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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.

电力系统的大规模停电将导致数百万美元的收入损失。它干扰了商业活动，甚至对环境和公共安全构成威胁。保护电力系统免受大规模停电的影响无疑是重中之重。早期发现随机元件故障和预测连锁故障对于防止大规模停电至关重要，但只有当电网具备足够的系统理解、态势感知和应急响应能力时，才能实现这些目标。随着各种有源控制器、可再生能源和存储器的增加，现代电力系统变得越来越复杂。由于模型方程组的复杂性和不确定性，传统的基于密集计算的模型方程组求解方法不再适用于实时分析和控制。在这个项目中，我们利用数据科学的最新进展来提高电力系统的可靠性，安全性和弹性。特别是，我们建议使用深度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相关的核心算法。本文的研究工作主要包括三个方面：（1）实时潮流分析，（2）实时异常检测和原因分析，（3）实时事故分析和最优紧急控制。预计该项目将在可靠的能源输送方面取得重大突破。它不仅有利于电力系统的研究和运营，还将通过促进物理定律辅助的机器学习来推进数据科学研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估而被认为值得支持。审查标准。