NDLI: Simulation analysis of time-frequency based on waveform detection technique for power quality application

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 automatic detection and classification of power quality disturbances has become a significant issue in modern power industry, because of electric load sensitive to power transient signal. This paper presents a novel approach for detection and location of power quality disturbances based on wavelet transform and artificial neural network. The wavelet transform is the projection of a discrete signal into two spaces: the approximation space and a series of detail spaces. The implementation of the projection operation is done by discrete-time subband decomposition of input signals using filtering followed by downsampling. The wavelet transform is utilized to produce representative feature vectors that can accurately capture the characteristics of power quality disturbance, exploring feature extraction of disturbance signal to obtain dynamic parameters. The feature vector obtained from wavelet decomposition coefficients are utilized as input variables of neural network for pattern classification of power quality disturbances. The training algorithm shows great potential for automatic power quality monitoring technique with on-line detection and classification capabilities. The combination performance of wavelet transform with neural network is evaluated by simulation results, approving that the proposed method is effective for analysis of power quality signal.
Starting Page	2529
Ending Page	2532
File Size	188702
Page Count	4
File Format	PDF
ISBN	9781424451814
DOI	10.1109/CCDC.2010.5498768
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	artificial neural network power quality disturbance Filtering Input variables Artificial neural networks Time frequency analysis dynamic parameter Discrete wavelet transforms feature vector Analytical models Power quality Neural networks Feature extraction Power industry Automatic detection and classification
Content Type	Text
Resource Type	Article

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