CAREER: Time-Synchronized Estimation in Power Systems: Unique Challenges and Innovative Solutions

职业：电力系统中的时间同步估计：独特的挑战和创新的解决方案

• 批准号: 2145063
• 负责人: Anamitra Pal
• 金额: $ 50万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145063&HistoricalAwards=false
• 关键词: CAREER; Time; Synchronized; Estimation; Power

Over the next two decades, the need for high-speed, high-precision monitoring, protection, and control of the electric power infrastructure will increase considerably as more renewable energy resources are added, electric vehicles become abundant, and frequency and intensity of extreme weather events rise. Time-synchronized measurements can satisfy this need and ensure resilience of this critical infrastructure only if the fundamental concerns regarding limited sensor coverage, lack of data and model interpretability, and heavy online computational burden are successfully addressed. This NSF CAREER project aims to alleviate these concerns by combining recent advances in robust statistics and machine learning to enable accurate and fast time-synchronized estimation in power systems. The project will bring transformative change by bridging the gap between physics-based and data-driven modeling and ensuring creation of algorithms that adapt to the needs of the data and not vice-versa. The intellectual merits of the project lie in creating new mathematical techniques in convex programming, interval-theoretic learning, and distributed optimization. The broader impacts of the project include engaging high school students in intellectually stimulating yet fun problem-solving projects that will expose them to STEM concepts as well as creating a power system workforce that is knowledgeable about data-driven methods in science and engineering.The goal of this project is to explore a new class of optimization problems that are fundamental to time-synchronized parameter, tracking, and dynamic estimation, respectively, in power systems. The proposed research will make new discoveries in two areas: (1) Linear estimation: by creating robust techniques that account for unknown noise characteristics and/or bounded perturbations in both dependent and independent variables. (2) Severely ill-structured estimation: by producing fast, valid, physics-compliant solutions using Bayesian inference and machine learning for problems in which classical methods fail to provide a consistent answer. The methodological and theoretical outcomes of this project achieved through the cross-pollination of ideas from power systems, data science, information theory, and statistics, will significantly boost the use of time-synchronized measurements for operational decision-making.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20年，随着可再生能源的增加、电动汽车的丰富以及极端天气事件的频率和强度的增加，对电力基础设施进行高速、高精度监测、保护和控制的需求将大幅增加。只有在成功解决传感器覆盖范围有限、缺乏数据和模型可解释性以及沉重的在线计算负担等基本问题的情况下，时间同步测量才能满足这一需求，并确保这一关键基础设施的弹性。这个NSF职业项目旨在通过结合稳健统计和机器学习方面的最新进展来缓解这些担忧，以实现电力系统中准确和快速的时间同步估计。该项目将带来变革性的变化，弥合基于物理的建模和以数据为驱动的建模之间的差距，并确保创建适应数据需要的算法，而不是相反。该项目的智力优势在于在凸规划、区间理论学习和分布式优化方面创造了新的数学技术。该项目的更广泛影响包括让高中生参与智力刺激但有趣的问题解决项目，使他们接触STEM概念，以及培养一支了解科学和工程中数据驱动方法的电力系统员工队伍。该项目的目标是探索一类新的优化问题，这些问题分别是电力系统中时间同步参数、跟踪和动态估计的基础。拟议的研究将在两个领域取得新的发现：(1)线性估计：通过创建稳健的技术来解释因变量和自变量中的未知噪声特征和/或有界扰动。(2)严重的结构不良估计：通过使用贝叶斯推理和机器学习为经典方法无法提供一致答案的问题产生快速、有效、符合物理规范的解决方案。该项目通过交叉授粉电力系统、数据科学、信息论和统计学的思想而取得的方法论和理论成果，将极大地促进时间同步测量在运营决策中的使用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models

• DOI: 10.3390/en16041636
• 发表时间: 2023-02
• 期刊: Energies
• 影响因子: 3.2
• 作者: Dhaval Dalal;Muhammad Bilal;Hritik Shah;Anwarul Islam Sifat;A. Pal;Philip Augustin

Transmission Line Parameter Estimation Under Non-Gaussian Measurement Noise

• DOI: 10.1109/tpwrs.2022.3204232
• 发表时间: 2022-08
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: A. Varghese;A. Pal;Gautam Dasarathy

State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks.

使用深度神经网络对不可观测的配电系统进行状态和拓扑估计。

• DOI: 10.1109/tim.2022.3167722
• 发表时间: 2022
• 期刊: IEEE transactions on instrumentation and measurement
• 影响因子: 5.6
• 作者: Azimian,Behrouz;Biswas,ReetamSen;Moshtagh,Shiva;Pal,Anamitra;Tong,Lang;Dasarathy,Gautam
• 通讯作者: Dasarathy,Gautam

Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes

在存在拓扑变化的情况下使用图神经网络进行时间同步状态估计

• DOI: 10.1109/naps58826.2023.10318579
• 发表时间: 2023
• 期刊: IEEE
• 作者: Moshtagh, Shiva;Sifat, Anwarul Islam;Azimian, Behrouz;Pal, Anamitra
• 通讯作者: Pal, Anamitra