Artificial Intelligence Approach for Enhancing Energy Yield in Solar PV Arrays Using CNNs

Abstract

Maintaining the best power output under different environmental circumstances presents major difficulties for solar Photovoltaic (PV) systems. Extraction of maximum available power depends on Maximum Power Point Tracking (MPPT) algorithms; nonetheless, traditional methods can have difficulty with fast-changing weather circumstances and partial shade conditions. This article provides a unique method for MPPT in solar PV systems based on Convolutional Neural Networks (CNN). The proposed CNN-based technique precisely forecasts the maximum power point under various operating situations using current and voltage patterns as input characteristics. Unlike conventional methods depending on iterative processes, this deep learning method can quickly find the global maximum power point even in complicated partial shading situations. With 98.7% tracking efficiency, the CNN-based MPPT greatly surpasses conventional Perturb and Observe (P&O) and incremental conductance techniques experimental results show. The technology reduces power oscillations and increases general energy yield by over 15% annually by reacting to abrupt changes in irradiation within milliseconds. Furthermore suited for both small-scale and utility-grade solar installations is the trained model since it requires less processing resources for deployment on embedded systems. This AI-driven method marks significant progress in renewable energy optimization, hence improving solar PV system grid integration capacity and economic viability.