The monitoring of power quality is one of the important tasks of power information acquisition system. As for the disturbance identification and classification of power quality

a kind of classification method based on wavelet support vector machine（SVM） is proposed. For the simulation of normal voltage signal waveform and of the disturbance signal waveform of 6 kinds of power quality

the wavelet decomposition for various disturbance signal is made firstly so to extract the energy entropy of each level of the wavelet packet coefficients as the feature vector; then the particle swarm optimization（PSO） algorithm is adopted to optimize the SVM kernel function parameters. Finally

the SVM with optimized parameters is used to perform the classification and identification the disturbance test set.It is shown by the simulation result that the accuracy rate of the method in the disturbance identification and classification is up to 97.28 compared with BP neural network classification method

also the training time and test time are reduced.