Intelligent Monitoring of Photovoltaic Panels Based on Infrared Detection

With the continuously increasing application of photovoltaic (PV) panels, how to effectively manage these valuable facilities has become an issue of concern. A new PV panel condition monitoring and fault diagnosis technique is developed in this paper. The new technique uses a U-Net neural network and a classifier in combination to intelligently analyse the PV panel’s infrared thermal images taken by drones or other kinds of remote operating systems. In the research, 295 infrared images were taken first from the PV panels in different health states, and then their ‘masks’ were manually created using the software LabelMe. Secondly, enlarge the number of image samples using image expansion methods to establish the image sample database for training and testing the U-net neural network. Thirdly, use 1852 infrared images and their mask images stored in the database to train a U-Net neural network until the trained U-net neural network can create mask images as accurately as the software LabelMe does. Finally, four potential criteria are proposed to mathematically characterise the contour of mask images and then based on the calculation results of these four criteria, diagnose different types of PV panel faults with the aid of three classifiers. The research results have shown that the combined use of a well-trained U-Net neural network and decision tree can diagnose the PV panel faults with the highest accuracy. Therefore, it may arguably provide a promising intelligent tool for condition monitoring the PV panels