Implementation of a Deep Learning-based MPPT System for Maximizing Energy Extraction in Solar PV Modules

In this paper, we propose a novel AI-based Maximum Power Point Tracking (MPPT) system for solar PV modules, leveraging a hybrid Transformer + LightGBM model. The system addresses the limitations of traditional MPPT methods such as Perturb and Observe (P&O) and Incremental Conductance, which struggle with slow response times and reduced accuracy under dynamic environmental conditions. By utilizing the sequential learning capabilities of Transformers and the computational efficiency of LightGBM, the proposed system ensures rapid, precise, and robust MPPT. The hybrid model trained and tested on diverse solar PV datasets, achieving an energy extraction efficiency of 98.7%, a significant improvement over traditional methods such as P&O (92.5%) and Incremental Conductance (94.3%). The system demonstrated an average response time of 45ms, outperforming conventional approaches. Additionally, the model effectively minimized voltage oscillations during rapid irradiance changes, reducing instability by over 80% compared to traditional methods. Field tests confirmed the system’s scalability and real-time adaptability, showing consistent performance across varying environmental conditions. These results highlight the transformative potential of AI-based MPPT in maximizing solar PV energy output.