# Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction
## 1 Introduction
However, the controller failed with high turbulence intensity which was about 0.21. This was because, with high turbulence intensity, the controller could not distinguish power variations due to wind speed change from those due to wind farm control. More recently, the concept of power increase by regulating the power of the most upstream wind turbine was reinvestigated with a relatively high fidelity wind farm simulation model [16]. In the research, it was found that the power partialization percentage, defined as the power output to the available power of the most upstream wind turbine to achieve the maximum power of a wind farm, is a function of the wind turbine spacing but is not much affected by the wind speed. Therefore, the power regulation of the upstream wind turbine for power maximization is achieved not by a constant blade pitch angle but by a constant power partialization of the most upstream wind turbine. This means that the blade pitch angle of the most upstream wind turbine must be adjusted when the wind speed varies. In the study, a power increase of 4.1% was observed with ten wind turbines in series with a spacing of 4 RD when the partialization of the most upstream wind turbine was 0.825. However, in that study, the wind was not a turbulent wind but a constant wind, and the study did not include any wind farm control algorithm, but did include results with manual power adjustments of the most upstream wind turbine for better understanding of power increase mechanism. There have also been conflicting results in the literature that the active induction of the most upstream wind turbine does not increase the overall power output from the wind farm [17; 18]. They are mostly based on the simulation results with the LES (Large Eddy Simulation) method. [PERSON] et al. [17] applied the LES to simulate the Lillgrund wind farm and showed the simulation results with the ambient turbulence intensity of 5.6% that the manual blade pitching of the first wind turbine by multiples of 2\({}^{\circ}\) does not increase the total power. However, in their study, the wind turbine dynamic and control was not considered. In addition, [PERSON] et al. [18] applied the high fidelity simulation tool, SOWFA (Simulation fOr Wind Farm Applications) to investigate the power increase of a small wind farm of five wind turbines by power curtailment. However, the manual blade pitching of the most upstream wind turbine did not increase the total farm power, but rather decreased it approximately by 8.9%. In their simulation results with five wind turbines in series, the powers of all four wind turbines except the fifth one decreased with the power curtailment of the most upstream wind turbine. They concluded that for the total power increase, a suitable wind farm control is necessary. [PERSON] and [PERSON] [PERSON] and [PERSON] (2014) applied an optimal control strategy to the LES simulation with an actuator disk model to investigate the total power increase of a wind farm with 50 wind turbines. Unlike other LES simulations with the fixed blade pitching, they found the power increase of 16% compared with the uncontrolled case. However, the wind turbine dynamics and power control algorithms of wind turbines including blade pitch (most upstream wind turbine only) and generator torque (all wind turbines) controls were not considered in their study. In this study, the previous study [PERSON] and [PERSON] (2014) was further expanded to develop a model based wind farm control method with a simple but effective open-loop wind farm control algorithm based on a function minimization method. The command from the wind farm controller to the wind turbine was the active power of the wind turbine and the wind farm control algorithm was validated with the relatively high fidelity wind farm model constructed previously ([PERSON] and [PERSON], 2014) but modified for this study. The originality of this study compared with the previous research can be summarized by the following factors.