SGER: Precursors for Impending Instability in Power Systems

电力系统即将出现不稳定的前兆由于电力需求增加和输电能力缺乏扩展的持续趋势，马里兰大学的埃亚德·阿贝德学院公园电力系统正在接近其稳定极限。在高应力电力系统中，通过先进的建模和仿真预测的稳定特性与实际系统的实际稳定性之间存在不匹配。这种不匹配导致了过于乐观的稳定性评估，有时会带来灾难性的后果。SGER建议的目标是开始开发一种非基于模型的方法来实时检测不确定电力系统与不稳定的接近程度。智能优点：将寻求基于信号的技术，以实时检测和监测压力条件下电力网络的不稳定程度。这项工作中的一个重要工具是不稳定性前兆的概念，即当系统接近其稳定极限时出现的测量系统输出的特征。该方法将被应用于电力系统模型，该模型将被用来模拟在应力条件下具有不确定模型的稳定裕度评估的真实情况。更广泛的影响：这一努力的成功不仅在避免电力系统停电方面至关重要，而且在地震警报系统、医疗器械设计(例如用于管理心脏病发作和脑癫痫发作)以及为避免经济或金融市场发生灾难性事件而制定政府经济政策方面也是至关重要的。在这些应用中，仅靠数学建模不能准确地预测不稳定的开始。

SGER: Precursors for Impending Instability in Power SystemsEyad AbedUniversity of Maryland, College ParkPower systems are being operated closer to their stability limits due to the continuingtrends of increasing power demand and lack of expansion in transmission capacity. There is a mismatch in highly stressed power systems between the stability characteristics predicted by advanced modeling and simulation and the actual stability of the real system. This mismatch results in overly optimistic stability assessment, withsometimes catastrophic consequences. The goal of this SGER proposal is to begin the development of a non-model-based approach to detecting closeness to instability of uncertain power systems in real time. Intellectual Merit: Signal-based techniques will be sought for real-time detection and monitoring of closeness to instability for electric power networks in stressed conditions. An important tool in this work is the concept of precursors for instability, signatures of measured system outputs that appear when a system approaches its stability limits. The methods will be applied to electric power system models which will be used to mimic real conditions of stability margin assessment with uncertain models in stressed conditions. Broader Impact:Success in this effort can be of critical importance not only in power system blackout avoidance, but also in earthquake warning systems, medical device design (such as for management of heart attacks and brain seizures), and development of government economic policy for avoidance of catastrophic events in the economy or in financial markets. Mathematical modeling alone is incapable of accurately predicting the onset of instability in any of these applications.