Collaborative Research: Advancing Robust Control and State Estimation of Converter-Based Power Systems

合作研究：推进基于转换器的电力系统的鲁棒控制和状态估计

• 批准号: 2151571
• 负责人: Ahmad Taha
• 金额: $ 26.5万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151571&HistoricalAwards=false
• 关键词: Collaborative; Research; Advancing; Robust; Control

Future power grids, the nation’s most critical infrastructure, will be extremely difficult to manage due to large-scale integration of renewable energy resources. The strategy proposed in this project is aided by new grid technologies (converter-based assets in wind/solar farms and high-frequency sensing devices) that are developed and deployed to allow new real-time control-theoretic algorithms to be implemented with little overhead---while guaranteeing grid stability and resilience. The literature in this area had addressed various scientific research questions, but mostly adopted simplified models that cannot adequately capture the real-time operation of future grids. This project addresses this science gap by developing a new set of real-time algorithms, leading to a more robust operation of future power grids characterized with high penetration of renewable energy resources. These control algorithms can be implemented by grid operators throughout the nation. The project will also include: a) hosting an outreach workshop on renewable energy systems for a low-income, minority-majority, and female-only high school in San Antonio; b) organizing a technical industry workshop that showcases the created algorithms in the state of Iowa; c) disseminating the created scientific methods within the curricula at the University of Texas at San Antonio and Iowa State University.This project aims at modernizing grid control methods which has traditionally relied on linear systems theory. In particular, the control-theoretic literature addressed a plethora of grid challenges with a focus on linearized, differential equation models whereby algebraic constraints (i.e., power flows) are eliminated. This is in contrast with the more realistic, complex nonlinear differential algebraic equation (NDAE) models. Linearizing grid models around operating points and eliminating algebraic constraints have proven to be a reliable strategy---a trade-off between complexity and tractability. Yet as grids are increasingly pushed to their limits via intermittent renewables, their physical states risk escaping operating regions due to a poor prediction of wind or solar. In lieu of linear differential equation models, control of NDAEs is highly beneficial for grids that are characterized by highly uncertain renewables. This guarantees grid stability for larger operating conditions. Given the limitations of present power system models and the lack of theoretical foundations for control and dynamic state estimation of grid NDAEs, this project will: 1) create a physically representative NDAE model of a power system with a mix of conventional machines and a variety of converter-based technologies; 2) investigate a general theory of dynamic state estimation and robust feedback control algorithms that consider the uncertain nature of power grids modeled via higher-order NDAEs; 3) obtain computationally tractable routines that can be implemented in control centers of power grids. The created theoretical foundations have applications in wide area control, converter-based control, centralized and decentralized and robust dynamic state estimation. This research is critical to guarantee acceptable performance of modern and future power systems and will lead to advancing the state-of-the-art of grid control studies.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.

未来的电网是国家最关键的基础设施，由于可再生能源的大规模整合，将极难管理。该项目中提出的策略得到了新的电网技术（风能/太阳能发电场中基于转换器的资产和高频传感设备）的帮助，这些技术的开发和部署允许以很少的开销实现新的实时控制理论算法，同时保证电网的稳定性和弹性。该领域的文献解决了各种科学研究问题，但大多采用简化模型，无法充分捕捉未来电网的实时运行。该项目通过开发一套新的实时算法来解决这一科学差距，从而使未来电网的运行更加稳健，其特点是可再生能源的高渗透率。这些控制算法可以由全国各地的电网运营商实施。该项目还将包括：a)为圣安东尼奥的一所低收入、少数族裔占多数的女子高中举办可再生能源系统外联讲习班；b)在爱荷华州组织技术行业研讨会，展示所创建的算法；c)在德克萨斯大学圣安东尼奥分校和爱荷华州立大学的课程中传播所创造的科学方法。该项目旨在使传统上依赖于线性系统理论的电网控制方法现代化。特别是，控制理论文献解决了大量的网格挑战，重点放在线性化的微分方程模型上，从而消除了代数约束（即功率流）。这与更现实的、复杂的非线性微分代数方程（NDAE）模型形成对比。在操作点周围线性化网格模型并消除代数约束已被证明是一种可靠的策略——在复杂性和可追溯性之间进行权衡。然而，随着间歇性可再生能源越来越多地将电网推向极限，由于风能或太阳能的预测不佳，电网的物理状态有可能脱离运行区域。替代线性微分方程模型，NDAEs的控制对于可再生能源具有高度不确定性的电网非常有利。这保证了电网在更大的运行条件下的稳定性。考虑到现有电力系统模型的局限性以及电网NDAE控制和动态估计的理论基础的缺乏，本项目将：1)创建一个具有物理代表性的电力系统NDAE模型，该模型混合了传统机器和各种基于变流器的技术；2)研究动态估计的一般理论和鲁棒反馈控制算法，这些算法考虑了通过高阶NDAEs建模的电网的不确定性；3)获得可在电网控制中心实现的计算可处理的例程。所建立的理论基础在广域控制、基于变换器的控制、集中和分散以及鲁棒动态估计等方面具有广泛的应用价值。这项研究对于保证现代和未来电力系统的可接受性能至关重要，并将推动电网控制研究的发展。该奖项反映了美国国家科学基金会的法定使命，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

Optimal Placement of PMUs in Power Networks: Modularity Meets A Priori Optimization

PMU 在电力网络中的最佳布局：模块化满足先验优化

• DOI: 10.23919/acc55779.2023.10156230
• 发表时间: 2023
• 期刊: American Control Conference 2023
• 作者: Kazma, Mohamad H.;Taha, Ahmad F.
• 通讯作者: Taha, Ahmad F.

Revisiting the Optimal PMU Placement Problem in Multi-Machine Power Networks

重新审视多机电力网络中的最佳 PMU 放置问题

• 发表时间: 2023
• 期刊: IEEE transactions on control systems technology
• 影响因子: 4.8
• 作者: Kazma, Mohamad;Taha, Ahmad F.
• 通讯作者: Taha, Ahmad F.

Robust Feedback Control of Power Systems With Solar Plants and Composite Loads

• DOI: 10.1109/tpwrs.2023.3323222
• 发表时间: 2023-10
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Muhammad Nadeem;MirSaleh Bahavarnia;Ahmad F. Taha

On Updating Static Output Feedback Controllers Under State-Space Perturbation

状态空间扰动下更新静态输出反馈控制器

• 发表时间: 2023
• 期刊: IEEE transactions on automatic control
• 影响因子: 6.8
• 作者: Bahavarnia, MirSaleh;Taha, Ahmad F.
• 通讯作者: Taha, Ahmad F.

State-robust Observability Measures For Sensor Selection In Nonlinear Dynamic Systems

非线性动态系统中传感器选择的状态鲁棒可观测性测量

• 发表时间: 2023
• 期刊: Proceedings of the IEEE Conference on Decision Control
• 作者: Kazma, Mohamad;Nugroho, Sebastian;Haber, Aleksandar;Taha, Ahmad
• 通讯作者: Taha, Ahmad