**1. Introduction**

Over the past years, wind turbines have increased significantly in size in an effort to reduce the cost of wind energy and make it a competitive source of energy. So far, this has been a successful approach. An example of this success can be seen in the increased installation numbers of new wind turbines in Germany [1]. Yet increasing the turbine size also has its drawbacks. A larger rotor will be subjected to higher aerodynamic and gravitational loads. For example, the aerodynamic bending moments and the moments due to the self-weight of the blade scale as the third and fourth power of the rotor diameter respectively ([2], (pp. 97–123)). As a consequence, the structure of the turbine components such as rotor blades has to be stiffer, which requires more or stronger material. This leads to an increase in the cost of energy. A way of counteracting the load increase seen in larger wind turbines is through the use of advanced wind turbine controllers. This allows for less material use in the design of the different components and hence results in a decrease of the cost of energy.

The most common actuator used for load alleviation is the blade pitch actuator. This comes from it being already used in the power regulation strategy of modern turbines. Many advanced load alleviation strategies using pitch actuators have been proposed. One of the best-known ones is the Individual Pitch Control (IPC) strategy [3], which commonly relies on the out-of-plane Blade Root Bending Moments (BRBM) of the individual blades as input signals. It has been used in combination with different input sensors—such as inflow sensors [4,5]—and also in combination with different actuators—such as active

**Citation:** Perez-Becker, S.; Marten, D.; Nayeri, C.N.; Paschereit, C.O. Implementation and Validation of an Advanced Wind Energy Controller in Aero-Servo-Elastic Simulations Using the Lifting Line Free Vortex Wake Model. *Energies* **2021**, *14*, 783. https://doi.org/10.3390/en14030783

Academic Editor: Robert Castilla Received: 6 January 2021 Accepted: 28 January 2021 Published: 2 February 2021

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trailing edge flaps [6–9]. Other studies have studied advanced model-based [10] or adaptive [11] controllers for load alleviation. A controller that uses neural networks as part of its architecture has also been recently proposed in [12].

Oftentimes, the results of these studies are difficult to compare and reproduce. This is partly because many research groups use self-developed baseline controller strategies as a basis for load reduction comparative studies. The source code of these controllers is rarely available. Alternatively, studies that use the NREL 5 MW Reference Wind Turbine (RWT) [13] also use the baseline controller available in the model definition. This is an older controller—based on [14,15]—that offers limited functionality and is inconvenient to use with other turbine models. This is because the controller parameters are hard-coded in the source file and the controller has to be recompiled every time the parameters change.

As part of the UpWind Project, a more advanced baseline controller was developed which allows for better power tracking and smoother transitions between the operating regions. These features lead to better energy capture and to reduced loading compared to the NREL 5 MW controller. Many of the aforementioned features are also implemented in the Basic DTU Wind Energy Controller [16].

In recent years, the wind energy community has started to address the problem of reproducible controller results by introducing several modern, open-source reference wind turbine controllers. Mulders and van Wingerden publish the Delft Research Controller (DRC) [17], which is expanded by Abbas et al. into NREL's Reference Open-Source Controller (ROSCO) [18]. Meng et al. also extend the Basic DTU Wind Controller to form the DTU Wind Energy Controller (DTUWEC), which includes advanced industrial features [19].

All of the cited reference controllers use classical controller architectures such as PID controllers. As discussed in ([20], (pp. 506–518)), there are several reasons for this choice. An important one is that with classical controller architectures, controller stability can be guaranteed. This is of utmost importance since modern large wind turbines are expected to operate reliably under all circumstances with minimum supervision. Another reason is that individual features can be added to the controller without the need to re-calibrate the whole controller. Also, the integration of a supervisory controller is much more straightforward when classical controller architectures are used. Other control techniques such as neural networks are powerful techniques that could potentially be used to solve specific control objectives. They are therefore well suited to be part of advanced controller features. They are however less appropriate as candidates for reference baseline controllers. Because of their black-box model nature, it is difficult to guarantee their stability, add specific features or integrate them with supervisory controllers.

Having a reference controller is one aspect of accurate and comparable load reduction estimation. In order to have a complete picture of the design loads of different wind turbine components, we need to simulate the wind turbine in realistic aeroelastic scenarios that often include controller faults or other unforeseen events. Current industry standards prescribe a large number of aeroelastic simulations of the complete turbine [21]. These are grouped together into Design Load Case (DLC) groups, each considering a particular scenario in the wind turbine's design life. Most of the aforementioned studies only include a selection of DLC groups, such as the power production DLC group. While being a good approximation for certain component's loads, using a selection of DLCs will not give a complete picture of the load reduction capabilities of the different turbine controllers. This is in part because many of the available wind turbine controllers do not feature a supervisory controller. The latter oversees the pitch and torque controller and reacts to unforeseen events, shutting down if threshold values of certain signals are passed in order to ensure the structural integrity of the turbine components. Only a full load calculation according to industry standards will give an accurate estimate of the load reduction capabilities of advanced controller strategies.

Another important aspect for accurate load estimation is the use of appropriate models in the aeroelastic simulations. Current aeroelastic codes mostly rely on the Blade Element Momentum (BEM) aerodynamic model to calculate aerodynamic loads ([20], (pp. 57–66)). BEM models are attractive because they are computationally inexpensive. Yet in order to capture the more challenging unsteady aerodynamic phenomena present in DLCs, BEM models require a series of engineering corrections. These corrections have been developed and tested so that they work for a wide range of operating conditions. If the wind turbine operates in a condition outside this range, then the BEM corrections could introduce inaccuracies and overestimate the aerodynamic loading on the turbine. Examples of this include turbine operation in extreme yawed conditions or inflow conditions that are inhomogeneous across the rotor. This could arise if the turbine is operating in the partial wake of another turbine, in sheared and/or turbulent inflow due to the Earth's boundary layer or if there is a large difference in the individual pitch angles of the blades (e.g., with pitch actuator faults) [22–25].

Vortex methods such as the Lifting Line Free Vortex Wake (LLFVW) aerodynamic model have a higher order representation of the unsteady aerodynamic phenomena and are capable of modeling these with far fewer assumptions than BEM models [26]. This is because in the LLFVW method the wake is explicitly modeled. In [27] the authors show that there are significant differences in fatigue and extreme loading if the aeroelastic simulations are performed with a BEM aerodynamic model compared to a LLFVW model, with the BEM model predicting increased fatigue loads. If we wish to accurately asses the load reduction potential of advanced control strategies based on individual pitch action, we require an accurate representation of the local aerodynamic effects that occur on each blade. Since the advanced controller action will be individual on each blade, the resulting induction field will be non-homogeneous. This in turn requires a higher-order aerodynamic model such as the LLFVW model to accurately estimate the effect of the resulting aerodynamic loads.

In this study, we address the aforementioned issues by introducing the TUB Controller (TUBCon). It is an open-source reference wind turbine controller with advanced load reduction strategies that features a complete supervisory controller. It can therefore be used to perform a full load calculation so that an accurate load picture of the turbine loads is obtained. Furthermore, this controller is fully compatible with the aeroelastic software QBlade [28]—which features the LLFVW aerodynamic model. In combination, QBlade and TUBCon can be used to accurately calculate wind turbine loads and also evaluate the performance of advanced load reduction strategies. We give an example of this by analyzing the load reduction capabilities of the well-known IPC strategy in power production with turbulent wind conditions. This paper is structured as follows: In Section 3 we present and fully describe TUBCon, including its advanced load reduction strategy and supervisory controller. In Section 4, we validate TUBCon in steady and turbulent wind conditions by comparing its performance to an established wind turbine controller from the literature. In Section 5 we analyze TUBCon's advanced load reduction capabilities in turbulent wind conditions by simulating a reference wind turbine using the baseline and IPC variants of TUBCon. Conclusions are drawn in Section 6.

#### **2. Methods**

We chose the DTU 10 MW RWT as the turbine model for this study as it is representative of the new generation of wind turbines and has been used in several research studies. The complete description of the turbine can be found in [29].

As explained in [25], large rotors such as the one from the DTU 10MW RWT have similar scales to the scales from turbulent wind found in Earth's boundary layer. As a consequence, the wind field that the turbine rotor sees will be much less homogeneous than the wind field seen by a smaller turbine rotor. BEM codes typically average the induction factor across a blade annulus, thus leading to inaccurate load predictions. In addition, large blade deflections that occur in modern large blades may introduce radial flow, thus violating the assumptions of many BEM models. Both of the aforementioned issues are addressed with higher order aerodynamic models such as the LLFVW model found in TU Berlin's

aeroelastic simulation tool QBlade. We did the testing and simulation of the TUB Controller presented in this study using QBlade.

#### *2.1. QBlade*

To model the unsteady aerodynamics, QBlade uses the Lifting Line Free Vortex Wake (LLFVW) method [28]. Here, the blade aerodynamic forces are evaluated on a blade element basis using standard airfoil polar data. The wake is modeled with vortex line elements. These are shed at the blade's trailing edge during every time step and then undergo free convection behind the rotor. Vortex methods can model the wake with far less assumptions and engineering corrections compared to BEM methods. Especially when the wind turbine is subject to unsteady inflow or varying blade loads, the LLFVW method increases the accuracy compared to BEM methods [27]. To model the dynamic stall of the blade elements, QBlade uses the ATEFlap unsteady aerodynamic model [30], modified so that it excludes contribution of the wake in the attached flow region [31].

Regarding the structural model, QBlade uses the open-source multi-physics library CHRONO [32]. It features a multi-body representation of the turbine which includes Euler-Bernoulli beam elements in a co-rotational formulation. With it, QBlade is able to accurately simulate blade deflections including the blade torsion, which has a significant influence on the blade loads. A detailed comparison between QBlade and OpenFAST can be found in [27].
