An Enhanced Fault Classification Method for Photovoltaic Modules Using Texture Features

In the field of photovoltaic (PV) power generation, PV systems are prone to faults such as cells and cracks due to prolonged operation in complex environments, which reduce power generation efficiency and pose safety risks. While traditional fault diagnosis methods can detect typical faults, they struggle to capture texture features. Texture features directly reflect the physical degradation process of PV modules and are significant indicators of faults. However, existing deep learning methods, although effective at extracting image features, do not focus on this crucial aspect, leading to insufficient sensitivity when classifying complex fault patterns (such as shadowing and aging overlap). This results in unsatisfactory classification performance. To address this issue, this paper proposes a texture-feature-enhanced fault classification method for PV modules. First, texture features from fault images were extracted using the gray-level co-occurrence matrix (GLCM). Then, a pilot study was conducted to verify the close relationship between texture features and PV faults. A feature fusion module was designed to combine these texture features with global features extracted by the ResNet50 network, enhancing the model. Ultimately, the texture-enhanced model achieved accurate fault classification, significantly improving the model’s sensitivity to fault textures. The proposed model was evaluated on a test dataset and compared with four other classical models. Experimental results showed that the proposed model outperforms existing models in both accuracy and recall, further validating the key role of texture features in model performance and providing a new reference for research in PV module fault classification.