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
• 负责人: Ganesh Venayagamoorthy
• 金额: $ 24万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2131070&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; DP

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).As use of renewable energy sources, such as wind and solar power, continue to increase, distributed Artificial Intelligence (AI) is needed to synthesize the large amounts of predictive use indicators, such as weather data and Internet of Things (IoT) sensor data, in order to allow the electric power grid to continue to operate reliably with the high levels of variability and uncertainty associated with renewable energy sources. Since this requires real-time processing, a secure and efficient hardware platform is needed; AI software alone is not sufficient. The Cellular Computational Network (CCN) is a distributed AI framework, with a brain-inspired neural network architecture, which is suitable for critical networked systems, such as the electric power grid. Hence, utilizing secure and efficient CCN hardware implementations for power system applications will accelerate operations to achieve a real-time performance guarantee on representative large-scale networks without compromising accuracy, and will simultaneously provide resiliency to cyber-physical system attacks, thus enhancing sustainable and secure power system operation.This project develops both synchronous logic and asynchronous logic hardware implementations of CCN cells and overall CCN systems using reconfigurable Field Programmable Gate Arrays, and explores approximate computing opportunities for application to CCNs. The resulting CCN hardware systems will be tested via integration into Clemson University’s various Real-Time Power and Intelligent Systems (RTPIS) Laboratory testbeds, including for wide area predictive state estimation of power system variables, solving dynamic power flows, and predictions of spatial-temporal wind speed/power, solar irradiance/power, and energy consumption of buildings/rooms. Furthermore, this project partners a Minority/Hispanic Serving Institution, Texas A&M University – Kingsville (TAMUK), with Clemson University to involve many more TAMUK Computer Science faculty with RTPIS Lab related research, and establishes a pipeline of high-performing Hispanic students from TAMUK to pursue Computer Engineering or Computer Science PhD degrees.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项的全部或部分资金根据《2021 年美国救援计划法案》（公法 117-2）提供。随着风能和太阳能等可再生能源的使用不断增加，分布式人工智能 (AI)需要综合大量的预测使用指标，例如天气数据和物联网 (IoT) 传感器数据，以便使电网能够在与可再生能源相关的高度可变性和不确定性的情况下继续可靠地运行自此以来。需要实时处理，仅需要安全高效的硬件平台是不够的。细胞计算网络（CCN）是一个分布式人工智能框架，具有类脑神经网络架构，适合关键网络化。因此，利用安全高效的 CCN 硬件实现电力系统应用将加速运行，以在不影响准确性的情况下实现代表性大规模网络的实时性能保证，同时为网络提供弹性。 -物理系统攻击，从而增强该项目使用可重新配置的现场可编程门阵列开发CCN单元和整个CCN系统的同步逻辑和异步逻辑硬件实现，并探索应用于CCN的近似计算机会。最终的CCN硬件系统将进行测试。通过集成到克莱姆森大学的各种实时电力和智能系统 (RTPIS) 实验室测试平台，包括电力系统变量的广域预测状态估计、求解动态功率流以及预测此外，该项目还与德克萨斯农工大学金斯维尔分校 (TAMUK) 和克莱姆森大学合作，让更多的 TAMUK 参与其中。计算机科学系教师进行 RTPIS 实验室相关研究，并为来自 TAMUK 的表现优异的西班牙裔学生建立了攻读计算机工程或计算机科学博士学位的渠道。该奖项反映了 NSF 的法定使命和通过使用基金会的智力优点和更广泛的影响审查标准进行评估，该项目被认为值得支持。

项目成果

Distributed Volt-Var Curve Optimization Using a Cellular Computational Network Representation of an Electric Power Distribution System

使用配电系统的蜂窝计算网络表示的分布式伏特-无功曲线优化

• DOI: 10.3390/en15124438
• 发表时间: 2022
• 期刊: Energies
• 影响因子: 3.2
• 作者: Dharmawardena, Hasala;Kumar Venayagamoorthy, Ganesh
• 通讯作者: Kumar Venayagamoorthy, Ganesh

Scalable Residential Demand Response Management

• DOI: 10.1109/access.2021.3119270
• 发表时间: 2021
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Pramod Herath;G. Venayagamoorthy

Distributed Demand Response Management for a Virtually Connected Community With Solar Power

• DOI: 10.1109/access.2022.3141772
• 发表时间: 2022
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Chirath Pathiravasam;G. Venayagamoorthy

Adaptive Automatic Generation Control for Improved Stability of Power Systems with Utility-Scale Photovoltaic Plants

• DOI: 10.1109/globconht56829.2023.10087578
• 发表时间: 2023-03
• 期刊: 2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)
• 作者: Rajan Ratnakumar;G. Venayagamoorthy

Identification of Substation Configurations in Modern Power Systems using Artificial Intelligence

• DOI: 10.48550/arxiv.2207.05603
• 发表时间: 2022-07
• 期刊: ArXiv
• 作者: Dulip Madurasinghe;G. Venayagamoorthy