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3 October 2024 Wind turbine fault prediction based on genetic algorithm optimization
Quanting Liu, Huaiqian Jing, Lihui Gao, Jian Cui, Hongchen An, Yuman Liu, Haoran Xu, Haolin Li
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Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132720Q (2024) https://doi.org/10.1117/12.3048391
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China
Abstract
This paper proposes a wind turbine fault prediction model based on genetic algorithm optimization. Firstly, a large amount of wind turbine operation data is collected and feature engineering processing is completed. Secondly, a fault prediction model is established as a benchmark model based on BP neural network. Again, using genetic algorithm to optimize the parameters of this model to improve its prediction performance can effectively improve its prediction performance. The genetic algorithm continuously optimizes the model parameters to find the optimal solution by simulating the natural biological evolution process. Finally, the optimized model is experimentally verified and compared and analyzed with the traditional method. The experimental results show that the wind turbine fault prediction model optimized based on genetic algorithm achieves significant improvement in both accuracy and generalization ability. Compared with the traditional method, the optimized model is able to predict wind turbine faults more accurately, discover potential problems in advance and take corresponding maintenance measures, so as to improve the reliability and efficiency of the generator.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Quanting Liu, Huaiqian Jing, Lihui Gao, Jian Cui, Hongchen An, Yuman Liu, Haoran Xu, and Haolin Li "Wind turbine fault prediction based on genetic algorithm optimization", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132720Q (3 October 2024); https://doi.org/10.1117/12.3048391
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KEYWORDS
Genetic algorithms
Mathematical optimization
Wind turbine technology
Neural networks
Machine learning
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