Evolutionary algorithm based optimization for power quality disturbances classification using support vector machines

Abstract

Classification and detection of power signal disturbances are most essential to ensure the good power quality. The power disturbance signals are non-stationary in nature. Non-stationary signal classification is a complex problem and equally a difficult task. In this paper we present a new method for accurate classification of power quality signals using Support Vector Machines (SVM) with optimized time-frequency Kernels by a stochastic genetic algorithm. The Cohen's class of time-frequency-transformation has been chosen as the Kernel for the SVM. An Evolutionary Algorithm has been used to optimize the parameters of the Kernels. The proposed classification method with optimized parameters is promising for classification of such non-stationary signals. Comparative simulation results demonstrate a significant improvement in the classification accuracy in case of these optimized Kernels. The important contribution of the paper is the optimization of the Kernels for the power system signal classification problem.