Power quality event detection and recognition using wavelet analysis and intelligent neural network

A novel method to detect short duration disturbance of distribution power system combing complex wavelet network with the improved back-propagation algorithm is presented. The paper tries to explain to design complex supported orthogonal wavelets by compactly supported orthogonal real wavelets, and...

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Bibliographic Details
Published in:Chinese Control Conference pp. 486 - 489
Main Authors: Jia Ruijuan, Xu Chunxia
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2008
Subjects:
Complex wavelet
Feature extraction
Multiresolution analysis
Power quality
Power system
Power systems
Short duration disturbance
Signal detection
Transforms
Wavelet analysis
Wavelet network
Wavelet transforms
ISSN:1934-1768
Online Access:Get full text
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Summary:A novel method to detect short duration disturbance of distribution power system combing complex wavelet network with the improved back-propagation algorithm is presented. The paper tries to explain to design complex supported orthogonal wavelets by compactly supported orthogonal real wavelets, and then explore the extraction of disturbance signal to obtain the feature information, and finally propose several novel wavelet combined information to analyze the disturbance signal, superior to real wavelet analysis result. The feature obtained from WT coefficients are inputted into wavelet network for power quality disturbance pattern recognition. The power quality disturbance recognition model is established and the improved back-propagation algorithm is used to fulfill the network structure and parameter identification. 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 results of simulation analysis show that the complex wavelet transform combined with wavelet network are more sensitive to signal singularity, and found to be significant improvement over current methods in real-time detection and better noise proof ability.
ISSN:1934-1768
DOI:10.1109/CHICC.2008.4605122