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Recent Developments in Machine Learning for Energy Systems Reliability Management

Duchesne, Laurine ; Karangelos, Efthymios ; Wehenkel, Louis

Proceedings of the IEEE, 2020-09, Vol.108 (9), p.1656-1676 [Periódico revisado por pares]

New York: IEEE

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Título:
Recent Developments in Machine Learning for Energy Systems Reliability Management
Autor: Duchesne, Laurine ; Karangelos, Efthymios ; Wehenkel, Louis
Assuntos: Distributed generation ; Electric power systems ; Electric power systems (EPSs) ; Electrical & electronics engineering ; Engineering, computing & technology ; Ingénierie électrique & électronique ; Ingénierie, informatique & technologie ; Machine learning ; machine learning (ML) ; Management ; Power system control ; Power system dynamics ; Power system reliability ; Power system stability ; reliability ; Reliability analysis ; Reliability engineering ; Security ; Security assessment ; Security control ; System reliability
É parte de: Proceedings of the IEEE, 2020-09, Vol.108 (9), p.1656-1676
Notas: scopus-id:2-s2.0-85088917964
Descrição: This article reviews recent works applying machine learning (ML) techniques in the context of energy systems' reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of ML. The objective is to foster the synergy between these two fields and speed up the practical adoption of ML techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, microgrids, and multienergy systems.
Editor: New York: IEEE
Idioma: Inglês

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Recent Developments in Machine Learning for Energy Systems Reliability Management

Duchesne, Laurine ; Karangelos, Efthymios ; Wehenkel, Louis

New York: IEEE

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