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Composite Reliability Evaluation Using Monte Carlo Simulation and Least Squares Support Vector Classifier

Composite Reliability Evaluation Using Monte Carlo Simulation and Least Squares Support Vector Classifier,10.1109/TPWRS.2011.2116048,IEEE Transactions

Composite Reliability Evaluation Using Monte Carlo Simulation and Least Squares Support Vector Classifier  
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This paper presents a fast and efficient method which combines the Monte Carlo simulation (MCS) and the least squares support vector machine (LSSVM) classifier, for reliability evalu- ation of composite power system. LSSVM is used to accurately pre-classify the power system operating states as either success or failure states during the Monte Carlo sampling. These pre-classi- fied failure states are then evaluated for adequacy analysis using DC power flow to calculate reliability indices. As a result, the com- puting time to perform power flow analysis of the system success states is eliminated. The proposed hybrid method is applied to the IEEE Reliability Test System (IEEE-RTS-79) and simulation re- sults obtained using LSSVM with linear and nonlinear kernels are compared with that of nonsequential MCS. These promising re- sults demonstrate the efficacy of the proposed MCS-LSSVM based hybrid method in termsof bothclassification accuracyand compu- tational time in evaluating the composite power system reliability.
Journal: IEEE Transactions on Power Systems - IEEE TRANS POWER SYST , vol. 26, no. 4, pp. 2483-2490, 2011
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