Wind turbine blade icing diagnosis using B-SMOTE-Bi-GRU and RFE combined with icing mechanism

• Published in: Renewable energy 2024-02, Vol.221, p.119741, Article 119741
• Main Authors: Tao, Cheng, Tao, Tao, He, Shukai, Bai, Xinjian, Liu, Yongqian
• Format: Article
• Language: English
• Subjects: B-SMOTE; Bi-GRU; Hybrid features; Icing diagnosis; SCADA; Wind turbine blade
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2023.119741

Low temperature and high humidity in winter can easily lead to blade icing, which severely impacts the actual power output and safe operation of wind turbines. However, existing blade icing diagnostic methods suffer from inadequacies in feature extraction and the accuracy. Therefore, we propose an efficient and accurate blade icing diagnosis method. Initially, we develop hybrid features that include both short-time and long-time features. RFE is combined with icing mechanism to extract short-time features, and the sliding window algorithm is used to extract long-time features. Secondly, the B-SMOTE method is used to synthesize minority icing samples. Finally, the B-SMOTE-Bi-GRU model is applied to validate the superiority of the proposed method. Compared to the features extracted based on the icing mechanism and the sliding window algorithm, the average improvements in recall, TNR, and accuracy for each wind turbine is between 0.26% and 4.87 %, 0.23%–4.60 %, and 0.05%–2.73 %, respectively. In comparison to the other models, the B-SMOTE-Bi-GRU method proposed in this paper enhances the recall and TNR of each wind turbine by between 0.06%-13.10 % and 0.07%–12.01 %, respectively. The results indicate that the hybrid features and B-SMOTE-Bi-GRU model proposed in this paper offer a more effective and accurate approach to blade icing diagnosis. •A new feature extraction method is proposed based on RFE combined with icing mechanism.•Icing diagnostic model based on the B-SMOTE-Bi-GRU model is proposed.•The proposed new method of feature extraction enhances the accuracy of blade icing diagnosis.•The B-SMOTE-Bi-GRU model is superior to existing models.•The proposed method achieves higher accuracy for blade icing diagnosis.