Transformer based fault prediction for wind turbines

Home > Archive>Volume 47, Issue 13, 2024 >45-52

Transformer based fault prediction for wind turbines
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                        Zhu Yanmin1Zhu Yanmin
School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology，Baotou 014010, China
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Li Zhonghu1Li Zhonghu
School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology，Baotou 014010, China
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Wang Jinming1Wang Jinming
School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology，Baotou 014010, China
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Yang Liqing1Yang Liqing
School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology，Baotou 014010, China
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Zhang Xinyu2Zhang Xinyu
School of Mechanical Engineering, Inner Mongolia University of Science and Technology，Baotou 014010, China
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Affiliation:1.School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology，Baotou 014010, China; 2.School of Mechanical Engineering, Inner Mongolia University of Science and Technology，Baotou 014010, China
Clc Number:TM315; TN03
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Abstract:

To study fault prediction methods for wind turbines based on SCADA data, the SCADA data of a 2 000 kW doubly-fed wind turbine over 14 months is used as the research subject. First, the data is preprocessed to ensure its usability. Considering the issues with the traditional Transformer model, such as complex structure and numerous parameter settings, a Transformer model is constructed by introducing a linear decoder structure. This model is then used for fault prediction research on wind turbines. The study shows that the constructed algorithm model has long-term stability, can eliminate false predictions, and can predict faults 6 days in advance, providing a safeguard to prevent sudden shutdowns due to fault deterioration.

Key words:wind turbine unit;deep learning;data analysis;SCADA system;fault prediction

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Online: November 07,2024
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