Exploiting the Weighted Graph Laplacian for Power Systems: High-Degree Contingency, Machine Learning, Data Assimilation, and Parallel-in-Time Integration

利用电力系统的加权图拉普拉斯:高度偶然性、机器学习、数据同化和并行时间集成

基本信息

  • 批准号:
    2229378
  • 负责人:
  • 金额:
    $ 27.62万
  • 依托单位:
  • 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:
    2022-11-01 至 2025-10-31
  • 项目状态:
    未结题

项目摘要

Modernizing the power grid continues to be a major national and international challenge. The urgency of this modernization has been accentuated by the devastating effects of climate change on the power infrastructure and the societal consequences that can occur when only minor, let alone major, damages are made to this infrastructure. Mathematics and statistics will play a special role in this modernization since the power system is modeled by complex systems of mathematical equations. This project will develop new fast and accurate computational techniques for solving the core mathematical equations modeling the grid. The crux of these new techniques will be based on the grid's network structure, which exposes the flow of electricity in the power grid. A common and significant problem in these techniques is determining the most relevant generators, loads, and substations in the grid. Knowing these components will allow power grid engineers to determine which parts of the grid are most vulnerable to disruptions coming from natural disasters and cyber attacks, and hence, help design a more resilient grid. The project will provide training opportunities to graduate students. This project aims to develop fast and accurate algorithms for analyzing large-scale, real-world power systems. A common feature of these algorithms is exploiting the system's associated weighted graph Laplacian to expose the diffusion of electricity in the network. The fundamental component of this research is determining the dependency and relevancy of the network buses, which is accomplished through state-of-the-art algebraic multigrid techniques for weighted graph Laplacians and approximate diffusion distance measures. The dependency and relevancy will be used to develop (1) fast and accurate screening techniques for high-degree contingency analysis; (2) multi-scale graph neural networks (GNNs) for regression in power grid analysis; and (3) optimal parallel-in-time integrators for dynamical power systems. Moreover, to avoid the vanishing gradient problem in the stochastic gradient method and to permit the natural incorporation of uncertainties in the GNN setting, an ensemble Kalman filter method will be used to train the GNN weights. More robust models, robust with respect to uncertainties, will be constructed by appropriately combining the surrogates obtained from the multi-scale GNNs with traditional power system models.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.
电网现代化仍然是一项重大的国家和国际挑战。气候变化对电力基础设施的破坏性影响,以及对这一基础设施造成轻微破坏(更不用说重大破坏)时可能产生的社会后果,都突出了这种现代化的紧迫性。数学和统计学将在这一现代化中发挥特殊作用,因为电力系统是由复杂的数学方程系统建模的。这个项目将开发新的快速和准确的计算技术,用于解决模拟网格的核心数学方程。这些新技术的关键将是基于电网的网络结构,它暴露了电网中的电流。这些技术中一个常见且重要的问题是确定电网中最相关的发电机、负载和变电站。了解这些组件将使电网工程师能够确定电网的哪些部分最容易受到自然灾害和网络攻击的影响,从而帮助设计更具弹性的电网。该项目将为研究生提供培训机会。该项目旨在开发快速准确的算法,用于分析大规模,真实世界的电力系统。这些算法的一个共同特征是利用系统的相关加权图拉普拉斯算子来揭示网络中的电力扩散。这项研究的基本组成部分是确定网络总线的依赖性和相关性,这是通过最先进的代数多重网格技术的加权图拉普拉斯算子和近似扩散距离的措施。依赖性和相关性将用于开发(1)快速准确的筛选技术,用于高度应急分析;(2)多尺度图神经网络(GNNs),用于电网分析中的回归;以及(3)动态电力系统的最佳并行时间积分器。此外,为了避免随机梯度法中的消失梯度问题,并允许在GNN设置中自然地并入不确定性,将使用集合卡尔曼滤波器方法来训练GNN权重。通过将从多尺度GNN中获得的替代模型与传统电力系统模型适当结合,将构建更稳健的模型,对不确定性具有稳健性。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(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Barry Lee其他文献

PMU Placement for enhancing dynamic observability of a power grid
用于增强电网动态可观测性的 PMU 布局
Asynchronous Fast Adaptive Composite-Grid Methods for Elliptic Problems: Theoretical Foundations
椭圆问题的异步快速自适应复合网格方法:理论基础
A Novel Multigrid Method for Sn Discretizations of the Mono-Energetic Boltzmann Transport Equation in the Optically Thick and Thin Regimes with Anisotropic Scattering, Part I
具有各向异性散射的光厚和薄区域中单能玻尔兹曼输运方程 Sn 离散化的新型多重网格方法,第一部分
  • DOI:
    10.1137/080721480
  • 发表时间:
    2010
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Barry Lee
  • 通讯作者:
    Barry Lee
On the configuration of the US Western Interconnection voltage stability boundary
论美国西部互联电压稳定边界的配置
A Moment-Parity Multigrid Preconditioner for the First-Order System Least-Squares Formulation of the Boltzmann Transport Equation
玻尔兹曼输运方程一阶系统最小二乘公式的矩宇称多重网格预处理器
  • DOI:
    10.1137/s1064827502407172
  • 发表时间:
    2003
  • 期刊:
  • 影响因子:
    0
  • 作者:
    P. Brown;Barry Lee;T. Manteuffel
  • 通讯作者:
    T. Manteuffel

Barry Lee的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Barry Lee', 18)}}的其他基金

Conference: 2023 NSF Algorithms for Modern Power Systems (AMPS) Workshop
会议:2023 NSF 现代电力系统算法 (AMPS) 研讨会
  • 批准号:
    2226640
  • 财政年份:
    2023
  • 资助金额:
    $ 27.62万
  • 项目类别:
    Standard Grant
AMPS: Advanced Mathematical Algorithms for Model Reduction and Stochastic Modeling for the Emerging Power Grid
AMPS:用于新兴电网模型简化和随机建模的高级数学算法
  • 批准号:
    1734727
  • 财政年份:
    2017
  • 资助金额:
    $ 27.62万
  • 项目类别:
    Standard Grant
Risk and Resiliency of the Electric Power Grid: Mathematical and Statistical Challenges
电网的风险和弹性:数学和统计挑战
  • 批准号:
    1550666
  • 财政年份:
    2015
  • 资助金额:
    $ 27.62万
  • 项目类别:
    Standard Grant

相似海外基金

Spectral theory of Schrodinger forms and Stochastic analysis for weighted Markov processes
薛定谔形式的谱论和加权马尔可夫过程的随机分析
  • 批准号:
    23K03152
  • 财政年份:
    2023
  • 资助金额:
    $ 27.62万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
22 UKRI-SBE: Contextually and probabilistically weighted auditory selective attention: from neurons to networks
22 UKRI-SBE:上下文和概率加权听觉选择性注意:从神经元到网络
  • 批准号:
    BB/X013103/1
  • 财政年份:
    2023
  • 资助金额:
    $ 27.62万
  • 项目类别:
    Research Grant
Coupling PDE-Based Computational Inversion and Learning Via Weighted Optimization
通过加权优化耦合基于偏微分方程的计算反演和学习
  • 批准号:
    2309802
  • 财政年份:
    2023
  • 资助金额:
    $ 27.62万
  • 项目类别:
    Standard Grant
Experimental Design-based Weighted Sampling
基于实验设计的加权抽样
  • 批准号:
    2310637
  • 财政年份:
    2023
  • 资助金额:
    $ 27.62万
  • 项目类别:
    Standard Grant
Development and application of weighted adjacency matrix estimation methods based on multivariate data
基于多元数据的加权邻接矩阵估计方法的开发与应用
  • 批准号:
    23K01377
  • 财政年份:
    2023
  • 资助金额:
    $ 27.62万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Map Paravascular Fluid Dynamic Signatures of Key Aging and AD Processes Using Dynamics Diffusion-Weighted Imaging
使用动力学扩散加权成像绘制关键衰老和 AD 过程的血管旁流体动态特征
  • 批准号:
    10739365
  • 财政年份:
    2023
  • 资助金额:
    $ 27.62万
  • 项目类别:
CAREER: Weighted Fourier extension estimates and interactions with PDEs and geometric measure theory
职业:加权傅里叶扩展估计以及与偏微分方程和几何测度理论的相互作用
  • 批准号:
    2237349
  • 财政年份:
    2023
  • 资助金额:
    $ 27.62万
  • 项目类别:
    Continuing Grant
Improving the diagnostic accuracy of breast MRI without contrast agent using the parameters of abbreviated diffusion-weighted image.
利用简化扩散加权图像参数提高无造影剂乳腺 MRI 的诊断准确性。
  • 批准号:
    23K07211
  • 财政年份:
    2023
  • 资助金额:
    $ 27.62万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
SBE-UKRI: Contextually and probabilistically weighted auditory selective attention: from neurons to networks
SBE-UKRI:上下文和概率加权听觉选择性注意:从神经元到网络
  • 批准号:
    2414066
  • 财政年份:
    2023
  • 资助金额:
    $ 27.62万
  • 项目类别:
    Standard Grant
Weighted semigroup approach for Fokker-Planck-Kolmogorov equations
Fokker-Planck-Kolmogorov 方程的加权半群方法
  • 批准号:
    517982119
  • 财政年份:
    2023
  • 资助金额:
    $ 27.62万
  • 项目类别:
    WBP Fellowship
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了