Wavelet Entropy Measure Definition and Its Application for Transmission Line Fault Detection and Identification;(Part III: Transmission line faults transients identification)

Detecting and classifying transient signal have been concerned with by researchers recently, at home and broad. Especially, there are still many difficulties in classification. Based on the PSCAD/EMTDC model of a practical 500 kV transmission line set in part II, six transient signals are simulated, such as breaker operation, capacitor switching, single-phase to ground, primary arc, and lighting strokes with and without fault. As its definition is given in this paper, multi-scales wavelet energy spectrum entropy, combined with neural network, has excellent ability in transient signal classification, especially between fault and non-fault ones. The faulty phase selection of short-circuit is completed on the base of wavelet energy spectrum entropy and fuzzy logic defined in part I. Experiment results show that wavelet energy spectrum entropy of transient signal is characteristic, and BP neural network is efficient in classifying, and fault phase selection using fuzzy inference is effective too. From the research, it is obvious that wavelet entropy application has a bright future in electronic power system.