Research and Application of Photovoltaic Power Station On-line Hot Spot Detection Operation and Maintenance System Based on Unmanned Aerial Vehicle Infrared and Visible Light Detection

Guanglei Li, Yuejiao Wang, Zheng Xu, Wei-Hua Teng, Xingyou Zhang
{"title":"Research and Application of Photovoltaic Power Station On-line Hot Spot Detection Operation and Maintenance System Based on Unmanned Aerial Vehicle Infrared and Visible Light Detection","authors":"Guanglei Li, Yuejiao Wang, Zheng Xu, Wei-Hua Teng, Xingyou Zhang","doi":"10.1109/ICPSAsia52756.2021.9621375","DOIUrl":null,"url":null,"abstract":"The traditional operation and maintenance method of hot spot detection has some problems, such as low efficiency of inspection, difficult to identify the cause of hot spot under the influence of multiple factors. In this paper, based on the Unmanned Aerial Vehicle(UAV) inspection technology, combined with the slope constraint based infrared image and visible image registration method of hot spot location and based on the improved fish swarm gray combination prediction method, the hot spot information discrimination process was designed. On this basis, an on-line hot spot detection operation and maintenance system of photovoltaic power station(PVPS) based on UAV infrared and visible light detection was constructed, and the accuracy of hot spot detection results of the system was verified by experiments. The system has high accuracy of hot spot location, can actively screen out the external influencing factors of photovoltaic module hot spot, and realize automatic alarm and location investigation of complex hot spot.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPSAsia52756.2021.9621375","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

The traditional operation and maintenance method of hot spot detection has some problems, such as low efficiency of inspection, difficult to identify the cause of hot spot under the influence of multiple factors. In this paper, based on the Unmanned Aerial Vehicle(UAV) inspection technology, combined with the slope constraint based infrared image and visible image registration method of hot spot location and based on the improved fish swarm gray combination prediction method, the hot spot information discrimination process was designed. On this basis, an on-line hot spot detection operation and maintenance system of photovoltaic power station(PVPS) based on UAV infrared and visible light detection was constructed, and the accuracy of hot spot detection results of the system was verified by experiments. The system has high accuracy of hot spot location, can actively screen out the external influencing factors of photovoltaic module hot spot, and realize automatic alarm and location investigation of complex hot spot.
基于无人机红外与可见光检测的光伏电站在线热点检测运维系统研究与应用
传统的运维热点检测方法在多种因素的影响下存在检测效率低、热点原因难以识别等问题。本文基于无人机(UAV)检测技术,结合基于斜率约束的红外图像和可见光图像热点定位配准方法,基于改进的鱼群灰度组合预测方法,设计了热点信息识别过程。在此基础上,构建了基于无人机红外和可见光检测的光伏电站在线热点检测运维系统,并通过实验验证了系统热点检测结果的准确性。该系统热点定位精度高,能主动筛选出光伏组件热点的外部影响因素,实现复杂热点的自动报警和定位调查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信