Abstract This article presents a monitoring strategy based on multilayer principal component analysis (PCA) to detect and diagnose power system disturbances in large amounts of data collected by intelligent electronic devices in low voltage smart grids. The PCA models are built on multiple sliding windows, sized (in terms of length and sampling time) according to the type of phenomena to detect. Abnormalities are detected with use of two complementary statistical indexes, then diagnosed by computing the individual contributions of each monitored variable to the constraint violation of those statistics. As a result, its implementation enables an automatic analysis of multiple phenomena of interest in parallel over time using distinct electrical quantities. Furthermore, the method is demonstrated within the RESOLVD project with data from the OpenLV project containing measurements of active and reactive power gathered at different low voltage distribution substations.

中文翻译：

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用多层主成分分析监测低压电网

摘要 本文提出了一种基于多层主成分分析（PCA）的监测策略，以检测和诊断低压智能电网中智能电子设备收集的大量数据中的电力系统扰动。PCA 模型建立在多个滑动窗口上，根据要检测的现象类型调整大小（在长度和采样时间方面）。使用两个互补的统计指标检测异常，然后通过计算每个监控变量对这些统计的约束违反的单独贡献来诊断。因此，它的实现可以使用不同的电量自动分析多个感兴趣的现象随时间并行。此外，