微PMU测量中的无监督事件检测、聚类和用例说明
Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements
摘要：
配电级相量测量单元（也称为微型PMU）报告大量高分辨率相量测量值，这些测量值构成所有配电馈线上发生的不同现象的各种事件特征。为了实现一个基于事件的分析，有有用的应用程序的公用事业运营商，需要提取这些事件从大量的微PMU数据。然而，由于事件的不频繁、不定期和未知的性质，甚至要弄清楚什么样的事件需要捕获和仔细检查通常都是一个挑战。在本文中，我们试图通过开发一种无监督的方法来解决这个开放的问题，这种方法需要最少的先验知识。首先，我们开发了一种基于生成对抗网络（GAN）概念的无监督事件检测方法。它的工作原理是训练深度神经网络，学习微PMU测量中正常趋势的特征;并相应地在存在任何异常时检测事件。我们还提出了一个两步无监督聚类方法，基于一个新的线性混合整数规划制定。它帮助我们在第一步中根据事件的起源和第二步中的相似性对事件进行分类。所提出的聚类方法的主动性质使得它能够在持续的基础上识别新的事件聚类。提出的无监督事件检测和聚类方法适用于现实世界的微PMU数据。结果表明，他们可以优于文献中流行的方法。这些方法还有助于我们进一步分析，以识别重要的事件集群，从而揭示可能对公用事业运营商有价值的几个用例。
英文摘要：
Distribution-level phasor measurement units, a.k.a, micro-PMUs, report a large volume of high resolution phasor measurements which constitute a variety of event signatures of different phenomena that occur all across power distribution feeders. In order to implement an event-based analysis that has useful applications for the utility operator, one needs to extract these events from a large volume of micro-PMU data. However, due to the infrequent, unscheduled, and unknown nature of the events, it is often a challenge to even figure out what kind of events are out there to capture and scrutinize. In this paper, we seek to address this open problem by developing an unsupervised approach, which requires minimal prior human knowledge. First, we develop an unsupervised event detection method based on the concept of Generative Adversarial Networks (GAN). It works by training deep neural networks that learn the characteristics of the normal trends in micro-PMU measurements; and accordingly detect an event when there is any abnormality. We also propose a two-step unsupervised clustering method, based on a novel linear mixed integer programming formulation. It helps us categorize events based on their origin in the first step and their similarity in the second step. The active nature of the proposed clustering method makes it capable of identifying new clusters of events on an ongoing basis. The proposed unsupervised event detection and clustering methods are applied to real-world micro-PMU data. Results show that they can outperform the prevalent methods in the literature. These methods also facilitate our further analysis to identify important clusters of events that lead to unmasking several use cases that could be of value to the utility operator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
