TWC: TTP Option: Small: Collaborative: Integrated Smart Grid Analytics for Anomaly Detection

TWC：TTP 选项：小型：协作：用于异常检测的集成智能电网分析

• 批准号: 1421879
• 负责人: Samir Tout
• 金额: $ 22.27万
• 依托单位: Eastern Michigan University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421879&HistoricalAwards=false
• 关键词: TWC; TTP; Option; Small; Collaborative

The modernized electric grid, the Smart Grid, integrates two-way communication technologies across power generation, transmission and distribution, in order to deliver electricity efficiently, securely and cost-effectively. On the monitoring and control side, it employs real-time monitoring offered by a messaging-based advanced metering infrastructure (AMI), which ensures the grid?s stability and reliability, as well as the efficient implementation of demand response schemes to mitigate bursts demand. The efficient implementation of these features presents a number of challenges, but also opportunities for technology development in engineering, networking, and data analytics.The intellectual contributions include the development of a framework that encompasses fast signal processing and machine learning algorithms, together with multi-sensor information to assess the health of the network. Specifically, the study will investigate algorithms for adaptive detection over streaming, high-dimensional and potentially missing value data. Furthermore, the project, via industry collaborators and power provisioners, will provide a comprehensive empirical evaluation with real-world data; this includes an open-source proof-of-concept prototype for quickly inferring nefarious activity in home-area networks, and a cloud-based testbed for examining realistic scenarios in a wide-area setting.

现代化的电网，即智能电网，集成了发电、输电和配电的双向通信技术，以高效、安全、经济地提供电力。在监视和控制方面，它采用基于消息的高级计量基础设施（AMI）提供的实时监视，从而确保电网的安全。S的稳定性和可靠性，以及有效实施需求响应方案以减轻突发需求。这些特性的有效实现带来了许多挑战，但也为工程、网络和数据分析方面的技术发展带来了机遇。智力贡献包括开发一个框架，该框架包含快速信号处理和机器学习算法，以及用于评估网络健康状况的多传感器信息。具体而言，该研究将研究流式，高维和潜在缺失价值数据的自适应检测算法。此外，该项目将通过行业合作者和电力供应商，提供具有真实数据的全面实证评估；这包括一个开源的概念验证原型，用于快速推断家庭区域网络中的恶意活动，以及一个基于云的测试平台，用于在广域环境中检查现实场景。