Deep Reinforcement Learning for Optimizing Inverter Control With Fixed and Adaptive Gain Tuning Strategies for Power System Stability

This paper presents novel methods for tuning inverter controller gains using deep reinforcement learning (DRL). A Simulink-developed inverter model is converted into a dynamic-link-library (DLL) and integrated with a Python-based RL environment, leveraging multi-core deployment and accelerated computing to significantly reduce RL training time. A mechanism based on a neural network (NN) is developed to transform the cascaded PI controller into an actor network, allowing optimized gain tuning by an RL agent to mitigate scenarios such as subsynchronous oscillations (SSO) and initial transients. Two distinct tuning approaches are demonstrated: a fixed gain strategy, where controller gains are represented as RL policy (actor network) weights, and an adaptive gain strategy, where gains are dynamically generated as RL policy (actor network) outputs. A comparative analysis of these methods is provided, showcasing their effectiveness in stabilizing the transient performance of grid-forming (GFM) and grid-following (GFL) inverters and discussing deployment challenges in hardware. Experimental results demonstrate the enhanced robustness and practical applicability of the RL-tuned controller gains in real-world systems.