Thermal inspection of photovoltaic modules with deep convolutional neural networks on edge devices in AUV

Abstract

Solar photovoltaic (PV), a green energy harvesting system, is growing worldwide rapidly. It is a friendly environmental energy system. During operation, anomalies could appear in the PV modules, which reduce energy harvesting efficiency, shorten the lifetime and lead to an increase in the amount of heavy metals being released into the environment. In this research, a remote sensing method is proposed for the fast and efficient detection of anomalies in photovoltaic (PV) systems. An infrared radiation (IR) camera mounted on flying vehicles (e.g., drone) to capture IR images of solar panels. Then, convolutional neural networks (CNN) are developed to detect abnormal cells in the PV systems. The CNN model are then quantized and implemented on edge devices for real-time detection of anomalies. We evaluated these models on 11 types of anomalies in PV modules collected from 826 solar panels worldwide. Moreover, the classification decision of each layer of the CNN model can be visualized by the Gradient-weighted Class Activation Mapping method to reinforce the classification output. In addition, we implemented the models on an edge device (Jetson Nano) to evaluate real-time inspection capabilities. The proposed model is efficient compared to the other previous models, which takes only 1.04 ms to progress one image on average while still yields a high accuracy of 85.35% when running on the Jetson Nano board.