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An Unsupervised Neural Network Approach for Solving the Optimal Power Flow Problem
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering.ORCID iD: 0000-0002-2028-9847
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering.ORCID iD: 0000-0003-3111-4820
2023 (English)In: Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO / [ed] Gini G., Nijmeijer H., Filev D., SciTePress, 2023, Vol. 1, p. 214-220Conference paper, Published paper (Refereed)
Abstract [en]

Optimal Power Flow is a central tool for power system operation and planning. Given the substantial rise in intermittent power and shorter time windows in electricity markets, there’s a need for fast and efficient solutions to the Optimal Power Flow problem. With this in consideration, this paper propose an unsupervised deep learning approach to approximate the optimal solution of Optimal Power Flow problems. Once trained, deep learning models benefit from being several orders of magnitude faster during inference compared to conventional non-linear solvers.

Place, publisher, year, edition, pages
SciTePress, 2023. Vol. 1, p. 214-220
Series
Proceedings of the International Conference on Informatics in Control, Automation and Robotics, ISSN 2184-2809
National Category
Control Engineering
Research subject
Physics, Electrotechnology
Identifiers
URN: urn:nbn:se:lnu:diva-129963DOI: 10.5220/0012187400003543Scopus ID: 2-s2.0-85181567286OAI: oai:DiVA.org:lnu-129963DiVA, id: diva2:1865752
Conference
20th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2023, Rome, 13-15 November 2023
Available from: 2024-06-05 Created: 2024-06-05 Last updated: 2024-06-28Bibliographically approved

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Marcial, AlexanderPerninge, Magnus

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