Wind turbine blades icing monitoring based on dynamic latent variable regression

doi:10.3969/j.issn.2097-0706.2024.12.004

Abstract

Abstract:

The monitoring and warning of wind turbine blade icing is of great significance to ensure the safe and stable operation of wind turbines. Considering the accumulating effects of icing processes on the turbine performance， a wind turbine blade icing monitoring model is proposed that conforms to the dynamic operating characteristics of the unit，so as to improve the accuracy of wind turbine blade icing monitoring. The power main band is extracted by isolated forest algorithm to provide high quality data for the establishment of performance degradation model. A dynamic latent variable regression algorithm is used to maximize the projection of quality variable on the dynamic latent space， extract latent structured relations between process and quality variables in the dynamic operation of wind turbines based on minimizing regression errors. The vector auto regression model is used to calculate the dynamic monitoring indicators， and if the monitoring indicators of the output power exceed the limit， the warning of deterioration is carried out. Then the tip speed ratio and pitch angle of the degradation data are abnormally detected based on isolated forest and if the parameters are abnormal and the ambient temperature is below 0 ℃， a blade icing warning will be issued. The actual operation data of a wind turbine in southwest China is used as an example to verify the effectiveness of the method in this paper.

Key words: blade icing monitoring, fault alert, isolated forest, dynamic latent variable regression, feature engineering, new power system

CLC Number:

TK83

XIAO Bitao, LIU Yu, LAI Xiaolu. Wind turbine blades icing monitoring based on dynamic latent variable regression[J]. Integrated Intelligent Energy, 2024, 46(12): 29-35.

Figures/Tables 8

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