Application of Adaptive Wavelet Network for Power Quality Disturbance Recognition and Analysis
To improve power quality disturbance classification performance in distributed power system, a novel adaptive wavelet network based on wavelet transform and self-organizing learning array (SOLAR) system is proposed. The wavelet transform is useful in detecting and extracting signal features of various types of electric power quality disturbances because it is sensitive to signal irregularities. These feature vectors then are applied to SOLAR network for structure parameters training and disturbance pattern classification. By comparing with conventional neural network, it is concluded that SOLAR has better data driven learning and local interconnections performance. The research results between the proposed method and the other existing method are discussed. The simulation results demonstrate the proposed approach gives an effective way for improving classification accuracy of power quality disturbances.
Power quality disturbance adaptive wavelet network self-organizing learning array wavelet transform classification performance
Kang Shanlin Song Yuhai Kang Yuzhe
Hebei University of Engineering,Handan 056038 China Beijing University of Chemical Technology,Beijing 100029 China
国际会议
西安
英文
2007-08-16（万方平台首次上网日期，不代表论文的发表时间）