Open AccessA nonlinear inverse model for airborne wind energy system analysis, control, and design optimization
Author(s) -
Aull Mark,
Cohen Kelly
Publication year - 2021
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.2562
Subject(s) - nonlinear system , control theory (sociology) , wind power , trajectory , inversion (geology) , inverse , engineering , controller (irrigation) , computer science , simulation , control engineering , marine engineering , control (management) , mathematics , physics , geometry , quantum mechanics , artificial intelligence , electrical engineering , paleontology , agronomy , astronomy , structural basin , biology
Abstract This paper describes a nonlinear model inversion for performance analysis and system optimization for airborne wind energy (AWE) systems. Airborne wind energy systems are lighter and potentially lower in cost than comparable conventional wind turbines due to using a tether and bridle rather than blades and a tower which must support high bending and compressive loads. They are also easier to install due to using a tension anchor rather than a foundation. There are many AWE systems in various states of R&D, but no commercial AWE wind farms currently exist. Because AWE systems are novel and significantly more complex to design, analyze, and test than conventional wind turbines, better analysis tools are important for the technology to mature. AWE system design involves many interdependent design trade‐offs, which are difficult to analyze with traditional tools; using simulations to iterate through parameters for design optimization is undesirable. A nonlinear model inversion based on appropriate simplifying assumptions is better suited to this task. The inverse model uses a trajectory input then calculates attitudes, forces, and control values required to follow that trajectory as well as power produced. Applicable constraints ensure that the trajectory is realizable. A validation is presented, using the output of the inverse model as a feed‐forward controller for a high fidelity simulation. The analysis shows good agreement with the simulation, despite simplifications such as a straight rigid tether, constant lift and drag coefficients, and a simplified rotor model.