Abstract The rapid expansion of photovoltaic power generation increases the difficulty of maintaining stable performance, because traditional operation and maintenance methods cannot handle the efficiency loss caused by array faults. To improve the intelligent operation and energy performance of photovoltaic stations, this paper proposes a collaborative scheme integrating improved visual detection with intelligent optimization. An enhanced YOLOv7 model, incorporating a Hierarchical Attention Fusion Module and a Decoupled Detection Head, is designed to strengthen fault feature extraction. Furthermore, a dynamic reconfiguration method based on a Grow Optimizer with parameter self-adaptive characteristics is proposed to optimize array energy efficiency. Experimental results demonstrate that the proposed model achieves a detection accuracy of 97.45% in noise-free scenarios and maintains a robustness of over 84.75% under noise interference. Meanwhile, the reconfiguration algorithm achieves a maximum power increase of 78.99% and a system conversion efficiency of 97.24% under open-circuit, short-circuit, and complex shading conditions. These results prove that the method improves fault recognition accuracy and power generation efficiency. It also builds a reliable intelligent operation loop through multi-technology coordination and offers effective support for reducing cost and improving performance in photovoltaic stations.
Luo et al. (Tue,) studied this question.