深度学习在光伏组件热斑检测中的应用  

Research on Thermal Spot Fault Detection of Photovoltaic Modules Based on Improved YOLOv5s

作　　者：吕栋梁 贺兴亮 翟卓涛 赵文燕 LV Dongliang;HE Xingliang;ZHAI Zhuotao;ZHAO Wenyan(Shanxi Transportation Investment and Financing Group Co.,Ltd.,Taiyuan 030006,China;School of Electric Power and Architecture,Shanxi University,Taiyuan 030000,China)

机构地区：[1]山西交通运输投融资集团有限责任公司,山西太原030006 [2]山西大学电力与建筑学院,山西太原030000

出　　处：《电工技术》2025年第6期54-58,64,共6页Electric Engineering

摘　　要：针对传统光伏组件热斑检测方法精度低、效率低的问题,提出了一项利用无人机实施光伏电站智能化巡检的技术路线。该路线具备自动采集和分析光伏组件图像数据的能力,并采用基于STA-TCN模型的深度学习检测方法进行光伏组件热斑故障诊断,并根据相机POS数据及相机模型解算缺陷坐标,实现缺陷定位。STA-TCN模型为引入了一种融合了空间和时间注意力机制的多元时域卷积网络(TCN)前馈网络模型,以解决传统TCN未能充分考量外生序列对状态预测贡献的问题。最后通过对比试验证明了所提模型的有效性。实验结果显示,与传统的机器学习方法相比,STA-TCN模型在检测精度、召回率和mAP等关键指标上均显示出更高的性能和可靠性。Aiming at the problems of low precision and low efficiency of traditional PV module hot spot detection methods,this study proposes a technical route to implement intelligent inspection of PV power stations using UAVs.This route has the ability to automatically collect and analyze PV module image data,and adopts a deep learning detection method based on the STA-TCN model for PV module hot spot fault diagnosis,and realizes defect localization by solving the defect coordinates based on the camera POS data and the camera model.The STA-TCN model is a multivariate time-domain convolution network(TCN)feed-forward network model that introduces a multivariate TCN that integrates spatial and temporal attention mechanisms to address the failure of the traditional TCN to solve the problems of the traditional TCN.Feedforward network model to solve the problem that traditional TCN fails to fully consider the contribution of exogenous sequences to state prediction.Finally,the effectiveness of the model proposed in this study is demonstrated through comparative experiments.Based on the experimental results,the STA-TCN model shows higher performance and reliability in key metrics,such as detection accuracy,recall and mAP,compared with traditional machine learning methods.

关 键 词：光伏组件 热斑故障 深度学习 

分 类 号：TM615[电气工程—电力系统及自动化]