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Maximizing Average Power Output of an Airborne Wind Energy Generator Under Parametric Uncertainties

[+] Author Affiliations
Michelle A. Kehs, Hosam K. Fathy

Pennsylvania State University, University Park, PA

Chris Vermillion

University of North Carolina at Charlotte, Charlotte, NC

Paper No. DSCC2015-9764, pp. V002T21A001; 8 pages
doi:10.1115/DSCC2015-9764
From:
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5725-0
  • Copyright © 2015 by ASME

abstract

This paper presents a controller for maximizing the time-averaged power output from an airborne wind energy generator in uncertain wind conditions. This system’s optimal energy output often involves flying in periodic figure-8 trajectories, but the precise optimal figure-8 shape is sensitive to environmental conditions, including wind speed. The literature presents controllers that are able to adapt to uncertainties, and this work expands on the current literature by using an extremum seeking based method. Extremum seeking is particularly well-suited for this application because of its well understood stability properties. In this work, extremum seeking is used to search through a family of optimal trajectories (computed offline) that correspond to discrete wind speeds. The controller is efficient in that it only searches for the optimum trajectory over the uncertain parameter (in this paper, wind speed). Results show that the controller converges to the optimal trajectory, provided it is initialized to a stable figure-8. The speed of convergence is dependent on the difference between the initial average power output and the optimal average power output.

Copyright © 2015 by ASME

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