Reinforcement Learning for Dynamic Event-Driven Control of Multi-Machine Power Systems

Abstract

This brief investigates a decentralized event-driven control (EDC) problem of multi-machine power systems having asymmetric constraints imposed on inputs. Initially, the decentralized input-constrained EDC problem is transformed into a set of input-unconstrained optimal EDC subproblems by introducing enhanced cost functions for nominal subsystems. Then, with the construction of dynamic event-triggering mechanisms, the event-driven Hamilton-Jacobi-Bellman equations (ED-HJBEs) are derived for these subproblems. To approximately solve these ED-HJBEs, only critic neural networks are utilized in the reinforcement learning framework, and their weights are updated via the gradient descent approach. After that, based on Lyapunov method, uniform ultimate boundedness of the closed-loop multi-machine power systems is established. Finally, simulations are conducted on a two-machine power system to validate the developed decentralized EDC policy.