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Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Author

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  • Nastaran Gholizadeh

    (Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Petr Musilek

    (Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
    Applied Cybernetics, University of Hradec Králové, 500 03 Hradec Králové, Czech Republic)

Abstract

In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

Suggested Citation

  • Nastaran Gholizadeh & Petr Musilek, 2021. "Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges," Energies, MDPI, vol. 14(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3654-:d:577869
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    References listed on IDEAS

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