Intelligent Icing Detection Model of Wind Turbine Blades Based on SCADA data
Wenqian Jiang, Junyang Jin
TL;DR
This study tackles icing detection on wind turbine blades using SCADA data. It introduces two intelligent frameworks, PGANC and PGANT, that leverage parallel GANs to learn normal vs icing features, with CNN classification for sufficient-label scenarios and domain-adaptive transfer learning for label-scarce cases. Through a two-stage training approach and MMD-based domain alignment, the methods achieve improved detection accuracy and cross-turbine generalization, outperforming traditional classifiers and ablations. The findings demonstrate practical impact for real-world wind farms by enabling more reliable and scalable icing monitoring under varying operating conditions.
Abstract
Diagnosis of ice accretion on wind turbine blades is all the time a hard nut to crack in condition monitoring of wind farms. Existing methods focus on mechanism analysis of icing process, deviation degree analysis of feature engineering. However, there have not been deep researches of neural networks applied in this field at present. Supervisory control and data acquisition (SCADA) makes it possible to train networks through continuously providing not only operation parameters and performance parameters of wind turbines but also environmental parameters and operation modes. This paper explores the possibility that using convolutional neural networks (CNNs), generative adversarial networks (GANs) and domain adaption learning to establish intelligent diagnosis frameworks under different training scenarios. Specifically, PGANC and PGANT are proposed for sufficient and non-sufficient target wind turbine labeled data, respectively. The basic idea is that we consider a two-stage training with parallel GANs, which are aimed at capturing intrinsic features for normal and icing samples, followed by classification CNN or domain adaption module in various training cases. Model validation on three wind turbine SCADA data shows that two-stage training can effectively improve the model performance. Besides, if there is no sufficient labeled data for a target turbine, which is an extremely common phenomenon in real industrial practices, the addition of domain adaption learning makes the trained model show better performance. Overall, our proposed intelligent diagnosis frameworks can achieve more accurate detection on the same wind turbine and more generalized capability on a new wind turbine, compared with other machine learning models and conventional CNNs.