NDLI: Wavelet neural network applied to power disturbance signal in distributed power system

Content Provider	IEEE Xplore Digital Library
Author	Huang Weili Du Wei
Copyright Year	2009
Description	Author affiliation: Hebei University of Engineering, Handan 056038, China (Huang Weili; Du Wei)
Abstract	The power system load equipment is more sensitive to power quality disturbances than equipment applied in the past. Therefore, the electric supply quality has become a major concern of electric utilities and end-users. A novel approach to detect and locate power quality disturbance in distributed power system combining wavelet transform with neural network is proposed. By performing decomposition of transient waveform, the original signal is divided into two parts: the low-frequency and the high-frequency, corresponding to approximation part and details part respectively. The paper aims at complex wavelet analysis, and then explores feature extraction of disturbance signal to obtain dynamic parameters, superior to real wavelet analysis result. The characteristic vector obtained from wavelet decomposition coefficients are input data of neural network for power quality disturbance pattern recognition. The improved training algorithm is used to complete the network parameter identification. By means of simulation and experimental data, the disturbance pattern can be obtained from the neural network output. The simulation results show that the proposed method is effective for transient signal analysis, taking advantage of complex wavelet transform and neural network.
Starting Page	3162
Ending Page	3165
File Size	103544
Page Count	4
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5194640
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Power system transients signal decomposition Wavelet analysis training algorithm neural network Power quality disturbance Neural networks Power quality Power system analysis computing characteristic vector Feature extraction Power industry Power systems detect and locate Signal analysis
Content Type	Text
Resource Type	Article

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