Power quality classification based on wavelet and artificial neural network
MEI Xue, WU Wei-lin(Department of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)
To improve the power efficiency, it is necessary to detect the power quality signals sensitively, classify them accurately and clarify them effectively. This paper develops a novel method to classify power quality variations, which combines the aptitude of wavelet transform in analyzing non-stationary signals with the classification capabilities of artificial neural network (ANN). Power quality signals were decomposed with wavelet multi-resolution analysis and the feature vectors were extracted through the coefficients at different levels. Then ANN was used for automatic conversion of the power quality signals the feature vectors. Test results show that this method can effectively classifies voltage swell, voltage sag, voltage flicker, harmonic distortion and transient.
【CateGory Index】： TM71