Proliferation of advanced metering devices with high sampling rates in distribution grids, e.g., micro-phasor measurement units (μPMU), provides unprecedented potentials for wide-area monitoring and diagnostic applications, e.g., situational awareness, health monitoring of distribution assets. Unexpected disruptive events interrupting the normal operation of assets in distribution grids can eventually lead to permanent failure with expensive replacement cost over time. Therefore, disruptive event classification provides useful information for preventive maintenance of the assets in distribution networks. Preventive maintenance provides wide range of benefits in terms of time, avoiding unexpected outages, maintenance crew utilization, and equipment replacement cost. In this paper, a PMU-data-driven framework is proposed for classification of disruptive events in distribution networks. The two disruptive events, i.e., malfunctioned capacitor bank switching and malfunctioned regulator on-load tap changer (OLTC) switching are considered and distinguished from the normal abrupt load change in distribution grids. The performance of the proposed framework is verified using the simulation of the events in the IEEE 13-bus distribution network. The event classification is formulated using two different algorithms as; i) principle component analysis (PCA) together with multi-class support vector machine (SVM), and ii) autoencoder along with softmax classifier. The results demonstrate the effectiveness of the proposed algorithms and satisfactory classification accuracies.
pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events
介绍了第一个数据驱动的电力系统事件数据生成模型pmuGE（phasor measurement unit Generator of Events）。我们已经在数千个实际事件上训练了这个模型，并创建了一个名为pmuBAGE（生成的PMU事件的基准分类）的数据集。该数据集由近1000个标记事件数据实例组成，以鼓励对相量测量单元（PMU）数据分析进行基准评估。PMU数据很难获得，特别是那些涵盖事件周期的数据。然而，电力系统问题最近通过数据驱动的机器学习解决方案取得了惊人的进步。一个高度可访问的标准基准数据集将大大加速该领域成功的机器学习技术的发展。提出了一种基于电力系统事件参与分解的PMU数据学习方法，使系统异常时PMU数据生成模型的学习成为可能。该模型可以创建高度真实的事件数据，而不会损害用于训练它的PMU的差异隐私。该数据集可供任何研究人员或从业者在线使用，请访问pmuBAGE Github存储库：https://github.com/NanpengYu/pmuBAGE。
Robust Event Classification Using Imperfect Real-world PMU Data
University of Nevada Reno（内华达大学里诺分校）；California State University San Bernardino（加州州立大学圣贝纳迪诺分校）；Virginia Polytechnic Institute and State University（弗吉尼亚理工大学）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Arizona State University（亚利桑那州立大学）
Forecasting Binary Economic Events in Modern Mercantilism: Traditional methodologies coupled with PCA and K-means Quantitative Analysis of Qualitative Sentimental Data
