Few-shot fault diagnosis for pitch system of wind turbines based on prototypical network with Mahalanobis distance

JiaJian Yao; Yuxian Zhang; Likui Qiao

doi:10.1117/12.2647069

7 September 2022 Few-shot fault diagnosis for pitch system of wind turbines based on prototypical network with Mahalanobis distance

JiaJian Yao, Yuxian Zhang, Likui Qiao

Author Affiliations +

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123292S (2022) https://doi.org/10.1117/12.2647069
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

Wind turbine failure data is only a small part of the huge amount of operational data. Classification of a small amount of fault data often leads to overfitting of the model. To address the above problem, a few-shot learning method for wind turbine fault classification is proposed by using a prototypical network with Mahalanobis distance. This is done by preprocessing a small amount of high-dimensional fault data using PCA and LSTM methods to obtain training samples for a prototypical network of Mahalanobis distance. The distance function that fits best to the wind power fault data is found by training as the distance function of the prototypical Mahalanobis distance network. The experimental results show that The accuracy of few-shot learning for wind turbine fault classification using the Mahalanobis distance prototypical network is much higher than that of conventional machine learning classification.

Citation Download Citation

JiaJian Yao, Yuxian Zhang, and Likui Qiao "Few-shot fault diagnosis for pitch system of wind turbines based on prototypical network with Mahalanobis distance", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123292S (7 September 2022); https://doi.org/10.1117/12.2647069

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KEYWORDS

Wind turbine technology

Mahalanobis distance

Data modeling

Electrical breakdown

Statistical modeling

Data acquisition

Machine learning

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