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A meta-learning approach to the optimal power flow problem under topology reconfigurations
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Chen, Yexiang, Lakshminarayana, Subhash, Maple, Carsten and Poor, H. Vincent (2022) A meta-learning approach to the optimal power flow problem under topology reconfigurations. IEEE Open Access Journal of Power and Energy, 9 . pp. 109-120. doi:10.1109/OAJPE.2022.3140314 ISSN 2687-7910.
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WRAP-A-meta-learning-optimal-power-flow-topology-reconfigurations-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2763Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/OAJPE.2022.3140314
Abstract
Recently there has been a surge of interest in adopting deep neural networks (DNNs) for solving the optimal power flow (OPF) problem in power systems. Computing optimal generation dispatch decisions using a trained DNN takes significantly less time when compared to conventional optimization solvers. However, a major drawback of existing work is that the machine learning models are trained for a specific system topology. Hence, the DNN predictions are only useful as long as the system topology remains unchanged. Changes to the system topology (initiated by the system operator) would require retraining the DNN, which incurs significant training overhead and requires an extensive amount of training data (corresponding to the new system topology). To overcome this drawback, we propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The key idea behind this approach is to find a common initialization vector that enables fast training for any system topology. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems. The results show that the MTL approach achieves significant training speed-ups and requires only a few gradient steps with a few data samples to achieve high OPF prediction accuracy and outperforms other pretraining techniques.
| Item Type: | Journal Article | |||||||||
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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| Library of Congress Subject Headings (LCSH): | Power resources, Electric power systems , Electric power systems -- Control, Electric network topology, Neural networks (Computer science) | |||||||||
| Journal or Publication Title: | IEEE Open Access Journal of Power and Energy | |||||||||
| Publisher: | IEEE | |||||||||
| ISSN: | 2687-7910 | |||||||||
| Official Date: | 4 January 2022 | |||||||||
| Dates: |
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| Volume: | 9 | |||||||||
| Page Range: | pp. 109-120 | |||||||||
| DOI: | 10.1109/OAJPE.2022.3140314 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 27 January 2022 | |||||||||
| Date of first compliant Open Access: | 27 January 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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