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

Nastaran Gholizadeh and Petr Musilek
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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

Energies, 2021, vol. 14, issue 12, 1-18

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.

Keywords: machine learning; distributed learning; federated learning; assisted learning; power systems; privacy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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