Performance assessment of a rigid wing Airborne Wind Energy pumping system

Licitra, Giovanni; Koenemann, Jonas; Bürger, Adrian; Williams, Paul; Ruiterkamp, Richard; Diehl, Moritz

doi:10.1016/j.energy.2019.02.064

Cited by 41 publications

(40 citation statements)

References 6 publications

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“…There are several papers that deal with model-based AWE systems for kites [13,[26][27][28] and tethered aircraft [29][30][31] as the flying devices of an AWE system, including a simulated approach for training constrained Gaussian processes models [32]; however, there is a lack of papers based on experimental measurements [33] using data-driven methods [34,35], although, system identification was used in several papers [28,[36][37][38]. In a predecessor paper [39], experimental data from early flight tests was presented, but without any data analysis.…”

Section: Machine Learning Methods In Awementioning

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: Machine Learning Methods In Awementioning

confidence: 99%

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Rushdi

Dief

et al. 2020

Energies

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“…However, in this paper, we confine our main focus to simulating and controlling the aircraft in the transition phase only. The aircraft simulation model represents the AP-2 aircraft, which has been developed by the Ampyx Power company [22][23][24]. The AP-2 aircraft was not initially designed for VTOL, but we want to see what happens if we modify it by adding vertical takeoff-landing capabilities.…”

Section: Overview Of the Full Power Cyclementioning

confidence: 99%

“…where ρ ≈ 1.225kg/m 3 is the air mass density, V = √ u 2 + v 2 + w 2 is the aircraft speed, C X , C Y , C Z and C l , C m , C n are the non-dimensional body axes aerodynamic force and moment coefficients, respectively. It is common in the aerodynamic field to approximate the aerodynamic coefficients by linear terms in their series expansions [7,8,[22][23][24]…”

Section: Aerodynamic Forces and Momentsmentioning

confidence: 99%

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Simulation of the Transition Phase for an Optimally-Controlled Tethered VTOL Rigid Aircraft for AirborneWind Energy Generation

Rushdi

Hussein²,

Dief

et al. 2020

AIAA Scitech 2020 Forum

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“…O PERATING airborne wind energy (AWE) systems requires sophisticated control strategies that try to exploit the full physical capabilities of the system for maximum power generation without compromising safety. The major part of the existing literature about AWE control systems focuses on the former, to maximize the power output using trajectory optimization (see, for instance, [1][2][3][4]). The only recent publication that analyses reliability and safety of AWE systems is [5], which presents a failure mode and effect analysis along with a fault tree analysis for a flexible wing kite power system.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Resilience of Airborne Wind Energy Systems Through Upset Condition Avoidance

Rapp

Schmehl

2021

Journal of Guidance, Control, and Dynamics

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Airborne wind energy (AWE) systems are tethered flying devices that harvest wind resources at higher altitudes, which are not accessible to conventional wind turbines. To become a viable alternative to other renewable energy technologies, AWE systems are required to fly reliably and autonomously for long periods of time while being exposed to atmospheric turbulence and wind gusts. In this context, the present paper proposes a three-step methodology to improve the resilience of an existing baseline control system toward these environmental disturbances. In the first step, upset conditions are systematically generated that lead to a failure of the control system using the subset simulation method. In the second step, the generated conditions are used to synthesize a surrogate model that can be used to predict upsets beforehand. In the final step an avoidance maneuver is designed that keeps the AWE system operational while minimizing the impact of the maneuver on the average pumping cycle power. The feasibility of the methodology is demonstrated on the example of tether rupture during pumping cycle operation. As an additional contribution a novel transition strategy from retraction to traction phase is presented that can reduce the probability of tether rupture significantly.

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Performance assessment of a rigid wing Airborne Wind Energy pumping system

Cited by 41 publications

References 6 publications

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Simulation of the Transition Phase for an Optimally-Controlled Tethered VTOL Rigid Aircraft for AirborneWind Energy Generation

Enhancing Resilience of Airborne Wind Energy Systems Through Upset Condition Avoidance

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