Optimized RNN-oriented power quality enhancement and THD reduction for micro grid integration of PV system with MLI: Crow Search-based Harris Hawks Optimization concept

Abstract

Grid-connected Photo Voltaic (PV) power systems are becoming increasingly popular in several nations. The goal of achieving maximum power and acceptable power quality in a grid-connected PV power system is considered a major difficulty. Hence, this paper develops an artificial intelligence-based optimization concept for PV system and novel cascaded Multi Level Inverter (MLI) for the grid integration of PV system. The cascaded MLI was designed with fewer power electronic switches and can function at asynchronous voltage sources, making it the most suitable for PV systems. This novel inverter minimizes the THD at the output with the help of enhancing the output voltage level. It also improves the power quality of the system. The micro grid integration of the introduced inverter is controlled by Optimized Recurrent Neural Network (ORNN), where the hidden neurons are tuned by novel hybrid meta heuristic algorithm by merging Crow Search Algorithm (CSA) and Harris Hawks Optimization (HHO) leading to Crow Search-based Harris Hawks Optimization (CS-HHO). The proposed model is designed at several loading conditions and weather conditions. The simulation findings proved the efficiency of the developed system.