Robust Transient State Estimation for Three-Phase Power Systems

三相电力系统的鲁棒瞬态估计

• 批准号: 2330377
• 负责人: Ali Abur
• 金额: $ 35万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330377&HistoricalAwards=false
• 关键词: Robust; Transient; State; Estimation; Three

This NSF project aims to facilitate efficient integration of inverter-based (IB)renewable energy sources into future generation of power systems. The project will bring transformative change by better modeling and estimation of the dynamic behavior of these sources so that they can be readily incorporated in the analysis and optimal control and coordination of all other sources and devices in power grids. The overall impact will be cleaner, less costly and more reliable energy delivery to the customers. It should also be noted that implementation and utilization of the project’s outcomes will provide job and training opportunities for the next generation of power engineers in the nation. The intellectual merits of the project include improved reliability of network applications that incorporate renewable energy sources, facilitating detection and correction of errors associated with IB source models and associated measurements and, incorporation of IB renewable energy sources in overall generation dispatch of large power grids. The broader impacts of the project include training and education of students from underrepresented and minority groups via REU programs in the summers and as student researchers during regular semesters.The goal of this project is to develop a robust and efficient state estimator (SE) which can trace the fast dynamics of IB energy sources so that effective control feedback signals at time scales commensurate with such dynamics can be implemented. This goal will be accomplished by using fast data acquisition to capture voltage and current signals in discrete time. Such measurements will be used to incorporate discrete-time models of power system elements into state estimation formulation in a computationally robust and efficient manner; enabling fast and automatic rejection of errors in sampled measurements; exploitation of the special structure of discrete-time network model to develop a parallel computation framework facilitating scalability of the developed SE algorithm for very large scale systems including those operating under unbalanced three-phase operating conditions accounting for nonlinear elements and frequency dependencies of line/cable parameters.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.

该 NSF 项目旨在促进基于逆变器 (IB) 的可再生能源集成到下一代电力系统中，该项目将通过更好地建模和估计高效这些能源的动态行为来带来变革，以便它们可以轻松地纳入其中。电网中所有其他能源和设备的分析、优化控制和协调，总体影响将为客户提供更清洁、成本更低、更可靠的能源。为未来提供工作和培训机会该项目的智力优势包括提高包含可再生能源的网络应用的可靠性，促进与 IB 源模型和相关测量相关的错误的检测和纠正，以及将 IB 可再生能源纳入整体。该项目更广泛的影响包括通过 REU 项目在夏季对代表性不足和少数群体的学生进行培训和教育，并在常规学期作为学生研究人员。该项目的目标是建立一个强大而高效的团队。状态估计器 (SE)它可以跟踪 IB 能源的快速动态，以便在与这种动态相称的时间尺度上实现有效的控制反馈信号，这一目标将通过使用快速数据采集来捕获离散时间的电压和电流信号来实现。用于以计算稳健且高效的方式将电力系统元件的离散时间模型纳入状态估计公式中；利用离散时间网络模型的特殊结构来快速、自动地剔除误差；计算框架促进所开发的SE的可扩展性该算法适用于超大规模系统，包括在不平衡三相运行条件下运行的系统，考虑非线性元件和线路/电缆参数的频率依赖性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和技术进行评估，被认为值得支持。更广泛的影响审查标准。