Airborne wind energy (AWE) systems harness wind power using devices flying in controlled patterns, with two main concepts: onboard power generation in the 'drag mode' and ground-based generation in the 'pumping cycle'. Quasi-steady state models (QSM) efficiently predict parameters like tether force and kite velocity for smaller AWE systems but the neglect of inertial forces leads to inaccuracies for bigger systems. Various formulations of quasi-steadiness exist in AWE literature, but their validity limits remain poorly understood. This research presents a theoretical framework specifically tailored to crosswind tethered flight dynamics, which is used to obtain a consistent definition of quasi-steadiness and to quantify the validity of this assumption, through extensive solution space analysis. The study finds that QSM provides reasonable accuracy for time-averaged quantities but fails to predict other aspects such as amplitude or phase. Limitations arise from the use of a steady aerodynamic model and assumptions of a rigid tether.

The goal of this report is to outline the sub-system design of the local sensing system chosen as the final concept in [1], to satisfy the mission need statement: measure the atmospheric conditions with full three-dimensional coverage of a wind farm to optimize its operational performance and control. This statement is derived from the need to improve the control and performance of wind farms through more informed processes and decisions, a task that meteorological masts would usually take on. However, the providable coverage is very low in comparison to the one a UAV based system could provide. UAVs have the potential to significantly increase the measurement coverage around an entire wind farm and in turn return to the user more valuable data. To approach the finding of a solution to this problem, the project was divided into four: planning, concept definition, concept exploration and detailed design. From the first two phases came unique concepts exploring remote and local sensing options, combined with a range of UAV types including hybrid, fixed-wing and rotor. Through a detailed trade-off process and sensitivity analysis, the agreed upon final solution came to be a local sensing concept that makes use of many hybrid drones. In the fourth and final phase, where we now find ourselves, the detailed concept is unpacked and designed into a marketable system that is capable of satisfying the underlying MNS. In this stage the design was split into three design groups: UAV design, ground station design, swarm design.