Abstract Fault diagnosis of photovoltaic (PV) systems is a crucial task to guarantee security, increase productivity, efficiency, and availability. In this regard, numerous diagnosis methods have been developed. Methods requiring the interruption of power production are not adequate for economic reasons. The development of large-scale PV plants and the objective of maintenance cost reduction push toward the emergence of automatic on-line diagnosis methods that use available information. In this study, we propose two data-driven methods for partial shading diagnosis using only the maximum power point’s information. It does not require the interruption of production, nor does it require any additional equipment to obtain the I(V) curve. The analyses are conducted with principal component analysis (PCA) and linear discriminant analysis (LDA) to detect and classify the faults. The experimental dataset is collected from a 250 Wp PV module under four states of health (healthy, and three severities of partial shading) for several meteorological conditions. The classification results have a 100% success rate, and are robust to the variations of temperature and irradiance.

中文翻译：

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光伏系统中局部阴影检测和识别的最大功率点分析

摘要 光伏（PV）系统的故障诊断是保证安​​全、提高生产力、效率和可用性的关键任务。在这方面，已经开发了多种诊断方法。出于经济原因，需要中断电力生产的方法是不够的。大规模光伏电站的发展和降低维护成本的目标推动了使用可用信息的自动在线诊断方法的出现。在这项研究中，我们提出了两种仅使用最大功率点信息进行局部阴影诊断的数据驱动方法。它不需要中断生产，也不需要任何额外的设备来获得I(V)曲线。分析采用主成分分析 (PCA) 和线性判别分析 (LDA) 进行，以检测和分类故障。实验数据集是从 250 Wp PV 模块在四种健康状态（健康和三种严重程度的部分阴影）下针对几种气象条件收集的。分类结果具有 100% 的成功率，并且对温度和辐照度的变化具有鲁棒性。