Real‐time transmission switching with neural networks. Issue 3 (29th December 2022)

• Record Type: Journal Article
• Authors: Bugaje, Al‐Amin B.; Cremer, Jochen L.; Strbac, Goran
• Is Part Of: IET generation, transmission & distribution. Volume 17:Issue 3(2023)
• Page Start: 696
• Page End: 705
• Publication Date: 2022-12-29
• Subjects: artificial intelligence -- power transmission control -- real‐time systems; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/gtd2.12698
• Languages: English
• ISSNs: 1751-8687

Abstract

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. Abstract : This paper presents a real‐time switching heuristic based on Neural Networks that provides almost instantaneous switching actions. The key advantage over other approaches to performing real‐time transmission switching is its real‐time computational time whereas the alternative approach uses mixed‐integer optimisation which takes too long to run in real‐time. We show our findings 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.