DDDAS-TMRP: Data-Driven Power System Operations

DDDAS-TMRP：数据驱动的电力系统运营

• 批准号: 0540216
• 负责人: Alan Sussman
• 金额: $ 32万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540216&HistoricalAwards=false
• 关键词: DDDAS; TMRP; Data; Driven; Power

The main objective of this research is to develop new algorithms and tools for the distributed collection, sharing, mining and harnessing of data for cooperative health monitoring and vulnerability assessment of power systems in real time. Fundamental concepts and technology for achieving this objective exist today in the areas of power systems, system theory and computer science, but harnessing them to achieve this vision will require the significant multidisciplinary concerted effort proposed here. The project aims at making fundamental contributions to the necessarilymultidisciplinary systems required for data utilization in monitoring and control of interconnectedengineering systems. The application that this proposal focuses on is the creation of new data-driven tools for electric power system operation and control. The applications systems include traditional SCADA systems as well as the emerging Phasor Measurement Unit (PMU) data concentrators. This effort will focus on five specific task areas: Distributed agent-based system monitoring; Pattern recognition and change detection; Dynamic analysis of signatures for early warning; Data-driven, low-order modeling for security assessment; Automated visualization for power system health monitoring. This work will provide a broader impact in several areas. First, the results in data manipulation, visualization, and processing will have application in other domains. Any complex system that relies heavily on measurements for monitoring and control shares the same need to extract key information from massive amounts of data received over wide areas and time scales. Second, several of the investigators work very closely with the electric power industry through the Power Systems Engineering Research Center (PSERC), an NSF Industry/University Cooperative Research Center designed to stimulate collaboration among 13 universities and 35 companies. Results from this project will naturally influence that collaboration in the form of seminars, tutorials, and interaction with industry.

本研究的主要目标是开发新的算法和工具，用于分布式收集、共享、挖掘和利用数据，用于实时电力系统的协同健康监测和脆弱性评估。实现这一目标的基本概念和技术目前存在于电力系统、系统理论和计算机科学领域，但利用它们来实现这一愿景将需要这里提出的重要的多学科协同努力。该项目旨在为监测和控制互联工程系统所需的数据利用所必需的多学科系统做出基本贡献。本提案的应用重点是为电力系统运行和控制创建新的数据驱动工具。应用系统包括传统的SCADA系统以及新兴的相量测量单元（PMU）数据集中器。这项工作将集中在五个具体的任务领域：基于分布式代理的系统监控；模式识别与变化检测；预警特征的动态分析；用于安全评估的数据驱动的低阶建模；电力系统健康监测的自动可视化。这项工作将在几个领域产生更广泛的影响。首先，数据操作、可视化和处理的结果将在其他领域得到应用。任何严重依赖测量进行监测和控制的复杂系统都需要从广泛区域和时间尺度上接收的大量数据中提取关键信息。其次，一些研究人员通过电力系统工程研究中心（PSERC）与电力行业密切合作，PSERC是一个NSF工业/大学合作研究中心，旨在促进13所大学和35家公司之间的合作。这个项目的结果自然会以研讨会、教程和与行业互动的形式影响协作。