摘要
实现光伏组件快速准确的故障诊断是保证光伏发电系统安全高效运行的重要前提。在分析光伏发电系统主要故障产生原因及其对光伏组件运行参数影响的基础上,对比分析目前国内外光伏电站常用的故障诊断方法及其特点。根据光伏电站结构及运行数据特点,采用主元成分分析法建立针对光伏电站组件的故障诊断模型,并通过某光伏电站实际运行数据进行验证。同一光伏场站各光伏组件的运行数据具有较强的时空相关性,诊断模型选取逆变器两侧功率、电压和电流等具有强关联性的参数进行建模。结果表明,主元成分分析法能快速有效地识别运行异常的光伏组件。
Faster and more accurate fault diagnosis of photovoltaic modules is important prerequisite to ensure the safe and efficient operation of photovoltaic power generation system.Common fault diagnosis methods of photovoltaic power stations are compared and analyzed based on the analysis of the main fault and its impact of photovoltaic power generation system.According to the structure and operation data characteristics of photovoltaic power station,a fault diagnosis model for photovoltaic power station modules is established using the principal component analysis method.The operation data of each photovoltaic module in the same station has strong spatio-temporal correlation,so the diagnosis model selects parameters with strong correlation such as power,voltage and current on both sides of the inverter for modeling.The results show that PCA can quickly and effectively identify abnormal photovoltaic modules.
出处
《科技创新与应用》
2023年第9期77-81,85,共6页
Technology Innovation and Application
关键词
光伏发电
光伏组件故障
故障诊断
数据分析
主成分分析法
photovoltaic power generation
photovoltaic module fault
fault diagnosis
data analysis
principal component analysis
