Collaborative Research: A Deeply Integrated Physics-Based and Data-Driven Approach for Effective Resilience Management of the Power Grid

协作研究:基于物理和数据驱动的深度集成方法,用于有效的电网弹性管理

基本信息

  • 批准号:
    2000156
  • 负责人:
  • 金额:
    $ 24.96万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-09-01 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

This grant will develop a novel, deeply integrated physics-based data-driven approach to assess and enhance the resilience of power transmission systems impacted by climatic extremes. The US electricity infrastructure is increasingly prone to climatic risks that cause wide-spread and sustained outages, costing billions of dollars annually. While natural hazard-induced failures in the transmission grid lead to large-scale and costly impacts, the existing transmission expansion planning models largely neglect resilience consideration of the network facing natural hazards. The purely data-driven approaches prevalent in resilience analytics of power distribution systems, however, are not applicable to transmission systems due to relative scarcity of data. A unilateral reliance on physics-based models is not feasible either due to their extreme computational costs, limiting their ability to scale up to the network level. This NSF grant seeks to address this fundamental gap via deep integration of physics-based and data driven methods. The outcome of this research is expected to help key decision-makers for the transmission infrastructure to characterize resilience under various uncertain future scenarios and identify optimal adaptation or mitigation strategies. The research program is complemented with educating the next generation of scholars in modeling hazards and infrastructure resilience through an interdisciplinary, research-integrated educational program, a strong commitment to increased diversity in student training and broad dissemination of the results.The research approach is grounded in the latest developments in big data analytics as well as physics-based analysis of structural failures. The physics-guided, data-centric and multiscale framework allows for scalable assessment of network resilience and identification of optimal investment decisions under climate uncertainty. Using the state-of-the-art machine learning and computer vision, the project will generate new publicly accessible data on transmission network topology as well as hazards’ impacts to facilitate further research in transmission resilience planning within the scientific community. Novel limit state functions for failure quantification of transmission networks will be established and a scalable approach to uncertainty quantification of structural systems will be developed. The multiscale approach to modeling uncertain processes will effectively fuse data on transmission systems with computational models of the infrastructure. The developed methodologies and data will shed new lights on the vulnerability of the transmission system under future hazard scenarios, and enable assessing the impact of investment decisions on transmission system resilience.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.
这笔赠款将开发一种新的、深度集成的基于物理学的数据驱动方法,以评估和增强受极端气候影响的输电系统的弹性。美国的电力基础设施越来越容易受到气候风险的影响,导致大范围和持续的停电,每年造成数十亿美元的损失。虽然自然灾害引起的输电网故障会导致大规模和代价高昂的影响,但现有的输电扩展规划模型在很大程度上忽略了网络面临自然灾害的弹性考虑。然而,配电系统弹性分析中流行的纯数据驱动方法由于数据相对稀缺而不适用于输电系统。单方面依赖基于物理的模型也是不可行的,因为它们的计算成本极高,限制了它们扩展到网络级别的能力。NSF的这项资助旨在通过基于物理和数据驱动方法的深度整合来解决这一根本性差距。这项研究的结果,预计将有助于关键决策者的传输基础设施,以表征各种不确定的未来情景下的弹性,并确定最佳的适应或缓解战略。该研究计划与教育下一代学者在建模灾害和基础设施的弹性通过跨学科,研究综合教育计划,在学生培训和广泛传播的结果增加多样性的坚定承诺补充。研究方法是在大数据分析的最新发展,以及基于物理的结构故障分析接地。以物理为指导,以数据为中心的多尺度框架允许对网络弹性进行可扩展的评估,并在气候不确定性下确定最佳投资决策。利用最先进的机器学习和计算机视觉,该项目将生成关于输电网络拓扑结构以及灾害影响的新的公开数据,以促进科学界对输电弹性规划的进一步研究。将建立输电网络故障量化的新极限状态函数,并开发结构系统不确定性量化的可扩展方法。多尺度方法来建模不确定的过程将有效地融合数据传输系统的基础设施的计算模型。开发的方法和数据将为输电系统在未来灾害情景下的脆弱性提供新的线索,并能够评估投资决策对输电系统弹性的影响。该奖项反映了NSF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。

项目成果

期刊论文数量(15)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Adaptive Reliability Analysis for Multi-fidelity Models using a Collective Learning Strategy
  • DOI:
    10.1016/j.strusafe.2021.102141
  • 发表时间:
    2021-09
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Chi Zhang;Chaolin Song;A. Shafieezadeh
  • 通讯作者:
    Chi Zhang;Chaolin Song;A. Shafieezadeh
Metamodel-based subset simulation adaptable to target computational capacities: the case for high-dimensional and rare event reliability analysis
A Physics-Informed Graph Attention-based Approach for Power Flow Analysis
基于物理信息图注意力的潮流分析方法
Simulation-free reliability analysis with active learning and Physics-Informed Neural Network
通过主动学习和物理信息神经网络进行免仿真可靠性分析
  • DOI:
    10.1016/j.ress.2022.108716
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    8.1
  • 作者:
    Zhang, Chi;Shafieezadeh, Abdollah
  • 通讯作者:
    Shafieezadeh, Abdollah
Value of information analysis in non-stationary stochastic decision environments: A reliability-assisted POMDP approach
  • DOI:
    10.1016/j.ress.2021.108034
  • 发表时间:
    2022-01
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Chaolin Song;Chi Zhang;A. Shafieezadeh;Rucheng Xiao
  • 通讯作者:
    Chaolin Song;Chi Zhang;A. Shafieezadeh;Rucheng Xiao
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Abdollah Shafieezadeh其他文献

Shake table testing and computational investigation of the seismic performance of modularized suspended building systems
  • DOI:
    10.1007/s10518-020-00902-3
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    Zhihang Ye;Gang Wu;De-Cheng Feng;Abdollah Shafieezadeh
  • 通讯作者:
    Abdollah Shafieezadeh
System outage fragility for power systems: A robust data-driven framework for disparity analysis using multiple hurricane events
电力系统的系统停运脆弱性:一种使用多个飓风事件进行差异分析的稳健数据驱动框架
Optimal EDPs for Post-Earthquake Damage Assessment of Extended Pile-Shaft–Supported Bridges Subjected to Transverse Spreading
用于横向扩展的加长桩轴支撑桥梁震后损伤评估的最佳 EDP
  • DOI:
    10.1193/090417eqs171m
  • 发表时间:
    2019-08
  • 期刊:
  • 影响因子:
    5
  • 作者:
    Abdollah Shafieezadeh;Xiaowei Wang;Aijun Ye
  • 通讯作者:
    Aijun Ye
Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced data
风致输电线路中断脆弱性模型:一种针对极度不平衡数据的自适应生成对抗网络增强概率分类方法
  • DOI:
    10.1016/j.egyai.2025.100511
  • 发表时间:
    2025-05-01
  • 期刊:
  • 影响因子:
    9.600
  • 作者:
    Mazin Al-Mahrouqi;Abdollah Shafieezadeh;Jieun Hur;Jae-Wook Jung;Jeong-Gon Ha;Daegi Hahm
  • 通讯作者:
    Daegi Hahm
Robust wind turbine monitoring for digital twin integration: A physics-informed covariance-preserving deep learning approach
用于数字孪生集成的稳健的风力涡轮机监测:一种基于物理信息且保持协方差的深度学习方法
  • DOI:
    10.1016/j.renene.2025.123176
  • 发表时间:
    2025-09-01
  • 期刊:
  • 影响因子:
    9.100
  • 作者:
    Minhyeok Ko;Abdollah Shafieezadeh
  • 通讯作者:
    Abdollah Shafieezadeh

Abdollah Shafieezadeh的其他文献

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{{ truncateString('Abdollah Shafieezadeh', 18)}}的其他基金

Collaborative Research: Downburst Fragility Characterization of Transmission Line Systems Using Experimental and Validated Stochastic Numerical Simulations
合作研究:使用实验和验证的随机数值模拟来表征传输线系统的下击暴脆性
  • 批准号:
    1762918
  • 财政年份:
    2018
  • 资助金额:
    $ 24.96万
  • 项目类别:
    Standard Grant
Experimentally Validated Stochastic Numerical Framework to Generate Multi-Dimensional Fragilities for Hurricane Resilience Enhancement of Transmission Systems
经过实验验证的随机数值框架可生成多维脆弱性以增强传输系统的飓风弹性
  • 批准号:
    1635569
  • 财政年份:
    2016
  • 资助金额:
    $ 24.96万
  • 项目类别:
    Standard Grant
A Novel Dynamically Coupled Storm Surge Hazard-Infrastructure Model for Effective Real-Time Risk-Informed Decision Making
用于有效实时风险知情决策的新型动态耦合风暴潮灾害基础设施模型
  • 批准号:
    1563372
  • 财政年份:
    2016
  • 资助金额:
    $ 24.96万
  • 项目类别:
    Standard Grant
Collaborative Research: Novel Fractional Order Ground Motion Intensity Measures for High Confidence Risk Assessment of Distributed Infrastructures
合作研究:用于分布式基础设施高置信度风险评估的新型分数阶地震动强度测量
  • 批准号:
    1462183
  • 财政年份:
    2015
  • 资助金额:
    $ 24.96万
  • 项目类别:
    Standard Grant
Collaborative Research: Risk Informed Decision Making for Maintenance of Deteriorating Distribution Poles Under Extreme Wind Hazards
合作研究:在极端风灾下维护恶化的配电杆的风险知情决策
  • 批准号:
    1333943
  • 财政年份:
    2013
  • 资助金额:
    $ 24.96万
  • 项目类别:
    Standard Grant

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相似海外基金

Collaborative Research: A Deeply Integrated Physics-Based and Data-Driven Approach for Effective Resilience Management of the Power Grid
协作研究:基于物理和数据驱动的深度集成方法,用于有效的电网弹性管理
  • 批准号:
    2000140
  • 财政年份:
    2020
  • 资助金额:
    $ 24.96万
  • 项目类别:
    Standard Grant
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  • 批准号:
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  • 财政年份:
    2016
  • 资助金额:
    $ 24.96万
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    Standard Grant
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让学习者深入理解代数思想和方法的教育方法研究
  • 批准号:
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  • 财政年份:
    2005
  • 资助金额:
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Collaborative Research: Ecophysiology of Deeply-Branching Bacterial and Archaeal Communities
合作研究:深分支细菌和古菌群落的生态生理学
  • 批准号:
    0525500
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Collaborative Research: Ecophysiology of deeply-branching bacterial and archaeal communities
合作研究:深分支细菌和古菌群落的生态生理学
  • 批准号:
    0525561
  • 财政年份:
    2005
  • 资助金额:
    $ 24.96万
  • 项目类别:
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Collaborative Research: Ecophysiology of deeply-branching bacterial and archaeal communities
合作研究:深分支细菌和古菌群落的生态生理学
  • 批准号:
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Collaborative Research: Contrasting Styles of Ca. 1.4 Ga Tectonism in the Southern Rockies: Evidence for a Fossil Rheologic Transition in a Deeply Exhumed Intracontinental Orogen
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  • 批准号:
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  • 财政年份:
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Collaborative Research: Contrasting Styles of Ca. 1.4 Ga Tectonism in the Southern Rockies: Evidence for a Fossil Rheologic Transition in a Deeply Exhumed Intracontinental Orogen
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  • 批准号:
    0101314
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  • 资助金额:
    $ 24.96万
  • 项目类别:
    Standard Grant
Collaborative Research: Contrasting Styles of Ca. 1.4 Ga Tectonism in the Southern Rockies: Evidence for a Fossil Rheologic Transition in a Deeply Exhumed Intracontinental Orogen
合作研究:Ca 的风格对比。
  • 批准号:
    0003528
  • 财政年份:
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  • 资助金额:
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Collaborative Research: Contrasting Styles of Ca. 1.4 Ga Tectonism in the Southern Rockies: Evidence for a Fossil Rheologic Transition in a Deeply Exhumed Intracontinental Oroge
合作研究:Ca 的风格对比。
  • 批准号:
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