Model Predictive Control For DFIG-Based Wind Power Generation Under Unbalanced Grid Conditions

Recently, with the wind power generation continues to expand, and the proportion of wind power in power energy structure is increasing year by year, the impact on power system becomes increasingly significant. In a wind power sources incorporated modern power system, the control of the doubly-fed induction generator (DFIG) is quite important to ensure high load-following capability. It can be quite challenging when grid unbalance conditions happened. Model predictive control (MPC) is an advanced control strategy which can optimize the DFIG operation, particularly under the unbalanced grid conditions. Compared with other algorithms, The distinguished feature of MPC is that it can predict the inputs and outputs in the future by the history information based on a system model and handle constraints.The detailed modeling in the positive and negative synchronous reference frames resulting from the grid unbalance is presented in nonlinear state-space form after analyzing the reasons of unblanced grid. Then an unbalanced control scheme based on rotor current is presented that makes the positive and negative sequence components of rotor current have their calculation criterion. A nonlinear MPC is derived for DFIG control, based on the input-output feedback linearization (IOFL) technique. The MPC is derived by optimization of two targets that aims to eliminate active or reactive power pulsation at twice the line frequency to realize constant output active and reactive power. The simulation results show that power pulsations is effectively suppressed under unbalanced grid condition while ensuring the good track performance.