NDLI: Power Quality Disturbances Detection and Classification Using Complex Wavelet Transformation and Artificial Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Liu Hua Wang Yuguo Zhao Wei
Copyright Year	2007
Description	Author affiliation: Hebei Univ. of Eng., Handan (Liu Hua; Wang Yuguo; Zhao Wei)
Abstract	This paper presents a novel power quality disturbance detection and classification method of distribution power system based on complex wavelet transform (WT) and radial basis function (RBF) neural network. The complex supported orthogonal wavelets is employed to extract the feature information of disturbance signal, and finally proposed to explore several novel wavelet combined information (CI) to analyze the disturbance, superior to real wavelet analysis result. The feature obtained from WT coefficients are inputted into RBF network for power quality disturbance pattern classification. The power quality disturbance classification model is established and the synthesized method of recursive orthogonal least squares algorithm (ROLSA) with improved givens transform is used to fulfill the network structure parameters. By means of choosing enough samples to train the recognition model, the type of disturbance can be obtained when signal representing fault is inputted to the trained network. The simulation results demonstrate that the complex WT combined with RBF network are more sensitive to signal singularity, and found to be significant improvement for acquiring signal feature information.
Starting Page	208
Ending Page	212
File Size	201258
Page Count	5
File Format	PDF
ISBN	9787811240559
DOI	10.1109/CHICC.2006.4347508
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms signal detection power quality disturbance power system Artificial neural networks Wavelet analysis Data mining Information analysis Complex wavelet Power quality Power system analysis computing Radial basis function networks Feature extraction Signal analysis
Content Type	Text
Resource Type	Article

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