AMPS: Model Reduction for Analysis, Identification, and Optimal Design of Power Networks

AMPS：用于电力网络分析、识别和优化设计的模型简化

• 批准号: 1923221
• 负责人: Serkan Gugercin
• 金额: $ 37.65万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923221&HistoricalAwards=false
• 关键词: AMPS; Model; Reduction; Analysis; Identification

Power networks are a rich source of dynamical systems that play a critical role in the infrastructure of modern society. Costly computer simulations are needed to model the impact of virtually any planning, monitoring, control and stability analysis task of the power grid, due to its scale and complexity. Such simulations become only more complicated with the growth of renewable energy generation such as solar and wind. To expedite dynamic simulation and to aid the design and optimization of power grids, this project pursues a suite of new model reduction algorithms tailored to the special considerations of power network modeling. The proposed methods will take advantage of the large volume of observation data that is available, moving toward algorithms for reliably estimating the state of the grid. This project supports 2 graduate students each year of the 3 year project.This project will develop and analyze new approaches to model reduction that are specially adapted to the needs of power grid operation and analysis. The project will pursue both projection-based methods and data-driven algorithms, considering nonlinear power network equations and their linearization. The resulting methods seek reliable, high-fidelity, low-order models that preserve critical physics-based structural features and parametric dependence germane to power grid dynamics. Data-driven modeling frameworks will be refined to allow for rapid model updating using near real-time observation streams from phasor measurement units. Reduced models will also contribute to the development of cost-effective power grid optimization tools.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.

电力网络是动力系统的丰富来源，在现代社会的基础设施中发挥着关键作用。由于电网的规模和复杂性，需要昂贵的计算机模拟来模拟几乎任何电网规划、监测、控制和稳定性分析任务的影响。随着太阳能和风能等可再生能源发电的增长，这种模拟只会变得更加复杂。为了加快动态仿真和帮助电网的设计和优化，本项目追求一套新的模型降阶算法，适合于电力网络建模的特殊考虑。所提出的方法将利用大量可用的观测数据，朝着可靠估计电网状态的算法发展。本项目为期3年，每年资助2名研究生。本项目将开发和分析专门适应电网运行和分析需要的模型简化新方法。该项目将采用基于投影的方法和数据驱动的算法，考虑非线性电力网络方程及其线性化。由此产生的方法寻求可靠的，高保真的，低阶的模型，保持关键的物理为基础的结构特征和参数依赖密切相关的电网动态。将改进数据驱动的建模框架，以便利用相量测量单元的近实时观测流快速更新模型。简化模型还将有助于开发具有成本效益的电网优化工具。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

Structured vector fitting framework for mechanical systems

机械系统的结构化矢量拟合框架

• DOI: 10.1016/j.ifacol.2022.09.089
• 发表时间: 2022
• 期刊: IFAC-PapersOnLine
• 作者: Werner, Steffen W.R.;Gosea, Ion Victor;Gugercin, Serkan
• 通讯作者: Gugercin, Serkan

Efficient Topology Design Algorithms for Power Grid Stability

• DOI: 10.1109/lcsys.2021.3088888
• 发表时间: 2021-03
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Siddharth Bhela;Harsha Nagarajan;Deepjyoti Deka;V. Kekatos

Inferring Power System Dynamics From Synchrophasor Data Using Gaussian Processes

• DOI: 10.1109/tpwrs.2022.3144935
• 发表时间: 2021-05
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: M. Jalali;V. Kekatos;Siddharth Bhela;Hao Zhu;V. Centeno

A Unifying Framework for Interpolatory \({\boldsymbol{\mathcal{L}_2}}\)-Optimal Reduced-Order Modeling

插值({oldsymbol{mathcal{L}_2}})-最优降阶建模的统一框架

• DOI: 10.1137/22m1516920
• 发表时间: 2023
• 期刊: SIAM Journal on Numerical Analysis
• 影响因子: 2.9
• 作者: Mlinarić, Petar;Gugercin, Serkan
• 通讯作者: Gugercin, Serkan

Inferring Power System Frequency Oscillations using Gaussian Processes

• DOI: 10.1109/cdc45484.2021.9683760
• 发表时间: 2021-12
• 期刊: 2021 60th IEEE Conference on Decision and Control (CDC)
• 作者: M. Jalali;V. Kekatos;Siddharth Bhela;Hao Zhu