Genetic algorithm based support vector machine for on-line voltage stability monitoring

Abstract A Genetic Algorithm based Support Vector Machine (GA-SVM) approach for online monitoring of long-term voltage instability has been proposed in this paper. The conventional methods for voltage stability monitoring are highly time consuming hence infeasible for online application. Support vector machine is a powerful and promising function estimation tool. To improve the accuracy and minimize the training time of SVM, the optimal values of SVM parameters are obtained using genetic algorithm. The proposed approach uses the voltage magnitude and phase angle obtained from Phasor Measurement Units (PMUs) as the input vectors to SVM and the output vector is the Voltage Stability Margin Index (VSMI). The effectiveness of the proposed approach is tested using the New England 39-bus test system and the Indian Northern Region Power Grid (NRPG) 246-bus real system. The results of the proposed GA-SVM approach for voltage stability monitoring are compared with grid search SVM (GS-SVM) and artificial neural networks (ANN) approach with same data set to prove its superiority.

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