Novel Hybrid Optimization Maximum Power Point Tracking and Normalized Intelligent Control Techniques for Smart Grid Linked Solar Photovoltaic System

Introduction of photovoltaic (PV) systems in grid causes some complex power quality problems such as harmonics, voltage fluctuation, and load imbalance that can affect the grid coordinated PV system performance. Therefore, herein, a novel normalized reasoning‐based fuzzy neural adaptive (NRFNA) control technique is proposed for the double‐stage grid coupled solar PV system. In addition, the utmost power in a double‐stage PV system is tracked at dissimilar environmental states by the proposed novel hybrid krill herd spider monkey (HKHSM)‐based maximum power point tracking (MPPT) algorithm. The current computation control method preserves the grid current in unity power factor as sinusoidal for the system at point common coupling (PCC). The simulation of this proposed work is done on MATLAB/Simulink. Subsequently, the performance of the developed replica results is validated under dissimilar conditions of power quality. Furthermore, the proposed HKHSM‐based MPPT along with a normalized reasoning herd spider monkey (NRHSM) control technique in a grid coordinated solar PV system has achieved 1.70% load current harmonics and 3.7% grid current harmonics. Consequently, the simulation outcomes are compared with the various conventional methods for proving the significance of the proposed system. Herein, novel hybrid krill herd spider monkey based maximum power point tracking along with a normalized reasoning based fuzzy‐neural adaptive control technique is proposed for improving the solar photovoltaic linked grid coordinated system. The total harmonic distortion value of projected solar photovoltaic with a smart grid system is attained within the IEEE standard range compared to conventional control methods.