Enhanced ANN-Based MPPT for Photovoltaic Systems: Integrating Metaheuristic and Analytical Algorithms for Optimal Performance Under Partial Shading

The efficiency of photovoltaic (PV) systems significantly decreases under partial shading conditions (PSC), leading to challenges in accurately tracking the maximum power point (MPP). This paper presents an enhanced Artificial Neural Network (ANN) to improve the performance of MPP tracking (MPPT) in PV systems subject to PSC. The proposed algorithm is based on an advanced ANN model trained with widely known analytical and metaheuristic algorithms, providing higher accuracy and faster convergence than existing methods. Furthermore, the ANN model was developed and trained using an extensive dataset that includes diverse shading scenarios, irradiation levels, and temperature conditions, with metaheuristic algorithms playing a key role in enhancing its training process. The performance of the proposed system has been evaluated through extensive simulations and sensitivity analyses. The results demonstrate that the improved ANN-based MPPT algorithm consistently outperforms existing MPPT techniques, including the Perturb and Observe (P&O) and Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Particle Swarm Optimization (PSO) methods. The proposed approach achieves higher efficiency, faster response times, and improved stability under dynamic shading conditions. Specifically, its superior efficiency reaches up to 99.98% under clear sky conditions (CSC) and up to 99.97% under PSC, as verified through extensive simulations using MPPT efficiency metrics. This advancement holds significant potential for optimizing the energy yield of PV systems, promoting more reliable and efficient renewable energy solutions, especially when operating in challenging environmental conditions.