Photovoltaic arrays fault diagnosis based on an improved dilated convolutional neural network with feature-enhancement

Abstract

Photovoltaic (PV) arrays are installed outdoors and prone to abnormalities and various faults under harsh natural conditions, reducing power conversion efficiency and the life of the PV modules, and even causing electric shock and fire. Current fault diagnosis methods are unable to accurately identify and locate faults in PV arrays in PV power systems, leading to increased operation and maintenance costs. Therefore, the feature-enhancement improved dilated convolutional neural network (CNN) is proposed for fault diagnosis of PV arrays in this paper. Firstly, aim at the problem of information loss due to data structure and spatial hierarchy within the traditional CNN, and the loss of data after down-sampling, which leads to the inability to reconstruct information, a dilated convolution is introduced to obtain a larger perceptual field while reducing the computational effort. Meanwhile, the adaptive dual domain soft threshold group convolution attention module is proposed to enhance the essential features of faults and reduce the information redundancy given the ambiguity and blindness of the feature data in PV array fault extraction. Finally, the model performance of the proposed model is validated and the operability and effectiveness of the proposed method are verified experimentally. The diagnostic results show that the average diagnostic accuracy of the proposed model is 98.95% compared with other diagnostic models, with better diagnostic accuracy and more stable diagnostic performance.