Classification of multiple power quality disturbances based on the improved SVM

Abstract

To improve classification accuracy of the multiple power quality disturbances, a method is proposed based on the wavelet transform and the particle swarm optimization-support vector machine. Firstly, wavelet transform is applied to extract the wavelet energy difference of multiple power quality disturbances, and the extracted energy difference is used as feature vector. Secondly, particle swarm method is used to optimize the support vector machine to enhance the accuracy of classification. The simulation results indicate that the proposed method is effective to the classification of the seven single power quality disturbances and four multiple power quality disturbances. Compared with the original method, the new method has higher classification accuracy and strong ability to resist noise.