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Reinforcement learning for distributed transient frequency control with stability and safety guarantees
Z. Yuan, C. Zhao, J. Cortés
Systems and Control Letters 185 (2024), 105753

Abstract

This paper proposes a reinforcement learning-based approach for optimal transient frequency control in power systems with stability and safety guarantees. Building on Lyapunov stability theory and safety-critical control, we derive sufficient conditions on the distributed controller design that ensure the stability and transient frequency safety of the overall system. These conditions characterize the search space of control policies for our learning approach. We construct neural network controllers that parameterize such control policies and use reinforcement learning to train an optimal one. Simulations on the IEEE 39-bus network illustrate the guaranteed stability and safety properties of the controller along with its significant reduction in cost.

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