Collaborative Research: AMPS: Rare Events in Power Systems: Novel Mathematics, Statistics and Algorithms.
合作研究:AMPS:电力系统中的罕见事件:新颖的数学、统计和算法。
基本信息
- 批准号:2229011
- 负责人:
- 金额:$ 15万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-08-01 至 2025-07-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The project goal is to build a comprehensive theoretical and algorithmic framework of AI/ML for detection, tracking, forecasting and mitigation of extreme and rare but consequential events in power systems. The overwhelming majority of conventional applications of AI/ML involve learning the `middle' of the distribution. Applications have become mostly routine exercises in `interpolation' in both industry and academia, thanks to the Deep Learning (DL) breakthrough. Based on copious amounts of `typical' information, a generic DL task focuses on designing algorithms which extract and build features which represent the most common characteristics of the massive scientific data. The difference between this conventional DL situation and DL for extreme events is that, in the latter setting, the task is one of extrapolation. Moreover, massive scientific data, beneficial in normal regimes, becomes a curse for extrapolation which focuses on extracting rare but significant events -- the black swans -- which are, like a needle in a haystack, notoriously difficult to detect and track, and then use to make reliable forecasts and possible mitigations as events develop. In other words, based on very limited information, the research objective is to extract regularity patterns, which can persist over long spatial and temporal scales, that then lead to potential rare extremes. The PI will study models that relate to such as the extreme heat of the summer of 2020 or the extreme cold in Texas in the spring of 2021; power system blackouts, like the 2004 East Coast blackout. The methods will have even broader applicability, for example, if prediction and detection of failures in other physical and cyber networks. PI will investigate specific objectives in three areas: (A) Physics-Informed Statistical Modeling for power systems, (B) Computational Methods of Inference for Extremes in power systems, and (C) Learning and Quantification of Errors in the Models. They will apply the methodology developed within the novel framework to including early detection of rare but devastating cascading failures in power systems. The mathematical/theoretical core of the methodology will consist in integration of power-system-specific constraints into the general Extreme Value Theory (EVT). This integration will be achieved via synthesis of EVT with the complementary approaches from the Physics Informed Machine Learning, Probabilistic Graphical Models and Optimal Transport theory. On the computational side, PI will utilize EVT to develop efficient model calibration, inference and learning algorithms for large-scale stochastic systems, described via properly parameterized non-linear real or complex-valued algebraic and differential equations with random stochastic input. Moreover, their coupled theoretical and computational efforts will be useful in a broader context for extending the rare event control and prevention methodology to other systems and applications of national importance.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.
项目的目标是建立AI/ML的全面理论和算法框架,用于检测,跟踪,预测和缓解电力系统中极端和罕见但结果性事件。 AI/ML的绝大多数常规应用涉及学习分布的“中间”。得益于深度学习(DL)的突破,应用程序已成为行业和学术界的“插值”的常规练习。基于大量的“典型”信息,通用的DL任务着重于设计算法,这些算法提取和构建代表大量科学数据最常见特征的特征。这种常规的DL情况与极端事件的DL之间的区别在于,在后一种情况下,任务是推断之一。此外,大量的科学数据(在正常制度中有益)成为外推的诅咒,该诅咒着重于提取罕见但重要的事件 - 黑天鹅 - 像黑天鹅一样,就像在黑天花中的针头一样,臭名昭著地检测和跟踪,然后用来进行可靠的预测,并随着事件的发展而进行可靠的预测和可能的缓解。换句话说,基于非常有限的信息,研究目标是提取规则性模式,这些模式可以持续在长的空间和时间尺度上,从而导致潜在的罕见极端。 PI将研究与2020年夏季的极端热量或2021年春季德克萨斯州极度寒冷有关的模型;电力系统停电,例如2004年东海岸停电。这些方法将具有更广泛的适用性,例如,如果预测和检测其他物理和网络网络中的故障。 PI将研究三个领域的特定目标:(a)功率系统的物理信息统计建模,(b)电力系统中极端的推理计算方法,以及(c)模型中错误的学习和量化。他们将在新型框架中运用开发的方法,包括对电力系统中罕见但毁灭性级联故障的早期检测。该方法的数学/理论核心将包括将功率系统特定的约束集成到一般的极值理论(EVT)中。通过物理学知情的机器学习,概率图形模型和最佳传输理论的补充方法,将通过合成EVT来实现这种整合。在计算端,PI将利用EVT来开发有效的大规模随机系统的有效模型校准,推理和学习算法,这些系统通过正确参数化的非线性真实或复杂值估算的代数和微分方程和随机随机输入来描述。此外,在更广泛的背景下,他们的理论和计算努力将是有用的,可以将罕见的事件控制和预防方法扩展到其他国家重要性的其他系统和应用。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影响的审查标准来通过评估来通过评估来支持的。
项目成果
期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
专利数量(0)
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Jose Blanchet其他文献
A Model of Bed Demand to Facilitate the Implementation of Data-driven Recommendations for COVID-19 Capacity Management
床位需求模型促进实施数据驱动的 COVID-19 容量管理建议
- DOI:
10.21203/rs.3.rs-31953/v1 - 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Teng Zhang;Kelly A McFarlane;J. Vallon;Linying Yang;Jin Xie;Jose Blanchet;P. Glynn;Kristan Staudenmayer;K. Schulman;D. Scheinker - 通讯作者:
D. Scheinker
Optimal Sample Complexity of Reinforcement Learning for Uniformly Ergodic Discounted Markov Decision Processes
均匀遍历贴现马尔可夫决策过程的强化学习的最优样本复杂度
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Shengbo Wang;Jose Blanchet;Peter Glynn - 通讯作者:
Peter Glynn
When are Unbiased Monte Carlo Estimators More Preferable than Biased Ones?
什么时候无偏蒙特卡罗估计比有偏估计更可取?
- DOI:
10.48550/arxiv.2404.01431 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Guanyang Wang;Jose Blanchet;P. Glynn - 通讯作者:
P. Glynn
Modeling shortest paths in polymeric networks using spatial branching processes
使用空间分支过程对聚合物网络中的最短路径进行建模
- DOI:
10.1016/j.jmps.2024.105636 - 发表时间:
2023 - 期刊:
- 影响因子:5.3
- 作者:
Zhenyuan Zhang;Shaswat Mohanty;Jose Blanchet;Wei Cai - 通讯作者:
Wei Cai
Representation Learning for Extremes
极端情况下的表征学习
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
Ali Hasan;Yuting Ng;Jose Blanchet;Vahid Tarokh - 通讯作者:
Vahid Tarokh
Jose Blanchet的其他文献
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{{ truncateString('Jose Blanchet', 18)}}的其他基金
Collaborative Research: CIF: Medium: Statistical and Algorithmic Foundations of Distributionally Robust Policy Learning
合作研究:CIF:媒介:分布式稳健政策学习的统计和算法基础
- 批准号:
2312204 - 财政年份:2023
- 资助金额:
$ 15万 - 项目类别:
Continuing Grant
DMS-EPSRC: Fast Martingales, Large Deviations, and Randomized Gradients for Heavy-tailed Distributions
DMS-EPSRC:重尾分布的快速鞅、大偏差和随机梯度
- 批准号:
2118199 - 财政年份:2021
- 资助金额:
$ 15万 - 项目类别:
Continuing Grant
Robust Wasserstein Profile Inference
鲁棒 Wasserstein 轮廓推断
- 批准号:
1915967 - 财政年份:2019
- 资助金额:
$ 15万 - 项目类别:
Continuing Grant
An Approach to Robust Performance Analysis Using Optimal Transport
使用最佳传输进行鲁棒性能分析的方法
- 批准号:
1820942 - 财政年份:2018
- 资助金额:
$ 15万 - 项目类别:
Continuing Grant
Collaborative Proposal: Strong Stochastic Simulation of Stochastic Processes Theory and Applications
合作提案:随机过程理论与应用的强随机模拟
- 批准号:
1838576 - 财政年份:2018
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
Collaborative Proposal: Strong Stochastic Simulation of Stochastic Processes Theory and Applications
合作提案:随机过程理论与应用的强随机模拟
- 批准号:
1720451 - 财政年份:2017
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
Collaborative Research: Perfect Simulation of Stochastic Networks
合作研究:随机网络的完美模拟
- 批准号:
1538217 - 财政年份:2015
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
Collaborative Research: Modeling and Analyzing Extreme Risks in Insurance and Finance
合作研究:保险和金融极端风险的建模和分析
- 批准号:
1436700 - 财政年份:2014
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
Collaborative Research: Optimal Monte Carlo Estimation via Randomized Multilevel Methods
协作研究:通过随机多级方法进行最优蒙特卡罗估计
- 批准号:
1320550 - 财政年份:2013
- 资助金额:
$ 15万 - 项目类别:
Continuing Grant
CAREER: Efficient Monte Carlo Methods in Engineering and Science: From Coarse Analysis to Refined Estimators
职业:工程和科学中的高效蒙特卡罗方法:从粗略分析到精细估算器
- 批准号:
0846816 - 财政年份:2009
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
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相似海外基金
Collaborative Research: AMPS: Deep-Learning-Enabled Distributed Optimization Algorithms for Stochastic Security Constrained Unit Commitment
合作研究:AMPS:用于随机安全约束单元承诺的深度学习分布式优化算法
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2229345 - 财政年份:2023
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Standard Grant
Collaborative Research: AMPS: Rare Events in Power Systems: Novel Mathematics, Statistics and Algorithms.
合作研究:AMPS:电力系统中的罕见事件:新颖的数学、统计和算法。
- 批准号:
2229012 - 财政年份:2023
- 资助金额:
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Collaborative Research: AMPS: Rethinking State Estimation for Power Distribution Systems in the Quantum Era
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2229074 - 财政年份:2023
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- 资助金额:
$ 15万 - 项目类别:
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- 资助金额:
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