Research on Generic Diagnostic Methods for Short-Circuit Faults in AC Microgrids

Abstract

Micro grid fault of rapid detection and removal is the key to ensure its reliability. With the access of many distributed generations (DG) to the system, the characteristics of short-circuit faults of microgrids in grid-connected and islanded modes have changed, and the traditional protection methods can no longer be applied to both operating modes of microgrids simultaneously. Due to the advantages of wavelet energy spectrum in the identification of the mutation characteristics of the weak signal as well as that of neural network in the location accuracy, this paper proposes a short circuit fault detection and protection method in AC microgrids. The method takes the current at the detection point as the object of analysis, uses the wavelet energy spectrum transform to analyze the current waveforms under normal and fault operation states, and extracts the fault characteristic quantities. At the same time, considering the effect of transition resistance, a generalized fault area identification model for both grid-connected and islanded modes is established by using a neural network algorithm. Simulation and experimental results show that this method can realize accurate judgment and area location of short-circuit faults in different modes, different DG capacities, different fault types and different fault regions.