Fault Detection, Classification, and Location by Static Switch in Microgrids Using Wavelet Transform and Taguchi-Based Artificial Neural Network

Microgrids, which are small electric power systems, accommodate different distributed generations and energy storage system. When faults occur outside or inside a microgrid, the static switch between bulk power system and microgrid must immediately open the circuit. This paper presents a fault detection, classification, and localization method that is based on a multiresolution analysis of the discrete wavelet transform (DWT) and a Taguchi-based artificial neural network (ANN). The differences of wavelet entropies of the three-phase fault voltages, three-phase fault currents, and the wavelet entropies of the neutral fault current are inputs to the ANN, which is trained using orthogonal datasets that are obtained from Taguchi's experiments. The ANN identifies the faulty phase and the location of the fault. The proposed DWT and ANN are implemented in a Renesas RX62T microcontroller, which is verified by chip-in-the-loop simulation with a real-time digital simulator (OP5600, Opal Lab) and a 50 kVA static switch to show the applicability of the proposed method.