Transfer Learning of Model-Based Neural Network for Transfer Function Inversion and Load Monitoring of Wind Turbines - Archive ouverte HAL Access content directly
Journal Articles Journal of Physics: Conference Series Year : 2022

Transfer Learning of Model-Based Neural Network for Transfer Function Inversion and Load Monitoring of Wind Turbines

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Abstract

One main concern when using a generic model of a wind turbine is how can we apply it on real measurements for different turbines of the same type. This paper gives a first answer by proposing an inversion approach of a transfer function. Then that approach is tested on a real test case. Two transfer functions from two different turbines of the same farm are inverted on SCADA measurements with transfer learning of a model-base neural network. The inversion results show better predictions than ones with a transfer function classically trained with LIDAR data. An application to the prediction of damage equivalent load with a generic model is showed: the results of applying the inverted transfer function is compared to real DEL measurements and to the transfer function trained from LIDAR data.
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Dates and versions

hal-03910066 , version 1 (21-12-2022)

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Attribution - CC BY 4.0

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Frédéric Delbos, Fabien Caleyron, Jean-Marc Leroy. Transfer Learning of Model-Based Neural Network for Transfer Function Inversion and Load Monitoring of Wind Turbines. Journal of Physics: Conference Series, 2022, 2265, pp.032081. ⟨10.1088/1742-6596/2265/3/032081⟩. ⟨hal-03910066⟩

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