AMPS: Dynamics-Aware Algorithms for Real-Time Structured Fault Detection in Power Systems

AMPS：用于电力系统实时结构化故障检测的动态感知算法

基本信息

批准号：
1736448
负责人：
Enrique Mallada
金额：
$ 23.07万
依托单位：
Johns Hopkins University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1736448&HistoricalAwards=false
关键词：
AMPS Dynamics Aware Algorithms Real

项目摘要

The U.S. power grid is in the midst of its most fundamental transformation since its inception. Spurred by the need to reduce emissions, the electric generation mix is drifting away from traditional, reliable sources, towards volatile and uncertain renewable sources. Simultaneously, there is an unprecedented increase in the quantity, quality, and variety of sensing and monitoring devices. From phasor measurement units (PMUs) to smart meters, the power grid is soon to experience an overflow of data that has the potential of providing an extraordinary amount of information at the transmission and distribution levels. However, despite this burst in data availability, there is still a lack of analytic tools that can leverage the newly available telemetry to help operators face the paradigm shift that renewable sources pose. Moreover, the modernization of monitoring systems that use cyber resources to transmit information for processing and analysis begets new challenges and threats. Without the proper tools to correct and validate the collected data, undetected errors or maliciously modified data can mislead operators and bring the system towards blackouts. This work addresses these challenges by developing novel algorithmic tools that take into account intrinsic properties of the measurements.This project develops a theoretical framework and associated algorithms that can reliably utilize the newly available measurements to provide useful real-time information that can allow operators to use resources better and react to unforeseen events. More precisely, the PIs seek to combine tools from statistics, dynamical systems, and optimization to develop a data analytic approach to identify and prevent cyber-physical attacks, correct missing, and corrupted data, identify network structural changes, and recognize abrupt local changes in supply- demand imbalance. This research is unique within the existing the literature in power system monitoring in several ways. Firstly, it acknowledges and leverages the fact that there are spatial and temporal correlations between the measurements generated by the underlying dynamical system (the power grid). Secondly, it builds a unifying modeling framework that can jointly capture how grid measurements are affected by (a) topology changes in the network, (b) abrupt changes in supply or demand, and (c) measurement errors. Thirdly, it develops a novel algorithmic framework that exploits structural sparsity properties of the different network disturbances to discriminate and identify the source of a given grid transient behavior. The research will also build a large-scale simulation testbed to assess the accuracy and scalability of the designed algorithms and in this way bridge the gap between theoretical models and actual power systems.

自成立以来，美国的电网正处于其最根本的转型之中。由于需要减少排放的需求，发电混合物从传统，可靠的来源逐渐流动到挥发性和不确定的可再生能源。同时，传感和监测设备的数量，质量和种类量存在前所未有的增加。从相量测量单元（PMU）到智能电表，功率电网很快就会体验到具有在传输和分配水平上提供大量信息的数据溢出。但是，尽管数据可用性发生了这种情况，但仍然缺乏分析工具，可以利用新近可用的遥测方法来帮助操作员面对可再生资源构成的范式变化。此外，使用网络资源传输信息进行处理和分析的监视系统的现代化会带来新的挑战和威胁。如果没有适当的工具来纠正和验证收集的数据，未发现错误或恶意修改的数据可能会误导运算符并将系统带入停电。这项工作通过开发新的算法工具来解决这些挑战，这些工具考虑了测量的内在属性。本项目开发了一个理论框架和相关算法，这些算法可以可靠地利用新可用的测量值来提供有用的实时信息，以便可以使运营商更好地利用资源来更好地利用资源并为统一事件做出反应。更准确地说，PI试图将工具从统计，动力学系统和优化中结合起来，以开发数据分析方法，以识别和防止网络物理攻击，正确的缺失和损坏的数据，识别网络结构性变化，并识别供应不平衡的局部局部变化。这项研究在电力系统中现有文献中是独一无二的。首先，它承认并利用了一个事实，即基础动力学系统（功率网格）产生的测量之间存在空间和时间相关。其次，它构建了一个统一的建模框架，可以共同捕获网格测量如何受（a）网络拓扑变化的影响，（b）供应或需求的突然变化，以及（c）测量错误。第三，它开发了一种新型的算法框架，该算法框架利用了不同网络干扰的结构稀疏性能，以区分和识别给定的网格瞬态行为的来源。该研究还将建立一个大规模的模拟测试台，以评估设计算法的准确性和可扩展性，并以此方式弥合理论模型和实际功率系统之间的差距。