基于动态指标的光伏组件健康程度诊断
Health Diagnosis of Photovoltaic Modules Based on Dynamic Health Criteria
李智华 1张宇浩 1吴春华 1汪飞 1马浩强1
作者信息
- 1. 上海市电站自动化技术重点实验室(上海大学),上海市 宝山区 200444
- 折叠
摘要
为了对光伏组件的健康状况进行诊断,提出了一种基于光伏组件的光生电流,等效串联电阻,等效并联电阻 3 个指标的光伏组件健康程度定量检测方法.首先利用改进的布谷鸟算法对测量的光伏组件I-U线进行参数辨识,形成实测健康曲线,再利用光伏组件自然老化模型确定理论健康曲线,然后计算实测健康曲线与理论健康曲线之间的偏移量并结合灰色关联分析进行数值校正;定义健康程度 H 参量计算得到光伏组件健康程度量化值.仿真分析和实验测试结果表明该方法可以对不同环境下的光伏组件健康程度进行有效在线诊断,为光伏组件维护提供参考.
Abstract
In order to diagnose the health status of the photovoltaic modules,a method of quantitative detection for the health checking of the photovoltaic modules is proposed based on three indexes of the photogenerating current,the equivalent series resistance and the equivalent parallel resistance.Firstly,the improved cuckoo algorithm was used to identify the parameters of the measured I-U curve of the photovoltaic modules,forming the measured health curve.Then,the theoretical health curve was determined by the natural aging model of the photovoltaic modules.Next,the offset between the measured health curve and the theoretical health curve was calculated and the numerical correction was carried out with the grey correlation analysis.The health degree H parameter is defined to calculate the quantified value of the health status of the photovoltaic modules.Simulation analysis and experimental test show that this method is able to diagnose the health degree of the PV module effectively in different environments,providing a reference for the PV module maintenance.
关键词
光伏组件/参数辨识/健康程度/灰色关联分析/布谷鸟算法Key words
photovoltaic modules/parameter identification/health degree/grey correlation analysis/cuckoo algorithm引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据