Consequently, the optimization of PV systems relies heavily on the global maximum power point tracking (GMPPT) methods. It introduces an intelligent control technique with fuzzy-based pattern search (PS) optimization for the MPPT controller, enhancing energy conversion efficiency. The fuzzy-PS approach is further refined with PA optimization. A comprehensive performance evaluation compares it with various. This thesis aims a method of condition monitoring in photovoltaic systems using machine-learning techniques, more specifically artificial neural network learning. First, to approach from a solar cell level, a known model, the one-diode and five-parameter model is used to model it. Object detection with YOLOv5 models and image segmentation with Unet++, FPN, DLV3+ and PSPNet. Challenges arise due to the low resolution. During the use of photovoltaic panels, photovoltaic panels need to undergo regular inspections to avoid affecting photovoltaic power generation output or causing safety accidents due to abnormal number and status of photovoltaic panel components. This paper builds a photovoltaic panel equipment.
