a b s t r a c tConverting the traction power of kites into electricity can be a low cost solution for wind energy. Reliable control of both trajectory and tether reeling is crucial. The present study proposes a modelling framework describing the dynamic behaviour of the interconnected system components, suitable for design and optimization of the control systems. The wing, bridle, airborne control unit and tether are represented as a particle system using spring-damper elements to describe their mechanical properties. Two kite models are proposed: a point mass model and a four point model. Reeling of the tether is modelled by varying the lengths of constituent tether elements. Dynamic behaviour of the ground station is included. The framework is validated by combining it with the automatic control system used for the operation of a kite power system demonstrator. The simulation results show that the point mass model can be adjusted to match the measured behaviour during a pumping cycle. The four point model can better predict the influence of gravity and inertia on the steering response and remains stable also at low tether forces. Compared to simple one point models, the proposed framework is more accurate and robust while allowing real-time simulations of the complete system.

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Abstract. In wind energy research, airborne wind energy systems are one of the promising energy sources in the near future. They can extract more energy from high altitude wind currents compared to conventional wind turbines. This can be achieved with the aid of aerodynamic lift generated by a wing tethered to the ground. Significant savings in investment costs and overall system mass would be obtained since no tower is required. To solve the problems of wind speed uncertainty and kite deflections throughout the flight, system identification is required. Consequently, the kite governing equations can be accurately described. In this work, a simple model was presented for a tether with a fixed length and compared to another model for parameter estimation. In addition, for the purpose of stabilizing the system, fuzzy control was also applied. The design of the controller was based on the concept of Mamdani. Due to its robustness, fuzzy control can cover a wider range of different wind conditions compared to the classical controller. Finally, system identification was compared to the simple model at various wind speeds, which helps to tune the fuzzy control parameters.