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

Abstract

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.

Citation

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. https://doi.org/10.1017/eds.2023.9

• 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
• DOI: https://doi.org/10.1017/eds.2023.9
• Keywords: CycleGAN; decision support; ice detection; neural style transfer; wind turbines
• Public URL: https://hull-repository.worktribe.com/output/4306437