*5.1. Results*

Figure 11 shows a selection of representative time series from a baseline simulation and a simulation with active IPC strategy. Both simulations use the TUB Controller with the same controller parameters. The baseline simulation uses the CPC strategy and the IPC simulation uses the CPC and IPC strategies. In this figure we can see the effect of the IPC strategy on the controller signals and on the *M*BR *<sup>Y</sup>* loads. From Figures 11b,e it can be seen that the influence of the IPC strategy on Ω and *P* is small. This is mainly due to the frequency separation between the CPC and IPC strategies. Figure 11c shows the time series of *θ*1. We can clearly see the 1P oscillation of the IPC strategy on top of the CPC pitch signal. The load reducing effect of this strategy can be seen in Figure 11d at around 700 s of simulation time. The 1P variation of *M*BR *<sup>Y</sup>* are clearly reduced due to the additional pitch actuation. The below-rated power limitation of the IPC strategy can be seen in Figure 11c around 650 s of simulation time. When the mean pitch angle decreases to 0◦, the amplitude of the IPC signal is also reduced 0◦ to maximize power capture.

**Figure 11.** Time series selection of simulations with and without Individual Pitch Control (IPC) strategy from the 12 m/s wind speed bin. (**a**) Wind speed; (**b**) Rotor speed; (**c**) Blade 1 pitch angle; (**d**) Out-of-plane BRBM blade 1; (**e**) Generator power. Base = Collective Pitch Control (CPC) strategy.

## 5.1.1. Controller Signals

The effect of the IPC strategy on the controller signals can be seen in Figure 12. It shows the normalized averaged standard variations of Ω, *θ* and *P* respectively. Figure 12a shows that the normalized *σ*¯(Ω) of the IPC strategy is practically 1 for all wind speed bins except the bins corresponding to 12 and 14 m/s average speed. There we see that Ω from the IPC simulations oscillates less than Ω from the CPC simulations. This can be understood if we look at Figure 11b. The additional 1P fluctuation of *θ* does influence the value of Ω by reducing slightly the sensitivity of the rotor to changes in the wind speed. This is particularly marked around rated wind, where the pitch angle is close to 0◦ and there are high angles of attack in the outer span of the blades. The latter lead to large values of lift forces and changes in the lift force due to pitching have an increased effect on rotor thrust and torque.

Figure 12b shows the normalized *σ*¯(*θ*) vs. the wind speed bin. This is the controller signal that shows the largest differences. From 10 m/s onward, the value of *σ*¯(*θ*) increases up to values larger than 1.2. It is this region where the IPC strategy starts to function. The increase in pitch activity from the IPC strategy is directly linked to the fact that the 1P fluctuation of *M*BR *<sup>Y</sup>* increases with increasing wind speed [36].

Finally, Figure 12c shows the normalized values of *σ*¯(*P*). Here, there is almost no difference between the two strategies. The lower value in the IPC simulations seen in the wind speed bin of 14 m/s comes from the relatively small reference value of *σ*¯(*P*) for the CPC calculations. Because *P* is fairly constant in above-rated wind simulations, differences in the drop of *P* for temporarily low wind speeds (as seen in Figure 11e) will have a large effect on the normalized *σ*¯(*P*).

**Figure 12.** Averaged standard deviations of controller signals vs. wind speed. (**a**) Rotor speed; (**b**) Pitch angle; (**c**) Generator power. Nomenclature of the signals is found in Table 5. Base = CPC strategy.

#### 5.1.2. Fatigue Loads

Figure 13 shows the normalized lifetime DELs of the simulations with the CPC and the IPC strategies. We can see that the main effect of the IPC strategy is to reduce the lifetime DELs of *M*BR *<sup>Y</sup>* and *<sup>M</sup>*BR *<sup>Z</sup>* by 14% and 9.4% respectively. There are also smaller effects on other load sensors. The lifetime DELs of *M*BR *<sup>X</sup>* are 2.4% lower in the simulations with the IPC strategy compared to the CPC strategy. The load differences for these three sensors come directly from the IPC strategy. Since the strategy is minimizing the yaw and tilt moments of the rotor, it indirectly minimizes the load fluctuations of the individual blade root moments. These fluctuations arise from oscillations of the effective lift force on the individual blades. So, indirectly, the IPC strategy is also minimizing the fluctuation of the effective lift force on the individual blades. Although the out-of-plane component dominates in the lift force composition, there is also a smaller in-plane component. The former affects *M*BR *<sup>Y</sup>* and the latter *M*BR *<sup>X</sup>* . Reducing the lift fluctuations also reduces the pitching moment oscillations and oscillations in the out-of-plane deflection of the blades. These two quantities directly affect *M*BR *<sup>Z</sup>* . So by reducing the lift force fluctuation, all three load sensors are affected.

**Figure 13.** Normalized lifetime Damage Equivalent Loads (DELs) for considered sensors. Nomenclature is found in Table 5. Base = CPC strategy.

The yaw bearing sensors are practically unaffected by the IPC strategy. This is due to the nature of our metric. The 1P based IPC strategy focuses on reducing the steady or slow varying yaw and tilt moments of the rotor. Our calculation of the DELs does not consider the means but only the ranges of the load cycles. A reduction of the load averages is therefore not captured by our metric. An IPC strategy that additionally targets the 2P load fluctuations would also reduce the yaw bearing DELs, ([10,20], (pp. 501–503)). The tower base lifetime DELs show that the IPC strategy also has a small impact on the tower fatigue loads. The IPC strategy increases the *M*TB *<sup>X</sup>* DELs by 1.5% and decreases the *<sup>M</sup>*TB *<sup>Y</sup>* DELs by 0.9% compared to the CPC strategy.

## **6. Conclusions**

This paper presented and detailed TUBCon, an advanced open-source wind turbine controller that includes pitch, torque and supervisory controllers. The controller can therefore be used to perform a complete load calculation according to industry standards. It is compatible with the common aeroelastic simulation codes including QBlade, which features the higher order LLFVW aerodynamic method in addition to a standard unsteady BEM aerodynamic model.

TUBCon was validated against an established turbine controller from the literature by comparing aeroelastic simulations of the DTU 10 MW RWT in steady and turbulent wind conditions. For steady wind conditions, the controllers show practically the same performance. For turbulent wind conditions, TUBCon shows a more aggressive constant power regulation. This reduces the mean standard deviation of the generator power for the above-rated wind bins by up to 70%, but increases the standard deviation of the rotor speed by up to 3.7%. This difference also affects the lifetime DELs of the tower base bending moments. The lifetime DELs of *M*TB *<sup>X</sup>* and *<sup>M</sup>*TB *<sup>Y</sup>* are increased in the TUBCon simulations by 3% and 2% respectively.

We also investigated the advanced load reduction capabilities of TUBCon by comparing the performances of the IPC strategy against a baseline CPC strategy. This was done using QBlade's LLFVW aerodynamic method, which is able to calculate the aerodynamic effects of a non-homogeneous induction field due to individual pitching more accurately compared to most BEM-based aerodynamic methods. The results show that the IPC strategy is able to reduce the lifetime DELs of *M*BR *<sup>Y</sup>* and *<sup>M</sup>*BR *<sup>Z</sup>* by 14% and 9.4% respectively when compared to the baseline simulations. This comes at the cost of increased pitch activity from the IPC controller. The normalized *σ*¯(*θ*) of the IPC strategy increase to values up to 22.6% higher than the values of the CPC strategy for wind speed bins above rated wind speed.

Future work will include further development of TUBCon to add new features such as tower vibration damping and rotor speed exclusion. It is also planned to add a fully featured trailing edge flap controller and the necessary features so that TUBCon can be used as a controller in offshore floating wind turbine simulations. Furthermore, the controller will be extended to include power curtailment and wake steering capabilities so that it can be used in conjunction with a wind farm controller. In addition, the effect of QBlade's LLFVW aerodynamic model on advanced controller performance will be further analyzed by comparing the results from LLFVW-based simulations to more common BEM-based simulations and considering more DLC groups.

**Author Contributions:** Conceptualization, S.P.-B.; methodology, S.P.-B. and D.M.; software, S.P.-B. and D.M.; validation, S.P.-B. and D.M.; formal analysis, S.P.-B.; investigation, S.P.-B. and D.M.; resources, C.N.N. and C.O.P.; data curation, S.P.-B. and D.M.; writing–original draft preparation, S.P.-B.; writing—review and editing, D.M., C.N.N. and C.O.P.; visualization, S.P.-B.; supervision, C.N.N. and C.O.P.; project administration, C.N.N. and C.O.P.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** TUBCon and QBlade are open-source codes available online. The latest version of QBlade is available at https://www.qblade.org. The latest version of TUBCon is available at https://github.com/s-perez-becker/TUBCon. The time series for the load calculations are stored in the HAWC2 binary format. They can be made available upon request.

**Acknowledgments:** SPB wishes to thank WINDnovation Engineering Solutions GmbH for supporting his research.

**Conflicts of Interest:** The authors declare no conflict of interest.
