WAVELET BASED POWER QUALITY DISTURBANCE DETECTION AND CLASSIFICATION USING SVM AND PSO
Keywords:
support vector machine, radial basis function neural networks, wavelet transformation, power quality, particle swarm optimizationAbstract
This paper introduces a novel approach to detect and classify power quality disturbance in the power system using Support Vector Machine (SVM). The proposed method requires less number of features as compared to conventional approach for the identification. For the classification, 8 types of disturbances are taken in to account. The classification performance of SVM is compared with Radial basis Function neural network (RBNN).The classification accuracy of the SVM network is improved, just by rewriting the weights and updating the weights with the help of cognitive as well as the social behavior of particles along with fitness value by using Particle Swarm Optimization (PSO). The simulation results possess significant improvement over existing methods in signal detection and classification with lesser number of features.
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