Assessment of power loss caused by soiling PV modules using a dual branch multi-modality deep learning network framework

Soiling can reduce the output power and work efficiency of photovoltaic (PV) modules, causing serious economic losses to PV systems. The cleaning schedules can be optimized to save economic expenses through the methods capable of estimating the power loss of PV modules resulting from soiling. This paper proposes a deep learning framework that combines visible light and infrared image information with dual branch cross-modality feature fusion. Initially, the MobileNetV2 is applied as the backbone of the dual branch framework to enhance the training efficiency and reduce the computational complexity. Subsequently, a cross-modality differential aware fusion module based on the channel attention mechanism (CA-CMDAF) is introduced to improve the cross-modality feature fusion capability of the model. Moreover, a multi-cascade and cross-modality fusion network and a multi-scale fusion network are integrated to further facilitate the effectiveness of feature fusion and reduce the loss of visual details during the feature extraction. Lastly, extensive experiments are carried out on the multi-modality dataset. The comparison results demonstrate the superior performance of the proposed dual branch network framework with the average accuracy of 88.27 %, which is higher than that of the single-modality models trained on either visible light or infrared images alone.