CPS:Synergy:Collaborative Research: Real-time Data Analytics for Energy Cyber-Physical Systems

过去，对系统的理解不足和对情况的认识不足造成了大规模停电。 随着对可变能源供应源的依赖增加，复杂能源系统的系统理解和态势感知变得更具挑战性。该项目利用大数据分析的力量直接提高系统理解和态势感知。该研究提供了实时检测异常事件的方法，从而允许控制中心在小事件发展为大停电之前采取适当的控制措施。这对社会和电力行业的意义是深远的。能源供应商将能够防止大规模停电并减少收入损失，客户将受益于可靠的能源供应和服务保障。该项目将培养包括女性和代表性不足的群体在内的学生，以培养该领域未来的劳动力。该项目包括四个主要目标：1）从测量数据中实时检测异常; 2）实时事件诊断和解释网络状态的变化; 3）实时优化电网控制; 4）支撑网络物理系统的科学基础。该项目的主要成果是电网事件或故障检测和诊断的实用解决方案，以及大规模停电的预测和预防。

Inadequate system understanding and inadequate situational awareness have caused large-scale power outages in the past. With the increased reliance on variable energy supply sources, system understanding and situational awareness of a complex energy system become more challenging. This project leverages the power of big data analytics to directly improve system understanding and situational awareness. The research provides the methodology for detecting anomalous events in real-time, and therefore allow control centers to take appropriate control actions before minor events develop into major blackouts. The significance for the society and for the power industry is profound. Energy providers will be able to prevent large-scale power outages and reduce revenue losses, and customers will benefit from reliable energy delivery with service guarantees. Students, including women and underrepresented groups, will be trained for the future workforce in this area.The project includes four major thrusts: 1) real-time anomaly detection from measurement data; 2) real-time event diagnosis and interpretation of changes in the state of the network; 3) real-time optimal control of the power grid; 4) scientific foundations underpinning cyber-physical systems. The major outcome of this project is practical solutions to event or fault detection and diagnosis in the power grid, as well as prediction and prevention of large-scale power outages.