DynSyst_Special_Topics: Multiscale Dynamics and Information in Data Collection and Assimilation for Complex Systems

DynSyst_Special_Topics：复杂系统数据收集和同化中的多尺度动力学和信息

• 批准号: 1030144
• 负责人: N. Sri Namachchivaya
• 金额: $ 45.98万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1030144&HistoricalAwards=false
• 关键词: DynSyst_Special_Topics; Multiscale; Dynamics; Information; Data

This research focuses on understanding noisy nonlinear dynamical systems using multidisciplinary approaches from advanced measurement capabilities, modeling, and simulations. The unique features of the problem are interactions between information collection, information fusion and information assimilation. The subtleties of the interaction between these effects will lead to new and novel analytical techniques. The approach is to (1) develop novel data-driven lower-order modeling and multi-scale nonlinear filtering frameworks using probability-based decision-making approaches for state estimation of the power system; (2) develop new algorithms and tools for the distributed collection, sharing, mining and harnessing of data for cooperative health monitoring and vulnerability assessment of power systems in real time; and (3) enhance overall power system dynamic performance and stability through novel data fusion techniques and particle methods which are specifically adapted to the complexities of the underlying dynamical system.The daily operation of interconnected electric power systems must guard against failures due to natural disasters, human attack, and failures due to aging and unexpected conditions. At the heart of this effort is huge data collection, analysis, and control strategies spanning time scales from microseconds to minutes and hours. Through the creation of new techniques this project will advance our understanding of the critical operational issues of the next-generation power system and will lead to smarter power grids to provide better electric energy security, efficiency, and sustainability.Through this grant we will foster intensive training of engineering undergraduate students, developing their motivation to pursue graduate studies in this multidisciplinary area. The PIs anticipate a direct scientific benefit in training researchers who are well-versed in engineering, modeling, and applied mathematics. The Co-PI and his group have developed Applets designed for education of K-12 students in how power systems operate. An integral part of the final outcome of the proposed work will be to provide the community a predictive tool that will enhance visual understanding of the effect of noise and uncertainty on power system operations.

本研究的重点是使用先进的测量能力，建模和仿真的多学科方法来理解嘈杂的非线性动力系统。该问题的独特之处在于信息收集、信息融合和信息同化之间的相互作用。这些效应之间相互作用的微妙之处将导致新的和新颖的分析技术。该方法是（1）开发新的数据驱动的低阶建模和多尺度非线性滤波框架，用于电力系统状态估计的基于概率的决策方法;（2）开发新的算法和工具，用于分布式收集、共享、挖掘和利用数据，用于真实的实时的电力系统健康监测和脆弱性评估;（3）针对电力系统的复杂性，采用新的数据融合技术和粒子方法，提高电力系统的动态性能和稳定性。互联电力系统的日常运行必须防范自然灾害、人为攻击、老化和意外事故等故障。这项工作的核心是巨大的数据收集，分析和控制策略，时间范围从微秒到分钟和小时。通过新技术的创造，该项目将推进我们对下一代电力系统关键运行问题的理解，并将导致更智能的电网，以提供更好的电能安全性，效率和可持续性。通过这笔赠款，我们将促进工程本科生的强化培训，培养他们在这个多学科领域攻读研究生课程的动力。PI预期在培训精通工程，建模和应用数学的研究人员方面有直接的科学效益。Co-PI和他的团队开发了旨在教育K-12学生如何操作电力系统的小程序。拟议工作的最终结果的一个组成部分将是为社区提供一个预测工具，将提高对噪声和不确定性对电力系统运行的影响的视觉理解。