Abstract

This paper proposes an intelligent system for detecting shadows that affect solar cell efficiency using image processing techniques based on camera images (RGB/IR). Artificial intelligence algorithms, such as neural networks or hybrid algorithms (like ANFIS or SCFNN), are used to analyze image data and predict the impact of shadows on productivity. This methodology is implemented in the MATLAB environment using simulations that model the effect of shadows on Maximum Power Point Tracking (MPPT) systems. A dependable supply of shading on the surface, provided by the array’s capacity in a PV system, enhances collection. The reconfiguration efficiency process is crucial for photovoltaic (PV) arrays, particularly in partial conditions of shading. In this work. The effects of full or partial shading on solar cell arrays are studied, and their reconfiguration is demonstrated. Through this field study, it is proposed to improve the operational system performance by using image analysis processing based on the moving shadow. The research results are then verified using a real solar cell array to obtain a picture. This leads to effective results and thus increases the system's energy efficiency. In addition to comparison with other static reconfiguration techniques used for shading conditions, the results show a significant increase in output power across both hardware and software platforms.

Key words: Photovoltaic (PV), Partial Shading, MPPT, Fuzzy Logic Controller (FLC), ANN, ANFIS, SCFNN