Advanced MPPT Strategy for PV Solar Energy Systems Using ANN-GWO

The PV modules have a major impact on the global transition to renewable energy sources. However, one of the most critical challenges in PV systems is the Maximum Power Point Tracking (MPPT) control. The key objective of this paper is to implement intelligent MPPT based on an online optimization technique by Grey Wolf Optimizer (GWO) to Artificial Neural Network (ANN) called ANN-GWO for a PV modules power plant 400kW. The ANN-GWO methodology is designed to enhance the efficiency of the PV system by adapting to varying environmental conditions. The ANN-GWO aims to optimize the MPPT process dynamically, ensuring maximum power extraction from the PV modules. This novel approach outperforms the conventional Perturb and Observe (P&O) method, a Proportional-Integral-Derivative (PID) controller and ANN-GWO techniques by adapting more effectively to rapid changes in sun irradiance and temperatures applied to the proposed PV panels. Overall, the result of the proposed ANN- GWO is a more reliable and consistent energy yield, even under fluctuating weather conditions, making the ANN-GWO a promising solution for large-scale solar energy applications. MATLAB/SIMULINK (Version R2022b) has been utilized to execute the simulation and obtain the findings which provided a comprehensive analysis of the proposed photovoltaic (PV) system.