Equipment: MRI: Track 1 Acquisition of a Digital Real-Time Simulator to Enhance Research and Student Research Training in Next-Generation Engineering and Computer Science

NSF核磁共振奖支持购买最先进的旗舰数字实时模拟器（DRTS），该模拟器目前正在带来革命性的变化，为跨多个领域的建模和仿真提供支持，包括输配电电网、智能/微电网、机器人、网络物理系统、航空航天和电动汽车，根据用例，使用相同的硬件，在不同的仿真保真度水平上提供支持。获得这个非常强大、可扩展、灵活、计算优越的设计、测试和验证平台，将大大提高博伊西州立大学（BSU）进行前沿研究的能力，为使用前沿能力的学生研究人员提供多学科研究经验，并通过不同的多学科社区扩大对科学和工程研究的参与。拟议仪器的计划用途构成了令人兴奋的研究领域，能够推动采购请求，主要包括现代电力和控制系统、机器人和网络物理系统的安全性。计划中的项目将利用该研究工具：(1)推进爱达荷州电网的弹性研究；(2)建立退役电动汽车电池退化模型；(3)为开发用于建立和断开接触的机器人控制器铺平道路；(4)开发用于在电力线上安装一系列物品的无人机的控制算法；(5)增强现代电力系统的实时网络安全。这些研究计划将为学生研究人员提供最先进的测试、验证、示范设施和研究成果。这些机会将激励学生追求科学、技术、工程和数学（STEM）方面的教育。最终，这些努力将有助于培养高技能的STEM劳动力。集成研究仪器包括一个硬件在环（HIL）系统、一个网络物理仿真（CPS）附加组件和一个电力硬件在环（PHIL）微电网试验台，以及必要的软硬件组件，这些组件将促进现代电力和控制系统、机器人和网络物理系统安全方面的研究活动。该综合研究工具将实现的研究活动包括：1)通过综合考虑所有极端事件，确定适当的弹性指标，以增强爱达荷州电网的弹性；2)改进二次寿命电池储能系统（SLBESS）在高保真电力系统建模平台中的表示，以更好地反映其在提供电网服务方面的独特能力和限制；3)采用最先进的机器学习技术，从学习专家混合模型到采用注意机制，为完全自主的机器人系统开发基于被动的控制器，实现接触和断开接触；4)为能够在电力线上安装各种物品的无人机开发状态估计和控制算法；5)研究网络物理能源系统的网络攻击检测和预防技术，防止电网网络安全问题的灾难性后果，为分布式能源提供强大的网络安全解决方案；6)为低惯性微电网开发可互操作、具有成本效益的控制和保护解决方案的新方法，以推进BSU的微电网研究和研究培训；7)开发一个虚拟集成环境（VIE）框架，适应广泛的肢体模型和算法，使上肢假肢具有卓越的灵活性；8)为爱达荷州以可再生能源为主的电网评估基于机器学习的预测电网响应系统，以实施野火减灾战略；9)研究实现稳健的网络攻击检测和响应系统的解决方案，以保障智能微电网中der的集成点；10)提高对上游电网严重故障情况下微电网惯性仿真方法的理解。该项目由电气、通信和网络系统部（ECCS）和促进竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This NSF MRI award supports the acquisition of a state-of-the-art flagship digital real-time simulator (DRTS) that is currently bringing transformative change to provide support to model and simulate across multiple domains, including transmission and distribution grids, smart/micro grids, robotics, cyber-physical systems, aerospace, and e-mobility at varying levels of simulation fidelities with the same hardware depending on the use cases. The acquisition of this very powerful, scalable, flexible, computationally superior design, test, and validation platform will make a substantial improvement in Boise State University's (BSU) capabilities to conduct leading-edge research, to provide multi-disciplinary research experiences for student researchers using leading-edge capabilities, and to broaden the participation in science and engineering research by a diverse multi-disciplinary community. The planned uses of the proposed instrument constitute exciting research domains that enable and drive the acquisition request, primarily including modern power and control systems, robotics, and the security of cyber-physical systems. The planned projects will utilize the research instrument to (1) advance the resilience research of Idaho's electrical grid; (2) develop degradation models of retired electric vehicle batteries; (3) pave the way for the development of controllers for robots that make and break contact (4) develop control algorithms designed for drones installing a range of items on power lines; (5) enhance the real-time cybersecurity in modern power systems. These research initiatives will provide student researchers access to state-of-the-art tests, validation, demonstration facilities, and research endeavors. These opportunities will inspire students to pursue education in science, technology, engineering, and mathematics (STEM). Ultimately, these efforts will contribute to developing a highly skilled STEM workforce.The integrated research instrument includes a hardware-in-the-loop (HIL) system, a cyber-physical simulation (CPS) add-on, and a power hardware-in-the-loop (PHIL) microgrid test bench, along with the necessary software and hardware components that will catalyze research activities in modern power and control systems, robotics, and the security of cyber-physical systems. Research activities to be enabled by this integrated research instrument include 1) identifying appropriate resilience metrics to enhance the resilience of Idaho's electric grid through comprehensively considering all extreme events; 2) improving the representation of second-life-battery energy storage systems (SLBESS) in high-fidelity power systems modeling platforms to better reflect their unique capabilities and constraints in delivering grid services; 3) employing state-of-the-art machine learning techniques ranging from learning mixture-of-experts models to employing attention mechanisms for developing passivity-based controllers for fully autonomous robotic systems that make and break contact; 4) developing state estimation and control algorithms for drones with the capability to install a wide range of items on power lines; 5) researching cyber-attack detection and prevention techniques in cyber-physical energy systems to prevent the catastrophic consequences of cyber-security issues in power grids and providing robust cyber-security solutions for distributed energy resources (DERs); 6) developing new approaches to work towards interoperable, cost-effective control and protection solutions for low inertia microgrids to advance microgrid research and research training at BSU; 7) developing a virtual integration environment (VIE) framework that accommodates a wide range of limb models and algorithms, enabling the adequate management of upper-limb prosthetics with superior dexterity; 8) evaluating machine learning-based predictive grid response systems for Idaho's renewable-dominated power grid to implement a pre-wildfire disaster mitigation strategy; 9) researching solutions for implementing a robust cyber-attack detection and response system to safeguard the integration points of DERs in smart microgrids; 10) raising understanding of microgrid inertia emulation methods in case of severe upstream grid failures. This project is jointly funded by the Division of Electrical, Communications, and Cyber Systems (ECCS) and the Established Program to Stimulate Competitive Research (EPSCoR).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.