Study of Global Power System Dynamic Behavior Based on Wide-Area Frequency Measurements

基于广域频率测量的全球电力系统动态行为研究

• 批准号: 0523315
• 负责人: Yilu Liu
• 金额: $ 24万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0523315&HistoricalAwards=false
• 关键词: Study; Global; Power; System; Dynamic

The Virginia Tech research team will draw from the wealth of the wide area measurement information from their frequency monitoring network FNET to study power system dynamics from an entirely new and broader view. Specifically, the proposed research will focus on: (1) analyzing the measurement data and study power system oscillations and disturbance propagation characteristics, (2) developing algorithms to calculate the traveling speed of the disturbance, which is affected by factors such as generator inertia, grid parameters, and topology, (3) developing an automatic on-line generation trip event locator based on FNET data, and (4) finally, creating state-of-the-art education materials. Intellectual merit: With the help of the first-hand FNET measurement data, it will be possible to devise early warning systems and activate control actions to help damp system oscillations and reduce catastrophic power system failures. The event triangulation capability will serve as an important part of the homeland security and national critical infrastructure monitoring system. Broader Impact: FNET should allow the starting point of a blackout to be to pin-pointed within seconds rather than minutes. The proposed activities will foster the integration of research and education through utilization of the monitoring system in coursework. FNET will also serve as a laboratory for developing and testing distance-learning and remote education initiatives.

弗吉尼亚理工大学的研究团队将从其频率监测网络FNET的广域测量信息中汲取财富，从全新和更广泛的角度研究电力系统动态。具体而言，拟议的研究将侧重于：（1）分析测量数据，研究电力系统振荡和扰动传播特性，（2）开发扰动传播速度的计算算法，该算法受发电机惯性、电网参数和拓扑结构等因素的影响，（3）开发基于FNET数据的自动在线发电跳闸事件定位器，（4）最后，创造最先进的教育材料。智力优点：在第一手FNET测量数据的帮助下，将有可能设计早期预警系统并启动控制措施，以帮助抑制系统振荡并减少灾难性的电力系统故障。事件三角测量能力将作为国土安全和国家关键基础设施监测系统的重要组成部分。更广泛的影响：FNET应该允许在几秒钟而不是几分钟内确定停电的起点。拟议的活动将通过在课程中使用监测系统，促进研究与教育的结合。FNET还将作为开发和测试远程学习和远程教育倡议的实验室。