EAGER-DynamicData: A Scalable Framework for Data-Driven Real-Time Event Detection in Power Systems

电是我们社会的命脉；因此，提供可靠和高效的电力供应对于确保人类福利和可持续的经济增长至关重要。确保美国电网可靠运行的关键需求是开发复杂而强大的监测和异常检测工具。为此，该研究项目旨在开发健壮且可扩展的数据驱动推理算法，用于检测和隔离可能威胁网格完整性的不良事件的发生。在这方面，项目将依赖的工具和方法的组合，即(i)电力系统可靠性建模和分析，（ii）统计信号处理和检测，以及估计理论，将形成一个独特的跨学科合作计划。所提出的框架依赖于由整个系统中的相量测量单元（pmu）获得的大型数据集。通过利用从上述pmu获得的电压相角测量的统计特性，将开发算法来检测和识别电网中的不良事件，例如，输电线路和其他资产的中断，近乎实时。具体来说，本研究的最终目标是开发一个数据驱动的框架，用于实时检测电力系统中的不良事件，该框架具有鲁棒性和高度可扩展性。该框架建立在最快变化检测（QCD）理论的现有强大工具的基础上，并将提供用于划分描述电力系统连通性的图的技术，以及PMU的放置，以允许这些基于QCD的工具在大型系统（如美国电网）中被利用。此外，该研究将探索具有挑战性的问题，即在我们基于qcd的算法中明确地结合不良事件的稀疏结构，使其可扩展到多个事件。

Electricity is the lifeblood of our society; therefore providing a reliable and efficient electricity supply is vital for ensuring human welfare and sustainable economic growth. A pivotal need in ensuring reliable operation of the US power grid is the development of sophisticated and robust tools for monitoring and anomaly detection. To this end, this research project aims to develop robust and scalable data-driven inference algorithms for detecting and isolating the occurrence of undesirable events that could threaten the integrity of the grid. In this regard, the combination of tools and methods on which the project will rely, namely (i) power system reliability modeling and analysis, and (ii) statistical signal processing and detection, and estimation theory, will result in a unique interdisciplinary collaboration program.The proposed framework relies on large datasets obtained with phasor measurement units (PMUs) located across the system. By exploiting the statistical properties of voltage phase angle measurements obtained from the aforementioned PMUs, algorithms will be developed to detect and identify undesirable events in power grids, e.g., outages in transmission lines and other assets, in near real-time. Specifically, the ultimate objective of this research is to develop a data-driven framework for real-time detection of undesirable events in power systems that is robust and highly scalable. The framework builds on existing powerful tools from the theory of quickest change detection (QCD), and will provide techniques for partitioning the graph describing the connectivity of a power system, and PMU placement to allow these QCD-based tools to be exploited in large scale systems such as the US power grid. Additionally, the research will explore the challenging problem of explicitly incorporating the sparsity structure of the undesirable events in our QCD-based algorithms to make them scaleable to multiple events.