摘要
随着光伏发电的大规模推广,光伏系统中的故障检测问题成为研究热点。在技术不断革新的同时,能够预测和提前预防光伏系统故障的发生,保证系统的可靠运行,变得尤其重要。文章基于光伏组件的工作情况、组件结构、老化现象以及对应的等效电路模型参数变化,对光伏组件的健康状态进行划分,总结了影响光伏组件亚健康状态的三大指标,分别是透光率、串联电阻以及并联电阻。文章提出了一种模糊算法对光伏组件健康状态进行健康、亚健康、部分阴影与故障状态进行诊断。首先,对归一化处理后的光伏组件样本数据集进行模糊C均值(fuzzy C-means,FCM)聚类得到聚类中心;然后,利用聚类中心与测试样本代入高斯隶属函数对健康状态进行诊断,并通过仿真与实验验证了该方法的可行性,为光伏系统故障预警、老化检测提供参考。
With the large-scale popularization of photovoltaic(PV)power generation,the problem of fault detection in PV system has become a research hotspot.With the continuous technological innovation,it is particularly important to be able to predict and prevent the occurrence of PV system faults in advance and ensure the reliable operation of the system.In this paper,based on the working condition,module structure,aging phenomenon and corresponding equivalent circuit model parameter changes of PV modules,the health state of PV modules was divided,and three major indicators affecting the sub-health state of PV modules were summarized,namely,light transmittance,the series resistance and the parallel resistance.A fuzzy algorithm was proposed to diagnose the health state of PV modules,including health,sub-health,partial shadow and fault state.Firstly,Fuzzy C-Means(FCM)clustering was performed on the normalized PV module sample data set to obtain the clustering center.Then,the clustering center and test sample was substituted into Gaussian membership function to diagnose the health status.Finally,the feasibility of the proposed method is verified by simulation and experiment,which provides a reference for fault warning and aging detection in PV system.
作者
吴春华
俞薛颖
李智华
汪飞
马浩强
WU Chunhua;YU Xueying;LI Zhihua;WANG Fei;MA Haoqiang(Shanghai Key Laboratory of Power Station Automation Technology(Department of Electrical Engineering,Shanghai University),Baoshan District,Shanghai 200444China)
出处
《电网技术》
EI
CSCD
北大核心
2022年第5期1887-1896,共10页
Power System Technology
基金
国家自然科学基金项目(51677112)。
关键词
光伏系统健康状态
模糊C均值聚类
光伏组件老化
高斯隶属度函数
health status of photovoltaic system
fuzzy c-means clustering
aging of photovoltaic modules
gaussian membership function
