CNN based fault event classification and power quality enhancement in hybrid power system

Abstract

A resilient approach is presented in this study for detecting and classifying faults for power distribution systems integrating renewable energy sources (RES). Combining discrete wavelet transform (DWT) and convolutional neural network (CNN). The suggested framework addresses the challenges of RES intermittency and kinetic energy insufficiency. The recommended methodology is evaluated in a MATLAB platform, featuring a power distribution system with photovoltaic (PV) and wind energy conversion system (WECS), stabilized by a boost converter and cascaded fuzzy logic controller (CFLC) based maximum power point tracking (MPPT) for PV and a PI controller for WECS. Comparative analyses demonstrate the superior performance of the CNN classifier with an accuracy of 96.33%, outshining existing classifiers, including ANN. Furthermore, under various fault conditions, the CNN consistently achieves high accuracy, with 98% for Islanding, 95% for line-to-ground fault, and 96% for line-to-line fault. The proposed approach exhibits excellent computational efficiency, with a training time of 10.5 hours, inference speed of 5 milliseconds, and resource utilization of 85%, emphasizing its suitability for instantaneous fault identification in power systems.