Real-time transmission switching with neural networks
Al Amin B. Bugaje (Imperial College London)
Jochen L. Cremer (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Goran Strbac (Imperial College London)
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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.
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