Fault Classification in a DG Connected Power System using Artificial Neural Network

Abstract

Distributed generation is playing a major role in the power system to meet the growing load demand. Integration of Distributed Generator (DG) to grid leads to various issues of protection and control of power system structure. From the different fault issues that occur in a distributed generator integrated power system, classification of fault remains as one of the most vital issues even after years of in-depth research. Because researchers are attempting to detect and diagnose these faults as soon as possible in order to avoid financial losses, this work aims to investigate the sort of fault that happened in the hybrid system. This paper proposed artificial neural network-based approaches for fault disturbances in a microgrid made up of wind turbine generators, fuel cells and diesel generator. The voltage signal is retrieved at the point of common coupling (PCC). The extracted data are used for training and testing purposes. Artificial neural network technique is utilized for the classification of faults in the simulated model. Furthermore, performance indices (PIs) such as standard deviation and skewness are calculated for reduction of data size and better accuracy. Both the fault and parameters are varied to check the usefulness of the proposed method. Finally, the results are discussed and compared with different DG penetration.