To improve the performance of power quality (PQ) disturbances classification, a novel approach using wavelet neural network is proposed. The wavelet transform can accurately localizes signal characteristics in time-frequency domains The wavelet transform is utilized to extract feature vectors of different PQ disturbances. These feature vectors then are applied to the neural network for training and disturbance pattern classification. By comparing with classic neural network, it is concluded that the proposed neural network has better data driven learning and local interconnections performance. The research results between the proposed method and the other existing method are discussed and the proposed method can provide accurate classification results. The simulation results demonstrate the proposed method gives a new way for identification and classification of power quality disturbances.

Date Published: 9 October 2008
PDF: 4 pages
Proc. SPIE 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment, 712803 (9 October 2008); doi: 10.1117/12.806339

Published in SPIE Proceedings Vol. 7128:
Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment
Jiancheng Fang; Zhongyu Wang, Editor(s)