Edge Intelligence for Smart EL Images Defects Detection of PV Plants in the IoT-Based Inspection System

Abstract

Given the huge installed capacity of photovoltaic (PV) worldwide, the traditional defect detection system for PV plants is infeasible, especially for large-scale plants. In this article, unmanned aerial vehicles (UAVs) mounted with several sensors and a computer in the cloud are used cooperatively to establish an Internet of Things-based cloud-edge computing infrastructure, which can automatically detect defects with low latency, low cost, and high accuracy. The pretrained models trained in the cloud server are embedded into the processor in UAVs to implement online detection. Specifically, given the characteristic of defects in electroluminescence images, a two-stage algorithm is proposed to identify the defects with high performance. In the first stage, cells, the basic unit in the PV module, are extracted using an encoder–decoder network. Then, a vision-based incremental defect classification algorithm is proposed for defect detection that integrates deep learning with prior knowledge to maximize computing efficiency. The performance of the proposed system is evaluated through extensive experiments.