Reinforcement Learning for Adaptive Protection in Power Grids

Abstract

This thesis explores the use of reinforcement learning to improve protection strategies in power grids with high penetration of renewable energy. Conventional relay schemes often fail under changing fault conditions caused by inverter-based DERs. This thesis investigates how adaptive, data-driven control can overcome these challenges. A simulated environment based on DIgSILENT PowerFactory enables comparison between traditional protection and learning-based approaches.