Wavelet-transform based early detection method for short-circuit faults in power distribution networks
Abstract Detecting short-circuit faults is a critical part of fault processing in distribution networks, which is useful for the subsequent fault processing technique. Through analysing the features of short currents, an early detection method for short-circuit faults in 10-kV middle-voltage distribution networks based on the Mallat decomposition algorithm is proposed. The reliability of the proposed method under some abnormal non-fault conditions is studied as well. Various types of short-circuit faults in distribution systems are simulated in the MATLAB/SIMULINK environment. The wavelet detection method (WDM) and other methods are implemented to realize fast detection of faults, and the detection results are analysed and compared. In addition, the experiments for different faults are accomplished in a physical simulation system and current waveforms are sampled. The algorithm programs of the WDM are written in a hardware detection system to achieve real-time fault detection. The simulation and test results show that the proposed early detection method can realize millisecond-level fault detection, adapt different fault conditions effectively, and has sufficiently high calculation efficiency in hardware. Highlights Presenting an early short-circuit-fault detection method of distribution networks. Mallat algorithm based on the cubic B-spline function has been developed. Wavelet packet decomposition is used for early short-circuit-fault detection. The proposed method is verified on MATLAB/SIMULINK and physical systems.
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