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

电力系统的大规模停电将导致数百万美元的收入损失。它干扰了企业，甚至对环境和公共安全构成了风险。毫无疑问，保护电力系统免受大规模停电的影响是当务之急。早期检测随机元件故障和预测连锁故障是防止大规模停电的关键，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，才能实现这一点。随着各种有源控制器、可再生能源和蓄电池的加入，现代电力系统正变得越来越复杂。由于模型的复杂性和不确定性，传统的基于密集计算的求解模型方程组的方法不再适合实时分析和控制。一名研究生将在该奖项的第一年获得支持。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，来推进与电力系统分析和控制有关的核心算法。提出的工作包括三个主要方面：(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。它不仅将有利于电力系统的研究和运营，还将通过促进物理法律辅助机器学习来促进数据科学研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.