Pressure Based Lift Estimation and its Application to Feedforward
Load Control employing Trailing Edge Flaps

Bartholomay, Sirko; Wester, Tom T. B.; Perez‐Becker, Sebastian; Konze, Simon; Menzel, Christian; Hölling, Michael; Spickenheuer, Axel; Peinke, Joachim; Nayeri, Christian Navid; Paschereit, Oliver P.; Oberleithner, Kilian

doi:10.5194/wes-2020-91

Cited by 4 publications

(16 citation statements)

References 12 publications

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“…Other sensors have also been considered as input. For example, locally distributed multihole probes for angle of attack and velocity measurements might be beneficial as they measure the changing flow conditions before the blade is affected by them 8–10 . However, these sensors have practical problems if they are to be used in normal atmospheric conditions.…”

Section: Introductionmentioning

confidence: 99%

Repetitive model predictive control for load alleviation on a research wind turbine using trailing edge flaps

et al. 2022

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This paper presents the results of an advanced control strategy that employs active trailing edge flaps to reduce the fatigue loads of an experimental wind turbine. The strategy, called repetitive model predictive control, is a multiple-input multipleoutput controller that aims at the alleviation of out-of-plane blade root bending moments. The strategy incorporates the control commands, output errors, and state deviation from the previous rotation. This way, the time lag in the strain sensor input due to the blade inertia is compensated. Additionally, a strategy to limit the computational costs is presented. The load alleviation performance is evaluated at different yaw cases and compared with different individual flap control strategies. The repetitive model predictive control is able to reduce the fatigue loads by up to 23% compared with the better performing individual flap control strategy. This improvement in load reduction is accompanied by an increase in flap travel of up to 7% compared with the individual flap control strategies. K E Y W O R D S fatigue load control, individual flap control, repetitive control, trailing edge flaps | INTRODUCTIONWind energy has become one of the most important sources of renewable energy. In order for this trend to continue, the cost of energy of this technology needs to be as low as possible. A crucial factor for the cost of energy is the employed material for building a turbine. Lowering material input can be achieved by reducing the loads acting on the turbine components. These in turn can be decreased by a load control strategy specifically designed for this purpose. As an example, cyclic and individual pitch control has been studied by various institutes. [1][2][3][4] Substantial research was also carried out into smart rotor control which could supplement pitching of the rotor blades. In particular, employing locally distributed active flow control devices on the blades has drawn a lot of attention in the community. Due to their high bandwidth and control authority, trailing edge flaps are one of the most promising active flow control options. 5 In particular, it was shown that trailing edge flaps achieve the same load reduction for fatigue loads as individual pitch control. 3 A critical aspect of active load control is the choice of sensors for the control strategy. 6 Mostly, active flow control aims at the alleviation of the out-of-plane turbine loads. Therefore, employing a feedback control on the out-of-plane blade root bending moment is a common choice.

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Section: Introductionmentioning

confidence: 99%

Repetitive model predictive control for load alleviation on a research wind turbine using trailing edge flaps

et al. 2022

Self Cite

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“…The three-hole probes and surface pressure measurements were analyzed for attached flow conditions in a previous two-dimensional study. 14 The former showed a normalized root mean square deviation for the lift estimation of 20% and the latter of 8% for a reduced frequency of up to k ¼ 0:25. 14 In the current setup, a geometrical deviation of the three-hole probe and the surface pressure port position might affect the absolute measurement values.…”

Section: The Bertmentioning

confidence: 93%

“…The same wind tunnel is employed for a wing with active trailing edge flap. 14 Herein, different control strategies are compared for inflow disturbances at various reduced frequencies. Neunaber and Braud create an inversed EOG by rotating a bar into the inflow field of a wind tunnel.…”

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confidence: 99%

Experimental assessment of a blended fatigue‐extreme controller employing trailing edge flaps

et al. 2022

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This paper presents an experimental assessment of a blended fatigue‐extreme controller for load control employing trailing edge flaps on a lab‐scale wind turbine. The controller blends between a repetitive model predictive controller that targets fatigue loads and a dedicated extreme load controller, which consists of a simple on‐off load control strategy. The Fatigue controller uses the flapwise blade root bending moments of the three blades as input sensors. The Extreme controller additionally uses on‐blade angle of attack and velocity measurements as well as acceleration measurements to detect extreme events and to allow for a fast reaction. The experiments are conducted on the Berlin Research Turbine within the large wind tunnel of the TU Berlin. In order to reproduce test cases with deterministic extreme wind conditions that follow industry standards, the wind tunnel was redesigned. The analyzed test cases are extreme direction change, extreme coherent gust, extreme operating gust and extreme coherent gust with direction change. The test cases are analyzed by on‐blade angle of attack and velocity measurements. The load control performance of the Blended controller is compared to the pure fatigue oriented and the pure extreme load controller. The Blended controller achieves a maximum flapwise blade root bending moment reduction of 23%, which is comparable to the reduction achieved by the Extreme controller.

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“…Ideally, if a sufficient number of pressure sensors are available, then the sum of spatially weighted surface pressure measurements will give a good estimate of the instantaneous vector force (without the contribution of skin friction) from which the lift can be obtained. A recent example is a wind turbine application by Bartholomay, et al [3], who investigated the ability of a small number of surface pressure sensors on a wind turbine blade to identify the instantaneous lift. The measured lift was then used in a feed-forward controller to reduce the unsteady loads acting on the turbine blade.…”

Section: Lift Estimation With Surface Pressurementioning

confidence: 99%

Lift coefficient estimation for a rapidly pitching airfoil

Williams

Eldredge

et al. 2021

Exp Fluids

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We develop a method for estimating the instantaneous lift coefficient on a rapidly pitching airfoil that uses a small number of pressure sensors and a measurement of the angle of attack. The approach assimilates four surface pressure measurements with a modified nonlinear state space model (Goman-Khrabrov model) through a Kalman filter. The error of lift coefficient estimates based only on a weighted-sum of the measured pressures are found to be noisy and biased, which leads to inaccurate estimates. The estimate is improved by including the predictive model in an conventional Kalman filter. The Goman-Khrabrov model is shown to be a linear parameter-varying system and can therefore be used in the Kalman filter without the need for linearization. Additional improvement is realized by modifying the algorithm to provide more accurate estimate of the lift coefficient. The improved Kalman filtering approach results in a bias-free lift coefficient estimate that is more precise than either the pressure-based estimate or the Goman-Khrabrov model on their own. The new method will enable performance enhancements in aerodynamic systems whose performance relies on lift.

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Pressure Based Lift Estimation and its Application to Feedforward Load Control employing Trailing Edge Flaps

Cited by 4 publications

References 12 publications

Repetitive model predictive control for load alleviation on a research wind turbine using trailing edge flaps

Repetitive model predictive control for load alleviation on a research wind turbine using trailing edge flaps

Experimental assessment of a blended fatigue‐extreme controller employing trailing edge flaps

Lift coefficient estimation for a rapidly pitching airfoil

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