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

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