Fault Detection and Analysis of Distributed Power System Short-circuit Using Wavelet Fractal Network

A novel method based on wavelet network and fractal theory for detecting short-circuit fault is proposed. To increase the signal-noise-ratio of the fault signal, the signal denoising technology using the statistic rule is brought forward to determine the threshold and the decomposition level of each order of wavelet space. On the basis of the inter relationship of wavelet transform and fractal theory, the whole and local fractal exponents obtained 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 achieved by different fractal dimensions values 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 detection model incorporates the advantages of morphological filter and multi-scale wavelet transform to extract the feature of faults meanwhile restraining various noises. The simulation results demonstrates that the proposed model is verified effectively.