NDLI: Application of signal processing and neural network for transient waveform recognition in power system

Content Provider	IEEE Xplore Digital Library
Author	Shanlin Kang Huanzhen Zhang Yuzhe Kang
Copyright Year	2010
Description	Author affiliation: Beijing University of Chemical Technology, 100029, China (Yuzhe Kang) \|\| Hebei University of Engineering, Handan 056038, China (Shanlin Kang; Huanzhen Zhang)
Abstract	The electric utilities and end users of power system network have become more concerned about power quality issues due to technical and financial consequences that have resulted from electric power quality disturbances. The power quality monitoring technology has an effective on analyzing power quality related problems. This paper presents a novel study combining wavelet transform with pattern recognition technique to investigate voltage stability using for power quality events. The wavelet transformation possesses capabilities of time and frequency domain localizations, achieving a great impetus in signal singularity detection. The statistics-based denoising method is designed to filter the random noise and impulse noise in power quality disturbance signals, incorporating the advantages of wavelet transform to extract signal feature meanwhile restraining various noises. The wavelet decomposition coefficients as feature vector of neural network are presented for extracting disturbance signal. The neural network provides a means of determining a degree of belief for each identified disturbance waveform. The performance of the proposed approach is studied and a proper combination of wavelet transformation and neural network is identified.
Starting Page	2481
Ending Page	2484
File Size	239781
Page Count	4
File Format	PDF
ISBN	9781424451814
DOI	10.1109/CCDC.2010.5498788
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 Power system transients wavelet transform Pattern recognition feature vector Power system signal denoising power quality monitoring Neural networks Power quality Power system analysis computing Voltage Signal processing Power industry voltage stability Monitoring
Content Type	Text
Resource Type	Article

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