Finite Control Set Model Free Predictive Control of DFIG-DC Based on Data Driven NN Predictors

This paper presents a finite control set model free predictive control method for doubly fed induction generator for direct current generation system. The system model is fully unknown, and only the input and output data are measured. Firstly, data-driven neural network predictors are proposed to reconstruct the system model. Filtered data of system states are recorded and used to update the neural network parameters. The system of the data-driven NN predictors is proved to be input-to-state stable. Then, a finite control set model free predictive controller is designed based on the perdiction model. The optimal control actions are selected based on the receding horizon optimization with a finite control set. Simulation results show the effectiveness of the proposed model free predictive control of doubly fed induction generator based on data-driven neural network predictors.