Distributed Learning Applications in Power Systems

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ECE doctoral student Nastaran Gholizadeh has just published a Review of Distributed Learning Applications in Power Systems in MDPI Energies. Working with professor Petr Musilek, they provide an overview of distributed learning architectures, survey the most important applications of this modern machine learning approach, and identify the most significant gaps and challenges for future research.

Their open-access article can be accessed here: https://www.mdpi.com/1996-1073/14/12/3654

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.

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