Active trailing edge flaps are a promising technology that can potentially enable further increases in wind turbine sizes without the disproportionate increase in loads, thus reducing the cost of wind energy even further. Extreme loads and critical deflections of the blade are design-driving issues that can effectively be reduced by flaps. In this paper, we consider the flap hinge moment as a local input sensor for a simple flap controller that reduces extreme loads and critical deflections of the DTU 10 MW Reference Wind Turbine blade. We present a model to calculate the unsteady flap hinge moment that can be used in aeroelastic simulations in the time domain. This model is used to develop an observer that estimates the local angle of attack and relative wind velocity of a blade section based on local sensor information including the flap hinge moment of the blade section. For steady wind conditions that include yawed inflow and wind shear, the observer is able to estimate the local inflow conditions with errors in the mean angle of attack below 0.2

We include this observer as part of a simple flap controller to reduce extreme loads and critical deflections of the blade. The flap controller's performance is tested in load simulations of the reference turbine with active flaps according to the IEC 61400-1 power production with extreme turbulence group. We used the lifting line free vortex wake method to calculate the aerodynamic loads. Results show a reduction of the maximum out-of-plane and resulting blade root bending moments of 8 % and 7.6 %, respectively, when compared to a baseline case without flaps. The critical blade tip deflection is reduced by 7.1 %. Furthermore, a sector load analysis considering extreme loading in all load directions shows a reduction of the extreme resulting bending moment in an angular region covering 30

Wind turbines have increased dramatically in size over the past years in an effort to reduce the cost of wind energy and make it a competitive source of energy. The increased installation numbers of new wind turbines in Germany over the years is an example of the success that this strategy has had

A promising alternative to conventional full span pitch control is the concept of the smart rotor with distributed active flow control (AFC) devices.

In addition to AFC actuators, the choice of AFC sensors is another critical aspect for the load alleviation strategies of smart rotors.

Strain sensors measuring the flapwise BRBM have been a popular and effective choice because the main focus of TE flap control has been fatigue load reduction of the out-of-plane loads. The frequency content of these fatigue loads is fairly low

Out-of-plane fatigue loads are design driving for several turbine components. Yet if we focus on the wind turbine blade, flapwise fatigue loads are not necessarily the best objective to use TE flaps or other local AFC devices for.

Flapwise BRBM fatigue loads have a fairly low-frequency content. Most of the damage concentrates around the 1P frequency

Using TE flaps to mitigate 1P and 2P flapwise BRBM means that the high bandwidth capabilities of these actuators are not used effectively.

Using AFC for fatigue load reduction requires a high number of duty cycles of the actuator. This is a limiting factor in the choice of the actuator. It is also not in line with the current philosophy of wind turbine designs since wind turbines are designed to be low maintenance machines. Having a high number of duty cycles might result in high maintenance requirements for the AFC actuator.

Blade optimization studies that include fatigue-oriented TE flap control as part of their optimization features did not show significant additional blade mass reduction compared to an optimized blade design without flaps.

[t]he blade optimization problems addressed in this work are primarily driven by flapwise stiffness, with blade deflection, rotor thrust and flapwise ultimate stresses in the spar forming the design drivers (

From these findings one can conclude that for modern large rotor blades, the extreme value of the resulting BRBM and the blade tip-to-tower clearance will be decisive. In the edgewise direction, the design-driving loads will be dictated by the fatigue loads arising from the blade's mass. In this direction, the blade root has to endure a load fluctuation with an amplitude equaling the blade's static moment once per revolution (1P).

Should the objective of an AFC strategy shift to reducing flapwise extreme loads and critical deflections, it would also have a positive effect on the design-driving edgewise fatigue loads of the blade. When an effective controller reduces extreme loads and deflections, the spar cap of the blade can be optimized and hence the mass of the blade reduced. This in turn reduces the edgewise fatigue loads near the blade root. These loads are mainly caused by gravitational forces. In additional optimization loops, the blade mass could be further reduced, potentially creating a virtuous cycle.

If a TE flap control strategy is used to alleviate loads with high frequency content (such as design-driving extreme loads), the choice of a sensor for controller input becomes much more relevant. Using the flapwise BRBM as an input sensor for the TE flap controller has its advantages in the implementation and maintenance of the sensor but can have negative effects on the performance of the controller. Because sensor and actuator are far apart, aerodynamic loads and deflections at the blade tip are sensed at the root with a considerable time lag. This time lag carries through to the actuation response of the controller, limiting its effectiveness

A possible sensor that has not received much attention from the community is the TE flap hinge moment

The goal of the present study is to explore and quantify the potential of TE flaps to mitigate design-driving extreme loads and deflections. We are also interested in exploring the possibility of using a local and robust sensor as an input choice for a controller strategy. This paper presents a novel method that allows the use of the TE flap hinge moment as an input sensor. It is a model-based observer that estimates the effective angle of attack of the blade section given the hinge moment, the accelerations and the relative wind velocity of the section (the last quantity is also estimated with our method). In addition, we use this observer as part of a simple extreme load controller for flaps and analyze its performance in a power production scenario with extreme turbulent wind. Section

The DTU 10 MW RWT was chosen as the turbine model. 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

The blade of the DTU 10 MW RWT was modified to accommodate TE flaps. The flaps are modeled via dynamic polar sets that describe the airfoil with discrete flap angles. For this study we chose a flap that covers 10 % of the chord and has maximum deflection angles of

Trailing edge flaps on the DTU 10 MW RWT blade.

In total six flaps are integrated into the blade, as can be seen in Fig.

We did the development, testing and simulation of the methods presented in this study using two aeroelastic simulation tools: the first is NREL's FAST v8.15 and the second is TU Berlin's QBlade.

FAST uses AeroDyn

The used structural model in FAST is ElastoDyn. It has a combined multi-body and modal dynamics representation that is able to model the wind turbine with flexible blades and tower

QBlade uses the lifting line free vortex wake (LLFVW) method as its aerodynamic model

QBlade is able to model AFC devices such as TE flaps using two dynamic polar sets. They are defined for the inner and outer spanwise locations of the AFC device. It is thereby possible to model AFC elements that span over two different airfoils. The ATEFlap model is also capable of modeling unsteady aerodynamic effects of flap deflections at high reduced frequencies. This allows QBlade to accurately model the aerodynamics of TE flap actuators with high bandwidths. This is required if a flap control strategy aims at reducing extreme loads and deflections.

QBlade has a structural solver based on the open-source multi-physics library CHRONO

A more detailed comparison between QBlade and (Open)FAST can be found in

Sketch showing the angles and coordinate systems of a 2D airfoil section. The blade section is depicted at a rotor azimuth angle

For this study, we used the TUB Controller

In this section we present the flap hinge moment model used in this study and the observer based on this model. The latter is able to estimate the effective angle of attack and relative velocity of a flapped blade section by means of local and global sensors, the former being an accelerometer, a flap position sensor and the flap hinge moment sensor. These sensors and the hinge are assumed to be positioned at the span-wise center of the blade section with active flap. The global sensors are the rotor speed, the rotor azimuth angle and the blade pitch angle. In this study, we define the sign of the hinge moment to be the same as for the flap angle, i.e., positive if the flap moves to the pressure side (see Fig.

The observer comprises two parts: an angle of attack estimator and a relative velocity estimator. The first is a collection of linear observers that estimate the angles of attack for different constant relative wind speeds. Together they form a linear parameter varying (LPV) system which is parameterized by the relative wind velocity. The relative velocity estimator uses simple models to estimate the relative wind speed and serves as the parameter input for the LPV system.

Because we use polar data to derive the aerodynamic loads on a blade section, we cannot measure the unsteady hinge moment directly in the simulations. We therefore need a model to calculate the hinge moment on a blade section. Ignoring friction, the total hinge moment on a blade section with a TE flap can be determined by the sum of the hinge moment due to gravity loads, the moment due to the flap inertial loads and the moment due to aerodynamic loads

The gravity loads can be calculated by taking the fraction of the flap's center of mass that has an offset in the out-of-plane direction relative to the flap hinge. If

The hinge moment due to inertial loads is caused by the loads due to flap acceleration as well as centrifugal, Coriolis and gyroscopic loads. As a first approximation, we chose to neglect the effect of all inertial loads except those arising from the flap acceleration. The centrifugal forces will act in the radial direction (i.e., parallel to the blade span) if we assume a straight, undeflected blade without rotor cone angle. Hence their contribution to the hinge moment will be zero. This changes if we introduce a cone angle, a pre-bend and blade deflection. Since the resulting angle of the deflected blade to the rotational plane will be small, the contributions of the centrifugal loads on the hinge moment will be small. A similar argument can be made for the Coriolis forces. The gyroscopic loads will only arise if the turbine actively yaws. In the simulations considered within this study the turbine did not yaw, so gyroscopic loads were not present and hence not included.

We can therefore write

To model the unsteady aerodynamic hinge moment, we use a model for thin airfoils in inviscous incompressible flow presented in

It should be noted that the hinge moment model presented in the reference
only models the unsteady aerodynamic hinge moment due to airfoil and flap motion for a constant wind speed. A complete model should also include the unsteady hinge moment contribution of varying gust fields and changes in the relative wind speed of the airfoil. Nonetheless, the contribution of both can be neglected for our application, as discussed in

We included two minor changes to the model presented in the aforementioned reference. The first change is the inclusion of an offset in the hinge moment coefficient for

The unsteady hinge moment coefficient

If we assume that

In order to obtain the total hinge moment of a blade section with flap, we add the individual contributions:

Figure

Figure

We note that – although not shown in Fig.

We can use the model presented in the previous section to estimate the effective angle of attack of the blade section by means of a linear observer. The idea is to estimate the internal states of a system based on the measured inputs and outputs. If we cannot measure the quantities

In this study, we follow an approach used in

Figure

Representation of the linear observer for a constant

The output of the state-space representation does not include

For this study we use a simple signal model. It basically assumes that

If we combine the signal and hinge moment models, we get a state space whose input vector is

The final state-space representation of our observer has the form

The gain matrix

The derivation of the linear estimator above is only valid for one fixed value of

To estimate

The vertical variation part uses the once-per-revolution Coleman transform of the velocities of the blade sections normal to the chord to get an equivalent amplitude of the vertically varying velocity normal to the chord of the rotor annulus at the spanwise position of the flapped blade section.

The tower shadow variation part uses a simplified approximation of the tower shadow model from

Once we have calculated

We note that the expression for

Combining the

The derivation of the model and observer presented in this section is based on several assumptions. The first is the 2D airfoil analysis assumption. It is therefore only correct for an infinitely thin blade station. Changes in the wind distribution and angle of attack along the span of the blade section will have an integrated effect on the hinge moment and potentially deviate from the pure 2D analysis. In this study we assume that the aerodynamic conditions do not change significantly within a flapped blade section and therefore can be treated as effective quantities for the section.

To have an idea of the error made with this assumption, we take the innermost
3 m flapped section with the aforementioned sensors located at the center of the section span. The center of this section is situated at a blade radius of 65.5 m. For turbine operation at rated rotor speed, the difference of local in-plane wind speeds between the center of the blade section and the inner part of the section span is about 2 % of the in-plane velocity at the section center. For the outer flapped sections, the relative error decreases due to the increased wind speed used for reference. For the out-of-plane wind speed we can estimate the error by looking at the coherence function of the spatial distribution of the turbulence. Assuming a Kaimal wind model, the spatial coherence function of two points that are 1.5 m apart is 0.75 for a frequency corresponding to 1P and an average out-of-plane velocity of 10 m s

Ultimately the validity of this assumption is reflected in the controller performance and has to be assessed experimentally or in simulations.

The observer presented in this section is based on a thin airfoil model and
does not correspond to physical airfoils used in wind turbines. As we saw in Sect.

In this section we present the performance of the estimator using a series of
steady and turbulent wind fields in combination with the turbine model. To illustrate the performance of our estimator, we simulate one 3 m flapped blade section at the rotor spanwise position of 74.3 m. The flap angle is held constant at

Both the hinge moment model and the observer are included in the TUB Controller

As discussed in Sect.

The performance of our observer is evaluated by its ability to estimate the representative aerodynamic quantities at the center of each flapped blade section.

This modeling approach could be improved if we consider more aerodynamic blade elements so that several of them lie within a flapped blade section. These elements would be used to calculate the lift force acting on the whole section. Using a representative airfoil polar for the blade section, we could calculate the effective angle of attack at the center location of the section and use this as an input for our hinge model. It would also be this effective angle of attack that would be estimated by our observer.

The parameters of the steady wind simulations are listed in Table

Simulation parameters for aeroelastic calculations.

Behavior of the estimator for two different wind speeds as a function of the rotor azimuth angle.

Figure

Figure

Figure

Observer performance for steady-state calculations.

Figure

We also analyzed the performance of our estimator in fully turbulent wind load calculations. These situations are especially challenging for the observer because our relative velocity estimator does not model turbulent out-of-plane wind speeds. The setup for the turbulent calculations is shown in Table

Because the observer lacks the turbulent variation in the out-of-plane wind speed in the relative velocity estimator, the estimated

Figure

Time series of the estimator performance for two different turbulent wind speed simulations.

We can see in Fig.

The filtering introduces a time lag which could potentially be detrimental for a controller. The effects of the filter would certainly be more marked for an extreme load controller than for a fatigue load controller. Since we are interested in the extreme load reduction potential of flaps, we will be analyzing the more critical situation in this study. The description and performance of such an extreme loads controller are presented in the next section.

The estimated

In this section we integrate the estimator presented in Sect.

The controller used in this study is based on the extreme load controller presented by

We expanded this controller by including additional sensors and criteria to trigger flap action. The controller logic is shown in Fig.

Graphical representation of the controller logic. The conditions 1 to 4 are given by Eqs. (

By using the flap hinge sensor in the outer blade span (Eqs.

Preliminary simulations also revealed that blade deflection dynamics are critical for predicting extreme events of

If any of the above conditions are met for any blade, then all flaps of all blades are deployed to the target angle

The conditions explained so far are valid for cases where the strategy is used to mitigate the maxima of

We note that this controller treats all flaps in one blade as a single unit. Why should we make the additional effort to include multiple flaps per blade? One reason for having several flaps per blade instead of one is that a system of multiple independent flaps will still be able to reduce the loads in the event of one flap ceasing to function. This makes the system more redundant and thus more appropriate for extreme load reduction. A second reason is that the controller will have sensorial input from multiple sources. Averaging the signals of the six flaps smooths out possible local variations that could arise from using a single set of sensors. Finally, having multiple sensors and actuators per blade opens up the possibility of using local distributed control strategies in future studies.

The metrics considered in this study are max(

Because we are dealing with stochastic wind simulations, we chose to follow the averaging procedure for extreme values described in

A complete analysis should include other key turbine sensors to measure the overall effect of the controller on the turbine. We chose not to include these sensors in order to limit the scope of this study.

In this study we focused on the load case group that caused the highest

Although the number of simulations is rather low to determine the overall extreme values of

Figure

max(

As expected, the highest values of

A numerical comparison is shown in Fig.

Normalized values of max(

It is also interesting to see what effect an active flap system has on the different load directions of max(

When we look at the effect of the active flap system, we can see that the reduction of max(

The results obtained in the previous section show that active TE flaps are able to reduce extreme loads and critical deflections of the blade substantially. Given the constraint that the control strategy should not interfere with the normal power production (DLC 1.1), a reduction of 7.6 % in max(

Averaged max(

Figure

Selection of time series for two different simulations with

The second column shows a situation where the controller does not perform as desired. At around 685 s of simulation time, there is a sudden peak in

One challenge in this particular study is that the difference in extrema between the DLC 1.1 and 1.3 groups is relatively small. Given the fact that the flap system needs a certain amount of time to have an effect on

Another observed issue with the proposed strategy is the unwanted mutual influence between the pitch and the flap controllers. We can see an example of this in Fig.

A possible solution would be to decrease the value of

All of the limitations discussed above arise mainly from the chosen controller strategy. While being simple, robust and able to reduce extreme loads and deflections significantly, the proposed architecture shows its limits when used in such a challenging scenario. Other controller strategy types, such as, e.g., model-based or adaptive-data-driven controllers that include combined pitch and flap action, are possible candidates that could exploit the full potential of active flaps while overcoming the observed limitations in this study.

In this paper we explored the potential of active trailing edge flaps to reduce extreme loads and critical deflections of the modified DTU 10 MW RWT blade with flaps. We considered the flap hinge moment as a robust and available sensor that can deliver valuable local information about the inflow and enable the flap system to have more time to react to sudden extreme conditions.

In order to use the flap hinge moment as an input sensor for a controller strategy, we adapted an existing unsteady hinge moment model for thin airfoils from the literature to use it in aero-servo-elastic simulations in the time domain. Based on this model, we developed an observer that estimates the effective local angle of attack and relative wind velocity of a blade section from local sensors. The latter include the flap hinge moment and an accelerometer mounted on the blade section.

We evaluated the performance of the observer in aeroelastic simulations with steady and turbulent wind conditions. For steady wind conditions, the error between the estimated and real values of the mean

This observer was included in a simple flap controller strategy to mitigate extreme blade loads and critical blade deflections. It is based on a series of on-off criteria applied to several input sensors. The sensors included integrated load values – such as

We tested the performance of this strategy in aero-servo-elastic load calculations according to the DLC 1.3 group from the IEC standard. This group features the extreme turbulent wind model and is responsible for the maxima of

A more detailed look revealed that active flap systems in general have the potential to reduce the extreme loads even further. Yet a combination of challenging conditions – e.g., constraints in the parameter selection space in order to reduce extreme loads without interfering with normal power production – and a simple controller strategy were found to be the main limitations of the proposed system.

The results of this paper show that active TE flaps have a large potential to reduce design-driving extreme loads and critical deflections of wind turbine blades. They can therefore help create more competitive turbine designs that will reduce the cost of energy even further. A critical aspect was seen to be the reaction time of the active flap system, which can be greatly improved if local sensors – such as the flap hinge moment – are used as input for the control strategy. More work needs to be done in order to gain further insight into this topic and better quantify the aforementioned potential of flaps.

The model used for the hinge moment calculation could be improved to increase its accuracy. Further refinements could include a more detailed state-space representation of the aerodynamic loads, include other inertial loads such as centrifugal and gyroscopic loads, and also include friction.

In this study we used identical systems to model the flap hinge and as input for our observer. This is certainly unrealistic, and a robustness study should be done to quantify how model uncertainty and measurement noise affect the observer performance. This is especially of relevance due to the lower sensitivity of the hinge moment coefficient to changes in the angle of attack. A proof of stability for the LPV observer is also needed to guarantee that the observer will be stable at all times.

The model and observer should also be tested experimentally. On the one hand, the proposed model could be compared to experimental data obtained from a 2D airfoil with an active flap for different reduced frequencies. Alternatively or complementarily, a system identification could be performed using experimental data to create an unsteady hinge moment model from the data to be used in the observer. The observer and model could also be tested and analyzed experimentally using an experimental wind turbine in a wind tunnel.

Regarding the controller strategy, a better quantification of its performance can be obtained by including more DLC groups that include the load extrema in all directions. Also, the evaluation of the strategy should be extended to include more load sensors, such as hub and tower bending moments. In addition, other control strategies should be considered as possible candidates for extreme load and deflection control. In particular, model-based or adaptive-data-driven controllers that are able to control both the pitch and flap actuators are promising candidates to increase the ability of active flaps to reduce design-driving loads.

This appendix contains the explicit entries of the matrices used for the hinge model and the hinge model observer. The entries for the feedthrough matrix of the hinge model

For the hinge observer model, the explicit entries of

This section contains the list of symbols (Tables

List of Latin symbols used in this paper.

List of Greek symbols used in this paper.

List of sub- and superscripts.

Both FAST and QBlade are open-source codes available online. The newest version of FAST v8 is available at

SPB prepared the manuscript with the help of all co-authors. DM is the main developer of QBlade. DM and SPB implemented the interface for flap controllers in QBlade. SPB developed the hinge model observer and control strategy, performed the calculations, and analyzed the results. COP provided assistance with the paper review.

The authors declare that they have no conflict of interest.

Sebastian Perez-Becker wishes to thank WINDnovation Engineering Solutions GmbH for supporting his research. The authors wish to thank Horst Schulte from HTW Berlin and Sirko Bartholomay from TU Berlin for their reviews and insightful comments on this paper. We acknowledge the support of the Open Access Publication Fund of TU Berlin.

This open-access publication was funded by Technische Universität Berlin.

This paper was edited by Mingming Zhang and reviewed by Athanasios Barlas and Vasilis A. Riziotis.