Academic Journal of Engineering and Technology Science, 2022, 5(2); doi: 10.25236/AJETS.2022.050206.
Liwen Wang1, Yanlong Zhao2
1Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, China
2School of Control Science and Engineering, University of Jinan, Jinan, China
To deal with the icing problem of wind turbine blades, the traditional classification methods are introduced firstly in this paper, and the XGBoost model based on monitoring and data acquisition (SCADA) system is introduced to estimate the icing conditions of blades. Meanwhile, the generation process of the XGBoost model is introduced in detail. Finally, the superiority of the XGBoost model is verified by experiments. The results show that XGBoost has higher precision and efficiency than other methods.
XGBoost; Wind Turbine Blade Icing Detection; Supervisory Control and Data Acquisition (SCADA); Data-driven
Liwen Wang, Yanlong Zhao. Wind Turbine Blade Icing Fault Prediction Based on SCADA Data by XGBoost. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 2: 30-35. https://doi.org/10.25236/AJETS.2022.050206.
[1] Tong W. Wind power generation and wind turbine design [M]. WIT press, 2010.
[2] Liu Y, Cheng H, Kong X, et al. Intelligent wind turbine blade icing detection using supervisory control and data acquisition data and ensemble deep learning [J]. Energy Science & Engineering, 2019, 7(6): 2633-2645.
[3] Homola M C, Nicklasson P J, Sundsbø P A. Ice sensors for wind turbines [J]. Cold regions science and technology, 2006, 46(2): 125-131.
[4] Dervilis N, Choi M, Taylor S G, et al. On damage diagnosis for a wind turbine blade using pattern recognition [J]. Journal of sound and vibration, 2014, 333(6): 1833-1850.
[5] Shi Q. Model-based detection for ice on wind turbine blades [D]. NTNU, 2017.
[6] Kreutz M, Ait-Alla A, Varasteh K, et al. Machine learning-based icing prediction on wind turbines [J]. Procedia CIRP, 2019, 81: 423-428.
[7] Zhang R, Li B, Jiao B. Application of XGboost algorithm in bearing fault diagnosis [C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2019, 490(7): 072062.
[8] Loh W Y. Classification and regression tree methods [J]. Encyclopedia of statistics in quality and reliability, 2008, 1: 315-323.
[9] Wang Y, Sun S, Chen X, et al. Short-term load forecasting of industrial customers based on SVMD and XGBoost [J]. International Journal of Electrical Power & Energy Systems, 2021, 129: 106830.
[10] Samat A, Li E, Wang W, et al. Meta-XGBoost for hyperspectral image classification using extended MSER-guided morphological profiles [J]. Remote Sensing, 2020, 12(12): 1973.