Academic Journal of Computing & Information Science, 2022, 5(2); doi: 10.25236/AJCIS.2022.050213.
Yanlong Zhao1, Liwen Wang2
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
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.
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