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This paper used the multi-class classification for support vector machine and combined with the good amplitude-frequency characteristic of Fourier transform,the good time-frequency characteristics of wavelet transform and the excellent statistical learning ability of support vector machine to make the classification and recognition to the disturbances of power quality. Mathematical modeling for the 8 kinds of common power quality disturbances, namely voltage swell, voltage sag, voltage interruption, harmonic, voltage fluctuation, transient oscillation , transient pulse and frequency deviation, and then use Fourier transform and wavelet transform to extract the characteristics of the waveform of the generated samples, and input the characteristic value to the osu_svm and do the quality disturbances Multi-class Classification. The example shows that this method has a high recognition accuracy, a few training samples and training time is short, a good real-time performance, and is not sensitive to noise, etc. It is an effective method for Power quality disturbances classification.
Power Electronics and Intelligent Transportation System (PEITS), 2009 2nd International Conference on (Volume:1 )
Date of Conference: 19-20 Dec. 2009