Wind turbines power regulation using a low-complexity linear parameter varying-model predictive control approach

Abstract

In this paper, the model predictive control (MPC) approach is utilized to stabilize the output power of the wind turbines at the region above the rated wind speed. The controller is designed based on two different approaches and results have been compared. First, by putting the advantages of the MPC approach into practice, the optimal output power regulation of the wind turbine is obtained using a control oriented linear parameter varying (LPV) model of the wind turbine. However, this method inherently requires high computational cost and thus powerful hardware and processors. To cope with this limitation, an efficient suboptimal approach is proposed that significantly reduces the online computational complexity of the controller. In this approach, the main part of the controller design procedure is done off-line prior to the closed-loop wind turbine power generation and a set of optimal controllers were designed using the MPC scheme. Then, a convex combination of the calculated controllers is used for online power regulation of the wind turbine. It is noted that the selected wind turbine is a horizontal axis wind turbine operating at various speeds ranging from 10-25 m/s. Finally, using a set of simulation results we investigate the performance of the proposed approach.