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.
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