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iEnergy 2022, 1(4): 453-462 https://doi.org/10.23919/IEN.2022.0048
Article | Open Access | Issue | Published: 20 December 2022

Safety-assured, real-time neural active fault management for resilient microgrids integration

Show Author's Information Wenfeng Wan1,  Peng Zhang1( ),  Mikhail A. Bragin2,  Peter B. Luh2
Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06269, USA
We dedicate this paper to the loving memory of Professor Peter B. Luh. Along with this paper, his vision was to revolutionize the way difficult and important mathematical programming problems are solved through the innovative use of AI and machine learning. Yet, on 11/28/2022 he tragically left the world. Professor Luh will be dearly missed.
Keywords:
federated learning, resilience, Active fault management, microgrids, real-time safety assurance
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Wan W, Zhang P, Bragin MA, et al. Safety-assured, real-time neural active fault management for resilient microgrids integration. iEnergy, 2022, 1(4): 453-462. https://doi.org/10.23919/IEN.2022.0048
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Abstract Full text About this article

Federated-learning-based active fault management (AFM) is devised to achieve real-time safety assurance for microgrids and the main grid during faults. AFM was originally formulated as a distributed optimization problem. Here, federated learning is used to train each microgrid’s network with training data achieved from distributed optimization. The main contribution of this work is to replace the optimization-based AFM control algorithm with a learning-based AFM control algorithm. The replacement transfers computation from online to offline. With this replacement, the control algorithm can meet real-time requirements for a system with dozens of microgrids. By contrast, distributed-optimization-based fault management can output reference values fast enough for a system with several microgrids. More microgrids, however, lead to more computation time with optimization-based method. Distributed-optimization-based fault management would fail real-time requirements for a system with dozens of microgrids. Controller hardware-in-the-loop real-time simulations demonstrate that learning-based AFM can output reference values within 10 ms irrespective of the number of microgrids.


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About this article

Safety-assured, real-time neural active fault management for resilient microgrids integration

Show Author's information Wenfeng Wan1, Peng Zhang1( ), Mikhail A. Bragin2, Peter B. Luh2
Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06269, USA
We dedicate this paper to the loving memory of Professor Peter B. Luh. Along with this paper, his vision was to revolutionize the way difficult and important mathematical programming problems are solved through the innovative use of AI and machine learning. Yet, on 11/28/2022 he tragically left the world. Professor Luh will be dearly missed.

Abstract

Federated-learning-based active fault management (AFM) is devised to achieve real-time safety assurance for microgrids and the main grid during faults. AFM was originally formulated as a distributed optimization problem. Here, federated learning is used to train each microgrid’s network with training data achieved from distributed optimization. The main contribution of this work is to replace the optimization-based AFM control algorithm with a learning-based AFM control algorithm. The replacement transfers computation from online to offline. With this replacement, the control algorithm can meet real-time requirements for a system with dozens of microgrids. By contrast, distributed-optimization-based fault management can output reference values fast enough for a system with several microgrids. More microgrids, however, lead to more computation time with optimization-based method. Distributed-optimization-based fault management would fail real-time requirements for a system with dozens of microgrids. Controller hardware-in-the-loop real-time simulations demonstrate that learning-based AFM can output reference values within 10 ms irrespective of the number of microgrids.

Keywords: federated learning, resilience, Active fault management, microgrids, real-time safety assurance

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Publication history

Received: 23 August 2022
Revised: 10 October 2022
Accepted: 12 October 2022
Published: 20 December 2022
Issue date: December 2022

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Acknowledgements

Acknowledgements

This work was supported in part by the National Science Foundation under Grants No. OIA-2134840 and ECCS-1810108, and in part by Department of Energy under Grant No. DE-EE0009341. This work relates to Department of Navy award N00014-20-1-2858 issued by the Office of Naval Research. The United States Government has a royalty-free license throughout the world in all copyrightable material contained herein.

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Copyright: by the author(s). The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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