基于完全邻域保持嵌入的光伏组件热斑故障诊断  

Complete neighborhood preserving embedding-based hot-spot detection for photovoltaic modules

作　　者：宋远大 易辉 缪小冬 冒泽慧[2] 李红涛 SONG Yuanda;YI Hui;MIAO Xiaodong;MAO Zehui;LI Hongtao(College of Electrical Engineering and Control Science,Nanjing Tech University,Nanjing 211816,China;College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;China Electric Power Research Institute,Nanjing 210003,China)

机构地区：[1]南京工业大学电气工程与控制科学学院,江苏南京211816 [2]南京航空航天大学自动化学院,江苏南京210016 [3]中国电力科学研究院有限公司,江苏南京210003

出　　处：《计算机集成制造系统》2024年第2期460-468,共9页Computer Integrated Manufacturing Systems

基　　金：国家重点研发计划资助项目(2020YFB1711201);国家自然科学基金优秀青年科学基金资助项目(61922042);国家自然科学基金重点国际(地区)合作研究资助项目(62020106003);江苏省前沿引领技术基础研究重大资助项目(2022050029)。

摘　　要：针对局部阴影遮挡引起的光伏组件热斑,提出一种基于完全邻域保持嵌入的热斑故障诊断方法。该方法采用流形学习从光伏组件运行数据中提取非线性特征,在不借助红外图像的前提下实现组件状态的高灵敏检测。所提方法较传统方法能够更精准地反映组件运行状态,进而实现故障检测与故障分级。依托国家电网电力科学研究院实验平台,通过对15组不同程度的热斑故障样本进行性能验证与对比,所提方法能够有效实现故障检测与评估。For the hot spot of PV modules caused by local shadow shading,a hot spot diagnosis method based on Complete Neighborhood Preserving Embedding(CNPE)was proposed.The method used flow learning to extract nonlinear features from PV module operation data and achieved highly sensitive detection of module status without the help of infrared images.Compared with traditional methods,the proposed method could reflect the module operation status more accurately and then realize fault detection and classification.Relying on the experimental platform of the State Grid Electric Power Research Institute,the performance of the proposed method with 15 groups of hot spot samples of different degrees was validated and compared,and the result showed that the proposed method could effectively achieve fault detection and evaluation with good results.

关 键 词：数据驱动 光伏组件 热斑故障 评估函数 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TM914[自动化与计算机技术—控制科学与工程]