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Airborne wind energy (AWE) systems are tethered flying devices that harvestwind resources at higher altitudes which are not accessible to conventionalwind turbines. In order to become a viable alternative to other renewableenergy technologies, AWE systems are required to fly reliably for long periodsof time without manual intervention while being exposed to varying windconditions. In the present work a methodology is presented, which augments anexisting baseline controller with a prediction and prevention methodology toimprove the resilience of the controller against these external disturbances.In the first part of the framework, upset conditions are systematicallygenerated in which the given controller is no longer able to achieve itsobjectives. In the second part, the generated knowledge is used to synthesize amodel that predicts upsets beforehand. Eventually, this allows to trigger anavoidance maneuver which keeps the AWE system operational, however, leads to alower power production. The methodology is applied to the specific case oftether rupture prediction and prevention. Simulation results are used todemonstrate that the presented methodology leads indeed to a predictableeconomic benefit over systems without the proposed baseline controlleraugmentation.

Enhancing Control System Resilience for Airborne Wind Energy Systems Through Upset Condition Avoidance