Automated Flight and Recent Developments of Kite Power at TU …highwind/wp-content/... · 2012. 5....

AWE workshop 23-24 May 2012

Automated Flight and Recent Developments of Kite Power at TU Delft AWE workshop 23-24 May 2012, KU Leuven, Belgium Roland Schmehl

2 AWE workshop 23-24 May 2012

Current demonstrator system Kite Power Technology Outline

• Demonstrator system • Autopilot development • Modeling & simulation • Research group


Kite power system demonstration April 2012, Valkenburg, the Netherlands

Demonstrator system


Demonstrator system Pumping kite working principle

Traction phase: high cable force due to crosswind flight manoeuvers

Recovery phase: low cable force due to depowering (flagging) of kite

Presenter

Presentation Notes

The new course AE4W20 is mandatory for students in the MSc track “Aerodynamics & Wind Energy”. Its focal mission is to convey how flow energy (more specifically wind energy) can be converted into mechanical (and electrical, potential, …) power. As such the course connects the more fundamental disciplines within the field aerodynamics with a the application-oriented disciplines of power generation. Typical forms of output power can be mechanical power (shaft power, towing, …)


Demonstrator system Ground station

Kite power system demonstration

Global Windday 2010, Lelystad, the Netherlands


LEI tube kites

25 m2 bow kite G2 with bridle for improved depower

Demonstrator system


Control pod Demonstrator system


System test at Valkenburg

• Altitude: 150 – 300 m • Flight vel: 70 – 90 km/h • Cycle mean power: 6.5 kW • Wing loading: 30 kg/m2

Demonstrator system

Presenter

Presentation Notes

The new course AE4W20 is mandatory for students in the MSc track “Aerodynamics & Wind Energy”. Its focal mission is to convey how flow energy (more specifically wind energy) can be converted into mechanical (and electrical, potential, …) power. As such the course connects the more fundamental disciplines within the field aerodynamics with a the application-oriented disciplines of power generation. Typical forms of output power can be mechanical power (shaft power, towing, …)

rschmehl

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Watch video at http://www.youtube.com/watch?v=B_vI5UYJuLk http://vimeo.com/39860878

rschmehl

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rschmehl

Typewritten Text

rschmehl

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rschmehl

Typewritten Text

http://www.youtube.com/watch?v=B_vI5UYJuLk

rschmehl

Typewritten Text

http://vimeo.com/39860878


Autopilot development


• Power output determined by

• FT tether force, vr reel velocity

• Aim: Constant power output

• max. force by crosswind trajectories (figure-of-8)

• control reeling velocity to keep power constant

• Need for trajectory tracking controller

Principle Control Structure

!

t rP F v const= × »



Geodesic Distance

( ) ( )1cos sinK C Cvd d c c-= × ¾¾® = - × -r r &

• Kite velocity projected onto local tangent plane

• Geodesic distance decreases depending on orientation


Presenter

Presentation Notes

Important: Angle(s) X: read: chi X_C course angle: angle between direction of traj. and small-north X track angle, “yaw angle” (this is actually not correct): between v_bar and small-north X_cmd bearing angle, “desired yaw angle”: between desired flight direction and small-north Every vector is rotated along the geodesic to the local tangent plane (LTP) of the kite Flat projection on the right to be seen like “unrolling the situation along the geodesic” (das geht nur weil geodesic!) V_bar is the velocity projected onto LTP Kinematic Sideslip is neglected! For derivation of the Eq do a little bit of math


• Control task: Minimize geodesic distance

• Solution: Control yaw angle χ, so that kite smoothly aligns

• Lack of validated kite models à empirical yaw correlation

• c1, c2 constants, vapp apparent wind speed, PS steering input (function of steering line length), G(Ψ) gravitational term (orientation in resp. to gravity)

Empirical System Model

( )!

sin 0Cvd c c= - × - <&

( )1 2app Sc v P c Gc » × × + × ψ&


Presenter

Presentation Notes

Important: If anyone complains: Eq. (1) doesn’t have to be <0, but: sign(delta_dot) != - sign(delta) IFF delta is signed Chi_dot is here approx. the yaw rate (with neglecting kinem. sideslip/drift and rotation of the kite around groundstation) P_S is the steering input, the eventual signal that is sent to the motors PSI contains the orientation of the kite, e.g. in EULER-angles “This Eq. is the base of the ctrl-law, as it enable us to prescribe desired yaw rates … see later …”


Empirical System Model: Results Autopilot development

Presenter

Presentation Notes

Fit is not optimal, but surprisingly good Interesting facts: 1) vapp^1 is linear and not quadratic, as one would expect 2) yaw RATE is dependent on steering input (i.e. line length difference) and NOT yaw ACCEL.� could be due to veeery low inertia and the non-overshoot due to some aerodyn. damping effects We need to put a lot more investigation into it


• Cascaded control structure:

• Feedback linearization: Independent controller design

• Bearing Controller: Determine χcmd that would decrease δ

• Attitude Controller: Minimize orientation error χcmd –χ

• Adaption to increase robustness:

• Comparison of linearization quality with reference (MRAC)

• Superposition of adaption signal onto control signal

Tracking Controller Autopilot development

Presenter

Presentation Notes

Adaption *not drawn*: MRAC/model reference adaptive ctrl Direct or implicit approach (no *parameter identification* is performed)


Simulation Results Autopilot development

Presenter

Presentation Notes

2d plot of controller for different “aggressivenesses”, cf. delta0, of bearing controller Simulation environment: perfect system knowledge No actuator constraints or time delays


Experimental Results 22 May 2012 Autopilot development

Presenter

Presentation Notes

2d plot of controller for different “aggressiveness” of bearing controller Simulation environment: perfect system knowledge No actuator constraints or time delays


Modelling & simulation

20

Modeling & simulation Dynamic processes affecting kite operation

Structural dynamics

Fluid dynamics

Flight dynamics

FSI

FSI = Fluid-Structure Interaction


Modeling & simulation Model complexity


Modeling & simulation Structural submodel

Canopy à triangular, three node shell elements • Total Lagrangian formulation • Bending + Membrane (Felippa) • Use real material properties

Inflatable LE/struts à Beam elements • Total Langrangian formulation • Based on Euler-Bernoulli • Fitted material properties from experiments


Modeling & simulation Aerodynamic submodel

• Expand Cl, Cd to 5 forces • Keep Cm constant. • Assign weights with variations • Compare to CFD


Modeling & simulation Model complexity


Research group Core staff 2012 • Prof. dr. Wubbo Ockels

Director ASSET institute, chairholder

• Dr. Roland Schmehl

Associate professor, head of research group

• Aart de Wachter, MSc

Project manager Kite Power / Laddermill

• Rolf van der Vlugt, MSc

Kite development, mechanical engineering

• Uwe Fechner, MSc

Electronics, embedded systems

• Sergiy Ulyashyn, MSc

Electronics, embedded systems

• Claudius Jehle

• Allert Bosch


• Friesland Fernijt II (136 kEuro), 2010

> Development of control pod • Rotterdam Climate Initiative (1.6 MEuro), running

> Development & commercialization of complete system

Research group Co-funding 2010-2012


• ASSET positioned as interfaculty institute within TU Delft: • Aerospace Engineering • Electrical Engineering & Computer Science • Industrial Design • Mechanical Engineering

• MSc course “Kite Power Generation & Transportation”

2010: 12 students, 2011: 26 students, 2012: 45 students

• MSc course “Wind Power” (50% kite power) 2011: 49 students, 2012: 82 students

• Currently 10 MSc graduate researchers, tendency rising (especially from Germany)!!!

Research group Educational involvement

29

Thank you for your attention

Web: www.kitepower.eu Email: [email protected] Twitter: www.twitter.com/kite_power

Automated Flight and Recent Developments of Kite Power at TU …highwind/wp-content/... · 2012. 5....

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