Optimization of support vector machine parameters for voltage stability margin assessment in the deregulated power system

Recently, the electric power systems are operated relatively close to their operational limits due to worldwide deregulated electricity market policies. The power systems are being operated with high stress, and hence sufficient voltage stability margin is necessary to be managed to ensure secure operation of the power system. A particle swarm optimization-based support vector machine (SVM) approach for online monitoring of voltage stability has been proposed in this paper. The conventional methods for voltage stability monitoring are less accurate and highly time-consuming consequently, infeasible for online application. SVM is a powerful machine learning technique and widely used in power system to predict the voltage stability margin, but its performances depend on the selection of parameters greatly. So, the particle swarm optimization is applied to determine the parameter settings of SVM. The proposed approach uses bus voltage angle and reactive power load as the input vectors to SVM, and the output vector is the voltage stability margin index. The effectiveness of the proposed approach is tested using the IEEE 14-bus test system, IEEE 30-bus test system and the IEEE 118-bus test system. The results of the proposed PSO-SVM approach for voltage stability monitoring are compared with artificial neural networks and grid search SVM approach with same data set to prove its superiority.