权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Collaborative Research: AMPS Stochastic Algorithms for Early Detection and Risk Prediction of Hidden Contingencies in Modern Power Systems

合作研究：用于现代电力系统中隐藏突发事件的早期检测和风险预测的 AMPS 随机算法

基本信息

批准号：
2229108
负责人：
Gang George Yin
金额：
$ 10.98万
依托单位：
University of Connecticut
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229108&HistoricalAwards=false
关键词：
Collaborative Research AMPS Stochastic Algorithms

项目摘要

Modern power systems (MPS) are complex systems involving conventional and renewable generators, smart distribution networks, and advanced information exchanges. High penetration of random low-inertia renewable energy sources, increased natural disasters such as the 2021 Winter storm Uri, and unprecedented man-made cyber-physical attacks have posed a threat to the reliability and security of MPS. Several cascading failures in MPS started with smaller undetected contingencies such as California's wildfires (e.g., Camp Creek Fire, Zogg Fire, and Dixie Fire) caused by equipment failures. Smaller contingency events, particularly on the distribution side of the grid, may not be directly detected. This project focuses on the early detection and risk prediction of hidden contingencies in MPS. The research fits within efforts to enhance the resilience of the U.S. power grid and move toward carbon-free energy infrastructure. Therefore, it has broader impacts on the carbon-free economy and social welfare. This project will also enhance teaching, training, and learning in mathematics and statistics, renewable energy, smart grids, and green technologies. The team plans to develop new courses for undergraduate and graduate students to facilitate the training of next-generation scientists and engineers. Every effort will be made to promote the participation of underrepresented students in the research project.This research project introduces a novel framework of stochastic prediction, estimation, and early detection (SPEED) for MPS. Covering a broad range of cyber-physical contingencies (CPC), this research will have the following distinct and novel aims and outcomes. First, the project introduces a new stochastic hybrid system (SHS) model, consisting of continuous dynamics and discrete events. Second, the project will develop new estimation and prediction computational methods. Starting from the Wonham filter for hidden Markov chains, to detect discrete jump changes, this research will focus on finding more computationally feasible schemes. Furthermore, rates of convergence of the algorithms will be obtained, and extensive numerical experiments will be performed. Third, fundamental concepts such as joint observability will be introduced. New estimation algorithms will be developed for joint estimation and prediction of CPC in SHS. Fourth, since early and quick detection of abrupt changes is vitally important for the risk management of MPS, this project will provide a new computable scheme based on Markov chain approximation for optimal stopping and will quantitatively predict risks of potential near-future cascading CPC. Fifth, evaluation and validation of the theoretical findings will be conducted through utility-level operational data, large-scale power grid simulations, and hardware-in-the-loop emulation on a microgrid. The synthetic operational and summary data of the distribution power grids and transmission systems will be incorporated into the validation and evaluation of the study.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.

现代电力系统（MPS）是一个复杂的系统，涉及传统和可再生发电机、智能配电网络和先进的信息交换。随机低惯性可再生能源的高度渗透，2021年冬季风暴乌里等自然灾害的增加，以及前所未有的人为网络物理攻击，对MPS的可靠性和安全性构成了威胁。MPS中的一些级联故障始于较小的未被发现的突发事件，例如由设备故障引起的加利福尼亚野火（例如Camp Creek Fire， Zogg Fire和Dixie Fire）。较小的突发事件，特别是在电网的配电侧，可能无法直接检测到。本项目主要研究MPS中潜在突发事件的早期发现和风险预测。这项研究符合提高美国电网弹性和向无碳能源基础设施迈进的努力。因此，它对无碳经济和社会福利具有更广泛的影响。该项目还将加强数学和统计学、可再生能源、智能电网和绿色技术方面的教学、培训和学习。研究组计划为培养下一代科学家和工程师，开发面向本科生和研究生的新课程。将尽一切努力促进代表性不足的学生参与研究项目。本研究计划提出一种新的MPS随机预测、估计和早期检测（SPEED）框架。本研究涵盖了广泛的网络物理偶然性（CPC），将具有以下独特而新颖的目标和结果。首先，引入了一种新的随机混合系统（SHS）模型，该模型由连续动力学和离散事件组成。第二，开发新的估算和预测计算方法。本研究将从隐马尔可夫链的Wonham滤波器开始，寻找更多计算上可行的方案来检测离散的跳跃变化。此外，算法的收敛速度将得到，并将进行广泛的数值实验。第三，介绍联合可观测性等基本概念。将开发新的估计算法来联合估计和预测SHS的CPC。第四，由于突变的早期和快速检测对于MPS的风险管理至关重要，本项目将提供一种新的基于马尔可夫链近似的最优停止的可计算方案，并将定量预测潜在的近期级联CPC的风险。第五，将通过公用事业级运行数据、大规模电网仿真和微电网硬件在环仿真对理论结果进行评估和验证。配电网和输电系统的综合运行和汇总数据将纳入研究的验证和评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。