Boosting Reinforcement Learning for Tertiary Voltage Control by Transfer Learning from a Supervised Power Flow Simulation Task
Year: 2025
Type: conference-paper
Abstract: This work explores how transfer learning can improve reinforcement learning for tertiary voltage control, which is a simulation-intensive process. The model is pre-trained over the supervised learning of a power flow simulator, as a way of incorporating physics inside the model. Two transfer strategies are proposed and compared against a transfer-free baseline. The case60nordic test case, which provides diversified operating conditions, including topological variations, is used to assess performance. Results indicate that fine-tuning the pre-trained model can effectively improve performance or reduce training time on the target task. This study emphasizes the potential of transfer learning for accelerating the training of power grid control downstream tasks.
Institution: University of Liège
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7117623723