Fault analysis in grid-connected solar photovoltaic systems based on multi-objective grey wolf optimisation

Abstract

The renewable energy resources depend on environmental concerns. Photovoltaic (PV) systems are an interesting form of renewable energy resource. The two main problems associated with PV systems are nonuniformity of irradiation and temperature change. PV achieving maximum power point tracking are dependent on power electronics converters. Emissions are a key challenge for the future of conventional renewable energy resources, which play an important role in the generation system. It is crucial in applications and industry because the primary goal of optimization is to discover specific research. We offer using the population of search agents based on fitness, the multi-objective grey wolf optimizer, a more modern meta-heuristic based on the hunting habits and social structure of grey wolves, for optimizing adaptive controllers for failures in grid analysis and power system integration various faults in the grid cause several power quality issues. Harmonics, voltage sag and swell imbalances, and many power conditioning topologies. This was carried out using MATLAB/Simulink, and the results showed a superior and flexible performance in reducing voltage sag and voltage swell problems and imperfections at various loading situations.