Reinforced Grey Wolf Optimization Based Maximum Power Point Tracking Algorithm for Photovoltaic Systems Under Varying Irradiation Conditions

Maximum Power Point Tracking is a method used in photovoltaic systems to optimize the power output of solar panels by monitoring the point of maximum power. Traditional MPPT techniques like P&O and IC can analyze the MPP in a uniform shading environment, but they fall short when there is partial shading. This is due to their drawbacks such as frequent oscillations, getting stuck at local maxima, etc. Therefore, it is challenging to use these algorithms in a PV system to produce optimal and dependable power. This project makes use of a meta-heuristic algorithm named grey wolf optimization (GWO) for a photo voltaic system for MPPT. The GWO is a new optimization methodology to overcome these problems with different algorithm strategies, like decreased tracking efficiency and transients. To overcome the issues, a new algorithm called Reinforced GWO has been extracted by altering the original GWO's hunting equation and encircling equation. The proposed system is being studied for a PV array operating under PSCs that shows minimum fluctuations in power and output power stability around the MPP. The simulation and associated results demonstrate that the suggested MPPT algorithm performs better than Standard GWO in terms of superior stability and fewer output power fluctuations as well as overall effective efficiency. The proposed Reinforced GWO MPPT algorithm is implemented on PV systems using MATLAB SIMULINK.