Online power quality disturbances identification using incremental wavelet decomposition and support vector machine

Power quality disturbances identification is the important procedure for improving power quality, and online application has actual value. An efficient method for power quality disturbances identification is presented in this paper. Wavelet decomposition is used for extracting the features of various disturbances, and support vector machine in data mining is used for classifying the disturbances. For online application, sliding window model and incremental algorithms for wavelet decompositions are used. This method has low cost in memory and run time, it can identify different disturbances in high accuracy and less time. Simulation experiment using several typical disturbances, swell, sag, interrupt, harmonic, transient impulse, transient oscillation, is finished, and a primary experiment using data from real power system is implemented, the experimental results show effectiveness of proposed method.

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