基于深度边缘的太阳能电池板故障检测

Deep Edge-Based Fault Detection for Solar Panels.

作者信息

Ling Haoyu, Liu Manlu, Fang Yi

机构信息

School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China.

School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China.

出版信息

Sensors (Basel). 2024 Aug 19;24(16):5348. doi: 10.3390/s24165348.

DOI:10.3390/s24165348

PMID:39205042

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11359415/

Abstract

Solar panels may suffer from faults, which could yield high temperature and significantly degrade their power generation. To detect faults of solar panels in large photovoltaic plants, drones with infrared cameras have been implemented. Drones may capture a huge number of infrared images. It is not realistic to manually analyze such a huge number of infrared images. To solve this problem, we develop a Deep Edge-Based Fault Detection (DEBFD) method, which applies convolutional neural networks (CNNs) for edge detection and object detection according to the captured infrared images. Particularly, a machine learning-based contour filter is designed to eliminate incorrect background contours. Then faults of solar panels are detected. Based on these fault detection results, solar panels can be classified into two classes, i.e., normal and faulty ones (i.e., macro ones). We collected 2060 images in multiple scenes and achieved a high macro F1 score. Our method achieved a frame rate of 28 fps over infrared images of solar panels on an NVIDIA GeForce RTX 2080 Ti GPU.

摘要

太阳能板可能会出现故障，这可能导致温度升高并显著降低其发电能力。为了检测大型光伏电站中太阳能板的故障，已采用配备红外摄像头的无人机。无人机可能会拍摄大量红外图像。手动分析如此大量的红外图像是不现实的。为了解决这个问题，我们开发了一种基于深度边缘的故障检测（DEBFD）方法，该方法根据捕获的红外图像应用卷积神经网络（CNN）进行边缘检测和目标检测。特别地，设计了一种基于机器学习的轮廓滤波器来消除不正确的背景轮廓。然后检测太阳能板的故障。基于这些故障检测结果，太阳能板可分为两类，即正常的和有故障的（即宏观故障）。我们在多个场景中收集了2060张图像，并获得了较高的宏观F1分数。我们的方法在NVIDIA GeForce RTX 2080 Ti GPU上对太阳能板的红外图像实现了28帧/秒的帧率。