AMPS: Real-Time Algorithms for Power System Analysis: Anomaly, Causality, and Contingency

AMPS：电力系统分析实时算法：异常、因果关系和意外事件

基本信息

批准号：
1936873
负责人：
Maggie Cheng
金额：
$ 10.92万
依托单位：
Illinois Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936873&HistoricalAwards=false
关键词：
AMPS Real Time Algorithms Power

项目摘要

A large-scale blackout in power systems would result in millions of dollars in revenue loss. It interrupts businesses, and even poses risks to environment and public safety. Protecting power systems from large-scale outages is no doubt a top priority. Early detection of random component failures and prediction of cascading failures are critical to the prevention of large-scale blackouts, but they can be achieved only when the power grid is equipped with adequate capability for system understanding, situational awareness, and emergency response. Modern power systems are becoming increasingly complex with the addition of a variety of active controllers, renewable energy resources and storages. With the complexity and uncertainty involved, traditional approaches based on intensive computation to solve a system of model equations are no longer suitable for real-time analysis and control. One graduate student will be support in year 1 of this award.In this project, we leverage recent advances in data science to improve power systems reliability, security, and resilience. In particular we propose to use deep neural networks and data-driven uncertainty quantification, to advance the core algorithms pertaining to the analysis and control of power systems. The proposed work includes three major thrusts: (1) real-time power flow analysis, (2) real-time anomaly detection and causal analysis, and (3) real-time contingency analysis and optimal emergency control. The project is expected to make a significant breakthrough in reliable energy delivery. It will not only benefit the power system research and operation, but also advance data science research by promoting physical law-assisted machine learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

电力系统中的大规模停电将导致数百万美元的收入损失。它打断了企业，甚至对环境和公共安全构成风险。保护电源系统免受大规模停电的影响无疑是重中之重。早期发现随机组件故障和级联故障的预测对于预防大规模停电至关重要，但是只有当功率网格配备足够的能力以进行系统理解，情境意识和紧急响应时，才能实现它们。随着添加各种主动控制器，可再生能源资源和储藏量，现代电力系统变得越来越复杂。由于涉及的复杂性和不确定性，基于密集计算的传统方法不再适合实时分析和控制。一名研究生将获得该奖项的第一年的支持。在该项目中，我们利用数据科学的最新进展来提高电源系统的可靠性，安全性和弹性。特别是我们建议使用深层神经网络和数据驱动的不确定性量化，以推进与电力系统分析和控制有关的核心算法。拟议的工作包括三个主要推力：（1）实时功率流分析，（2）实时异常检测和因果分析，以及（3）实时应急分析和最佳应急控制。预计该项目将在可靠的能源传递方面取得重大突破。它不仅将使电力系统研究和运营受益，而且还通过促进物理法律辅助机器学习来提高数据科学研究。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来支持的。