EAGER-DynamicData: A Hierarchical Approach to Dynamic Big Data Analysis in Power Infrastructure Security

EAGER-DynamicData：电力基础设施安全动态大数据分析的分层方法

• 批准号: 1462530
• 负责人: Amir-Hamed Mohsenian-Rad
• 金额: $ 18.5万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462530&HistoricalAwards=false
• 关键词: EAGER; DynamicData; Hierarchical; Approach; Dynamic

This research will address a dynamic big data problem that is of urgent national interest: the need for efficient methods to diagnose faults and attacks in critical interconnected infrastructures, such as electricity power networks. Additionally, this project will investigate new methodologies to extract knowledge from the complex streams of data that come from various sensors in infrastructure systems and the models of their behavior. Results and findings in this project will be validated via industry-accredited power system simulators, and will be useful to the power industry in enhancing the safety, stability, and security of essential power infrastructure. This project will promote multi-disciplinary research involving expertise in big data analysis, machine learning, security, power systems, and control systems. This research will provide a powerful bridge between theory and real-world applications while serving as a training platform for a diverse new generation of engineers at the University of California, Riverside, one of America's most ethnically diverse research-intensive institutions. This project will foster the use of multi-resolution data-driven methods for the detection and classification of anomalies in critical dynamical infrastructures, with focus on power networks. This project has three novel, innovative, and potentially transformative technical elements: (1) A comprehensive statistical model, as an alternative to existing physics-based models, using Dynamic Bayesian Networks and Conditional Random Fields to model complex infrastructures subject to failures and attacks; (2) A hierarchical detection and classification method based upon machine learning concepts to tame and leverage the vast amount and diversity of dynamic multi-resolution data collected by spatially distributed sensors; (3) A systematic method to train and inform data-driven methodologies from model-based and analytical knowledge that come from power systems and control theory to build scalable and performing detection and classification mechanisms in power infrastructure security.

这项研究将解决一个具有紧迫国家利益的动态大数据问题：需要有效的方法来诊断关键互联基础设施（如电力网络）中的故障和攻击。此外，该项目还将研究新的方法，从基础设施系统中的各种传感器及其行为模型的复杂数据流中提取知识。该项目的结果和发现将通过行业认可的电力系统模拟器进行验证，并将有助于电力行业提高基本电力基础设施的安全性、稳定性和保障性。该项目将促进涉及大数据分析，机器学习，安全，电力系统和控制系统专业知识的多学科研究。这项研究将在理论和现实世界的应用之间提供一个强大的桥梁，同时作为加州大学滨江分校（美国最具种族多样性的研究密集型机构之一）多元化的新一代工程师的培训平台。该项目将促进使用多分辨率数据驱动方法，对关键动态基础设施的异常情况进行检测和分类，重点是电力网络。该项目有三个新颖、创新和潜在变革性的技术要素：（1）综合统计模型，作为现有基于物理的模型的替代方案，使用动态贝叶斯网络和条件随机场来模拟遭受故障和攻击的复杂基础设施;（2）基于机器学习概念的分层检测和分类方法，以驯服和利用动态多特征的大量和多样性。空间分布的传感器收集的分辨率数据;（3）一种系统的方法来训练和通知来自电力系统和控制理论的基于模型和分析知识的数据驱动的方法，以建立可扩展的和执行的电力基础设施安全检测和分类机制。