ABSTRACT

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.

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