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.

这个NSF职业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带来变革性的变化，以应对与具有不同操作模式和电路拓扑的现代电网相关的挑战，并在基于逆变器的资源（IBRS）的高渗透率下。这些挑战可以决定常规保护方案的性能，并可能导致宽度停电等有害影响。因此，有必要重新设计常规保护系统，并使其适应盛行的电网条件。这将通过设计一个可扩展的应用程序来实现，该应用程序可以在传输和分销电网中采取自适应保护操作。该项目的智力优点包括通过编码软件和硬件解决方案来提高基础保护系统的可靠性，选择性，敏感性和安全性，从而解决了IBR高渗透率的保护挑战。该项目的更广泛的影响包括扩大代表性不足的团体在动力工程中的参与，并将实用和现实世界中的实际概念整合到现有的动力工程课程中。这将通过组织夏令营和其他范围内的K-12和大学生的其他外展活动来实现这一目标，并为新墨西哥大学的本科和研究生设计新的课程主题（UNM）。该项目的研究目标是（i），以设计适应性保护和实现极端事件，以实现ALGITY ALGITY ALGITIASE ALGITY ALGITHM，以设计对实现的互动，以响应ALGORITY ALGORITY ALGITIASE，并为ALGORIADE ALGITIASE ALGITIONS II II INTERAIGE ALGITIADESSITION提供响应（保护模块。拟议的研究将制定一个多阶段随机优化问题，以确定可行的继电器设置，以满足继电器的协调时间间隔的约束，以造成极端事件引起的不同电路拓扑场景。 On the other hand, the local adaptive protection module will be designed using unsupervised conditional generic adversarial network (C-GAN) for fault detection and physics-informed machine learning algorithms will utilize the postfault sequential component networks’ equations for regulated fault location and resistance.This award reflects NSF's statutory mission and has been deemed honestly of support through evaluation using the Foundation's intellectual merit and broader impacts审查标准。