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Proceedings Paper

Dynamic time-frequency analysis method for transient signal in power system network
Author(s): Baoqun Zhao; Yuanyang Wang
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Paper Abstract

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.

Paper Details

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
Show Author Affiliations
Baoqun Zhao, Hebei Univ. of Engineering (China)
Yuanyang Wang, Beijing Univ. of Aeronautics and Astronautics (China)


Published in SPIE Proceedings Vol. 7128:
Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment

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