Collaborative Research: SLES: Safety under Distributional Shift in Learning-Enabled Power Systems

合作研究：SLES：学习型电力系统分配转变下的安全性

• 批准号: 2331776
• 负责人: Javad Lavaei
• 金额: $ 40万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331776&HistoricalAwards=false
• 关键词: Collaborative; Research; SLES; Safety; under

This NSF project aims to revolutionize the design of learning-enabled, safety-critical systems, with a special focus on power systems. These systems face increasing challenges due to accelerated environmental and technological changes. The project will bring transformative change to the operation of such systems by introducing the concept of antifragility, which promotes change as an opportunity for system enhancement. This innovative viewpoint is crucial for effectively managing distributional shifts in our rapidly changing environment. This transformation will be achieved by pioneering proactive, memory-based antifragile systems, exploring multi-agent systems for cooperative decision-making, and applying advanced techniques for validation and rigorous stress testing. The intellectual merits of the project include a groundbreaking approach towards embracing change and uncertainty. Rather than perceiving these factors as detriments, the project uses them as catalysts for self-improvement, setting the stage for a resilient and adaptive way to operate safety-critical systems. The broader impacts of the project include enhancing the resilience and reliability of crucial infrastructures such as power systems to ensure uninterrupted access to vital services. The project also seeks to serve as a hub for cross-disciplinary dialogue on safe decision-making and public outreach activities to foster scientific literacy and diversity within the STEM community.In power system operation, safety is crucial and requires adherence to rigorous mathematical models that describe the dynamics of various parameters such as voltage, frequency, or the health of an equipment. The task of preserving end-to-end safety is becoming prohibitively complex amidst distributional shifts, driven by the growing complexity and unpredictability of the environment. Our project addresses these challenges through three interconnected research thrusts. The first thrust targets the creation of proactive, antifragile systems that anticipate and adapt to changes, using advanced techniques such as meta-safe learning and offline reinforcement learning. The second thrust bolsters system antifragility through multi-agent systems, encouraging exploration, cooperation, and distributed control to ensure resilience and safety, even under significant disturbances. The third thrust is devoted to validation and stress testing, employing multi-objective adversarial learning and real-world case studies to better handle rare or unexpected events. These research thrusts provide a comprehensive understanding of system fragility, distributed decision-making, and cooperative behavior. Supported by empirical analysis and mathematical guarantees, the proposed methodologies offer a robust approach to ensuring the safety of learning-enabled systems amidst evolving challenges, marking a significant advancement in the field.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.

这个NSF项目旨在彻底改变学习型安全关键系统的设计，特别关注电力系统。由于环境和技术的加速变化，这些系统面临着越来越多的挑战。该项目将通过引入反脆弱性概念，为此类系统的运作带来变革性变化，这将促进变革，使之成为系统增强的机会。这种创新观点对于在快速变化的环境中有效管理分配转变至关重要。这一转变将通过开拓主动的、基于记忆的反脆弱系统、探索用于合作决策的多智能体系统以及应用先进的验证和严格的压力测试技术来实现。该项目的智力价值包括拥抱变化和不确定性的开创性方法。该项目没有将这些因素视为障碍，而是将其用作自我改进的催化剂，为以弹性和适应性的方式运行安全关键系统奠定基础。该项目的更广泛影响包括提高电力系统等关键基础设施的复原力和可靠性，以确保不间断地获得重要服务。该项目还旨在成为安全决策和公共宣传活动的跨学科对话中心，以促进STEM社区的科学素养和多样性。在电力系统运行中，安全至关重要，需要遵守严格的数学模型，这些模型描述了电压，频率或设备健康等各种参数的动态。由于环境的日益复杂和不可预测性，在分布变化中，保护端到端安全的任务变得异常复杂。我们的项目通过三个相互关联的研究方向来应对这些挑战。第一个目标是使用元安全学习和离线强化学习等先进技术，创建预测和适应变化的主动、抗脆弱系统。第二个推力通过多智能体系统支持系统反脆弱性，鼓励探索，合作和分布式控制，以确保即使在重大干扰下也能恢复和安全。第三个重点是验证和压力测试，采用多目标对抗学习和真实案例研究来更好地处理罕见或意外事件。这些研究重点提供了对系统脆弱性，分布式决策和合作行为的全面理解。在经验分析和数学保证的支持下，所提出的方法提供了一种强大的方法，以确保学习系统在不断变化的挑战中的安全性，标志着该领域的重大进步。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。