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All Journal International Journal of Advances in Artificial Intelligence and Machine Learning
Yirui Qiao
Zhongyan Logistics Technology
Computer Science & IT

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Fault Warning Using Clustering Analysis Method on Wind Turbine Blade Yuli Guo; Bing Zeng; Yirui Qiao
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 2 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i2.662

Abstract

Background: In an attempt to solve the problem of internal damage and crack of wind turbine blades that are difficult to be evaluated, a method based on cluster analysis is proposed to study the fault warning of wind turbine blades using machine learning.Aims: Developing an early warning method (fault warning) for explicit damage (including surface cracks and internal damage) to wind turbine blades by applying cluster analysis (K-Means) and unsupervised learning to short-term historical operational data.Method: Firstly, 2 MW wind turbine was used to collect short-term historical operation data of wind turbine and perform data pre-processing. Secondly, the wind speed-power, wind speed-hub speed and wind speed-tip speed ratio were used to classify the parameters affecting the explicit faults of wind turbine blades by using the cluster analysis method, and the distance analysis was performed by using the combined weight method segmentation, and the axial vibration data of the abnormal data points were analyzed by using the Fourier series transformation. And then, establishing the wind turbine blade explicit fault evaluation rules based on the performance reliability theory. Lastly, the feasibility of the method is verified by cases.Result: The results demonstrated that the method can quickly warning the wind turbine blade explicit faults, especially for evaluation of the wind turbine blade surface explicit cracks and internal explicit fault. Conclusion: This work has achieved the fault warning of large components based on the wind turbine short-term historical operation data.
Co-Authors Bing Zeng Yuli Guo