Photovoltaic (PV) power generation serves as a core pillar for China′s energy transition and the achievement of the dual carbon goals, and the efficient and safe operation of its systems is of vital importance. However, PV panel faults can directly lead to a sharp drop in power generation, a rise in safety risks, and an increase in operation and maintenance costs, which have become a key bottleneck restricting the high-quality development of the PV industry. To address this issue, a PV panel fault diagnosis method based on cross-scale feature fusion is proposed. By leveraging the existing security equipment in PV power stations, this method can accurately detect six operational states of PV panels, including normal, dust accumulation, bird droppings adhesion, electrical loss, physical damage, and snow coverage, without incurring additional hardware costs. Specifically, this method firstly constructs a cross-scale feature fusion framework combining the Transformer and the convolutional neural network (CNN). The Transformer captures the global contextual features of PV panel images through its self-attention mechanism, providing global guidance for the CNN to extract local detailed features. Secondly, a dense connection mechanism is designed in the CNN branch, which enhances the propagation and reuse capabilities of features at different levels through cross-layer connections of feature maps. Meanwhile, a visible light dataset for six operating states of PV panels is developed in a targeted manner, covering different lighting conditions, weather situations, and fault types. Compared with other models, this method exhibits superior comprehensive performance, with a Top-1 accuracy of 93.33% and a Top-3 accuracy of 100%. Additionally, the model has a relatively lightweight parameter scale and low hardware computing power requirements. Furthermore, to comprehensively evaluate the engineering application value of the method, a three-dimensional comprehensive evaluation index system, model accuracy-usage efficiency-application scale, is established, which further verifies the feasibility of the method for practical deployment in PV fields.