Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges
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 l...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/12/3654 |
| _version_ | 1797529537482850304 |
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| author | Nastaran Gholizadeh Petr Musilek |
| author_facet | Nastaran Gholizadeh Petr Musilek |
| author_sort | Nastaran Gholizadeh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:15:14Z |
| format | Article |
| id | doaj.art-11d05078c4f7417881cc8f8557bf090e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T10:15:14Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-11d05078c4f7417881cc8f8557bf090e2023-11-22T00:49:03ZengMDPI AGEnergies1996-10732021-06-011412365410.3390/en14123654Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and ChallengesNastaran Gholizadeh0Petr Musilek1Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaElectrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaIn 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.https://www.mdpi.com/1996-1073/14/12/3654machine learningdistributed learningfederated learningassisted learningpower systemsprivacy |
| spellingShingle | Nastaran Gholizadeh Petr Musilek Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges Energies machine learning distributed learning federated learning assisted learning power systems privacy |
| title | Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges |
| title_full | Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges |
| title_fullStr | Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges |
| title_full_unstemmed | Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges |
| title_short | Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges |
| title_sort | distributed learning applications in power systems a review of methods gaps and challenges |
| topic | machine learning distributed learning federated learning assisted learning power systems privacy |
| url | https://www.mdpi.com/1996-1073/14/12/3654 |
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