Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 1

EXPERIMENTAL INVESTIGATION OF DYNAMIC LOAD CONTROL STRATEGIES USING ACTIVE MICROFLAPS ON

WIND TURBINE BLADES

O. Eisele, G. Pechlivanoglou, C.N. Nayeri, C.O. Paschereit

Hermann Föttinger Institute (ISTA), TU Berlin, Germany

EWEA 2011, March 14.-17. 2011Brussels, Belgium

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 2

- Motivation

- Test Model Configuration

- Wind Tunnel & Force Measurement Setup

- Experiment Description

- Direct Inverse Control

- Controller Design

- Results

- Conclusion

Contents

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 3

Motivation

→ Large blade deflections

→ Reduction of the blade lifetime due to fatigue

Unsteady aerodynamic loads

Tower Shadow

Wind Gusts

Wind Shear

Yaw Misalignment Gravitational Effects

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 4

Motivation

• Aim: Reduction of unsteady aerodynamic loads

• Solution: Local control surfaces along the span of WT-blades

• Adaptation of the aerodynamic characteristics of the blade

• Common Solutions: Deformable flaps, Microtabs, rigid flaps

• Problems: sensors, controllers required

Scope of the Project:

• Evaluation of dynamic lift load reduction potential using rigid TE-Microflap

• PID–Control vs. Direct Inverse Control

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 5

Test Model Configuration

Airfoil: AH 93-W-174

Chord: 60cm; Span: 154cm

Plain rigid flap, hinged at TE

Flap-chord: 1.6%c

Flap-thickness: 0.3%c

Max. flap deflections:

56.6° to pressure side

74° to suction side

Actuation with digital servos

Trailing Edge

Max. 74°

Max. 56.6°

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 6

Wind Tunnel & Force Measurement Setup

Closed loop wind tunnel placed at ISTA/HFI TU-Berlin

Test section: 2 x 1.41 m²

Nozzle contraction ratio: 6.25 : 1

Test model mounted on an external 6-component wind tunnel balance

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 7

Experiment Description

The Scenario:

Airfoil under arbitrary pitching motion in the wind tunnel

Controller determines flap deflection to achieve the reference lift

Sampling Rate: 20Hz

Reynolds number: 10⁶

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 8

Experiment Description

AoA-Signal generated from white noise sequence

Mean: 7°; Amplitude: 3°

Pitching rate: 2.2°/sec

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 9

The system to be controlled can be described by:

The inverse model:

The function g'-1 is obtained by teaching a neural network based on measured data

Direct Inverse Control

The inverse controller:

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 10

PID - Controller Direct Inverse Controller

• Discrete version of:

• Manual tuning:

•Step change in reference lift

•Observation of measured lift

•First estimation:

•Ziegler Nichols Method

•Fine tuning

• Controller design with NNCTRL-Toolkit

• 8000 data samples from closed loop experiment

• Teaching: 6500 samples

• Validation: 1500 samples

• Optimization of the neural network architecture

• Final network:

Controller Design

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 11

Validation of the Inverse Model:

Predicted control signal very close to the control signal applied by PID-Controller

Controller Design

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 12

PID-Control: Time Series

Results

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 13

PID-Control: Statistical Quantities

Lift Statistics Baseline PID

Mean: 0.57 0.53

Standard Deviation:

0.19 0.06

Load Reduction Potential: 70%

Results

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 14

Direct Inverse Control: Time Series

Results

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 15

Load Reduction Potential: 36.8%

Direct Inverse Control: Statistical Quantities

Lift Statistics Baseline DIC

Mean: 0.57 0.53

Standard Deviation:

0.19 0.12

Results

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 16

High potential for dynamic lift load reduction using TE-microflaps

In case of PID controlled microflap: 70%

In case of DIC controlled microflap: 36.8%

Unstable behavior of DIC, very active control signal

High performance of neural networks for dynamic system modelling

Further neural network based control approaches proposed

Conclusions

Chair of Fluid Dynamics, Hermann-Föttinger-Institute (HFI)C. O. Paschereit Institute of Fluid Mechanics and Acoustics 17

EXPERIMENTAL INVESTIGATION OF DYNAMIC LOAD CONTROL STRATEGIES USING ACTIVE MICROFLAPS ON

WIND TURBINE BLADES

O. Eisele, G. Pechlivanoglou, C.N. Nayeri, C.O. Paschereit

Hermann Föttinger Institute (ISTA), TU Berlin, Germany

THANK YOU VERY MUCH FOR YOUR ATTENTION...

EWEA 2011, March 14.-17. 2011Brussels, Belgium