Adaptive Multiple MPC for a Wind Farm with DFIG: a Decentralized-Coordinated Approach

Abstract

For low investment and flexible control, doubly fed induction generator (DFIG) is becoming the dominant type that is been used in the wind farms (WFs) today. The report researches about the rotor-side controller of DFIG where all design is based on the single machine infinite bus (SMIB) model. The interactions between the different generators have not been considered in the SMIB model, and the desired performance cannot be obtained by using the controller based on this model. In this situation, an adaptive decentralized-coordinated multiple model predictive control (ADM-MPC) is proposed. First, the interaction measurement method is developed to obtain the interaction measurement model of DFIG, where the interactions between the different generators have been considered. Next, an adaptive multiple MPC based on the obtained interaction measurement method of DFIG is employed to control the rotor-side converter of DFIG. In order to cope with the stochastic disturbance of wind turbine, the augment state structure is employed to improve the tracking control performance. An artificial neural network (ANN) trained online is employed as a weighting controller to cope with the nonlinearities and large operating range of DFIG. A simple, generic renewable power system (RPS) is used to demonstrate contributions. The results of both dominant eigenvalue analysis and time response simulations are represented to illustrate contributions to system damping and transient stability that the DFIG based WF can make with the proposed ADM-MPC controller.