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Real-time altitude control of airborne wind energy (AWE) systems can improveperformance by allowing turbines to track favorable wind speeds across a rangeof operating altitudes. The current work explores the performance implicationsof deploying an AWE system with sensor configurations that provide differentamounts of data to characterize wind speed profiles. We examine various controlobjectives that balance trade-offs between exploration and exploitation, anduse a persistence model to generate a probabilistic wind speed forecast toinform control decisions. We assess system performance by comparing powerproduction against baselines such as omniscient control and stationary flight.We show that with few sensors, control strategies that reward exploration arefavored. We also show that with comprehensive sensing, the implications ofchoosing a sub-optimal control strategy decrease. This work informs andmotivates the need for future research exploring online learning algorithms tocharacterize vertical wind speed profiles.

On Wind Speed Sensor Configurations and Altitude Control in Airborne Wind Energy Systems