A reference model for airborne wind energy systems for optimization and control. (September 2019)

• Record Type: Journal Article
• Authors: Malz, E.C.; Koenemann, J.; Sieberling, S.; Gros, S.
• Is Part Of: Renewable energy. Volume 140(2019)
• Page Start: 1004
• Page End: 1011
• Publication Date: 2019-09
• Subjects: Wind energy -- Airborne wind energy -- Modeling -- Validation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.03.111
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Airborne Wind Energy (AWE) is a promising new technology, and attracts a growing academic and industrial attention. Important research efforts have been deployed to develop prototypes in order to test the technology, generate control algorithms and optimize the efficiency of AWE systems. By today, a large set of control and optimization methods is available for AWE systems. However, because no validated reference model is available, there is a lack of benchmark for these methods. In this paper, we provide a reference model for pumping mode AWE systems based on rigid wings. The model describes the flight dynamics of a tethered 6 degrees of freedom (DOF) rigid body aircraft in form of differential-algebraic equations, based on Lagrange dynamics. With the help of least squares fitting the model is assessed using real flight data from the Ampyx Power prototype AP2. The model equations are smooth and have a low symbolic complexity, so as to make the model ideal for optimization and control. The information given in this paper aims at providing AWE researchers with a model that has been validated against flight data and that is well suited for trajectory and power output simulation and optimization. Highlights: The proposed AWE system reference model is capable of representing adequately real flight data. The average power output during one pumping cycle is estimated by the model with a relative error of 0.92% to the data. The assumption of a straight tether is fair as the modeled variables related to the tether present a good fit with the data.