Online Power Quality Disturbances Detection and Classification using One-Pass Wavelet Decomposition and Decision Tree

An efficient method for power quality disturbances detection and classification is presented in this paper. Wavelet decomposition is used for extracting the features of various disturbances, and decision tree is used for classifying the disturbances. For online application, sliding window model and one-pass scan algorithms for wavelet decompositions are used. This method has low cost in memory and run time, it can detect and identify different disturbances in high accuracy and rapid speed. Simulation experiment using several typical disturbances, swell, sag, interrupt, harmonic, show the effectiveness of proposed method.