Reinforcement learning tuned decentralized synergetic control of power systems

In this paper, decentralized synergetic controllers with varying parameters are developed to dampen oscillations in electric power systems via the excitation systems of the generators. Each generator is treated as a subsystem for which a synergetic controller is designed. Each subsystem is a dynamical system driven by a function that estimates the effect of the rest of the system. A particle swarm optimization (PSO) technique is employed to initialize the controllers' gains. Then, reinforcement learning (RL) is used to vary the gains obtained after implementing the PSO so as to adapt the system to various operating conditions. Simulation results for a two area power system indicate that this technique gives a better performance than synergetic fixed gains controllers, or conventional power system stabilizers. Simulation results are obtained using the power analysis toolbox (PAT). (C) 2011 Elsevier B.V. All rights reserved.