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Modeling and Sensor Fusion for Improved State and Parameter Estimation in Airborne Wind Energy


M. Polzin

Master Thesis, HS15 (10498)

A new estimator for kite power systems with ground-based actuation and generation is presented in this thesis. Estimation of the kite state, including position and heading, with line sensing only limits the achievable cycle efficiency of such airborne wind energy systems due to unobservable tether dynamics. The developed estimator actively addresses the effects of tether dynamics, such as tether sag and induced dead-time, by fusing additional sensor measurements through a newly developed kinematic model. The latter was developed from a systematic analysis of experimental data from a ground-based kite power system using a visual tracking system. Inspired by leader follower dynamics in pattern formation, we model the kite and tether dynamics as a system of coupled unicycles where the tethers follow the kite. An Unscented Kalman filtering scheme has been implemented to simultaneously estimate the kite state and parameters to describe the coupling which include a time-varying delay of the line sensors and the kite velocity. The presented estimator is finally demonstrated for simulated and experimental flight data where a visual tracking system serves as ground truth, and compared to state-of-the-art estimators.

Supervisors: Henrik Hesse, Tony Wood, Roy Smith


Type of Publication:

(12)Diploma/Master Thesis

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2016:IFA_5454
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