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.

A review of existing knowledge regarding how to manage ice during offshore work in cold waters has been carried out. The objective has been to contribute to increased safety and efficiency in future projects by learning from world-wide experiences. It was found that offshore drilling has been carried out in a wide range of ice conditions and at water depths spanning from five metres to more than a thousand metres. Icebergs in open waters have been handled safely over several years and the possibility of detecting icebergs is considered good. With respect to icebergs frozen in sea ice, both detection and deflection is considered difficult and the technology for doing so is not proven. Good ice management systems are considered to represent the main factors for operating successfully in the covered waters. Future work should focus on how to include the effect of ice management statistically in a design process.