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

• 批准号: 2229109
• 负责人: Masoud Nazari
• 金额: $ 20万
• 依托单位: Wayne State University
• 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=2229109&HistoricalAwards=false
• 关键词: Collaborative; Research; AMPS; Stochastic; Algorithms; Collaborative; Research; AMPS; Stochastic; Algorithms

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）是涉及常规和可再生发电机，智能分销网络和高级信息交换的复杂系统。随机低惯性可再生能源的高渗透，诸如2021年冬季风暴URI之类的自然灾害以及前所未有的人造网络物理攻击对MPS的可靠性和安全构成了威胁。国会议员的几次级联失败始于较小的未被发现的突发事件，例如加利福尼亚的野火（例如Camp Creek Fire，Zogg Fire和Dixie Fire）由设备故障引起。可能无法直接检测到较小的应急事件，特别是在网格的分布侧。该项目重点介绍了国会议员中隐藏意外情况的早期发现和风险预测。该研究符合增强美国电网的弹性并朝着无碳能源基础设施发展的努力。因此，它对无碳经济和社会福利产生了更大的影响。该项目还将增强数学和统计，可再生能源，智能电网和绿色技术的教学，培训和学习。该团队计划为本科和研究生开发新课程，以促进下一代科学家和工程师的培训。将尽一切努力促进代表性不足的学生参与研究项目。本研究项目介绍了一个新颖的随机预测，估计和早期检测（速度）（速度）的新框架。该研究涵盖了广泛的网络物理意外事件（CPC），将具有以下独特的新颖目标和结果。首先，该项目引入了一个新的随机混合系统（SHS）模型，该模型由连续的动态和离散事件组成。其次，该项目将开发新的估计和预测计算方法。从隐藏的马尔可夫链的Wonham滤镜开始，要检测离散的跳跃变化，这项研究将着重于寻找更多计算可行的方案。此外，将获得算法的收敛速率，并将进行广泛的数值实验。第三，将引入基本概念，例如联合可观察性。将开发新的估计算法，以用于SHS中CPC的联合估计和预测。 第四，由于对突然变化的早期和快速检测对于MPS的风险管理至关重要，因此该项目将基于马尔可夫链近似值提供新的可计算方案，以实现最佳停止，并将定量预测潜在的近乎未能的级联CPC的风险。第五，理论发现的评估和验证将通过公用事业级的操作数据，大规模的电网模拟以及微电网上的硬件仿真进行。分配功率电网和传输系统的合成操作和摘要数据将纳入研究的验证和评估中。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来通过评估来获得支持的。