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

EAGER-DynamicData：电力系统中数据驱动的实时事件检测的可扩展框架

• 批准号: 1462311
• 负责人: Alejandro Dominguez-Garcia
• 金额: $ 18.5万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462311&HistoricalAwards=false
• 关键词: EAGER; DynamicData; Scalable; Framework; Data

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.

电力是我们社会的命脉，因此提供可靠和高效的电力供应对于确保人类福祉和可持续经济增长至关重要。确保美国电网可靠运行的一个关键需求是开发用于监测和异常检测的复杂而强大的工具。为此，本研究项目的目的是开发强大的和可扩展的数据驱动的推理算法，检测和隔离不良事件的发生，可能会威胁到网格的完整性。在这方面，该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，估计理论，将导致一个独特的跨学科的合作program.The拟议的框架依赖于大型数据集获得的相量测量单元（PMU）位于整个系统。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期望事件，例如，输电线路和其他资产的中断，几乎实时。具体而言，本研究的最终目标是开发一个数据驱动的框架，实时检测电力系统中的不良事件，是强大的和高度可扩展的。该框架建立在现有的强大的工具，从理论的最快变化检测（QCD），并将提供技术分区的图形描述的电力系统的连通性，和PMU的位置，使这些QCD为基础的工具被利用在大规模的系统，如美国电网。此外，研究将探讨明确纳入我们的QCD为基础的算法，使他们可扩展到多个事件的稀疏性结构的不良事件的挑战性问题。