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

智能电网等具有社会重要性的网络物理系统（CPS）的可靠运行对于充满活力的经济的无缝运行至关重要。持续停电可能导致大面积的重大中断，造成数百万美元的损失。因此，有效的计算技术和工具，通过执行故障诊断和故障诊断来减少这种系统故障是必要的。智能电网是CPS：它由物理组件（包括发电，输电和配电设施）与网络组件（如智能传感器，通信网络和控制软件）接口的网络组成。这笔赠款提供资金，用于开发新的方法，为智能电网建立模型，代表物理和网络组件中的故障依赖性。这些模型将用于建立一个综合的全系统解决方案，用于诊断故障和预测未来的故障传播，可以解释现有的保护机制。这项工作的最初贡献将是在一个大型分布式网络物理系统的多个层次上的故障的集成建模和开发新的，分层的，强大的，在线的诊断和故障诊断算法。如果成功的话，基于模型的故障诊断和故障诊断技术将通过识别即将发生的故障传播和确定关键故障的时间来提高大型系统中隔离故障的有效性，这将提高系统的可靠性并减少由于故障而产生的损失。 这项工作将弥合计算机科学和电力工程中所需的故障管理方法之间的差距，因为电网变得更智能，更复杂，更密集的数据。该项目的成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。

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.