Detection and classification of faults in a microgrid using wavelet neural network

Abstract

It is necessary to detect the fault disturbances as quick as possible to improve performance of microgrid. Keeping an eye to the above issue this paper introduces a novel technique for the detection and classification of different faults in microgrid consisting of as Wind Turbine (WT), diesel generator, Solid Oxide Fuel Cell (SOFC) and micro-turbine. Wavelet transform (WT) and Wavelet Packet Transform (WPT) are used for detection and feature extraction to characterize the various faulted signals by using multi-resolution technique. Further, taking the input feature information of all fault disturbances, artificial neural network (ANN), neuro-fuzzy (NF) and Wavelet Neural Network (WNN) are implemented to accurately classify various faults. Two practically relevant 3-bus system and 14-bus microgrid system comprised with various types of distribution generations are considered for the protection analysis which is simulated using MATLAB/SIMULINK environment. Validation of the proposed technique has been done and compared with other two well proven and extensively used methods like ANN and NF under different operating scenarios.