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A de-ambiguous condition monitoring scheme for wind turbines using least squares generative adversarial networks

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  • Wang, Anqi
  • Qian, Zheng
  • Pei, Yan
  • Jing, Bo

Abstract

Wind turbine condition monitoring (WTCM) plays an important role in reducing operation & maintenance (O&M) cost and improving the reliability of wind farms. Supervisory control and data acquisition (SCADA) data have advantages such as easy access and strong timeliness and are used widely for WTCM. However, it is difficult to distinguish and label historical SCADA data as healthy or faulty accurately during the model training process. Therefore, a De-ambiguous Condition Monitoring scheme with Transfer layer (DCMT) based on SCADA data is proposed to overcome this problem. This scheme provides a fault early warning for wind turbines. In this scheme, an improved auto-encoder (AE) network with a transfer layer is designed to eliminate the effect of SCADA data in the ambiguous status (ambiguous data) and enhance the reliability of a training dataset. Meanwhile, a structure of Siamese encoder is designed to calculate the residuals between latent features, i.e., the outputs of the Siamese encoders. These residuals can be utilised to identify wind turbine operational conditions. Further, least squares generative adversarial networks (LSGAN) is introduced to learn the distribution of health data while restricting the discriminator and realising the augmentation of health data for model training. The proposed method is applied to two cases of generator winding and gearbox bearing over-temperature faults of wind turbines from northwest China. Compared with other methods, the proposed method effectively detects potential abnormal conditions in advance without raising false alarms.

Suggested Citation

  • Wang, Anqi & Qian, Zheng & Pei, Yan & Jing, Bo, 2022. "A de-ambiguous condition monitoring scheme for wind turbines using least squares generative adversarial networks," Renewable Energy, Elsevier, vol. 185(C), pages 267-279.
    • Handle: RePEc:eee:renene:v:185:y:2022:i:c:p:267-279
    DOI: 10.1016/j.renene.2021.12.049
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    References listed on IDEAS

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    1. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
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    Cited by:

    1. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    2. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    3. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).
    4. Xiaobo Liu & Haifei Ma & Yibing Liu, 2022. "A Novel Transfer Learning Method Based on Conditional Variational Generative Adversarial Networks for Fault Diagnosis of Wind Turbine Gearboxes under Variable Working Conditions," Sustainability, MDPI, vol. 14(9), pages 1-15, April.

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