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Power System Security Margin Prediction Using Radial Basis Function Networks Guozhong Zhou Iowa State University

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Power System Security Margin Prediction Using Radial Basis Function Networks Guozhong Zhou Iowa State University Computer Science Technical Reports Computer Science 6-27-1997 Power System Security Margin Prediction Using Radial Basis Function Networks Guozhong Zhou Iowa State University James D. McCalley Iowa State University, [email protected] Vasant Honavar Iowa State University Follow this and additional works at: http://lib.dr.iastate.edu/cs_techreports Part of the Artificial Intelligence and Robotics Commons, Information Security Commons, and the Systems Architecture Commons Recommended Citation Zhou, Guozhong; McCalley, James D.; and Honavar, Vasant, "Power System Security Margin Prediction Using Radial Basis Function Networks" (1997). Computer Science Technical Reports. 154. http://lib.dr.iastate.edu/cs_techreports/154 This Article is brought to you for free and open access by the Computer Science at Iowa State University Digital Repository. It has been accepted for inclusion in Computer Science Technical Reports by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Power System Security Margin Prediction Using Radial Basis Function Networks Abstract This paper presents a method to predict the postcontingency security margin using radial basis function (RBF) networks. A genetic algorithm-based feature selection tool is developed to obtain the most predictive attributes for use in RBF networks. The proposed method is applied to a thermal overload problem for demonstration. Simulation results show that the proposed method gives satisfactory results and the running time decreases by a factor of 10 compared with using multilayer perceptrons. Keywords Security margin, power systems, radial basis function networks, genetic algorithms, feature selection Disciplines Artificial Intelligence and Robotics | Information Security | Systems Architecture This article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/cs_techreports/154 Power System Security Margin Prediction Using Radial Basis Function Networks TR 97-10 Guozhong Zhou, James D. McCalley and Vasant Honavar June 27, 1997 ACM Computing Classi cation System Categories 1991: I.2.6 [Arti cial Intel ligence] Learning | connectionism and neural nets Keywords: power systems, security margin feature subset selection, genetic algorithms, radial basis function networks Dr. McCalley's research is partially supp orted through grants from National Science Foundation and Paci c Gas and Electric Company. Dr. Honavar's research is partially supp orted through grants from National Science Foun- dation and the John Deere Foundation. This pap er will app ear in: Pro ceedings of the 29th Annual North American Power Symp osium, Oct. 13-14. 1997, Laramie, Wyoming. Arti cial Intelligence Research Group Department of Computer Science 226 Atanaso Hall Iowa Sate University Ames, Iowa 50011-1040, USA Power System Security Margin Prediction Using Radial Basis Function Networks Guozhong Zhou James D. McCalley Vasant Honavar [email protected] [email protected] [email protected] Department of Electrical and Computer Engineering Department of Computer Science Iowa State University Iowa State University Ames, Iowa 50011 Ames, Iowa 50011 Abstract This pap er presents a metho d to predict the p ostcontingency se- curity margin using radial basis function networks with a fast training metho d. A genetic-based feature selection to ol is develop ed to obtain the most predictive attributes for use in RBF networks. The prop osed metho d is applied to a thermal overload problem for demonstration. The simulation results show that the prop osed metho d gives satisfac- tory results and the running time decreases by a factor of 10 compared with using multilayer p erceptrons. 1 Intro duction 1.1 Power system security margin prediction Security is a very imp ortant problem for power system op erations. Power system states can be identi ed as secure or insecure. In addition, it is very useful for op erators to know the margin, i. e., how far the state is from the security b oundary. Knowledge of security margins is b ecoming more and more essential as transmission lines are op erating closer and closer to their capacities in to day's deregulated environment. The security margin prediction problem can b e expressed as follows: Given an operating condition characterized by a set of critical parameters chosen with respect to a particular contingency and postcontingency perfor- mance measure, nd the \distance" between the operating condition and the boundary de ned by a threshold on the postcontingency performancemeasure. Fig. 1 shows the pro cedure for security margin prediction. 1 Intelligent Information Processing Step 1 Step 2 Step 3 Step 4 Identify AUTOMATIC SECURITY FEATURE SELECTION Security Construct ASSESSMENT SOFTWARE (GANN) Basecase KEEP Problem - structured Monte Carlo FILE - genetic algorithm sampling (Large + accuracy - industrial grade analysis Database) + cardinality software +controllability - modularity of analysis - simple neural network Step 6 Step 5 TRAIN NEURAL NETWORK VISUALIZATION & PRESENTATION Encoded Input/ x1 - margin prediction Out put Relation R x2 x3 Figure 1: Pro cedure for security margin prediction There are various security problems in p ower systems. We consider only thermal overload problem here. In this case, the p ostcontingency p erfor- mance is the ow on the line at risk of overload and the threshold is the emergency rating of this line. Let x denote the critical parameter candi- s dates, vector x denote the selected critical parameters, and R denote the s that p erformance measure, our goal is to obtain a relationship R = f x predicts the contingency e ects using knowledge of only precontingency con- ditions. Neural networks are used for this purp ose b ecause of their capability to handle nonlinear functions of many variables. A sample system is shown in Fig. 2. The load in the area, during high loading conditions, is greater than the generation capacities in this area. Therefore, a signi cant amount of power must be imp orted into the area to meet the demand. There are several ties b etween this area and the remaining part of the system. Thermal overload o ccurs on tie line 5 when tie line 3 is outaged. So the p ostcontingency p erformance measure is the ow on tie line 5. The emergency rating of tie line 5 is I = 600A. Therefore, the threshold 0 value for this constraintisI = 600A, and when the p erformance measure is 0 normalized according to I I 0 R = I 0 wehave R = 0 on the b oundary,i. e., the margin is zero. Thus, the value of 0 R represents the security margin. The more negative the margin, the more secure the system. 2 Intertie 1 Area Tie 1 Gen B Tie 2 Subarea Tie 3 Gen A Gen C Tie 4 Load Tie 5 Intertie 2 Figure 2: A sample system 1.2 Feature Selection for Security Margin Prediction Security assessment studies have traditionally dep ended on engineering judg- ment to select the critical parameters, i.e., to select the parameters to be used in predicting the margin for each security problem. Our goal here is to develop an automatic approach to critical parameter selection, i.e., feature selection. We do not intend that automatic feature selection replace engi- neering judgment, but rather enrichitby con rming and extending physical understanding. Feature selection is a searchover the space of all combinations of features where a function is used to evaluate what is b est. So two comp onents of feature selection are evaluation function and search approach. For the purp ose of feature selection in power systems, op erating param- eters can b e classi ed as independent or dependent. A parameter is indep en- dent if it is included in the input data to a power ow program; examples include MW injection or voltage magnitude at a generator bus or load level MW or MVAR injection at a load bus. A parameter is dep endent if it is computed as a result of a power ow program solution; examples include bus voltages at a load bus or line ows. Indep endent critical parameters can be further divided according to op erator controllability. MW injections and 3 voltage magnitudes at generator buses are controllable indep endent param- eters; load levels at load buses are noncontrollable indep endent parameters. The problem for feature selection in power system security assessment can b e describ ed as follows: Given a database having columns consisting of a number of features and a single performancemeasure, determine a subset of the total attribute space that can be used to train a neural network that predicts the postcontingency performance measure such that the fol lowing criteria are satis ed: Set Suciency accuracy: The feature set must contain sucient in- formation to allow prediction of the p ostcontingency p erformance mea- sure within a desired accuracy for all op erating conditions within the study scop e. Set Cardinality: The feature set should b e chosen as the set of minimum size that satis es the set suciency criterion. Control lability Constraint: At least one feature within the set must be controllable by the op erator so that the op erating p oint can be adjusted, with resp ect to the b oundary, using preventive actions. Researchers in the statistics and pattern recognition communities have investigated feature subset selection problems for decades [10]-[12]. Many of these metho ds are based only on the ability to predict the output. There are no ecient way to account for cardinality constraints on the feature set although there are several techniques that have attempted to nd minimum feature subsets see [16] for a discussion. Furthermore, there is no capability to preselect some parameters into the solution. Some of the mo dels select the feature sets that satisfy the suciency condition, but no mechanism has b een prop osed to ensure that the chosen subset will satisfy the cardinality and the controllability conditions. To satisfy all three parts of the stated criteria, we have develop ed a genetic algorithm approach that searches only p ortions of the solution space of a sp eci ed cardinality level containing certain attributes.
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