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

过去，不充分的系统理解和不充分的态势感知造成了大规模的停电。随着对可变能源供应来源依赖的增加，复杂能源系统的系统理解和态势感知变得更加具有挑战性。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能力。该研究提供了实时检测异常事件的方法，从而使控制中心能够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，客户将受益于有服务保证的可靠能源供应。学生，包括妇女和代表性不足的群体，将接受这一领域未来劳动力的培训。该项目包括四大重点：1)从测量数据中实时检测异常；2)实时事件诊断和网络状态变化解释；3)电网实时最优控制；4)支撑网络物理系统的科学基础。本项目的主要成果是切实解决电网中事件或故障的检测与诊断，以及大规模停电的预测与预防。

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.