太阳能Issue(8)：45-53,9.DOI:10.19911/j.1003-0417.tyn20240812.01
基于改进DenseNet的光伏组件故障诊断研究
RESEARCH ON FAULT DIAGNOSIS OF PV MODULE BASED ON IMPROVED DenseNet
摘要
Abstract
Aiming at the fault detection problem of PV module infrared thermal images,this paper proposes an improved DenseNet model,DenseNet-46 model,which addresses the issues of large network parameters,excessive feature channels,and effective features less prominent in the original DenseNet model.The DenseNet-46 model is used for feature extraction and analysis of PV module infrared thermal images,and the J48 algorithm is employed for feature selection from the extracted features.The accuracy of four models,namely DenseNet-46,DenseNet-121,ResNet-50,and VGG-19,is compared analysis in faults diagnosing in PV modules.The research results show that the J48 algorithm achieves high efficiency in feature selection,with a classification accuracy of 96.82%.The DenseNet-46 model achieves a recognition accuracy of PV module fault of 99.2%,significantly outperforming the other three models and demonstrating excellent performance in PV module fault diagnosis.DenseNet-46 model not only reduces model complexity but also minimizes feature information loss,providing an efficient and accurate solution for PV module fault detection,and contributing to the intelligent operation and maintenance of PV power generation systems.关键词
DenseNet/J48算法/光伏组件/故障诊断/特征提取/红外热成像Key words
DenseNet/J48 algorithm/PV modules/fault diagnosis/feature extraction/infrared thermography分类
信息技术与安全科学引用本文复制引用
何婷,乔俊强..基于改进DenseNet的光伏组件故障诊断研究[J].太阳能,2025,(8):45-53,9.基金项目
甘肃自然能源研究所软科学专项项目(2024SR-02) （2024SR-02）
甘肃省自然科学基金项目(23JRRA1664) （23JRRA1664）
