CAREER: Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems

职业:利用智能技术和神经网络进行可扩展的学习和适应,以实现复杂系统的重新配置和生存能力

基本信息

  • 批准号:
    1231820
  • 负责人:
  • 金额:
    $ 2.39万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2012
  • 资助国家:
    美国
  • 起止时间:
    2012-01-13 至 2013-05-31
  • 项目状态:
    已结题

项目摘要

Recently, intelligent techniques and adaptive critic designs have received increasing attention. The dynamic stochastic optimization (DSO) of complex systems such as the electric power grid and its parts can be formulated as minimization and/or maximization of certain quantities. The electric power grid is faced with deregulation and an increased demand for high-quality and reliable electricity for our digital economy, and coupled with interdependencies with other critical infrastructures, it is becoming more and more stressed. Intelligent systems technology will play an important role in carrying out DSO to improve the network efficiency and eliminate congestion problems without seriously diminishing reliability and security. This project proposes to investigate ways in which the power grid can be dynamically optimized, as a testbed for advanced brain-like stochastic identifiers and controllers.This project will advance knowledge and understanding on how to carry out optimization of a dynamic stochastic system. A novel local and global dynamic stochastic optimization strategy for a large scale complex system will be designed. The operating safety margins that currently exist on the large complex systems such as the electric power grid will be minimized, thus, allowing maximum utilization of existing resources with increased system reliability and security with optimal settings on devices throughout the entire system. The capability of carrying out dynamic stochastic optimization is the dream of today. This proposal is a first step in unfolding this dream to reality using brain-like systems with learning and adaptation based on approximate dynamic programming, advanced neural networks and other intelligent techniques on complex systems. In addition, system survivability and availability will be increased by improving reliability and fault tolerance of digital hardware, where the critical algorithms are implemented, using evolution and intelligent techniques. Fault tolerant designs to the unpredictable means robustness, security and safety. The project will also include a major component of educational outreach and of international collaboration including intellectual exchange via faculty and student exchanges between the U.S. and Nigeria, and US and Brazil.
近年来,智能技术和自适应批评设计受到越来越多的关注。电网及其组成部分等复杂系统的动态随机优化(DSO)可以表述为一定数量的最小化和/或最大化。电网面临着放松管制和数字经济对高质量、可靠电力需求的增加,再加上与其他关键基础设施的相互依赖,电网的压力越来越大。在不严重降低可靠性和安全性的前提下,智能系统技术将在DSO的实施中发挥重要作用,提高网络效率,消除拥塞问题。该项目提出研究电网动态优化的方法,作为先进的类脑随机识别器和控制器的试验台。这个项目将促进对如何进行动态随机系统优化的认识和理解。针对大型复杂系统设计了一种新的局部和全局动态随机优化策略。目前存在于大型复杂系统(如电网)上的操作安全余量将被最小化,从而允许最大限度地利用现有资源,提高系统可靠性和安全性,并在整个系统的设备上进行最佳设置。实现动态随机优化的能力是当今的梦想。这个建议是将这个梦想变为现实的第一步,使用基于近似动态规划、先进神经网络和其他复杂系统智能技术的类脑系统进行学习和适应。此外,通过改进数字硬件的可靠性和容错性,系统的生存能力和可用性将得到提高,其中关键算法是使用进化和智能技术实现的。对不可预测的容错设计意味着鲁棒性、安全性和安全性。该项目还将包括教育推广和国际合作的主要组成部分,包括通过美国与尼日利亚、美国与巴西之间的教师和学生交流进行知识交流。

项目成果

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Ganesh Venayagamoorthy其他文献

Ganesh Venayagamoorthy的其他文献

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{{ truncateString('Ganesh Venayagamoorthy', 18)}}的其他基金

Collaborative Research: MoDL: Graph-Optimized Cellular Connectionism via Artificial Neural Networks for Data-Driven Modeling and Optimization of Complex Systems
合作研究:MoDL:通过人工神经网络进行图优化的细胞连接,用于复杂系统的数据驱动建模和优化
  • 批准号:
    2234032
  • 财政年份:
    2023
  • 资助金额:
    $ 2.39万
  • 项目类别:
    Standard Grant
Collaborative Research: CISE-MSI: DP: IIS RI: Research Capacity Expansion via Development of AI Based Algorithms for Optimal Management of Electric Vehicle Transactions with Grid
合作研究:CISE-MSI:DP:IIS RI:通过开发基于人工智能的算法来扩展研究能力,以实现电动汽车与电网交易的优化管理
  • 批准号:
    2318612
  • 财政年份:
    2023
  • 资助金额:
    $ 2.39万
  • 项目类别:
    Standard Grant
Collaborative Research: CISE-MSI: DP: CCF: SHF: MSI/HSI Research Capacity Building via Secure and Efficient Hardware Implementation of Cellular Computational Networks
合作研究:CISE-MSI:DP:CCF:SHF:通过安全高效的蜂窝计算网络硬件实现进行 MSI/HSI 研究能力建设
  • 批准号:
    2131070
  • 财政年份:
    2021
  • 资助金额:
    $ 2.39万
  • 项目类别:
    Standard Grant
Collaborative Research: Planning Grant: I/UCRC for Real-Time Intelligence for Smart Electric Grid Operations (RISE)
合作研究:规划资助:I/UCRC 智能电网运营实时智能 (RISE)
  • 批准号:
    1464637
  • 财政年份:
    2015
  • 资助金额:
    $ 2.39万
  • 项目类别:
    Standard Grant
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
  • 财政年份:
    2014
  • 资助金额:
    $ 2.39万
  • 项目类别:
    Standard Grant
Scalable Intelligent Power Monitoring and Optimal Control of Distributed Energy Systems Using Adaptive Critics
使用自适应批评的分布式能源系统的可扩展智能电力监控和优化控制
  • 批准号:
    1308192
  • 财政年份:
    2013
  • 资助金额:
    $ 2.39万
  • 项目类别:
    Standard Grant
AIR Option 2: Research Alliance Situational Intelligence for Smart Grid Optimization and Intelligent Control
AIR选项2:智能电网优化和智能控制研究联盟态势智能
  • 批准号:
    1312260
  • 财政年份:
    2013
  • 资助金额:
    $ 2.39万
  • 项目类别:
    Standard Grant
Collaborative Research: Computational Intelligence Methods for Dynamic Stochastic Optimization of Smart Grid Operation with High Penetration of Renewable Energy
合作研究:可再生能源高渗透智能电网运行动态随机优化的计算智能方法
  • 批准号:
    1232070
  • 财政年份:
    2012
  • 资助金额:
    $ 2.39万
  • 项目类别:
    Standard Grant
EFRI-COPN: Neuroscience and Neural Networks for Engineering the Future Intelligent Electric Power Grid
EFRI-COPN:用于设计未来智能电网的神经科学和神经网络
  • 批准号:
    1238097
  • 财政年份:
    2012
  • 资助金额:
    $ 2.39万
  • 项目类别:
    Standard Grant
RAPID: Impact of Earthquakes on the Electricity Infrastructure
RAPID:地震对电力基础设施的影响
  • 批准号:
    1216298
  • 财政年份:
    2012
  • 资助金额:
    $ 2.39万
  • 项目类别:
    Standard Grant

相似国自然基金

Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
  • 批准号:
  • 批准年份:
    2024
  • 资助金额:
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职业:随机优化和基于物理的机器学习,用于电力系统的可扩展和智能自适应保护
  • 批准号:
    2338555
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职业:石墨烯/MoS2/石墨烯人工神经元和突触的可扩展整体集成,用于加速机器学习
  • 批准号:
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