Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2022, 5(2); doi: 10.25236/AJCIS.2022.050213.

Wind Turbine Blade Icing Detection Based on Random Forest


Yanlong Zhao1, Liwen Wang2

Corresponding Author:
Yanlong Zhao

1School of Electrical Engineering, University of Jinan, Jinan, China

2Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, China


In this paper, a multimodal random forest method is proposed to solve the problem that the traditional linear method has low accuracy in predicting the icing of generator blade. This paper uses the random forest algorithm to model and analyze the icing event of wind turbine blades, and describes in detail the process of using the C4.5 algorithm to generate a decision tree, and then randomly selecting samples and sample features to train to generate a random forest. The effectiveness of the method in this paper is experimentally verified by SCADA data, and the results show that the multi-modal random forest algorithm reduces the error rate to 1.97% in wind turbine blade icing prediction, which is more accurate than the traditional method.


Ensemble Learning; Random Forest; Icing Detection; SCADA Data

Cite This Paper

Yanlong Zhao, Liwen Wang. Wind Turbine Blade Icing Detection Based on Random Forest. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 2: 81-86. https://doi.org/10.25236/AJCIS.2022.050213.


[1] Kreutz, Markus, et al. "Machine learning-based icing prediction on wind turbines." Procedia CIRP 81 (2019): 423-428.

[2] Liu, Yao, et al. "Intelligent wind turbine blade icing detection using supervisory control and data acquisition data and ensemble deep learning." Energy Science & Engineering 7.6 (2019): 2633-2645.

[3] Ghani, Rizwan, and S. Virk Muhammad. "Experimental study of atmospheric ice detection on wind turbine blade using thermal infrared technique." Wind Engineering 37.1 (2013): 71-77.

[4] Chen, Xu, and Chunhui Zhao. "Condition-Driven Soft Transition Modeling and Monitoring Strategy for Complex Nonstationary Process." IFAC-PapersOnLine 54.3 (2021): 445-450.

[5] Zhang, Zhao hui, Wen song Zhou, and Hui Li. "Icing estimation on wind turbine blade by the interface temperature using distributed fiber optic sensors." Structural Control and Health Monitoring 27.6 (2020): e2534.

[6] Tian, Jing, et al. "Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with K-nearest neighbor distance analysis." IEEE Transactions on Industrial Electronics 63.3 (2015): 1793-1803.

[7] Bodla, Muhammad Kamran, et al. "Logistic regression and feature extraction based fault diagnosis of main bearing of wind turbines." 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2016.

[8] Chen, Xu, and Chunhui Zhao. "Linear and nonlinear hierarchical multivariate time delay analytics for dynamic modeling and process monitoring." Journal of Process Control 107 (2021): 83-93.

[9] Laouti, Nassim, Nida Sheibat-Othman, and Sami Othman. "Support vector machines for fault detection in wind turbines." IFAC Proceedings Volumes 44.1 (2011): 7067-7072.

[10] Abdallah, Imad, et al. "Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data." Safety and Reliability–Safe Societies in a Changing World. CRC Press, 2018. 3053-3061.

[11] Que, Zijun, and Zhengguo Xu. "A data-driven health prognostics approach for steam turbines based on xgboost and dtw." IEEE Access 7 (2019): 93131-93138.

[12] Myles, Anthony J., et al. "An introduction to decision tree modeling." Journal of Chemometrics: A Journal of the Chemometrics Society 18.6 (2004): 275-285.

[13] Fayyad, Usama M., and Keki B. Irani. "On the handling of continuous-valued attributes in decision tree generation." Machine learning 8.1 (1992): 87-102.

[14] Singh, Sonia, and Priyanka Gupta. "Comparative study ID3, cart and C4. 5 decision tree algorithm: a survey." International Journal of Advanced Information Science and Technology (IJAIST) 27.27 (2014): 97-103.

[15] Liaw, Andy, and Matthew Wiener. "Classification and regression by randomForest." R news 2.3 (2002): 18-22.

[16] Chen, Xu, and Chunhui Zhao. "Multivariate Time Delay Estimation Based on Dynamic Characteristic Analytics." 2020 39th Chinese Control Conference (CCC). IEEE, 2020.