Collaborative Research: An Intelligent Restoration System for a Self-healing Smart Grid (IRS-SG)

合作研究：用于自愈智能电网的智能恢复系统（IRS-SG）

• 批准号: 1408141
• 负责人: Ganesh Venayagamoorthy
• 金额: $ 17万
• 依托单位: Clemson 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: 2014-08-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408141&HistoricalAwards=false
• 关键词: Collaborative; Research; Intelligent; Restoration; System

How can we restore power more effectively after a major outage, such as a hurricane, a cascade blackout or a more local outage? Can we use modern computer-based methods to get better performance than what we have today, in a system which is mainly informal and based on guesswork under conditions of great stress and limited information? This new collaborative project will bring together an expert on power restoration with a pioneer of new intelligent computation methods for the power grid, in hopes of finding a more powerful and modern way to restore power more effectively. The goals are; (1) to develop an online adaptive restoration tool using advanced scalable computational intelligence techniques; (2) investigate a novel scheme to use renewable resources in system restoration; (3) explore a blackstart unit investment strategy to improve the self-healing capability; and (4) real-time implementation and demonstration of the new system on benchmark and utility power systems. The grant will include travel to New Zealand, to discuss use of the new system to help provide better response to events like earthquakes. It will also include outreach to Native Americans in the Dakotas. The problem of efficient restoration is very difficult from a technical point of view. A small number of researchers, like the lead PI, have developed a few tools of practical use in this problem, but it is still largely an unsolved problem. The main justification for NSF involvement in this area, and for significant hope of success, is the use of intelligent systems methods far beyond what anyone has applied in the past to this problem, methods pioneered in the intelligent systems part of the EPCN program at NSF(described for example in the book Handbook of RLADP edited by Frank Lewis and Derong Liu). Data from new sources such as synchrophasors will be part of this work. The underlying algorithms are designed from the start to run on massively paralleldistributed systems, such as Cellular Neural Network hardware, which allow much faster real-time computation than traditional sequential computers and algorithms.

大规模停电，例如飓风，级联停电或更本地的中断之后，我们如何才能更有效地恢复电力？我们能否使用现代基于计算机的方法来获得比今天的基于非正式的系统，并且基于在巨大的压力和有限的信息条件下进行猜测的系统中获得更好的性能？这个新的合作项目将通过用于电网的新智能计算方法的先驱，将电力恢复的专家汇集在一起​​，希望找到一种更强大，更现代的方法来更有效地恢复电力。目标是； （1）使用高级可伸缩计算智能技术开发在线自适应修复工具； （2）调查一种新的计划，以在系统恢复中使用可再生资源； （3）探索一个黑色的单位投资策略，以提高自我修复能力； （4）实时实施和演示基准和公用事业电源系统上的新系统。该赠款将包括前往新西兰的旅行，讨论新系统的使用，以帮助对地震等事件提供更好的反应。它还将包括向达科他州的美洲原住民宣传。从技术角度来看，有效恢复的问题非常困难。少数研究人员（例如PID PI）在此问题中开发了一些实际使用工具，但这在很大程度上是一个未解决的问题。 NSF参与该领域的主要理由是为了获得成功的希望，是使用智能系统方法远远超出了过去对此问题应用的任何人，在NSF的智能系统中率先使用EPCN计划的一部分（例如，在Frank Lewis和Derong Liu的Rladp编辑中描述）。来自同步主管等新来源的数据将成为这项工作的一部分。从一开始就设计了基础算法，以便在诸如细胞神经网络硬件之类的大规模分配系统上运行，这些系统可以比传统的顺序计算机和算法更快地实时计算。