screens can arrive at a realistically small set f cntingencies t be prcessed by the simulatr. Simulatr: Determine the cnfiguratin and the transient state f the system at the end f the simulated time segment using numerical integratin techniques. Terminatin Cnditin Check: Predict whether the system will cme t a steady-state in the presently simulated cnfiguratin. If s, the simulatin can be terminated. Als, determine whether the cntingency is s severe that further simulatin is irrelevant. Security Mnitr: Evaluate the acceptability (security) f the pst-cntingency state. This includes checking fr branch verlads, bus vltage and reactive generatin limit vilatins as well as the lcatin and amunt f lst lad/ generatin. Als, generate security measures t be cmmunicated t the dispatcher. Crrective Actins: Deterinine if timely pstcntingency crrective actins are pssible t mve the system t an acceptable state. Preventive Actins: In cases where apprpriate crrective actins are nt available, determine the necessary precntingency preventive actins. Security Knwledge Base Update: Add significant new infrmatin abut the cntingency and the initial perating cnditins t the existing knwledge base. Based n a careful examinatin f the needs f each f the abve functins, it was determined that expert systems technlgy can be useful in many f the abve functins. One f the mst prmising areas is the selectin f ptentially severe cntingencies n the basis f the pre-cntingency steady state slutin f the pwer system. Anther prmising applicatin f AI technlgy identified was the use f Neural Netwrk Methdlgy fr cntingency screening based n cmpsite indices cmputed n the basis f the pwer system cnditins immediately after fault clearance as described in [2]. Table 1 presents the typical mdel siz.e and number f cntingencies t be cnsidered in n-line DSA alng with estimated CPU time requirements. The CPU time required t perfrm 20 secnd transient stability simulatins f SOO cntingencies in a 2500 bus pwer system n a 6 MIPS cmputer (e.g. V AX8700) is estimated t be 240 hurs. Fr an n-line 882

3 implementatin, this shuld be reduced t abut 10 minutes implying an imprvement by a factr f Based n ur recent experience we suggest that this magnitude f imprvements can be realiz.ed by a cmbinatin f intelligent cntingency selectin methds, cntingency screening methds, faster simulatin techniques, and faster CPU's. The expected speedups due t each f these methds are presented in table 1. This paper reprts n newly develped AI techniques fr cntingency selectin and cntingency screenllig which make such speedups feasible. ef)j SIMULATION SPEED REQUIREMENTS 2,500 Bus Pwer Sptem 20 Secnd Slabilly Slmu4atln 500 Cnllnganctee 30,,.,.,,.. per Slmulalln days CPU tkn MIP RISC prce sr SPEED-UP METHODS Cntingency electin 3 x 1-211sys-1 - (150 cantlng...cies) CPU lime Cntingency screens x t50 MIP ALPHA prces r 0 (30 cnllngenctes) Imprved slmulalln 8 x.5 day CPU Um" m lhds On-line CSA req<*ement Faster CPU I 10 minutes CPU Um" 0 15 x x -~ Table 1: Speed-up Requirements fr On-Line DSA Criteria fr Imrtant Lines: These rules mimic the human expert wh cnsiders varius attributes f the transmissin lines that may be detrimental t system dynamic security and defines threshld values fr each f the attributes based n previus experience. An utage f a line is cnsidered imprtant fr cntingency analysis if any f the fllwing quantities are large: the real pwer, apparent pwer, reactive pwer, the rati f reactive pwer t real pwer, r the phase angle acrss the line. Criteria fr Imrtant Buses; Similar t the line rules, the bus rules mimic the human expert's capability t fcus n ptential prblems. A fault at a bus is cnsidered imprtant if any f the fllwing quantities is large: the ttal pwer flw twards r away frm the bus, the ttal real r reactive lad r generatin at the bus. Knwledge f the imprtant buses helps t prune the number f lines selected fr further analysis. Fr example: if a bus is nt imprtant, then utages f lines cnnected t that bus are nt selected fr study. if the system is secure fr the utage f a line carrying the maximum amunt f pwer t (r away frm) an imprtant bus, then it is als secure fr utages f ther lines carrying pwer t (r away frm) that bus. 3. RELEVANT CONTINGENCY SELECTION Selectin f cntingencies fr further evaluatin shuld be based n the perating cnditins f the pwer system. These cnditins change as a result f frced and scheduled utages as well as changes in custmer demand. Therefre, a set f cntingencies selected independently f the perating cnditins will be either t large r t small. On the ther hand, it is imprtant that n ptentially severe credible cntingency is verlked. It is, hwever, acceptable t select a few marginal cases. At present, there is n universally accepted autmatic scheme fr the selectin f relevant cntingencies. This sectin reprts n an expert system apprach t achieve this gal. 3.1 Knwledge Base The rule-based expert system selects relevant cntingencies based n pre-cntingency steady-state data f the pwer system. The fllwing generic rules were frmulated n the basis f a survey f pwer industry experts s as t handle a variety f pwer system mdels. Criteria fr Imprtant interfaces: The human expert first prunes the definitely secure r less imprtant areas f the pwer system and then delves int a mre careful assessment f varius factrs t select nly a few lines fr analysis. Usually such selectin invlves system specific cnsideratins. Hwever, all such system specific criteria can be dealt with as instances f general criteria fr imprtant interfaces. Here, an interface is a grup f lines which represent the vulnerable prtin f the system. These interfaces may be specified explicitly by a human expert based n experience r autmatically generated by tplgical scan f the pwer system. Each interface is assciated with a sending area and a receiving area. A given interface is imprtant if any f the fllwing quantities are large: the ttal reactive pwer f all the lines f the interface, the rati f the reactive pwer t the real pwer at either end, r the maximum phase angle acrss the interface. An interface is imprtant if the pwer carried by it is large with respect t the generatin in the sending area, lad in the receiving area, r inertia in either the sending area r receiving area. 883

4 It can be assumed that if a system is secure with respect t the utage f the line carrying the mst pwer in an interface, then the system is secure with respect t the utage f any single line utage in that interface. Therefre, it is adequate t select nly the mst imprtant line(s) f the interface. Autmatic Identificatin f Interfaces: Interfaces can be autmatically defined as cutsets in the pwer system netwrk. The number f all pssible cutsets in a netwrk is an expnential functin f the size f the netwrk. In practice, it is nt necessary t identify all pssible cutsets. A pwer system can be visualiz.ed as a directed graph (accrding t the directin f real pwer flws) with the up stream buses n the left and the dwn stream buses n the right. The fllwing restrictins can be used t e)jminate mst spurius cutsets frm cnsideratin and reduce the number f admissible cutsets t a practical level: - Only minimal cutsets are f interest (i.e. cutsets which can be described as a cmbinatin f cutsets d nt have t be cnsidered explicitly). - The sending area and receiving area must nt have any cnnectins ther than thse f the cutset. Generating Mre Cmplex Cntingencies: The abve rules tgether yield imprtant single line utages t be studied. Multiple cmpnent utages can be easily defined frm data cncerning prbable cmmn mde failures. Fr example, a relatin cmmn-twer can be defined fr transmissin lines. Then any transmissin lines related in this manner with ne f the selected lines will be candidates fr duble-line utages. l additin, based n the breaker cnnectivity ne can generate cntingencies assciated with stuck breakers. 3.2 User Interface The expert system shell used fr DSA f a large-scale pwer system shuld use bject riented implementatin where attributes, methds, rules, and relatins assciated with an bject class can be defined nly nce and autmatically inherited by each bject f the class. Methds fr graphical manipulatin f icns can be embedded int the bjects resulting in a pwerful graphical user interface. A glimpse f ne such user interface used fr relevant cntingency selectin is given in Figure 2. This interface has the capability t present a magnified map f a prblem area alng with a navigatin map. It can als present the necessary infrmatin in pp-up windws and prvide a cntrl interface fr peratr actins. l!l'i :1--T-----i~~,~ ~~ ~ Figure 2: User Interface 3.3 Cntingency Selectin: Test Results A prttype expert system fr cntingency selectin has been implemented and tested n three different pwer system mdels nrmally used by Ontari Hydr, Nrthern States Pwer and Pacific Gas & Electric Cmpany in their planning and peratinal studies. The size f these mdels, the number f imprtant lines selected by the line rules alne, by line and bus rules, and by the interface rules are presented in Table 2. The same threshld values were used fr all three cases. In final implementatins, these values can be tuned. System Size: OH NSP PG&E I f Generatrs I f Buses I f Lines I f lines Selected by Line Rules B by Una & Bus Rules by Interface Rules Table 2. Cntingency Selectr: Results Amng the selected line utages, apprximately ne third are usually studied by the respective utilities. Anther ne third are transfrmers which are nt studied as cntingencies because the prbability f a frced transfrmer utage is small. Hwever, their (scheduled) utages are cnsidered in pstulating severe pre-cntingency perating cnditins. Abut 10 t 15 % f the selected lines were lumped equivalents f less imprtant lines which culd be easily e1iminated. The remainder represent a degree f cnservatism f the selectin methd. 884

5 The expert system cnsidered sme f the line utages usually studied by the utilities hut did nt select them because they were deemed less severe than the nes selected in the given precntingency perating cnditin. 4. COMPOSITE INDICES FOR CONTINGENCY SCREENING BY NEURAL NETWORK Apprpriate selectin f cntingencies fr further prcessing is f extreme imprtance. Emphasis has t he placed n ensuring that n severe cntingencies are excluded and that a reasnable reductin in the number f cntingencies is achieved. The usual apprach f using all state variables as input t a neural netwrk is nt feasible in the case f largescale pwer system mdels because f the "curse f dimensinality" assciated with the generatin f training cases. The cncept f severity indices, develped fr static security analysis, can be adpted t slve this prblem. The idea is t define a small number f cmpsite measures f system state t describe the pwer system and use them as inputs t a neural netwrk. The number f these cmpsite indices is independent f the size f the pwer system mdel resulting in a fixed size and architecture f the neural netwrk. The selected indices describe the change in cnditins f the pwer system between the pre-fault steady-state and immediately fllwing fault clearing, i.e., the deviatin frm the pre-fault steady-state. Several illustrative types f indices are listed belw: Change in generatr rtr angles Change in bus vltages Change in speed f generatrs Change in kinetic energy f generatrs Acceleratin f generatrs The cmputatin f each cmpsite index cnsists f the fllwing steps: Calculate the change fr each variable relevant t the index (e.g. acceleratin f each generatr). Nrmalize the change (e.g. divide by the rating f the crrespnding generatr). Raise the nrmalized value t a high expnent. Sum all these terms (sum ver all generatrs) t get a cmpsite index fr the system. Fr additinal details are presented in [17,22]. A judicius chice f the indices allws ne t use training cases which are cncentrated in the vicinity f the security bundary with nly a few very stable r very unstable cases. 4.1 Cntingency Sqeening; Test Results A 436 bus system mdel f a majr utility with 2400 lines and 88 generatrs was used in testing f the cmpsite input methd. Many cases were simulated using different pre-cntingency cnditins, cntingencies and fault clearing times. Fr each case, a set f 53 indices was cmputed immediately fllwing fault clearing. A class-mean feature extractin prcedure, similar t the ne described in [18], selected 24 f the indices fr use as inputs t a neural netwrk. In the training mde, the desired utput f the neural netwrk is set t 0.0 fr stable cases (definitely harmless) and 1.0 fr unstable (ptentially harmful) cases. In the testing mde, an utput value greater than 0.5 is cnsidered t imply instability (ptentially harmful). Neuial netwrks f different cmplexity were tested. It was determined that a three layer neural netwrk is sufficient t classify cntingencies in DSA. The first layer cnsists f ne nde fr each cmpsite input. The final layer cnsists either f ne nde fr the entire pwer system r a few ndes, ne fr each prblem area in the system. The intermediate layer cnsists f nly a few ndes (3-8 hidden ndes were used in these tests). Several tests were designed t evaluate the accuracy f cntingency classificatin with the neural netwrk methdlgy. The results f tw selected tests are presented belw : Neural Netwrk Trained with Several Cntingencies: This test was designed t test the feasibility f using a single neural netwrk t classify cntingencies under different lading cnditins in the pwer system. A single pre-cntingency tplgy f the pwer system was cnsidered. Seven different levels f generatin at a majr plant were used. 378 cases were generated by simulating 6 different cntingencies. Fr each f these cntingencies, 9 different fault clearing times were simulated. The results f 300 randmly selected cases were used t train the neural netwrk and all cases were used in testing. The classificatin by the neural netwrk was very gd. One marginally stable training case and ne marginally stable test case were classified as ptentially severe. All thers were classified crrectly as either stable r unstable. 885

6 The utput f the neural netwrk fr cases invlving a specified cntingency is presented in Fig. 3 in the frm f a cntur plt. In this figure, the X and Y axes z w 4.0 (/) ( FAULT CLE.ARING {CYCLES) Figure 3: Stability Bundary by ANN 1 :t represent the fault clearing time and the generatin level respectively. The piece-wise linear curve t the left f the cnturs represents the envelpe f the stable cases as determined by step-by-step simulatins. The piece-wise linear curve t the right is the envelpe f unstable cases. The thick cntur representing the stability bundary determined by the neural netwrk, i.e., utput = 0.5, lies entirely within the bundaries implied by the training cases. Neural Netwrk Trained with Several Pre Cntingency Cnfi1Uf3tim; This test was designed t test the feasibility f using a single neural netwrk fr different pwer system tplgies with respect t a single cntingency. A neural netwrk f the same structure as that in the previus test was used. Again, the classificatin f the cntingencies by the neural netwrk was very precise. One marginally stable case was classified as ptentially severe. All ther cases were classified apprpriately.. The typical CPU times required fr training the neural netwrk in the abve tests were in the range f 5 t 50 secnds n a 15.6 MIPS Sun wrkstatin. This training is dne in an ff-line envirnment. A neural netwrk s trained can perfrm n-line classificatin f a case (as definitely harmless r ptentially severe) within millisecnds. S. CONCLUSIONS The results f the paper shw that AI techniques can play an essential rle in n-line DSA..In particular, a new technique fr cntingency preselectin using the expert system methdlgy was presented and evaluated n realistic mdels f three diverse utilities. The fllwing cnclusins can be drawn based n the evaluatin and experience reprted: - The state-f-the-art in expert system shells and related MMI and databases bas matured enugh t perfrm useful functins in the design and peratin f largescale pwer systems. - The cntingency selectr perfrmed at a level cmparable t human experts. The autmatic cntingency selectr can select relevant cntingencies based nly n the pre-cntingency steady-state slutin. The paper als presented a new methd t deal with the curse f dimensinality in the applicatin f neural netwrks t large-scale systems. Cmpsite indices were develped t capture the dynamic behavir f the pwer system. The fllwing cnclusins can be drawn frm the results: - The CPU time requirements are made independent f the siz.e f the pwer system mdel by using a fixed number f cmpsite indices as inputs t a neural netwrk. - The develped methdlgy requires very few training cases relative t ther published methds. This is a result f using nly a few cmpsite indices as inputs t the neural netwrk:. - A neural netwrk: can be used t dasmfy cntingencies in a number f pre-cntingency cnfiguratins in a given area f a pwer system. - The test results clearly illustrates the feasibility f using the neural netwrk methdlgy in classifying cntingencies in large pwer system mdels. A prttype system based n the new techniques reprted here is being implemented t demnstrate the feasibility f n-line DSA using realistic mdels f large pwer systems. 886

7 ACKNOWLEDGEMENT This paper is based n part f the wrk perfrmed under EPRI RP n "Dynamic Security Analysis Using Artificial Intelligence Techniques, Phase! Feasibility Evaluatin". The invaluable technical advice and discussins with Mr. Chi Tang f Ontari Hydr, Mr. Peter Mackin f Pacific Gas & Electric Cmpany, Mr. Michael McMullen f Nrthern States Pwer and Mr. Jseph Wrubel f Public Service Electric & Gas cmpany are gratefully acknwledged. REFERENCES [ 1] A.B.R. Kumar, et.al., "Dynamic Security Assessment fr Pwer Systems: Research Plan", EPRI EL-4958, Final reprt n RP [ 2] A.B.R. Kumar, A. Ipakchi, V. Brandwajn, M.A. El-Sbarkawi, G. Cauley "Neural Netwrks fr Dynamic Security Assessment f Large-Scale Pwer systems: Requirements Overview", Prceedings f the First Frum n Applicatin f Neural Netwrks t Pwer Systems, Seattle, Washingtn, July 23-26,1991 [ 3] Prceedings f the Secnd Sympsium n Expert Systems Applicatin t Pwer Systems,University f Washingtn, Seattle, washingtn, July 17-20, [ 4] Y. Akimt, et.al, "Transient Stability Expert",IEEE Trans.n Pwer Systems, February 1989, [ 5] D. Reichelt, H. Glavitsch, "Features f Optimizatin Using AI-Techniques t Integrate Analytical Sftware",ibid.,pp [ 6] Y. Xue, et.al., A Planning Decisin Supprt Expert System fr Transient and Dynamic Security f Pwer Systems",ibid.,pp [ 7] D. J. Sbajic, Y. H. Pa, "An Artificial Intelligence System fr Pwer System Cntingency Screening",IEEE Trans. n Pwer Systems, Vl.3,N.2, May [ 8] Tarun Khanna, "Fundatins f Neural Netwrks", Addisn-Wesley Publishing Cmpany, Reading, Massachusetts, [ 9] M. M. Nelsn and W. T. Illingwrth, A Practical Guide t Neural Nets, Addisn-Wesley Publishing Cmpany, Reading, Massachusetts, [10] Prceedings f the IEEE, September, 1990, Special Issue n Neural Netwrks, I: Thery and Mdeling. [11] Prceedings f the IEEE, Octber, 1990, Special Issue n Neural Netwrks, II: Analysis, Techniques and Applicatins. [12] Reprt n "DARPA Neural Netwrk Study, Octber 1987-February 1988", Bernard Widrw, Study Directr; Published by AFCEA Internatinal Press, A Divisin f the Armed Frces Cmmunicatins and Electrnics Assciatin, 4400 Fair Lakes Curt, Fairfax, Virginia , USA. [13] M. A. El-Shark.awi, et.al., "Dynamic Security Assessment f Pwer Systems Using Back Errr Prpagatin Artificial Neural Netwrks", pp , Prceedings f the Secnd Sympsium n Expert Systems Applicatin t Pwer Systems, University f Washingtn, Seattle, Washingtn, July 17-20, [14] Y. H. Pa, " Artificial Neural-Net Based Dynamic Security Assessment Fr Electric Pwer Systems", IEEE Trans. n Pwer Systems, Vl.4, N.l, Feb [15] H. Mri, Y. Tamar, S. Tsuzuki, "An Artificial Neural-Net Based Technique fr Pwer System Dynamic Stability with the Khnen Mdel, pp , Prceedings f PICA, May 7-10, 1991, Baltimre. [16] Y. H. Pa, D. J. Sbajic, "Cmbined Use f Unsupervised and Supervised Learning fr Dynamic Security Assessment", pp , Prceedings f PICA, May 7-10, 1991, Baltimre. [17] V. Brandwajn, M.G. Lauby, "Cmplete Bunding Methd fr AC Cntingency Analysis", Trans. n PWRS, May,1989, pp [18] Siri Weerasriya, M.A. El-Sharkawi, "Feature Selectin fr Static Security Assessment Using Neural Netwrks", IEEE Internatinal Sympsium n Circuits and Systems, San Dieg, Califrnia, May 10-13,1992. [19] Niebur, D., and Germnd, A.J., "Unsupervised Neural Net Classificatin f Pwer System Static Security Status", Prceedings f ESAPS 3, [20] Prceedings f the IEEE, Special Issue n Knwledge-Based Systems in Electric Pwer Systems, May [21] Internatinal Jurnal f Electrical Pwer and Energy Systems, Special Issue n Expert System Applicatins in Pwer systems, vl. 14, N 2&3, April, 1992 and June, [22] Final Reprt n EPRI RP "Pwer System Dynamic Security. Analysis Using Artificial Intelligence Techniques, Phase I - Feasibility Evaluatin. 887