Novel dynamics are emerging in the power system due to the new Smart Grid (SG) environment. The high sampling rate of the Phasor Measurement Units (PMUs) enables them to capture the dynamic fluctuations in the power system measurements. Understanding the statistical and dynamic characteristics of the PMU data requires advanced data analytics techniques capable of performing accurate modeling of the power system variables (voltage, frequency, phase angle, and rate of change of frequency (ROCOF)). In this article, we provide evidence of the non-stationarity and fractality of PMU data collected from Europe. We adopt the Autoregressive Fractionally Integrated Moving Average (ARFIMA) models with non-integer differencing parameter to model the short-range and long-range correlations in the PMU data. Furthermore, the goodness-of-fit of the ARFIMA model is confirmed by analyzing the correlation and independence of the model residuals. Anomaly detection is among the promising applications of the PMU ARFIMA models. It is shown that the 2012 Indian blackout is accompanied by a change point in the differencing parameter opening the road to event (anomaly) detection by ARFIMA monitoring.

中文翻译：

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PMU数据的分数动态

由于新的智能电网（SG）环境，电力系统中出现了新的动力。相量测量单元（PMU）的高采样率使它们能够捕获电力系统测量中的动态波动。要了解PMU数据的统计和动态特性，需要能够对电源系统变量（电压，频率，相角和频率变化率（ROCOF））进行精确建模的高级数据分析技术。在本文中，我们提供了从欧洲收集的PMU数据的非平稳性和分形性的证据。我们采用具有非整数微分参数的自回归分数积分移动平均值（ARFIMA）模型来对PMU数据中的短时和长时相关性进行建模。此外，通过分析模型残差的相关性和独立性，可以确定ARFIMA模型的拟合优度。异常检测是PMU ARFIMA模型的有前途的应用之一。结果表明，2012年印度电力供应中断时，差异参数中出现了一个变化点，这为ARFIMA监测打开了通往事件（异常）检测之路。