Multi-machine power system control based on dual heuristic dynamic programming
Zhen Ni, Yufei Tang, Haibo He, Jinyu Wen · 2014
In this paper, we integrate a goal network into the existing dual heuristic dynamic programming (DHP) architecture, and study its damping performance on the multi-machine power system. There are four types of neural network in our proposed design: a goal network, a critic network, an action network and a model network. The motivation of this design is to build a general mapping between the system variables and the partial derivatives of the utility function, so that these required derivatives can be directly obtained and adaptively tuned over time. However, the existing DHP design can only obtain a predefined (fixed) external utility function (or its derivatives). We apply both the proposed approach and the existing DHP approach on the multi-machine power system, and compare the damping performance on a four-machine two-area power system. The simulation results demonstrate the improved control performance with the proposed design.