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EWEA 2011, March 14.-17. 2011 Brussels, Belgium. 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. Contents. - PowerPoint PPT Presentation
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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