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

CPS：协同：协作研究：能源网络物理系统的实时数据分析

• 批准号: 1854077
• 负责人: Maggie Cheng
• 金额: $ 33.61万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854077&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Real

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.

过去，系统理解不足和情境意识不足导致大规模停电。 随着对可变能源供应源的依赖，系统的理解和对复杂能源系统的情境意识变得更具挑战性。该项目利用大数据分析的力量直接提高系统理解和情境意识。该研究提供了实时检测异常事件的方法，因此允许控制中心在次要事件发展为主要停电之前采取适当的控制措施。对社会和电力行业的意义是深远的。能源提供商将能够防止大规模停电并减少收入损失，客户将从可靠的能源交付带有服务保证中受益。包括妇女和代表性不足的群体在内的学生将在该领域的未来劳动力中接受培训。该项目包括四个主要推力：1）测量数据中的实时异常检测； 2）实时事件诊断和网络状态变化的解释； 3）实时对电网的最佳控制； 4）基础网络物理系统的基础。该项目的主要结果是对电网中事件或故障检测和诊断的实用解决方案，以及预测和预防大规模停电。