CPS: Synergy: Collaborative Research: Diagnostics and Prognostics Using Temporal Causal Models for Cyber Physical Systems- A Case of Smart Electric Grid

CPS：协同：协作研究：使用网络物理系统的时间因果模型进行诊断和预测 - 以智能电网为例

• 批准号: 1329800
• 负责人: Srdjan Lukic
• 金额: $ 20万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1329800&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Diagnostics

Reliable operation of cyber-physical systems (CPS) of societal importance such as Smart Electric Grids is critical for the seamless functioning of a vibrant economy. Sustained power outages can lead to major disruptions over large areas costing millions of dollars. Efficient computational techniques and tools that curtail such systematic failures by performing fault diagnosis and prognostics are therefore necessary. The Smart Electric Grid is a CPS: it consists of networks of physical components (including generation, transmission, and distribution facilities) interfaced with cyber components (such as intelligent sensors, communication networks, and control software). This grant provides funding to develop new methods to build models for the smart grid representing the failure dependencies in the physical and cyber components. The models will be used to build an integrated system-wide solution for diagnosing faults and predicting future failure propagations that can account for existing protection mechanisms. The original contribution of this work will be in the integrated modeling of failures on multiple levels in a large distributed cyber-physical system and the development of novel, hierarchical, robust, online algorithms for diagnostics and prognostics. If successful, the model-based fault diagnostics and prognostics techniques will improve the effectiveness of isolating failures in large systems by identifying impending failure propagations and determining the time to critical failures that will increase system reliability and reduce the losses accrued due to failures. This work will bridge the gap between fault management approaches used in computer science and power engineering that are needed as the grid becomes smarter, more complex, and more data intensive. Outcomes of this project will include modeling and run-time software prototypes, research publications, and experimental results in collaborations with industry partners that will be made available to the scientific community.

诸如智能电网等社会重要性的网络物理系统（CPS）的可靠操作对于充满活力的经济的无缝运作至关重要。持续的停电可能会导致损失数百万美元的大面积的重大干扰。因此，有必要通过执行故障诊断和预后来减少这种系统故障的有效计算技术和工具。智能电网是CPS：它由与网络组件（例如智能传感器，通信网络和控制软件）相连的物理组件（包括生成，传输和配电设施）网络组成。该赠款为开发新方法提供了资金，以建立代表物理和网络组件中故障依赖性的智能网格模型。这些模型将用于构建整体系统范围的解决方案，用于诊断故障并预测可以解释现有保护机制的未来故障传播。这项工作的最初贡献将是大型分布式网络物理系统中多个级别的失败的集成建模，以及用于诊断和预后学的新颖，等级，健壮的在线算法的开发。如果成功，基于模型的故障诊断和预后技术将通过确定即将到来的失败传播并确定将增加系统可靠性并减少因故障而造成的损失来提高大型系统中隔离故障的有效性。这项工作将弥合计算机科学和动力工程中使用的故障管理方法之间所需的差距，因为电网变得更聪明，更复杂，并且密集程度更大。该项目的结果将包括建模和运行时软件原型，研究出版物以及与行业合作伙伴合作的实验结果，这些结果将提供给科学界。