Abstract: The doubly-fed induction generator（DFIG）is the mainstream model in the wind power market.

Since there are multiple time-scale control objectives in the wind turbine，it is difficult for conventional linear

control methods to optimize the multi-objective. Model predictive control（MPC）is an effective method for wind

turbine control due to the high dynamic response performance and multi-objective optimization capacity. However，model predictive control achieves multi-objective optimization through a single cost function，which leads to the coupling of the control objectives，making it difficult to design the weight factors and determine the control priority.Therefore，a dynamic sequential model predictive control（DSMPC）strategy was proposed，which a single cost function was replaced by an optimization structure composed of multiple cascade cost functions. The control objectives were ranked according to the average value of all cost functions of each control objective，and the priority was dynamically adjusted. In addition，the control threshold was used to adjust the number of candidate switch vectors from the control pre-stage to the control post-stage. This method realized the overall optimal control of multiple objectives without using weight factors. The effectiveness of the proposed method was proved by the

hardware-in-the-loop results.

      Key words: doubly-fed induction generator（DFIG）；dynamic sequential model predictive control（DSMPC）；

dynamic priority；control threshold