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A new Approach of Power Quality Disturbance Classification Based on Rough Membership Neural Networks

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3 Author(s)
Wang Lixia ; Coll. of Electr. Eng., Southwest Jiaotong Univ. Chengdu, Chengdu, China ; He Zhengyou ; Zhao Jing

Building a power quality monitoring and analysis system is important to improve power quality and avoid equipment damage. A new approach for power quality disturbance classification based on linear time-frequency distribution and rough membership neural networks is presented in this paper. Taken the advantages of windowed Fourier transform and S-transform, the approach presented five features to characterize the disturbance signals, than classify them with rough membership neural networks. The simulation results of 7 common kinds of disturbances indicate that the method has good performance of accuracy and efficiency.

Published in:

Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific

Date of Conference:

28-31 March 2010