Implementation and performance results of wavelet network for analysis of fault signal in power system

Weili Huang; Wei Du

doi:10.1117/12.806322

9 October 2008 Implementation and performance results of wavelet network for analysis of fault signal in power system

Weili Huang, Wei Du

Proceedings Volume 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment; 712802 (2008) https://doi.org/10.1117/12.806322
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

Abstract

A new method is proposed to detect fault signal in power system using wavelet transform and neural network. The wavelet transform can decompose the original signal into several other signals with different levels of resolution. From these decomposed signals, the original time-domain signal can be recovered without losing any information. In order to increase the signal-noise-ratio, statistic rule is used to determine the wavelet decomposition level and threshold. Considering the inter relationship of wavelet decomposition coefficients, the signal features obtained from wavelet transform are presented for fault pattern classification. According to threshold value of each type of fault signal in each frequency band, the correlation between the type of signal and the signal features can be figured to fault pattern classification. As to this model, the advantages of morphological filter and wavelet transform are used to extract the fault feature meanwhile restraining various noises. The simulation results demonstrate that the proposed approach is verified to be effective.

Citation Download Citation

Weili Huang and Wei Du "Implementation and performance results of wavelet network for analysis of fault signal in power system", Proc. SPIE 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment, 712802 (9 October 2008); https://doi.org/10.1117/12.806322

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KEYWORDS

Wavelets

Wavelet transforms

Signal detection

Fractal analysis

Network security

Signal analyzers

Continuous wavelet transforms

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