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）的可再生能源有效集成到未来的电力系统中。 该项目将通过更好地建模和估计这些源的动态行为带来变革性的变化，以便它们可以很容易地纳入电网中所有其他源和设备的分析和优化控制和协调。 总体影响将是更清洁，成本更低，向客户提供更可靠的能源。 还应该指出的是，该项目成果的实施和利用将为该国下一代电力工程师提供就业和培训机会。 该项目的智力优势包括提高网络应用的可靠性，包括可再生能源，促进检测和纠正与IB源模型和相关测量相关的错误，并将IB可再生能源纳入大型电网的整体发电调度。 该项目的更广泛的影响包括通过夏季REU项目对来自代表性不足和少数群体的学生进行培训和教育，并在常规学期担任学生研究员。该项目的目标是开发一种强大而有效的状态估计器（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实现，以捕获离散时间内的电压和电流信号。 这种测量将用于以计算上鲁棒和有效的方式将电力系统元件的离散时间模型并入状态估计公式;使得能够快速和自动地拒绝采样测量中的误差;利用离散元的特殊结构，时间网络模型，以开发一个并行计算框架，促进所开发的SE算法的可扩展性，用于非常大规模的系统，包括那些操作该奖项反映了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。