WIND TURBINE BLADE DAMAGE DETECTION AND ANALYSIS BASED ON TRANSFER LEARNING

Yin Xiaoju; Pan Xue; Zuo Yanbin; Guan Xin

doi:10.19912/j.0254-0096.tynxb.2023-0925

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (10) : 506-511. DOI: 10.19912/j.0254-0096.tynxb.2023-0925

WIND TURBINE BLADE DAMAGE DETECTION AND ANALYSIS BASED ON TRANSFER LEARNING

Yin Xiaoju¹, Pan Xue¹, Zuo Yanbin², Guan Xin¹

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Abstract

Aiming at the problems of complex causes of wind turbine blade damage, low fault identification efficiency, and insufficient accuracy, a wind turbine blade damage detection method is propsed based on improved DenseNet network improved by transfer learning is proposed. A mathematical model of DenseNet network improved by transfer learning （DenseNet-TL ） is established to improve the feature extraction capability, and the recognition and analysis of wind turbine blade images are carried out under the model to determine the damage state of the blades. The offline training and testing is carried out with a wind farm dataset, and the results show that, compared with AlexNet and ResNet models, the model effectively saves the training time, improves the generalization ability of the model, and the average training accuracy reaches more than 90%, which verifies the validity and accuracy of the method.

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transfer learning / image recognition / damage detection / wind turbine blades / wind turbines

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Yin Xiaoju, Pan Xue, Zuo Yanbin, Guan Xin. WIND TURBINE BLADE DAMAGE DETECTION AND ANALYSIS BASED ON TRANSFER LEARNING[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 506-511 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0925

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