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Experimental Aerodynamics & Concepts Group Micro Renewable Energy Systems Laboratory Georgia Institute of Technology [email protected] Validation of a Prediction Model for Control of Micro Wind Turbines Ryan McGowan and Narayanan Komerath Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta, Georgia USA INTERNATIONAL CONFERENCE ON POWER, SIGNALS, CONTROL AND COMPUTATIONS Trissur, India, January 3-06, 2012

Validation of a Prediction Model for Control of Micro Wind Turbines

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Validation of a Prediction Model for Control of Micro Wind Turbines. Ryan McGowan and Narayanan Komerath Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta, Georgia USA . INTERNATIONAL CONFERENCE ON POWER, SIGNALS, CONTROL AND COMPUTATIONS - PowerPoint PPT Presentation

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Page 1: Validation of a Prediction Model for Control of Micro Wind Turbines

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Validation of a Prediction Model for Control of Micro

Wind TurbinesRyan McGowan and Narayanan Komerath

Daniel Guggenheim School of Aerospace EngineeringGeorgia Institute of Technology

Atlanta, Georgia USA

INTERNATIONAL CONFERENCE ON POWER, SIGNALS, CONTROL AND COMPUTATIONSTrissur, India, January 3-06, 2012

Page 2: Validation of a Prediction Model for Control of Micro Wind Turbines

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SUMMARYAim

•Develop micro wind turbine for single-family use in India•Vertical axis vs. horizontal axis•Local manufacture & maintenance•Lifecycle safety & environment•Technically complex to design!

•Operation at low wind speeds•Low blade tip speed

Optimize design for low tip-speed ratio

TSR = Blade tip tangential\ Windspeed

•Aerodynamics simulation validated•Low Reynolds number corrections•Wind tunnel testbed

•Azimuthal modification strategy to improve operation•Real-time control options

TSR

Objective

Methods

Page 3: Validation of a Prediction Model for Control of Micro Wind Turbines

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Vertical Axis Wind Turbine

1. Bicycle-based 1m VAWT >270rpm, >70 w (mechanical)2. 2m 1kW VAWT for high coastal winds.

Issues: 1. Optimal tip speed ratio 2 to 5 – too high.2. Variable power transmission3. Nonlinear pitch control4. Flexible blade operation5. Benign failure modes6. Hybrid devices: power conditioning, storage

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54-inch Horizontal Axis Wind Turbine Electrical Output vs. Wind Speed, with 50 Ohm resistive load (2 tests)

Page 5: Validation of a Prediction Model for Control of Micro Wind Turbines

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Horizontal Axis Wind Turbine Simulation: Power Coefficient vs. Tip Speed Ratio

Page 6: Validation of a Prediction Model for Control of Micro Wind Turbines

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Measured VAWT mechanical power in two tests vs. preliminary predictions from momentum streamtube theory.

40

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40302010

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Vt = - Vb

Vertical Axis Wind Turbine, looking from above, showing relative velocity on the Advancing Blade Side, and the blade wake.

Solidity, defined as diameter divided by blade chord times number of blades.

As solidity increases, blade-wake interaction increases.So we need Multiple StreamTube Theory to account for power loss from streamtubes due to power extraction or drag due to blades

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Blade element predictions of the mechanical power output of the 3-armed, bi-bladed VAWT, for different choices of blade chord compared to the baseline chord, with fixed span

Aspect Ratio Effect on Optimal Tip Speed Ratio

Baseline AR =Span/chord. ~9

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Power coefficient vs. tip speed ratio for various solidities (Sandia data and simulation)

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Comparison of (GT) and Sandia performance predictions at a solidity of 0.18.

Page 11: Validation of a Prediction Model for Control of Micro Wind Turbines

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Positive Torque (driving) occurs when net force has a positivecomponent along the tangential direction, driving the blade

Tangential coefficient vs. alpha for a NACA 0015 airfoil

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Wind

•Enable startup in low winds•Provide some power•Limit performance at high•speeds

• Passive control of local angle of attack• Eliminate blade stall/negative torque• Allow self-starting

Drag Channels

Guide Vanes

Passive Starting Mechanisms

To reach optimal TSR, one needs powered start

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ConclusionsBegun to integrate prediction, design and testing of a 2m x 2m vertical axis wind turbine with slanted double blades.

Operating point predictions with multiple streamtube theory are validated against published results. Reynolds number effects are clearly seen in the predictions, and their proper inclusion allows the predictions to match experimental data well.

Low performance at Tip Speed Ratio <1 is as per predictions. Simulation used to study how to eliminate negative torque at low tip speed ratio.

A self-starting device using drag tubes isbeing simulated.

Limiting turbine speed for safety implies that high tip speed ratio is best achieved at low wind speeds by taking the turbine to operating speed using human pedaling action or an electric motor.

Time-resolved simulations and thus to control algorithms for adapting to given wind patterns and optimizing power extraction and safety.

Page 14: Validation of a Prediction Model for Control of Micro Wind Turbines

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ACKNOWLEDGEMENTS

The work reported in this paper was made possible by resources being developed for the “EXTROVERT” cross-disciplinary learning project under NASA Grant NNX09AF67G S01. Mr. Anthony Springer is the Technical Monitor.