Automatic defect identification of PV panels with IR images through unmanned aircraft

In order to improve the reliability and performance of photovoltaic systems, a fault diagnosis method for photovoltaic modules based on infrared images and improved MobileNet-V3 is proposed. Firstly, the defect images of open-source photovoltaic modules and their existing problems are analysed; based on the existing problems, image enhancement and data enhancement are performed on the infrared defect images of photovoltaic modules, so that the infrared images meet the requirements of image availability and sample quantity. Finally, the basic MobileNet-V3 network is improved to realize fault classification of photovoltaic modules. The experimental results show that, compared with the traditional CNN and the basic MobileNet V3, the proposed fault classification method not only has high accuracy and fast diagnosis speed, but also has a high recognition rate for various fault categories, which has good practicability and application value. © 2023 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.