Abstract—This paper presents power disturbance recognition using back-propagation neural networks (BPNN). First, the discrete wavelet transform is used to extract the features of the power disturbance waveforms in the form of series coefficients of several levels. The Parseval theory is then utilized to calculate the energy of each level so that the number of coefficients can be reduced; then, the extracted results are used for recognition by the BPNN. Multi-event power disturbances are also fed to the recognition system for testing. From experiment results, the recognition rate is at least 83.67%. It proves the feasibility of the proposed method.
Index Terms—Discrete wavelet transforms (DWT), power quality, back-propagation neural networks, parseval theory.
The author is with the Department of Electrical Engineering, National Changhua University of Education, Chang-hua, Taiwan (e-mail:cswang@cc.ncue.edu.tw).
Cite: Chau-Shing Wang, "Power Disturbance Recognition Using Back-Propagation Neural Networks," International Journal of Engineering and Technology vol. 4, no. 4, pp. 430-433, 2012.
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