Design and Control of a Scaled Wind Turbine
by
Chaeeun Kim, BSME
A Thesis
In
Mechanical Engineering
Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCES
Approved
Dr. Beibei Ren Chair of Committee
Dr. Siva Parameswaran
Dr. Andy Swift
Mark Sheridan Dean of the Graduate School
December, 2019
Copyright 2019, Chaeeun Kim Texas Tech University, Chaeeun Kim, December 2019
ACKNOWLEDGMENTS
I would like to express my sincere gratitude to my advisor, Dr. Beibei Ren, whose guidance made the completion of this thesis possible.
I would also like to thank the faculty and staff at Whitacre College of Engineering of Texas Tech University for their constant encouragement; I am especially grateful to the Mechanical Engineering Shop staff for their help in manufacturing the wind turbine components. I extend thanks to the National Wind Institute for allowing me to use their test equipment, which was instrumental in conducting experiments. I wish to acknowledge the members of the Dynamic Intelligent Systems, Control, and Optimization (DISCO) Group for their collaboration and expertise. I would also like to thank our undergraduate research assistant, Abigail Baker, for her invaluable contributions.
Finally, I would like to thank my friends and family for their unwavering belief in me; their support has been essential throughout not only the completion of this thesis, but my education.
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TABLE OF CONTENTS
ACKNOWLEDGMENTS ...... ii ABSTRACT ...... iv LIST OF TABLES ...... v LIST OF FIGURES ...... vi CHAPTER I ...... 1 INTRODUCTION ...... 1 Motivation ...... 1 Background ...... 2 Organization ...... 3 CHAPTER II ...... 4 WIND TURBINE DESIGN ...... 4 Scaled Wind Turbine Design ...... 4 Wind Turbine Blade Design ...... 5 Wind Turbine Tower Design ...... 11 Wind Turbine Hub Design ...... 13 Manufacturing Process ...... 13 Conclusion ...... 17 CHAPTER III ...... 18 OPEN LOOP CONTROL ...... 18 Open Loop Control ...... 18 Experimental Setup ...... 19 Experimental Results ...... 22 Conclusion ...... 30 CHAPTER IV ...... 31 CLOSED LOOP CONTROL ...... 31 Closed Loop Control ...... 31 DC-DC Converter ...... 31 Maximum Power Point Tracking [9] ...... 32 Experimental Setup ...... 33 Conclusion ...... 37 CHAPTER V ...... 39 COMPARISON BETWEEN QBLADE AND EXPERIMENTS ...... 39 Comparison ...... 39 Conclusion ...... 42 CHAPTER VI ...... 44 CONCLUSION ...... 44 Conclusion ...... 44 Future Work ...... 45 BIBLIOGRAPHY ...... 46
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ABSTRACT
As demand for renewable energy in the United States rises, wind power is becoming an increasingly important source of environmentally friendly energy. While wind power is being considered to be one of the largest, and fastest growing, sources of renewable energy to supply electricity, the challenge of modelling blade performance remains. Wind turbine models are often designed and assessed using simulation software such as QBlade. There are often discrepancies between simulation and field data, especially for smaller scaled wind turbines; however, there is a lack of study into explanations or solutions for these deviations.
In this project, a scaled model of a three-blade horizontal axis wind turbine (HAWT) was designed using QBlade to find an optimal aerodynamic blade design and was fabricated using an additive manufacturing process. The scaled model was taken to the National Wind Institute’s Boundary Layer Wind Tunnel at Reese Technology Center where experimental data was obtained. During the experiment, a gradient-based extremum seeking algorithm, maximum power point tracking (MPPT), was applied to achieve the maximum power output of the scaled wind turbines while a DC-DC boost converter was adopted for voltage regulation. Discrepancies between simulations and experiments are attributed to assumptions made in QBlade, which creates difficulties in modelling performance in blades with low Reynolds numbers, as well as limitations in manufacturing, and parameter data in simulations.
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LIST OF TABLES
Table 1 QBlade Parameter ...... 8 Table 2 Components and Their Manufacturing Methods ...... 17 Table 3 Experimental Results for Blades with 18° Twist at Wind Speed of 4.69 m/s ...... 23 Table 4 Experimental Results for Blades with 30° Twist at Wind Speed of 4.69 m/s ...... 23 Table 5 Experimental Results for Blades with 35° Twist at Wind Speed of 4.69 m/s ...... 24 Table 6 Experimental Results for Blades with 18° Twist at Wind Speed of 6.00 m/s ...... 25 Table 7 Experimental Results for Blades with 30° Twist at Wind Speed of 6.00 m/s ...... 25 Table 8 Experimental Results for Blades with 35° Twist at Wind Speed of 6.00 m/s ...... 25 Table 9 Experimental Results for Blades with 18° Twist at Wind Speed of 7.31 m/s ...... 26 Table 10 Experimental Results for Blades with 30° Twist at Wind Speed of 7.31 m/s ...... 27 Table 11 Experimental Results for Blades with 35° Twist at Wind Speed of 7.31 m/s ...... 27 Table 12 Experimental Results for Blades with 18° Twist at Wind Speed of 8.18 m/s ...... 28 Table 13 Experimental Results for Blades with 30° Twist at Wind Speed of 8.18 m/s ...... 28 Table 14 Experimental Results for Blades with 35° Twist at Wind Speed of 8.18 m/s ...... 29 Table 15 Comparison of Theoretical and Empirical Power Values ...... 40 Table 16 Comparison of Theoretical and Empirical Power Coefficient Values ...... 41
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LIST OF FIGURES
Figure 1 Components of Wind Turbine [2]...... 2 Figure 2 Assembly of the Wind Turbine Model ...... 4 Figure 3 Exploded Isometric Drawing of the Wind Turbine Model ...... 5 Figure 4 Geometry of the Airfoil [3]...... 5 Figure 5 Forces and Moments of the Airfoil [3] ...... 5 Figure 6 Airfoils Used in QBlade ...... 6 Figure 7 Hub Radius ...... 7 Figure 8 Angle of Twist, Position, and Chord ...... 7 Figure 9 Angle of Twist vs. Position ...... 9 Figure 10 Power Coefficient Graph from QBlade ...... 10 Figure 11 Power vs. Rotational Speed Graph from QBlade ...... 11 Figure 12 Two Parts of the Wind Turbine Tower ...... 12 Figure 13 Height and Rotor Diameter of Wind Turbine [4] ...... 12 Figure 14 Hub Design ...... 13 Figure 15 FDM Printing Process [5] ...... 14 Figure 16 Schematic of an FDM Printer [5] ...... 14 Figure 17 SLA Printing Process [6] ...... 15 Figure 18 Wind Turbine Blade from QBlade (left) and Wind Turbine Blade with Hub Connection (right) ...... 16 Figure 19 Block Diagram of Open Loop Control ...... 19 Figure 20 Boundary Layer Wind Tunnel [7] ...... 19 Figure 21 Wind Turbine in the Wind Tunnel...... 20 Figure 22 TI Microcontroller ...... 21 Figure 23 Control Box ...... 21 Figure 24 Experimental Setup ...... 22 Figure 25 Power vs. Resistance Graph for Wind Speed of 4.69 m/s ...... 24 Figure 26 Power vs. Resistance Graph for Wind Speed of 6.00 m/s ...... 26 Figure 27 Power vs. Resistance Graph for Wind Speed of 7.31 m/s ...... 27 Figure 28 Power vs. Resistance Graph for Wind Speed of 8.18 m/s ...... 29 Figure 29 Wind Turbine Block Diagram ...... 31
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Figure 30 Circuit Diagram for DC-DC Boost Converter [8] ...... 32 Figure 31 Extremum Seeking Based MPPT Algorithm ...... 33 Figure 32 Experimental Setup with MPPT Algorithm ...... 34 Figure 33 Real Time Data ...... 34 Figure 34 MPPT Results for Blades with 18° Twist ...... 35 Figure 35 MPPT Results for Blades with 30° Twist ...... 36 Figure 36 MPPT Results for Blades with 35° Twist ...... 37
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CHAPTER I INTRODUCTION
Motivation There has been an increase in demand for environmentally friendly energy sources, such as wind farms; coincidentally, the interest in pursuing such technology has been on the rise. Wind power is considered one of the largest and fastest growing sources of renewable energy to supply electricity – the U.S. Department of Energy predicts that 20 percent of the U.S.’ electricity will be supplied by wind by 2030, and the contribution of the wind energy will only increase [1]. While there are multiple software packages for the design and assessment of the aerodynamics of wind turbine models, such as Ashes, Bladed, FAST, FOCUS6, HAWC2, and QBlade, only two from the list, FAST and QBlade, are open source software. National Renewable Energy Laboratory’s FAST is one of the most used software in the wind energy industry, but requires certification to be downloaded. Knowledge of working with command prompt is also necessary, and the user needs to download other software to visualize the output. Conversely, QBlade tends to be more user-friendly – it can be downloaded easily without any authentication and both the design and the analysis can be done.
Scaled wind turbines are often built and tested in wind tunnels in academia – conducting experiments with the scaled wind turbines allows students and scholars to gain further insight in the effects of various design parameters. The experiments can also help to find the optimal design of the wind turbine. Unfortunately, most of the work conducted tend to focus on the mechanical design of the wind turbine and often neglect the electrical design including the controller. Nevertheless, while many scholars experience the discrepancies between the values obtained from simulations and experiments for a scaled wind turbine, there have been limited studies examining possible causes for these differences.
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Background Commercial wind turbines consist of the anemometer, brake, controller, gearbox, generator, high-speed shaft, low-speed shaft, nacelle, pitch system, rotor, wind vane, yaw drive, and yaw motor, as shown in Figure 1.
Figure 1 Components of Wind Turbine [2]
The principle of electricity generation through a wind turbine is as follows: 1. The wind rotates the rotor – hub and blades 2. As the rotor rotates, the low-speed shaft rotates 3. The gearbox, which connects the low-speed shaft to the high-speed shaft, increases the rotational speed 4. The high-speed shaft derives the generator For wind turbines with a pitch system, the blades will be turned to control the wind speed. For wind turbines with yaw drive, the turbine will rotate according to the direction of the wind.
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The efficiency of the design is assessed by looking at the power coefficient, Cp, which is calculated by Equation 1: 푅표푡표푟 푃표푤푒푟 퐶 = (1) 푝 푃표푤푒푟 푖푛 푊푖푛푑 It is important to note that according to the Betz Theory, the maximum power that can be extracted from wind energy is 59 percent.
Organization In this thesis, a scaled model of a three-blade horizontal axis wind turbine was designed, manufactured, then studied. In Chapter II, the design process of the scaled wind turbine model is discussed. Wind turbine blades were designed, and their aerodynamics were studied using QBlade. The hub and the tower were designed to allow for interchangeability so that different blades and motors could be tested. Then, the manufacturing processes were discussed for the components – the hub was machined while the blades and the tower were printed. In Chapter III, an open loop control was explained and the experimental results were discussed. In Chapter IV, a closed loop control was considered and the experimental results were analyzed. To optimize the power produced by the model, the maximum power point tracking (MPPT) algorithm was applied, and a DC-DC boost converter was used for voltage regulation. In Chapter V, overall experimental results obtained using the National Wind Institute’s Boundary Layer Wind Tunnel at Reese Technology Center was compared to the simulation data and explained. In Chapter VI, the findings were discussed, and further work was recommended.
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CHAPTER II WIND TURBINE DESIGN
In this chapter, a design process for a scaled wind turbine model is discussed. Wind turbine blades were designed using QBlade, and a hub and tower were designed using computer-aided design software, such as Inventor and SolidWorks. Then, the hub was machined and the remaining parts were 3D printed using different materials.
Scaled Wind Turbine Design The design of a scaled wind turbine was kept simple to focus on the aerodynamic design of the wind turbine blades. Twists were added to the wind turbine blades for the consistency of the pitch, and the nacelle was replaced by a Direct Current motor. The turbine model consisted of the hub, three blades, the motor housing, and the tower. Since multiple motors and blade designs were considered, the parts were fabricated to be interchangeable. Figure 2 shows the assembly of all parts and Figure 3 shows an exploded isometric view of the wind turbine model.
Figure 2 Assembly of the Wind Turbine Model
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Figure 3 Exploded Isometric Drawing of the Wind Turbine Model
Wind Turbine Blade Design Since the rotor is the starting point of electricity generation, the aerodynamic design of the wind turbine blade is crucial. The characteristics of the airfoil are shown in Figure 4.
Figure 4 Geometry of the Airfoil [3]
The lift and drag forces of the airfoil are shown in Figure 5.
Figure 5 Forces and Moments of the Airfoil [3]
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To optimize the design, multiple airfoils were utilized to vary the thickness throughout the blade. Figure 6 shows the airfoils considered in the design using QBlade: NACA 0012, NACA 0015, NACA 0018, NACA 0021, NACA 0024, and NACA 0025.
Figure 6 Airfoils Used in QBlade
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The four parameters considered were hub radius, angle of twist, position, and chord. For the consistency of the assembly, the twist of the blades was considered rather than the pitch. Each parameter is shown in Figures 7 and 8.
Hub Radius
Figure 7 Hub Radius
Angle of Twist
Position
Chord Figure 8 Angle of Twist, Position, and Chord
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Due to the size of bed for 3D printer, the length of the blades had to be restricted. Taking the hub connector into consideration, the length was determined to be 192 mm. As any changes in the parameter were made, the values were interpolated as shown in Table 1.
Table 1 QBlade Parameter Position (mm) Chord (mm) Twist Foil Polar 1 0 4 0 Circular Foil CD=1.2 360 Polar 2 3 4 18 Circular Foil CD=1.2 360 Polar 3 4.5 9.2 18 NACA 0025 NACA0025 360M 4 6 15 18 NACA 0025 NACA0025 360M 5 7.5 20.7 18 NACA 0025 NACA0025 360M 6 9 25 18 NACA 0025 NACA0025 360M 7 10.5 28.1 18 NACA 0025 NACA0025 360M 8 12 30 18 NACA 0025 NACA0025 360M 9 42 26 15 NACA 0024 NACA0024 360M 10 72 22 12 NACA 0021 NACA0021 360M 11 102 18 9 NACA 0018 NACA0018 360M 12 132 14 6 NACA 0015 NACA0015 360M 13 162 10 3 NACA 0012 NACA0012 360M 14 192 6 0 NACA 0012 NACA0012 360M
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Figure 9 shows the twist angle with respect to the position.
40
35
30
25
) º 20 18°
Twist Twist ( 15 30° 35° 10
5
0 0 20 40 60 80 100 120 140 160 180 200 Position (mm)
Figure 9 Angle of Twist vs. Position
Initially, blades with twists of 8º, 10º, and 12º were considered for this project since the blades with lower twists suggested higher power coefficient values in QBlade. However, the rotor did not rotate during experiments using blades with lower twists. This can be explained by the geometry of the blades—if there is not enough twist at the base of the blades, the rotor would not rotate due to lack of torque. For the experiments, the blades with higher angle of twists were considered.
Figure 10 shows a graph of the power coefficients with respect to TSR of the three final designs – blades with a chord of 30 mm and twist of 18° at the base, blades with a chord of 30 mm and twist of 30° at the base, and blades with a chord of 35 mm and twist of 35° at the base.
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twist= 18°, chord= 30mm twist= 30°, chord= 30mm
twist= 35°, chord= 35mm
푝
퐶
,
퐶표푒푓푓푖푐푖푒푛푡
푃표푤푒푟
푇푖푝 푆푝푒푒푑 푅푎푡푖표, 푇푆푅
Figure 10 Power Coefficient Graph from QBlade
The blue line represents the blades with twist of 18°, the red line represents the values for blades with twist of 30°, and the green line corresponds to the values for blades with twist of 35°.
Tip speed ratio, λ, is calculated according to Equation 2: 푇푖푝 푆푝푒푒푑 표푓 퐵푙푎푑푒 휔푅 휆 = = (2) 푊푖푛푑 푆푝푒푒푑 푣 where ω is the angular velocity of the blades, R is the rotor radius, and v is the velocity of the wind.
Figure 10 suggests that the blades with 35° twist should perform the best until a TSR of 5.0 is reached. The blades with 30° twist should be the second-best until they are outperformed at a TSR of 3.8. The blades with 18° twist start off as the least effective design, then exceed the performance of the 30° at a TSR of 3.8, followed by 35° at a TSR of 5.0. With smaller scaled wind turbines, bigger twist at the base of the blades assists the initial rotation of the rotor, resulting in higher power coefficients at a lower TSR.
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Figure 11 shows the graphical representation of power produced by the three blade designs with respect to the rotational speed of the blades at wind speed of 8.18 m/s.
twist= 18°, chord= 30mm twist= 30°, chord= 30mm
twist= 35°, chord= 35mm
푃표푤푒푟
푅표푡푎푡푖표푛푎푙 푆푝푒푒푑 Figure 11 Power vs. Rotational Speed Graph from QBlade
Figure 11 suggests a similar performance prediction to the one in Figure 9. Blades with bigger twists are expected to perform better than the blades with twist of 18°, but once the blades reach the rotational speed of 1550 rpm, blades with 18° twist begin to outperform the other designs, eventually producing the most power by the rotational speed of 1950 rpm. It is important to note that each blade design would have different rotational speed even in the same setting for the wind speed. Not to mention, because of the concavity of the graph, the maximum power point tracking (MPPT) algorithm would be used as part of the controller design, which would be discussed further in Chapter IV.
Wind Turbine Tower Design Based on the rotational speed considered in Figure 11, a DC motor with a capability to spin up to 9000 rpm was chosen as a generator. The motor’s maximum rotational speed would be more than enough to account for the rotational speed of the blades. Taking into account the possibility of considering different motors, the motor tower was
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Figure 12 Two Parts of the Wind Turbine Tower
Literature suggests that typically, the height of the wind turbine is 1 to 1.5 times the rotor diameter [3].
Figure 13 Height and Rotor Diameter of Wind Turbine [4]
Since the rotor diameter of the model used was 400 mm, the height of the tower was determined to be 400 mm, which is one times the rotor diameter. 12
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Wind Turbine Hub Design To minimize the manufacturing cost, a hub that can be assembled with all three different blade designs was desired. Figure 14 shows the design of the hub with four holes in the base – one for the motor shaft and three for the blades with three holes on the sides for set screws to keep the motor in place.
Figure 14 Hub Design
Manufacturing Process The simplistic design of the hub allowed it to be machined. To reduce the weight of the rotor, aluminum was used to manufacture the hub. The overall shape of the hub was machined using a lathe, and all the holes necessary for the screws for the assembly were drilled using a milling machine.
To preserve the design, the wind turbine blades and the tower were both manufactured by additive manufacturing. Depending on the complexity of the geometry, two different methods of 3D printing were used: Fused Deposition Modeling (FDM) and Stereolithography Apparatus (SLA).
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FDM fabricates parts by extruding materials – a spool of thermoplastic polymer filaments is melted, and an object is printed by depositing the material layer by layer. This process is shown in Figure 15, and the schematics are shown in Figure 16.
Figure 15 FDM Printing Process [5]
Figure 16 Schematic of an FDM Printer [5]
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While FDM is one of the most cost-effective 3D printing methods, it is recommended for parts with simpler geometries, due to limitations in resolution [5]. To print the wind turbine tower, the following steps were carried out: 1. Use slicer software to convert the CAD model to G-code file 2. Print the tower parts 3. Sand to smooth out the edges as necessary
For wind turbine blades, the FDM printer struggled to capture their complex geometry. An SLA printer was then used instead. SLA can produce high quality prints with smooth surfaces, even for parts with complex geometries. However, the print takes longer and is more expensive. SLA constructs parts by hardening liquid resin – a photosensitive liquid in a reservoir is converted to 3D solid plastic layer by layer using a laser [6]. Figure 17 shows the printing process for SLA printers.
Figure 17 SLA Printing Process [6]
For an SLA printer, two materials were considered: Tough Resin and Rigid. Tough Resin contains similar characteristics as ABS plastic. While it possesses high stiffness and can withstand cyclic loads, it is brittle and has a low heat deflection temperature. Rigid is filled with ceramic. It possesses high stiffness, is suitable for parts with fine
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To print the wind turbine blades, the following steps were carried out: 1. Export the blade geometry file from QBlade 2. Add the hub connection as shown in Figure 15 using Fusion 360 3. Use slicer software to convert the CAD model to G-code file 4. Print the blades 5. Cut off supports and sand the edges as necessary
Figure 18 shows the blade model exported from QBlade and the blade with hub connection.
Figure 18 Wind Turbine Blade from QBlade (left) and Wind Turbine Blade with Hub Connection (right)
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To allow for interchangeability of the blades with different designs with the same hub, a hub connection was added at the base of each blade before it was printed. The hub connection included a hole for the screw to pass by and an arc at the bottom so the blades would be 120º apart on the hub.
Blades printed using the FDM printer had imperfections such as holes and jagged edges and could not spin on their own, even at a wind speed of about 10 m/s. The Rigid blades provided much better resolution than the FDM printed ones and seemed to work well in the beginning of the experiments. However, as the experiments went on and the cyclic loads added up, the hub connection broke, resulting in the blades flying off into the wind tunnel and breaking. The Tough Resin blades printed by the SLA printer were durable enough to continue on with the experiments.
Table 2 represents components of the scaled model and manufacturing method chosen for each. Table 2 Components and Their Manufacturing Methods Components Manufacturing Method Blade Additive Manufacturing (SLA) Hub Machined (Lathe, Milling Machine) Tower Additive Manufacturing (FDM)
Conclusion The aerodynamics of three different blades were analyzed using QBlade. Blades with 35° twists were expected to perform the best followed by blades with 18° twists and 30° twists. Since the graph of power produced with respect to the angular speed showed a concave function, the MPPT algorithm was recommended as part of the controller design. The hub and the tower designed focused on the interchangeability of parts, since multiple blade designs and motors were considered. Finally, FDM and SLA 3D printing techniques were discussed, as well as comparison of different materials.
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CHAPTER III OPEN LOOP CONTROL
Since wind turbines’ main goal is to provide power, wind turbine control was considered. Before any optimization methods were considered, the capabilities of the scaled wind turbine model were assessed. This chapter focuses on an open loop control and the experimental results.
Open Loop Control For this project, the Direct Current motor was used as the generator. Direct Current motor, commonly referred to as a DC motor, contains permanent magnets and a spinning armature. As electricity goes through the electromagnet in the armature, the magnetic field created begins to spin the armature. As the polarity of the electromagnet changes, the armature will continue to spin, resulting in the rotational motion of the rotor. In this case, the DC motor works in the opposite way.
As the wind energy rotates the wind turbine blades and the hub, the shaft rotates. The shaft ends up spinning the generator to generate electricity. The equation for the power of the wind is described as the following: 1 푃 = 휌 퐴푉3 (3) 푤 2 푎푖푟 where Pw is the power of the wind, air is the density of air, A is the area covered by rotating blades, and V is the velocity of the wind.
The equation for the density of air is as shown in Equation 4:
휌푎푖푟 = 1.225 − (0.00011푧) (4) where z is the elevation above sea level. In Lubbock, Texas, the density of air is 1.112 kg/m3.
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Figure 19 shows the block diagram of the open loop control used.
푉푔, 퐼푔
Figure 19 Block Diagram of Open Loop Control
where Vg is the voltage from the generator and Ig is the current from the generator. The variable resistor was added to consider multiple loads and find the maximum power that can be produced with each design.
Experimental Setup For the experiment, the National Wind Institute’s Boundary Layer Wind Tunnel at Reese Technology Center was used. The closed-circuit wind tunnel, shown in Figure 20, is 6 ft wide and 4 ft high [7].
Figure 20 Boundary Layer Wind Tunnel [7]
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The scaled wind turbine model assembly was placed in the boundary layer section of the wind tunnel. As shown in Figure 21, the position of the model was marked with tape and the model was bolted into the wooden panel so the location of the model would be consistent throughout the experiments.
Figure 21 Wind Turbine in the Wind Tunnel
The wires were connected from the DC motor of the wind turbine model to a Texas Instruments (TI) microcontroller for accurate readings of the voltage and current produced by the wind turbine, as shown in Figure 22. For safety measures, a battery was connected to the controller.
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Figure 22 TI Microcontroller
Wind speed inside the wind tunnel was regulated by a control box, as shown in Figure 23. The percentage input is converted to the speed of m/s using Equation 8. 푣 = 0.4366푥 − 0.5514 (5) where v is the speed in m/s and x is the setting in percent.
Figure 23 Control Box
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The overall experimental setup is shown in Figure 24. The wind turbine model shown in Figure 21 and placed in the Boundary Layer Wind Tunnel shown in Figure 20.
Boundary Layer Wind Tunnel
Wind Turbine Model
TI Controller
Control Box
Figure 24 Experimental Setup
Experimental Results The experiments included testing the three blade designs at varying wind speed settings of 12%, 15%, 18%, and 20% which were 4.69 m/s, 6.00 m/s, 7.31 m/s, and 8.18 m/s, respectively. The voltage produced was recorded at open circuit followed by various loads of approximately 24.5 Ω, 10.8 Ω, 5.7 Ω, and 2.0 Ω. The purpose of this experiment was to determine the highest power that can be produced by each design at respective wind speeds.
Once the voltage values were recorded for five resistance values, power was calculated using Equation 6, 푉2 푃 = + 4퐼2 (6) 푅 where the second term, 4I2, was added to account for the power consumed by the DC motor with resistance of 4 Ω. By definition, there is no power produced in an open
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The results of the experiments were first recorded in tabular form then graphed for visual representation. The tables contain the resistance and voltage values obtained during the experiments followed by the current and power calculated. The graphs demonstrate the power produced by each blade with respect to loads. There will only be four points, since there is no power associated with an open circuit. For all the graphs, the blue line represents the values obtained from the blades with 18° twist, the red line represents the values gathered from the blades with 30° twist, and the green line represents the data recoded from the blades with 35° twist.
The experimental results for the wind setting of 12% and wind speed of 4.69 m/s is shown in Tables 3, 4, and 5, and Figure 25. Table 3 Experimental Results for Blades with 18° Twist at Wind Speed of 4.69 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 3.70 N/A N/A 2.0 0.24 0.12 0.086 5.7 0.73 0.13 0.159 10.8 1.45 0.13 0.267 24.5 2.33 0.10 0.258
Table 4 Experimental Results for Blades with 30° Twist at Wind Speed of 4.69 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 2.90 N/A N/A 2.0 0.28 0.140 0.118 5.8 0.88 0.152 0.226 11.4 1.54 0.135 0.281 25.4 2.10 0.083 0.201
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Table 5 Experimental Results for Blades with 35° Twist at Wind Speed of 4.69 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 4.10 N/A N/A 2.0 0.50 0.250 0.375 5.5 1.64 0.298 0.845 10.5 2.00 0.190 0.526 24.4 2.96 0.121 0.418
1
0.8
0.6 18°
0.4 30° Power Power (W) 35° 0.2
0 2.0 7.0 12.0 17.0 22.0 27.0 Resistance (Ω)
Figure 25 Power vs. Resistance Graph for Wind Speed of 4.69 m/s
The blades with 18° twist produced a maximum power of 0.251 W with 10.8 Ω load. The highest power generated by the blades with 30° twist was 0.281 W with resistance of 11.4 Ω. The blades with 35° twist produced a power of 0.845 W at 5.5 Ω load. Comparing all data points, the blades with 35° twist performed the best throughout all resistance values, and the blades with 30° twist were outperformed by the blades with 18° twist at resistance of about 12 Ω.
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The experimental results for the wind setting of 15% and wind speed of 6.00 m/s is shown in Tables 6, 7, and 8 and Figure 26.
Table 6 Experimental Results for Blades with 18° Twist at Wind Speed of 6.00 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 5.40 N/A N/A 2.0 0.53 0.265 0.421 5.9 1.64 0.278 0.765 11.3 2.70 0.239 0.873 24.6 4.10 0.167 0.794
Table 7 Experimental Results for Blades with 30° Twist at Wind Speed of 6.00 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 4.10 N/A N/A 2.0 0.56 0.280 0.470 5.9 1.79 0.303 0.912 10.9 2.35 0.216 0.693 25.3 3.00 0.119 0.412
Table 8 Experimental Results for Blades with 35° Twist at Wind Speed of 6.00 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 5.90 N/A N/A 2.0 1.38 0.690 2.857 5.7 2.70 0.474 2.176 10.6 3.55 0.335 1.638 25.7 4.50 0.175 0.911
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3
2.5
2
1.5 18°
30° Power Power (W) 1 35°
0.5
0 2.0 7.0 12.0 17.0 22.0 27.0 Resistance (Ω)
Figure 26 Power vs. Resistance Graph for Wind Speed of 6.00 m/s
The maximum power produced by the blades with 18° twist was 0.873 W with 11.3 Ω load. The highest power generated by the blades with 30° twist was 0.912 W with a resistance of 5.9 Ω. The blades with 35° twist produced the power of 2.857 W at 2.0 Ω load. Looking at all data points, the blades with 35° twist produced the most power despite different resistance values, and the blades with 30° twist was outperformed by the blades with 18° twist at resistance of around 9 Ω.
The experimental results for the wind setting of 18% and wind speed of 7.31 m/s is shown in Tables 9, 10, and 11 and Figure 27. Table 9 Experimental Results for Blades with 18° Twist at Wind Speed of 7.31 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 7.03 N/A N/A 2.0 0.91 0.455 1.242 5.1 2.35 0.461 1.932 10.3 3.88 0.377 2.029 22.6 5.40 0.239 1.519
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Table 10 Experimental Results for Blades with 30° Twist at Wind Speed of 7.31 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 5.40 N/A N/A 2.0 1.20 0.600 2.160 5.1 2.22 0.435 1.724 10.5 3.18 0.303 1.330 25.2 4.10 0.163 0.773
Table 11 Experimental Results for Blades with 35° Twist at Wind Speed of 7.31 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 7.80 N/A N/A 2.0 2.03 1.015 6.181 5.0 3.58 0.716 4.614 11.1 5.00 0.450 3.064 25.0 6.22 0.249 1.795
7
6
5
4 18° 3
30° Power Power (W)
2 35°
1
0 2.0 7.0 12.0 17.0 22.0 27.0 Resistance (Ω)
Figure 27 Power vs. Resistance Graph for Wind Speed of 7.31 m/s
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The blades with 18° twist produced maximum power of 2.029 W with 10.3 Ω load. The highest power generated by the blades with 30° twist was 2.160 W with resistance of 2.0 Ω. The blades with 35° twist produced the power of 6.188 W at 2.0 Ω load. Comparing all data points, the blades with 35° twist performed the best throughout all resistance values, and the blades with 30° twist was outperformed by the blades with 18° twist at resistance of about 5 Ω.
The experimental results for the wind setting of 20% and wind speed of 8.18 m/s is shown in Tables 12, 13, and 14 and Figure 28.
Table 12 Experimental Results for Blades with 18° Twist at Wind Speed of 8.18 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 7.90 N/A N/A 2.0 1.56 0.780 3.650 5.9 3.20 0.542 2.912 10.7 4.86 0.454 3.033 23.3 6.10 0.262 1.871
Table 13 Experimental Results for Blades with 30° Twist at Wind Speed of 8.18 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 6.24 N/A N/A 2.0 1.59 0.795 3.792 6.0 2.75 0.458 2.101 9.7 3.70 0.381 1.993 24.6 4.85 0.197 1.112
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Table 14 Experimental Results for Blades with 35° Twist at Wind Speed of 8.18 m/s Resistance (Ω) Voltage (V) Current Power (W) 0.0 9.06 N/A N/A 2.0 2.43 1.215 8.857 5.9 4.65 0.789 6.149 10.4 5.89 0.566 4.619 24.0 7.33 0.305 2.612
10
8
6 18°
4 30° Power Power (W) 35° 2
0 2.0 7.0 12.0 17.0 22.0 27.0 Resistance (Ω)
Figure 28 Power vs. Resistance Graph for Wind Speed of 8.18 m/s
The blades with 18° twist produced maximum power of 3.650 W with 2.0 Ω load. The blades with 30° twist’s highest power generated was 3.792 W with resistance of 2.0 Ω. The blades with 35° twist produced the power of 8.857 W at 2.0 Ω load. Comparing all data points, the blades with 35° twist performed the best throughout all resistance values, and the blades with 30° twist was outperformed by the blades with 18° twist at resistance of about 3 Ω.
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Conclusion In this chapter, open loop control was used to test the capabilities of the three blade designs. Based on the experimental results, the blades with the twist of 35° consistently performed the best, and the blades with higher twists tended to reach their maximum power output at a lower resistance than the blades with lower twists. As expected, as wind speed increased, more power was produced for all designs. Blades with 18° twist outperformed the blades with 30° with different loads.
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CHAPTER IV CLOSED LOOP CONTROL
Once maximum power produced by each blade was found in Chapter III, an optimization technique was applied to constantly produce maximum power possible. This chapter focuses on the controller design of the scaled wind turbine model. Controller design included the DC-DC boost converter as well as the MPPT controller.
Closed Loop Control The block diagram for wind turbine control is shown in Figure 29:
Figure 29 Wind Turbine Block Diagram
DC-DC Converter The main purpose of the DC-DC Converter is to step up the voltage. As current goes through the inductor, a magnetic field is created. As the MOSFET switch disconnects the circuit, the subsequent magnetic field travels to the other side of the full circuit, resulting in an increase of the voltage.
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The circuit diagram of the DC-DC Converter is shown in Figure 30.
푉푔, 퐶푔
Figure 30 Circuit Diagram for DC-DC Boost Converter [8]
In Figure 19, Vg is the input voltage of the DC-DC boost converter, Cg is the input capacitance, L is the inductance, iL is the current of the inductor, Su is the control signal of pulse width modulation (PWM), Cdc is the output capacitance, and Vdc is the output voltage of the DC-DC boost converter.
Assuming a virtual output resistance, R, to the DC-DC Converter shown in Figure 30, the model becomes:
푉푑푐 푖퐿 푖퐿푢 푉̇푑푐 = − + − (7) 푅퐶푑푐 퐶푑푐 퐶푑푐
푉 푉 푢 푉푔 (8) 푖̇ = − 푑푐 + 푑푐 + 퐿 퐿 퐿 퐿 where u is the duty cycle of Su and within the range of 0 and 1 [8].
Maximum Power Point Tracking [9] As mentioned in Chapter II, the concavity of the power function suggests the application of the MPPT algorithm.
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Figure 31 shows the block diagram for the gradient-based extremum-seeking MPPT algorithm.
∗ 푢 푃푔 = 푉푔퐼푔
푢̂ 푔̂ 푃푔 − 푃̅푔
asin (휔푡) sin (휔푡)
Figure 31 Extremum Seeking Based MPPT Algorithm
The voltage and current from the wind turbine system is first read to calculate the power produced. Then, the signal goes through the high-pass filter, which allows the DC part to be filtered out. Then, the perturbation signal, asin(ωt), is applied and the maximum power output is observed. After that, the signal goes through the low-pass filter, where the double frequency of the perturbation signal would be filtered out. If the gradient value is positive at that point, the input voltage of the DC-DC boost converter is smaller than the optimal value. Another perturbation signal, sin(ωt), is added to increase the voltage. In the opposite case where the gradient is negative, the input voltage of the DC- DC boost converter is larger than the optimal value, the added perturbation signal, sin(ωt), allows the voltage to decrease. The signal, u*, affects the voltage read from the generator, Vg, which is related to the rotational speed of the generator, which is used to calculate the tip speed ratio. Consequently, the resulting voltage will always be at its optimal value for the maximum power output.
Experimental Setup he general experimental setup was similar as the one from the open loop control in Figure 24; for the closed loop control, the experiments included the implementation of
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Texas Tech University, Chaeeun Kim, December 2019 the MPPT algorithm to optimize the power produced by the wind turbine. Figure 32 shows the experimental setup with MPPT algorithm, where real time data recorded is displayed on the laptop screen, as shown in Figure 33.
Wind Tunnel Wind Turbine Model
TI Controller
MPPT Algorithm
Battery
Figure 32 Experimental Setup with MPPT Algorithm
Figure 33 Real Time Data
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Figure 34 represents the graphical results of the experiments conducted for the blades with 18° twist with MPPT algorithm at wind setting of 20%, 22.5%, and 25%, which are equivalent to 8.18 m/s, 9.27 m/s, and 10.36 m/s, respectively.
13.1 W 10.2 W
7.9 W
Wind Speed Wind Speed Wind Speed 20% 22.5% 25% (8.18 m/s) (9.27 m/s) (10.36 m/s)
Figure 34 MPPT Results for Blades with 18° Twist
At wind speed of 8.18 m/s, the average power produced was 7.9 W. With 9.27 m/s wind, 10.2 W of power was generated. Finally, with 10.36 m/s of wind, 13.1 W of power was produced. From the experiments with open loop control, the maximum power produced by the blades with 18° twist at the wind speed of 8.18 m/s was 3.650 W with 2 Ω load. The MPPT algorithm was able to successfully increase the power by 2.16 times.
Figure 35 represents the graphs obtained on the second set of experiments using the blades with 30° twist and MPPT algorithm at the same wind setting of 20%, 22.5%, and 25%, which are equivalent to 8.18 m/s, 9.27 m/s, and 10.36 m/s, respectively.
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9.1 W
6.7 W 4.6 W
Wind Speed Wind Speed Wind Speed 20% 22.5% 25% (8.18 m/s) (9.27 m/s) (10.36 m/s)
Figure 35 MPPT Results for Blades with 30° Twist
At wind speed of 8.18 m/s, the average power produced was 4.6 W. With 9.27 m/s wind, 6.7 W of power was generated. And with 10.36 m/s of wind, 9.1 W of power was produced. Looking at the experimental results from the experiments conducted with open loop control, the maximum power generated by blades with 30° twists was 3.792 W with resistance of 2 Ω. Comparing this value to the power obtained via MPPT, 4.6 W, there was an increase of the power by 1.21 times.
The values obtained for the blades with 35° twist with the application of MPPT algorithm at the wind setting of 20%, 22.5%, and 25%, which correspond to 8.18 m/s, 9.27 m/s, and 10.36 m/s, respectively, are shown in Figure 36.
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13.9 W 12.3 W
9,8 W
Wind Speed Wind Speed Wind Speed 20% 22.5% 25% (8.18 m/s) (9.27 m/s) (10.36 m/s)
Figure 36 MPPT Results for Blades with 35° Twist
At wind speed of 8.18 m/s, the average power produced was 9.8 W. With 9.27 m/s wind, 12.3 W of power was generated. At a wind speed of 10.36 m/s, 13.9 W of power was produced. Looking at the experimental results from the first set of experiments conducted with various loads, the maximum power generated by blades with 35° twists was 8.857 W with resistance of 2 Ω. The power obtained using MPPT was 9.8 W, which is 1.11 times the power obtained in previous experiments.
Conclusion In this chapter, the controller design of the scaled wind turbine was discussed and implemented. The closed loop wind turbine control consisted of a DC motor, which was used as a generator, a DC-DC boost converter, an MPPT controller, a PWM signal generator, and batteries. The DC-DC converter and MPPT algorithm were further discussed to explain the optimization technique. The DC-DC converter regulated the voltage by stepping up the voltage. The gradient-based extremum-seeking MPPT algorithm was discussed and applied. Using the voltage and current produced by the wind turbine model and applying sinusoidal waves as perturbation signal, the power
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Texas Tech University, Chaeeun Kim, December 2019 output was calculated and optimized. As the wind applied to the model fluctuated, the power produced by the model varied accordingly. The controller would always strive to achieve the maximum power the model can produce at each wind speed.
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CHAPTER V COMPARISON BETWEEN QBLADE AND EXPERIMENTS
This chapter focuses on the simulation and experimental results of the scaled wind turbine model. The discrepancies and the causes of the inconsistencies between the simulation and experimental results are deliberated.
Comparison The experimental results followed the same pattern as the simulation – the blades with 35° twist consistently showed the best performance, while the blades with 30° twist performed better than the ones with 18° twist until a certain point, upon which the blades with 18° twist started to outperform them. In the simulations, the blades with 18° twist began to outperform the blades with 30° twist at TSR of about 3.8.
For the experiments using open loop control, as the wind speed increased, the point of blades with 18° twist producing more power than the blades with 30° twist occurred with less load; the blades with 30° twist was outperformed at the resistance values of 12 Ω, 9 Ω, 5 Ω, and 3 Ω at wind speeds of 4.69 m/s, 6.00 m/s, 7.31 m/s, and 8.18 m/s, respectively. Since the speed of the blades was not measured in the experiments, these values could not be compared with TSR values obtained in the simulations.
Comparing the experiments with open loop and closed loop control, the MPPT algorithm was successfully implemented. At the same wind speed of 8.18 m/s, the power produced by the wind turbine blades with MPPT exceeded the maximum power generated from the open loop control.
While the rotational speed of the rotor was not measured during the experiments, the power and power coefficient values obtained from the experiments with MPPT algorithm and from the QBlade simulation were compared. Unfortunately, the discrepancies between simulation and first experimental values were evident.
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According to QBlade, the maximum power that can be produced by the blades with 18° twist, 30° twist, and 35° twist are 16 W, 12 W, and 15.5 W, respectively. Using the estimated efficiency of the generator used based on the manufacturer datasheet, 85%, and Equation 9, the values obtained from QBlade was calibrated.
푃푒푙푒푐 = 퐶푃,푟표푡표푟 ∙ 휂푔푒푛푒푟푎푡표푟 ∙ 푃푤 (9) where Pelec is the electrical power, CP,rotor is the power coefficient of the rotor, ƞgenerator is the efficiency of the generator, and Pw is the power from wind.
After calculations, the power values become 13.6 W, 10.2 W, and 13.2 W for blades with 18º, 30º, and 35º twists. These values are higher than the maximum power generated by the three different blade designs: 7.9 W, 4.6 W, and 9.8 W. The percent differences between empirical and theoretical power values were 41.9% for blades with 18° twist, 54.9% for blades with 30° twist, and 25.6% for blades with 35° twist.
Table 15 Comparison of Theoretical and Empirical Power Values
Twist Power Theoretical (W) Power Experimental (W) % Difference (%) 18º 13.5 7.9 41.9 30 º 10.2 4.6 54.9 35 º 13.2 9.8 25.6
Looking at the experimental data of wind setting of 20% (wind speed of 8.18 m/s), the power coefficient of blades with 18° twist was 0.22, 0.13 for 30°, and 0.28 for 35°. From the QBlade graph, the maximum power coefficients were 0.39, 0.28, and 0.37, respectively. For blades with 18° twists, the percent difference between empirical and theoretical power coefficient values was 43.5%. The blades with 30° had even bigger percent difference of 54.2%. The blades with 35° twist showed a percent difference of 26.1%. Table 16 shows these results in a tabular form.
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Table 16 Comparison of Theoretical and Empirical Power Coefficient Values
Twist Cp,Theoretical Cp,Experimental % Difference (%) 18º 0.39 0.22 43.5 30 º 0.28 0.13 54.2 35 º 0.37 0.27 26.1
There are a couple explanations for the discrepancies. First, QBlade makes certain assumptions to carry out the simulations. It assumes there is no aerodynamic interaction between the fluid elements of adjacent annular rings and does not account for the radial flow [10]. The difference is more evident in smaller wind turbine models, since the smaller size in geometry is associated with low Reynolds number. At low Reynolds number, boundary layer flow is complicated, which can affect airfoil characteristics [11].
Second, there were slight differences between the simulation and real-life models. Simulations only showed the blade design, whereas the real-life model had an extruder added to connect the blades to the hub and the tower. Additionally, the experimental blades might not have been as smooth as the simulated blades – due to the complex geometry of the blades, 3D printed parts often had to be sanded down to achieve the smoothness. Moreover, wind turbine blades are made of polyester while the scaled model blades consisted of plastic. Difference in material would alter the weight and stiffness of the blades, affecting the overall performance of the wind turbine. Not to mention, the generator loss may have been underestimated.
While optimal environments cannot be achieved in experimental settings, some modifications can be made to reduce discrepancies between simulation and experimental models. The quality of the blades can be enhanced by experimenting with different printing materials, changing the manufacturing process, e.g. designing a mold for blades and pouring silicone, or using a higher quality 3D printer. Different wind tunnel can also be considered to compare different experimental results. Simulations
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Texas Tech University, Chaeeun Kim, December 2019 can also be improved by further refining results within QBlade, such as adding wind field and turbine data.
Conclusion This chapter focused on the comparison of the simulation and experimental data. Simulation data obtained during wind turbine blade design process was summarized. Based on the graphs populated in QBlade, the blades with 35° twist were anticipated to produce the most power at the same wind speed setting, followed by blades with 18° twist, and finally the blades with 30° twist.
The model was taken to Reese Technology Center for experiments to be performed using National Wind Institute’s Boundary Layer Wind Tunnel. The scaled wind turbine model was placed in the closed-circuit wind tunnel and the wires were connected to the TI controller. For the experiments with open loop control, the blades were tested at four different wind speeds and with five different loads. The four wind settings were 4.69 m/s, 6.00 m/s, 7.31 m/s, and 8.18 m/s; and the five different resistance values were 0.0 Ω (open circuit), 2.0 Ω, 5.7 Ω, 10.8 Ω, and 24.5 Ω. The voltage outputs were recorded and the power outputs were calculated and graphed. As expected, the blades with 35° twist performed the best, while the blades with 30° twist was outperformed by the blades with 18° twists at different points throughout the experiment. For the experiments with closed loop control, MPPT algorithm was applied. Comparing the maximum power values from the open loop control and the power generated from the closed loop experiment, the MPPT was successful in all three cases, meeting or exceeding the power output.
Looking at the power and power coefficients from the simulations and the experiments, the discrepancies were apparent. The blades with 18° twist suffered a percent difference of 41.9% in power output and 43.5% in power coefficient. The blades with 30° twist experienced a percent difference of 54.9% and 54.2% in power output and power
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Texas Tech University, Chaeeun Kim, December 2019 coefficient, respectively. Finally, the blades with 35° twist achieved a percent difference of 25.6% for power generated and 26.1% for the power coefficient.
The discrepancies among simulations and two sets of experiments are explained by the assumptions made in QBlade and modifications made in manufacturing and assembling the parts.
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CHAPTER VI CONCLUSION
Conclusion The purposes of these experiments were to find the optimal aerodynamic blade design, apply MPPT algorithm to a scaled wind turbine, and compare the simulations and experiments. For wind turbine design, different airfoils were studied and NACA 00 series was chosen for the airfoils used in the wind turbine blade design. Using QBlade, different hub radius, angle of twist, position of the airfoils, and chords of the airfoils were studied and analyzed. In the end, blades with a chord of 30 mm and twist of 18° at the base, blades with a chord of 30 mm and twist of 30° at the base, and blades with a chord of 35 mm and twist of 35° at the base were chosen. Once the blade designs were finalized, a hub and tower were designed in accordance of the dimensions of the blades. While the hub was easily machined, in order to preserve the accuracy of the manufactured parts, the blades and the tower were 3D printed. Since tower design was simple, the tower parts were printed using FDM printer, and after a trial and error process, the blades were printed with SLA printer.
For wind turbine design, the gradient-based extremum-seeking maximum power point tracking (MPPT) method was proposed for the controller. For MPPT algorithm, voltage and current values are read for power calculation and perturbation signals are applied to constantly achieve the optimal power. Then, the circuit diagram of the DC-DC boost converter was reviewed to step up and regulate the voltage. While the MPPT algorithm was able to increase the power output of the wind turbine blades, the maximum power was only reached for blades with 18° twist. The incremental gain value would need to be adjusted for the blades with 30° and 35° twists.
Simulation and experimental model values were consistent in pattern for all wind blade geometries. Blades with 35° twist performed best – they produced the most power at all wind speeds. Blades with 30° twist outperformed the blades with 18° twist until certain
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Texas Tech University, Chaeeun Kim, December 2019 point in all simulations and experiments. Power coefficients were focused on to compare efficiency of blade designs. At a wind speed of 8.18 m/s, the percent difference between maximum power suggested from QBlade graphs and obtained in experiments were 41.9%, 54.9%, and 25.6% for blades with 18°, 30°, and 35° twist, respectively. Comparing the power coefficient values obtained from QBlade graphs and experiments were 43.5% for blades with 18° twist, 54.2% for blades with 30° twist, and 26.1% for the blades with 35º twist.
Explanations for the disparity in simulation and experimental values include assumptions of optimal environments in simulations and imperfect manufacturing processes in experiments. QBlade, the simulation model used, relies assumptions such as no interaction between fluid elements and no radial flow; however, real-life conditions are not so independent of its surroundings. Low Reynolds number in smaller scaled models causes challenges in simulating boundary layer flow, affecting airfoil characteristics. Furthermore, the geometry of the wind turbine used in experiments required modifications for rotor assembly and the surface of the 3D printed blades were not as smooth as ones in simulations.
Future Work Further effort is needed to reduce the gap between the simulations and experiments. Additional parameters should be considered in QBlade simulations to enhance models and more accurately reflect real-life environments. Different materials and manufacturing processes for wind blades should be tested to improve quality of the print.
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BIBLIOGRAPHY
[1] U.S. Department of Energy. Wind Vision: A New Era for Wind Power in the United States. Springfield: U.S. Department of Commerce, 2015. [2] “Inside of a Wind Turbine.” Office of Energy Efficiency and Renewable Energy. Accessed December 15, 2018. https://www.energy.gov/eere/wind/inside-wind- turbine. [3] Manwell, James, McGowan, Jon, and Anthony Rogers. Wind Energy Explained: Theory, Design and Application. Chichester: John Wiley & Sons Ltd, 2009. [4] Hansen, Martin O. L. Aerodynamics of Wind Turbines. Sterling: Earthscan, 2008. [5] Varotsis, Alkaios Bournias. “Introduction to FDM 3D printing.” 3D Hubs. Accessed May 9, 2019. https://www.3dhubs.com/knowledge- base/introduction-fdm-3d-printing. [6] “Stereolithography (SLA 3D printing) – Simply Explained.” All3DP. August 1, 2019. https://all3dp.com/2/stereolithography-3d-printing-simply-explained/. [7] “Boundary Layer Wind Tunnel.” National Wind Institute. Texas Tech University. Accessed September 13, 2019. https://www.depts.ttu.edu/nwi/research/facilities/wind-tunnel.php. [8] Wang, Yeqin, and Beibei Ren. “Fault Ride-Through Enhancement for Grid-Tied PV Systems With Robust Control.” IEEE Transactions on Industrial Electronics 65, no. 3 (2018): 2302-2312. doi: 10.1109/TIE.2017.2740858. [9] Leblanc, M. “Sur l’electrification des chemins de fer au moyen de courants alternatifs de frequence elevee.” Revue Generale de l’Electricite 12, no. 8 (1922): 275-277. [10] Marten, Dennis, Wendler, Johannes, Pechlivanoglou, George, Nayeri, Christian N., and Christian O. Pascherit. “QBlade: An Open Source Tool for Design and Simulation of Horizontal and Vertical Axis Wind Turbines.” International Journal of Emerging Technology and Advanced Engineering 3, no. 3 (2013): 264-269. https://ijetae.com/files/Conference%20ICERTSD- 2013/IJETAE_ICERTSD_0213_41.pdf. [11] Kadrowski, Damian, Kulak, Michal, Lipian, Michal, Malgorzata, Stepien, Baszczynski, Piotr, Zawadzki, Karol, and Kaciej Karczewski. “Challenging Low Reynolds – SWT Blade Aerodynamics.” MATEC Web of Conferences 234 (2018). doi: 10.1051/matecconf/201823401004.
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