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.

该奖项全部或部分由2021年美国救援计划法案资助（公法117-2）。随着风能和太阳能等可再生能源的使用不断增加，需要分布式人工智能（AI）来综合大量的预测性使用指标，如天气数据和物联网（IoT）传感器数据，以便允许电网在与可再生能源相关联的高度可变性和不确定性的情况下继续可靠地运行。由于这需要实时处理，因此需要一个安全高效的硬件平台;仅凭人工智能软件是不够的。细胞计算网络（CCN）是一种分布式AI框架，具有大脑启发的神经网络架构，适用于关键的网络系统，如电网。因此，将安全且高效的CCN硬件实现用于电力系统应用将加速操作，以在代表性的大规模网络上实现实时性能保证，而不影响准确性，并同时提供对网络物理系统攻击的弹性。从而提高电力系统的可持续和安全运行。该项目开发了CCN单元的同步逻辑和异步逻辑硬件实现，整个CCN系统使用可重构现场可编程门阵列，并探讨近似计算的机会，应用到CCN。由此产生的CCN硬件系统将通过集成到克莱姆森大学的各种实时电力和智能系统（RTPIS）实验室测试平台进行测试，包括电力系统变量的广域预测状态估计，求解动态功率流，以及时空风速/功率，太阳辐照度/功率和建筑物/房间能耗的预测。此外，该项目的合作伙伴少数民族/西班牙裔服务机构，得克萨斯州A M大学-金斯维尔（TAMUK），与克莱姆森大学，涉及更多的TAMUK计算机科学教师与RTPIS实验室相关的研究，并建立了一个管道的高性能西班牙裔学生从TAMUK追求计算机工程或计算机科学博士学位。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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

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

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

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