Real‐time transmission switching with neural networks
Abstract The classical formulation of the transmission switching problem as a mixed‐integer problem is intractable for large systems in real‐time control settings. Several heuristics have been proposed in the past to speed up the computation time, which only limits the number of switchable lines. In...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-02-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12698 |
| _version_ | 1811173467929706496 |
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| author | Al‐Amin B. Bugaje Jochen L. Cremer Goran Strbac |
| author_facet | Al‐Amin B. Bugaje Jochen L. Cremer Goran Strbac |
| author_sort | Al‐Amin B. Bugaje |
| collection | DOAJ |
| description | Abstract The classical formulation of the transmission switching problem as a mixed‐integer problem is intractable for large systems in real‐time control settings. Several heuristics have been proposed in the past to speed up the computation time, which only limits the number of switchable lines. In this paper, a real‐time switching heuristic based on neural networks that provides almost instantaneous switching actions, are presented. The findings are shown on case studies of the IEEE 118‐bus test system, and the results show that the proposed heuristic is robust to out of distribution data. Additionally, the proposed heuristic has significant computational savings while all other performance metrics like accuracy are similar to state‐of‐the‐art machine learning methods proposed for transmission switching. |
| first_indexed | 2024-04-10T17:48:29Z |
| format | Article |
| id | doaj.art-4c5fa994db9e45db935972e089bebebd |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-10T17:48:29Z |
| publishDate | 2023-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-4c5fa994db9e45db935972e089bebebd2023-02-03T04:08:35ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952023-02-0117369670510.1049/gtd2.12698Real‐time transmission switching with neural networksAl‐Amin B. Bugaje0Jochen L. Cremer1Goran Strbac2Department of Electrical and Electronic Engineering Imperial College London London UKDepartment of Electrical Sustainable Energy TU Delft Delft The NetherlandsDepartment of Electrical and Electronic Engineering Imperial College London London UKAbstract The classical formulation of the transmission switching problem as a mixed‐integer problem is intractable for large systems in real‐time control settings. Several heuristics have been proposed in the past to speed up the computation time, which only limits the number of switchable lines. In this paper, a real‐time switching heuristic based on neural networks that provides almost instantaneous switching actions, are presented. The findings are shown on case studies of the IEEE 118‐bus test system, and the results show that the proposed heuristic is robust to out of distribution data. Additionally, the proposed heuristic has significant computational savings while all other performance metrics like accuracy are similar to state‐of‐the‐art machine learning methods proposed for transmission switching.https://doi.org/10.1049/gtd2.12698artificial intelligencepower transmission controlreal‐time systems |
| spellingShingle | Al‐Amin B. Bugaje Jochen L. Cremer Goran Strbac Real‐time transmission switching with neural networks IET Generation, Transmission & Distribution artificial intelligence power transmission control real‐time systems |
| title | Real‐time transmission switching with neural networks |
| title_full | Real‐time transmission switching with neural networks |
| title_fullStr | Real‐time transmission switching with neural networks |
| title_full_unstemmed | Real‐time transmission switching with neural networks |
| title_short | Real‐time transmission switching with neural networks |
| title_sort | real time transmission switching with neural networks |
| topic | artificial intelligence power transmission control real‐time systems |
| url | https://doi.org/10.1049/gtd2.12698 |
| work_keys_str_mv | AT alaminbbugaje realtimetransmissionswitchingwithneuralnetworks AT jochenlcremer realtimetransmissionswitchingwithneuralnetworks AT goranstrbac realtimetransmissionswitchingwithneuralnetworks |