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Proceedings Paper

Detection and analysis of distribution power quality disturbance based on complex wavelet transform and RBF network
Author(s): Lin Liu; Songhua Shen; Qiang Liu
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Paper Abstract

A novel method to detect power quality disturbance of distribution power system combing complex wavelet transform (WT) with radial basis function (RBF) neural network is presented. The paper tries to explain to design complex supported orthogonal wavelets by Morlet 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 (CI) to analyze the disturbance signal, superior to real wavelet analysis result. The feature obtained from WT coefficients are inputted into RBF network for power quality disturbance pattern recognition. The power quality disturbance recognition 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 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 WT combined with RBF 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.

Paper Details

Date Published: 28 October 2006
PDF: 7 pages
Proc. SPIE 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, 635820 (28 October 2006); doi: 10.1117/12.717967
Show Author Affiliations
Lin Liu, Beijing Univ. of Aeronautics and Astronautics (China)
Songhua Shen, Beijing Univ. of Aeronautics and Astronautics (China)
Qiang Liu, Capital Engineering and Research Inc., Ltd. (China)


Published in SPIE Proceedings Vol. 6358:
Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation
Jiancheng Fang; Zhongyu Wang, Editor(s)

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