System identification, fuzzy control and simulation of a kite power system with fixed tether length

Abstract

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.

Adaptive Flight Path Control of Airborne Wind Energy Systems

“…The approach presented in this paper was derived for the 20 kW kite power system of Delft University of Technology, as illustrated in Figures 1 and 2. The mathematical model was previously formulated and demonstrated in [12,28]. The flight path planning (FPP) and flight path control (FPC) approaches were both designed on the basis of this model.…”

“…We propose an algorithm that estimates the parameters of the kite's dynamic model in real time using system identification (SI) [27][28][29][30]. The employed SI algorithm is a noniterative technique based on Plackett's algorithm due to its ability to calculate the parameters of the system (dynamic model) with high accuracy without singularity.…”

“…We further propose an adaptive controller to improve the robustness of the system and stabilize the kite in different wind conditions. Moreover, SI is considered as a part of the adaptive control strategy, and the estimated parameters are used to obtain the control gains [30], which means that these gains are updated in real time on the basis of changes in the dynamic model [28].…”

“…The remainder of this paper is divided as follows. Section 2 outlines the components of the mathematical model, and discusses it briefly on the basis of the concepts and implementations described in [12,28]. The mathematical model includes a simplified kite system model, designed as a single-input single-output (SISO) model relating the relative steering input (input) to the course angle (output) of the kite, a flight path planner (FPP), and a flight path controller (FPC).…”

“…Subsequently, Section 4 presents the adaptive controller based on the estimated parameters from the SI algorithm that is used to stabilize the kite in real time. In Section 5, simulation results are presented for the classical controller described in [12,13,28], and for the combination of adaptive controller and SI algorithm, and compared for two different flight conditions. Finally, Section 6 summarizes the experimental results using measurement data acquired during a physical flight test; then, these results are analyzed using the SI algorithm derived in Section 3.…”