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Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning

Chatterjee, Joyjit; Alvela Nieto, Maria T; Gelbhardt, Hannes; Dethlefs, Nina; Ohlendorf, Jan-Hendrik; Greulich, Andreas; Thoben, Klaus-Dieter


Maria T Alvela Nieto

Hannes Gelbhardt

Jan-Hendrik Ohlendorf

Andreas Greulich

Klaus-Dieter Thoben


Wind energy’s ability to liberate the world from conventional sources of energy relies on lowering the significant costs associated with the maintenance of wind turbines. Since icing events on turbine rotor blades are a leading cause of operational failures, identifying icing in advance is critical. Some recent studies have utilized deep learning (DL) techniques to predict icing events with high accuracy by leveraging rotor blade images, but these studies only focus on specific wind parks and fail to generalize to unseen scenarios (e.g., new rotor blade designs). In this paper, we aim to facilitate ice prediction on the face of lack of ice images in new wind parks. We propose the utilization of synthetic data augmentation via a generative artificial intelligence technique—the neural style transfer algorithm to improve the generalization of existing ice prediction models. We also compare the proposed technique with the CycleGAN as a baseline. We show that training standalone DL models with augmented data that captures domain-invariant icing characteristics can help improve predictive performance across multiple wind parks. Through efficient identification of icing, this study can support preventive maintenance of wind energy sources by making them more reliable toward tackling climate change.


Chatterjee, J., Alvela Nieto, M. T., Gelbhardt, H., Dethlefs, N., Ohlendorf, J., Greulich, A., & Thoben, K. (2023). Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning. Environmental Data Science, 2, 1-15.

Journal Article Type Article
Acceptance Date May 10, 2023
Online Publication Date Jun 7, 2023
Publication Date 2023
Deposit Date Jun 8, 2023
Publicly Available Date Jun 8, 2023
Journal Environmental Data Science
Publisher Cambridge University Press
Peer Reviewed Peer Reviewed
Volume 2
Article Number e12
Pages 1-15
Keywords CycleGAN; decision support; ice detection; neural style transfer; wind turbines
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Copyright Statement
© The Author(s), 2023. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.

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