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Proceedings Paper

Early detection for short-circuit fault in low-voltage systems based on fractal exponent wavelet analysis
Author(s): Shanlin Kang; Bingjun Wang; Yuzhe Kang
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Paper Abstract

By combining wavelet transform (WT ) with fractal theory, a novel approach is put forward to detect early short-circuit fault. The application of signal denoising based on the statistic rule is brought forward to determine the threshold of each order of wavelet space, and an effective method is proposed to determine the decomposition adaptively, increasing the signal-noise-ratio (SNR). In a view of the inter relationship of wavelet transform and fractal theory, the whole and local fractal exponents obtained from WT coefficients as features are presented for extracting fault signals. The effectiveness of the new algorithm used to extract the characteristic signal is described, which can be realized by the value of the fractal dimensions of those types of short-circuit fault. In accordance with the threshold value of each type of short-circuit fault in each frequency band, the correlation between the type of short-circuit and the fractal dimensions can be figured to perform extraction. This model incorporates the advantages of morphological filter and multi-scale WT to extract the feature of faults meanwhile restraining various noises. Besides, it can be implemented in real time using the available hardware. The effectiveness of this model was verified with the simulation results.

Paper Details

Date Published: 6 November 2006
PDF: 7 pages
Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 63574Z (6 November 2006); doi: 10.1117/12.717510
Show Author Affiliations
Shanlin Kang, Hebei Univ. of Engineering (China)
Bingjun Wang, Anyang Univ. (China)
Yuzhe Kang, Beijing Univ. of Chemical Technology (China)


Published in SPIE Proceedings Vol. 6357:
Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence
Jiancheng Fang; Zhongyu Wang, Editor(s)

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