Airborne wind energy resource analysis

Bechtle, P.; Schelbergen, Mark; Schmehl, Roland; Zillmann, Udo; Watson, Simon J.

doi:10.1016/j.renene.2019.03.118

Cited by 83 publications

(56 citation statements)

References 34 publications

(47 reference statements)

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“…The design, as a tensile structure, substantially reduces the material use, which leads to lower system costs and environmental footprint. It also allows for a dynamic adjustment of the operational altitude to the available wind resources, which can greatly increase the capacity factor [14]. For a HAWT, almost 30% of the power is generated by the tip of the rotor blades while the rest of the rotor functions mainly as a support structure for the crosswind motion of the blades [1,2].…”

Section: Airborne Wind Energymentioning

confidence: 99%

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Rushdi

Dief

et al. 2020

Energies

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Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the material required for conventional wind turbines. In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models. The system is designed for 7 kW traction power and comprises an inflatable wing with suspended kite control unit that is either tethered to a fixed ground anchor or to a towing vehicle to produce a controlled relative flow environment. A measurement unit was attached to the kite for data acquisition. To predict the generated tether force, we collected input–output samples from a set of well-designed experimental runs to act as our labeled training data in a supervised machine learning setting. We then identified a set of key input parameters which were found to be consistent with our sensitivity analysis using Pearson input–output correlation metrics. Finally, we designed and tested the accuracy of a neural network, among other multivariate regression models. The quality metrics of our models show great promise in accurately predicting the tether force for new input/feature combinations and potentially guide new designs for optimal power generation.

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Section: Airborne Wind Energymentioning

confidence: 99%

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Rushdi

Dief

et al. 2020

Energies

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“…Developers and operators of large conventional wind turbines, AWES and drones require accurate wind data to estimate power output and mechanical loads. They currently rely on oversimplified approximations such as the logarithmic wind profile (Optis et al, 2016) or coarsely resolved re-analysis data sets (Archer and Caldeira, 2009;Bechtle et al, 2019) as the applicability of conventional spectral wind models (Burton, 2011) has not been verified for these altitudes. The first investigations (Fechner and Schmehl, 2018) resorting to the Mann model (Mann, 1994;IEC, 2005) have been conducted.…”

Section: Introductionmentioning

confidence: 99%

Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

et al. 2019

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Abstract. Airborne wind energy systems (AWESs) aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind light detection and ranging (lidar) measurements and mesoscale models both have their advantages and disadvantages when assessing the wind resource at such heights. This study investigates whether assimilating measurements into the mesoscale Weather Research and Forecasting (WRF) model using observation nudging generates a more accurate, complete data set. The impact of continuous observation nudging at multiple altitudes on simulated wind conditions is compared to an unnudged reference run and to the lidar measurements themselves. We compare the impact on wind speed and direction for individual days, average diurnal variability and long-term statistics. Finally, wind speed data are used to estimate the optimal traction power and operating altitudes of AWES. Observation nudging improves the WRF accuracy at the measurement location. Close to the surface the impact of nudging is limited as effects of the air–surface interaction dominate but becomes more prominent at mid-altitudes and decreases towards high altitudes. The wind speed frequency distribution shows a multi-modality caused by changing atmospheric stability conditions. Therefore, wind speed profiles are categorized into various stability conditions. Based on a simplified AWES model, the most probable optimal altitude is between 200 and 600 m. This wide range of heights emphasizes the benefit of such systems to dynamically adjust their operating altitude.

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“…The devices operate at higher altitudes than conventional wind turbines where the wind is generally stronger and steadier. () Consequently, the availability of power and the capacity factor are expected to increase significantly compared with the conventional wind turbines. Also, the cost of the system is expected to decrease dramatically since less materials are used due to the absence of the massive support structure (tower and long blades) of the wind turbine.…”

Section: Introductionmentioning

confidence: 99%

Boundary layer transition modeling on leading edge inflatable kite airfoils

2019

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We present a computational fluid dynamic analysis of boundary layer transition on leading edge inflatable kite airfoils used for airborne wind energy generation. Because of the operation in pumping cycles, the airfoil is generally subject to a wide range of Reynolds numbers. The analysis is based on the combination of the shear stress transport turbulence model with the γ−trueR˜eθt transition model, which can handle the laminar boundary layer and its transition to turbulence. The implementation of both models in OpenFOAM is described. We show a validation of the method for a sailwing (ie, a wing with a membrane) airfoil and an application to a leading edge inflatable kite airfoil. For the sailwing airfoil, the results computed with transition model agree well with the existing low Reynolds number experiment over the whole range of angles of attack. For the leading edge inflatable kite airfoil, the transition modeling has both favorable and unfavorable effects on the aerodynamics. On the one hand, the aerodynamics suffer from the laminar separation. But, on the other hand, the laminar boundary layer thickens slower than the turbulent counterpart, which, in combination with transition, delays the separation. The results also indicate that the aerodynamics of the kite airfoil could be improved by delaying the boundary layer transition during the traction phase and tripping the transition in the retraction phase.

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Airborne wind energy resource analysis

Cited by 83 publications

References 34 publications

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

Boundary layer transition modeling on leading edge inflatable kite airfoils

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