Fault Diagnosis for Solar Panels using Convolutional Neural Network

Fault diagnosis of solar panels is essential for production capacity and safety of solar energy, and has caught considerable attention. This paper presents an efficient method based on a convolution neural network (CNN) to detect solar panels' edges in infrared images. An accurate classifier is proposed to identify defective solar panels with the obtained obtained edges of solar panels. The location and classification algorithms of solar panels with faults are realized through a lightweight CNN, and implemented on embedded devices. With many images collected from different photovoltaic plants, we verified the effectiveness of the proposed fault diagnosis algorithms.