Fault Detection and Classification in Interconnected System with Wind Generation Using ANN and SVM

Abstract

Protective relays are installed in generation, transmission, and distribution system for detection, classification, and estimation of faults. To match the future load demand and to get uninterrupted power supply, use of renewable energy sources are increasing day by day. Faults can occur in transmission lines, transformers, generators, and busbars but the nature of these faults may change many times when renewable energy sources are considered. This research paper introduce techniques to detect and classify different faults on transmission line in the presence of wind energy sources using efficient tools of artificial intelligence. The main challenges of the system fault detection, in presence of wind turbine lie in their non-linearity, uncertainty and unknown disturbances. PSCAD/EMTDC software tool is used to simulate the power system model with RES which is implemented in MATLAB and Python software. Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms have been used to classify and detect faults on transmission lines connected with wind energy source. The proposed technique has been validated for internal faults on transmission line and external faults on power system. In total of 4320 internal and external fault cases with wide variation in system parameters have been used for validation of the proposed model. The proposed model gives an overall fault zone identification accuracy of more than 99% in presence of wind energy source. The results obtained from validation show that the performance of SVM classifier is better than ANN in term of efficacy and classification time.