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An intelligent technique for optimal power quality reinforcement in a grid‐connected HRES system: EVORFA technique

This manuscript proposes the optimal power quality reinforcement in grid‐connected hybrid renewable energy sources like solar photovoltaic, wind turbine, battery storage using an intelligent approach. The proposed hybrid approach is the consolidation of Egyptian vulture optimization algorithm (EVOA) and random forest algorithm (RFA); hence, it is known as EVORFA technique. The major objective of this research is voltage stabilizing, power loss reduction, extenuating harmonic distortion. EVOA is mainly used to the offline way to differentiate the perfect combination and forms the dataset of proportional integral gain parameters; load current, DC‐link voltage, and voltage sources are based on reduced error objective function. In EVOA, multiple parameters are considered that is identified to the power quality (PQ) issues. The RFA predicts most optimal control signal with minimum error based on the accomplished dataset. The proposed EVORFA approach is executed in MATLAB/Simulink work site. The EVORFA approach performance is carried out in two modes, that is, simultaneous PQ reinforcement and RES power injection PRES > 0 and PQ reinforcement (PRES = 0). By then the experimental results are compared to the existing methods like gravitational search algorithm (GSA) and RFA.