通过分析老化故障的产生和演化机理以及对热斑、隐裂和电势诱导衰减（PID）故障时间序列特性的差异性比较,确定老化故障时间序列具有独特的变化规律;通过光伏组串等效电路模型参数计算,研究和验证这种变化规律对模型参数的影响作用和相关性,确定老化故障诊断特征向量;采用模糊C均值聚类算法,提出基于时间序列特征提取的光伏组件老化故障诊断方法。仿真和实验结果表明：所获得的模型参数计算结果能很好地描述时间序列的特性变化;所确定的故障诊断特征量,能有效表征老化故障的发生和演化过程;所提出的故障诊断方法能可靠地实现老化故障判定、程度等级划分和程度估算。
Abstract
By analyzing the generation and evolution mechanisms of aging faults and comparing the differences in time-series characteristics among thermal hotspots, micro-cracks, and potential-induced degradation （PID） faults, the unique variation patterns of aging faults in time-series data are determined. By calculating the parameters of the equivalent circuit model of PV string arrays, the influence and correlation of these variation patterns on the model parameters are studied and verified, resulting in the identification of aging fault diagnosis feature vectors. A fuzzy C-means clustering algorithm is employed to propose an aging fault diagnosis method based on time-series feature extraction for PV. Simulation and experimental results demonstrate that the computed model parameters effectively describe the characteristic variations in the time-series data. The identified fault diagnosis features effectively represent the occurrence and evolution processes of aging faults. The proposed fault diagnosis method reliably achieves cause determination, severity classification, and severity estimation of aging faults in PV systems.
关键词
光伏组件 /
故障诊断 /
时间序列 /
特征提取 /
老化 /
模糊C均值聚类
Key words
photovoltaic /
fault diagnosis /
time series /
feature extraction /
aging /
fuzzy C-means clustering
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基金
浙江省基础公益研究计划（LGG22E070003;LGG20E070003）
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