NDLI: Neural network-based detection and recognition method for power quality disturbances signal

Content Provider	IEEE Xplore Digital Library
Author	Wei Liao Hua Wang Pu Han
Copyright Year	2010
Description	Author affiliation: Hebei University of Engineering, Handan 056038, China (Wei Liao; Hua Wang) \|\| North China Electric Power University, Baoding 071003, China (Pu Han)
Abstract	With widespread use of various kinds of electric devices, the demand for clean power supply has been increasing in the past decades, which has great effect on the energy market. In order to improve supply quality of power system, the sources and causes of power quality disturbances must be known before appropriate mitigating operation. This paper used wavelet network-based neural classifier to automatically detect, localize, and classify the transient disturbance pattern, which can acquire the qualitative and quantitative results. The wavelet transform can offer a better compromise in terms of time-frequency domain, which decomposes the transient signal into a series of wavelet coefficients, corresponding to a specific octave frequency band containing more detailed information. To acquire the original information of transient signal, the wavelet-based denoising technology is discussed in a low signal noise ratio environment. The improved training algorithm is utilized to complete the neural network parameters initialization and classification performance. In order to satisfy power system observation, the power quality monitor configuration method is proposed. The testing results and analysis indicate that the proposed method is feasible and practical for analyzing power quality disturbances.
Starting Page	1023
Ending Page	1026
File Size	248171
Page Count	4
File Format	PDF
ISBN	9781424451814
DOI	10.1109/CCDC.2010.5498069
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Wavelet domain power quality disturbance Power supplies Power system transients Noise reduction signal noise ratio Time frequency analysis power system observation Energy market wavelet transformation Wavelet coefficients Working environment noise Neural networks Power quality neural classifier
Content Type	Text
Resource Type	Article

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