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Research on the Icing Diagnosis of Wind Turbine Blades Based on FS–XGBoost–EWMA

Jicai Guo1,2, Xiaowen Song1,2,*, Chang Liu1,2, Yanfeng Zhang1,2, Shijie Guo1,2, Jianxin Wu1,2, Chang Cai3, Qing’an Li3,*

1 College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China
2 Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot, 010051, China
3 Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing, 100190, China

* Corresponding Authors: Xiaowen Song. Email: email; Qing’an Li. Email: email

(This article belongs to the Special Issue: Wind Energy Development and Utilization)

Energy Engineering 2024, 121(7), 1739-1758. https://doi.org/10.32604/ee.2024.048854

Abstract

In winter, wind turbines are susceptible to blade icing, which results in a series of energy losses and safe operation problems. Therefore, blade icing detection has become a top priority. Conventional methods primarily rely on sensor monitoring, which is expensive and has limited applications. Data-driven blade icing detection methods have become feasible with the development of artificial intelligence. However, the data-driven method is plagued by limited training samples and icing samples; therefore, this paper proposes an icing warning strategy based on the combination of feature selection (FS), eXtreme Gradient Boosting (XGBoost) algorithm, and exponentially weighted moving average (EWMA) analysis. In the training phase, FS is performed using correlation analysis to eliminate redundant features, and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis (SCADA) data to build a normal behavior model. In the online monitoring phase, an EWMA analysis is introduced to monitor the abnormal changes in features. A blade icing warning is issued when the monitored features continuously exceed the control limit, and the ambient temperature is below 0°C. This study uses data from three icing-affected wind turbines and one normally operating wind turbine for validation. The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.

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Cite This Article

APA Style
Guo, J., Song, X., Liu, C., Zhang, Y., Guo, S. et al. (2024). Research on the icing diagnosis of wind turbine blades based on fs–xgboost–ewma. Energy Engineering, 121(7), 1739-1758. https://doi.org/10.32604/ee.2024.048854
Vancouver Style
Guo J, Song X, Liu C, Zhang Y, Guo S, Wu J, et al. Research on the icing diagnosis of wind turbine blades based on fs–xgboost–ewma. Energ Eng. 2024;121(7):1739-1758 https://doi.org/10.32604/ee.2024.048854
IEEE Style
J. Guo et al., "Research on the Icing Diagnosis of Wind Turbine Blades Based on FS–XGBoost–EWMA," Energ. Eng., vol. 121, no. 7, pp. 1739-1758. 2024. https://doi.org/10.32604/ee.2024.048854



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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