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.

该奖项是根据2021年《美国救援计划法》的全部或部分资助（公共法117-2）。由于使用可再生能源（例如风能和太阳能）继续增加，分布式人工智能（AI）需要大量的预测性指标，例如，诸如“天气数据和Intern Intern”（Iot of Etitional oter（Iot of），需要进行电动数据（IOT数据），需要进行分布式人工智能（AI）（AI），以允许使用（IOT）的效力（IOT）。与可再生能源相关的可变性和不确定性。由于这需要实时处理，因此需要一个安全有效的硬件平台。仅AI软件就不够。蜂窝计算网络（CCN）是一个分布式的AI框架，具有脑启发的神经网络体系结构，适用于关键的网络系统，例如电力电网。因此，在电力系统应用程序中利用安全有效的CCN硬件实施将加速操作，以实现实时性能保证，不影响准确性而代表大规模网络，并且只会为网络物理系统攻击提供弹性可重新配置的现场可编程门阵列，并探索用于应用CCN的近似计算机会。由此产生的CCN硬件系统将通过集成到Clemson大学的各种实时电力和智能系统（RTPIS）实验室测试床位，包括用于广泛的电力系统变量的范围预测状态估计，解决动态功率流以及空间 - 周期性风速/电力的预测，构建速度/电力/电力和能量耗材和能源摄入型房间。此外，该项目与克莱姆森大学（Clemson University）合作，得克萨斯州农工大学（Texas A＆M University）是少数族裔/西班牙裔服务机构，与克莱姆森大学（Clemson University）一起，与RTPIS实验室相关研究涉及更多的Tamuk计算机科学教师，并建立了从TOMESCORY和计算机科学统计的高表现的西班牙裔学生，并建立了一项高表现的西班牙裔学生。诚实地通过评估来诚实地使用基金会的智力优点和更广泛的影响审查标准。

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