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

弗吉尼亚理工大学的研究小组将从他们的频率监测网络FNET中获取丰富的广域测量信息，从一个全新的、更广阔的视角来研究电力系统动力学。具体而言，研究将集中在：(1)分析测量数据，研究电力系统振荡和扰动传播特性；(2)开发算法来计算受发电机惯性、电网参数和拓扑等因素影响的扰动传播速度；(3)开发基于FNET数据的自动在线发电跳程事件定位器；(4)最后，创建最先进的教育材料。智力优势：在第一手FNET测量数据的帮助下，可以设计早期预警系统并激活控制动作，以帮助阻尼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成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始点。拟议的活动将通过在课程作业中利用监测系统来促进研究和教育的结合。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。

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.