CAREER: Stochastic Optimization and Physics-informed Machine Learning for Scalable and Intelligent Adaptive Protection of Power Systems

职业：随机优化和基于物理的机器学习，用于电力系统的可扩展和智能自适应保护

• 批准号: 2338555
• 负责人: Ali Bidram
• 金额: $ 51.75万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2029-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338555&HistoricalAwards=false
• 关键词: CAREER; Stochastic; Optimization; Physics; informed

This NSF CAREER project aims to improve the resilience of power grids by designing a data-driven adaptive protection platform (APP). The project will bring transformative change by designing intelligent and adaptive protection schemes in response to challenges associated with modern power grids with different operational modes and circuit topologies and under high penetration of Inverter-based Resources (IBRs). These challenges can deteriorate the performance of conventional protection schemes and may result in detrimental impacts like widespread blackouts. Therefore, there is a need to redesign the conventional protection systems and make them adaptive to the prevailing power grid conditions. This will be achieved by designing a scalable APP that can take adaptive protection actions in transmission and distribution electric power grids. The intellectual merits of the project include addressing the protection challenges that rise from the high penetration of IBRs by incorporating software and hardware solutions that improve the reliability, selectivity, sensitivity, and security of the underlying protection system. The broader impacts of the project include broadening the participation of underrepresented groups in power engineering and integrating practical and real-world concepts into the existing curriculum of power engineering. This will be achieved by organizing summer camps and other outreach activities for underrepresented K-12 and college students and designing new course topics for undergraduate and graduate students at the University of New Mexico (UNM).The research objectives of this project are (i) to design an adaptive protection platform that is responsive to extreme events using a stochastic optimization algorithm for optimizing protection relay settings, and (ii) to create communication-free and adaptive local protection modules. The proposed research will formulate a multi-stage stochastic optimization problem to identify feasible relay settings that satisfy the relay’s coordination time interval constraints for different circuit topology scenarios caused by extreme events. On the other hand, the local adaptive protection module will be designed using unsupervised conditional generative adversarial network (C-GAN) for fault detection and physics-informed machine learning algorithms for fault location. The physics-informed machine learning algorithms will utilize the postfault sequential component networks’ equations for regularization of estimated fault location and resistance.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 NSF CAREER 项目旨在通过设计数据驱动的自适应保护平台（APP）来提高电网的弹性。该项目将通过设计智能和自适应保护方案来应对与不同运营的现代电网相关的挑战，从而带来变革。模式和电路拓扑以及基于逆变器的资源（IBR）的高渗透率，这些挑战可能会降低传统保护方案的性能，并可能导致大面积停电等不利影响。让他们适应这将通过设计一个可扩展的应用程序来实现，该应用程序可以在输配电网中采取自适应保护措施，该项目的智能优点包括通过整合软件来解决因 IBR 的高渗透率而带来的保护挑战。该项目的更广泛影响包括扩大代表性不足的群体对电力工程的参与，并将实用和现实世界的概念融入现有的电力课程。这将是工程。该项目的研究目标是 (i) 设计一个自适应保护平台，使用随机优化算法来优化继电保护设置，以响应极端事件，以及（ii）创建无通信和自适应本地保护模块。所提出的研究将制定多阶段随机优化问题来确定可行的。继电器设置满足另一方面，将使用无监督条件生成对抗网络（C-GAN）进行故障检测和基于物理的机器学习算法来设计本地自适应保护模块。基于物理的机器学习算法将利用故障后顺序组件网络方程对估计的故障位置和电阻进行正则化。该奖项授予 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。