IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i19p7008-d1256242.html
   My bibliography  Save this article

Anomaly Detection for Wind Turbines Using Long Short-Term Memory-Based Variational Autoencoder Wasserstein Generation Adversarial Network under Semi-Supervised Training

Author

Listed:
  • Chen Zhang

    (Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China)

  • Tao Yang

    (School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Intelligent anomaly detection for wind turbines using deep-learning methods has been extensively researched and yielded significant results. However, supervised learning necessitates sufficient labeled data to establish the discriminant boundary, while unsupervised learning lacks prior knowledge and heavily relies on assumptions about the distribution of anomalies. A long short-term memory-based variational autoencoder Wasserstein generation adversarial network (LSTM-based VAE-WGAN) was established in this paper to address the challenge of small and noisy wind turbine datasets. The VAE was utilized as the generator, with LSTM units replacing hidden layer neurons to effectively extract spatiotemporal factors. The similarity between the model-fit distribution and true distribution was quantified using Wasserstein distance, enabling complex high-dimensional data distributions to be learned. To enhance the performance and robustness of the proposed model, a two-stage adversarial semi-supervised training approach was implemented. Subsequently, a monitoring indicator based on reconstruction error was defined, with the threshold set at a 99.7% confidence interval for the distribution curve fitted by kernel density estimation (KDE). Real cases from a wind farm in northeast China have confirmed the feasibility and advancement of the proposed model, while also discussing the effects of various applied parameters.

Suggested Citation

  • Chen Zhang & Tao Yang, 2023. "Anomaly Detection for Wind Turbines Using Long Short-Term Memory-Based Variational Autoencoder Wasserstein Generation Adversarial Network under Semi-Supervised Training," Energies, MDPI, vol. 16(19), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:7008-:d:1256242
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/19/7008/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/19/7008/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Morrison, Rory & Liu, Xiaolei & Lin, Zi, 2022. "Anomaly detection in wind turbine SCADA data for power curve cleaning," Renewable Energy, Elsevier, vol. 184(C), pages 473-486.
    2. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
    3. Xiang, Ling & Yang, Xin & Hu, Aijun & Su, Hao & Wang, Penghe, 2022. "Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks," Applied Energy, Elsevier, vol. 305(C).
    4. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    5. Kong, Ziqian & Tang, Baoping & Deng, Lei & Liu, Wenyi & Han, Yan, 2020. "Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units," Renewable Energy, Elsevier, vol. 146(C), pages 760-768.
    6. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Song, Chaosheng & Chen, Dingliang & Zheng, Jie, 2022. "Fault detection of offshore wind turbine gearboxes based on deep adaptive networks via considering Spatio-temporal fusion," Renewable Energy, Elsevier, vol. 200(C), pages 1023-1036.
    7. Chen, Junsheng & Li, Jian & Chen, Weigen & Wang, Youyuan & Jiang, Tianyan, 2020. "Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders," Renewable Energy, Elsevier, vol. 147(P1), pages 1469-1480.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sergen Tumse & Mehmet Bilgili & Alper Yildirim & Besir Sahin, 2024. "Comparative Analysis of Global Onshore and Offshore Wind Energy Characteristics and Potentials," Sustainability, MDPI, vol. 16(15), pages 1-28, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhan, Jun & Wu, Chengkun & Yang, Canqun & Miao, Qiucheng & Wang, Shilin & Ma, Xiandong, 2022. "Condition monitoring of wind turbines based on spatial-temporal feature aggregation networks," Renewable Energy, Elsevier, vol. 200(C), pages 751-766.
    2. Huifan Zeng & Juchuan Dai & Chengming Zuo & Huanguo Chen & Mimi Li & Fan Zhang, 2022. "Correlation Investigation of Wind Turbine Multiple Operating Parameters Based on SCADA Data," Energies, MDPI, vol. 15(14), pages 1-24, July.
    3. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    4. Feng, Chenlong & Liu, Chao & Jiang, Dongxiang, 2023. "Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning," Renewable Energy, Elsevier, vol. 206(C), pages 309-323.
    5. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).
    6. Yao, Qingtao & Zhu, Haowei & Xiang, Ling & Su, Hao & Hu, Aijun, 2023. "A novel composed method of cleaning anomy data for improving state prediction of wind turbine," Renewable Energy, Elsevier, vol. 204(C), pages 131-140.
    7. Wang, Han & Zhang, Ning & Du, Ershun & Yan, Jie & Han, Shuang & Li, Nan & Li, Hongxia & Liu, Yongqian, 2023. "An adaptive identification method of abnormal data in wind and solar power stations," Renewable Energy, Elsevier, vol. 208(C), pages 76-93.
    8. Dao, Phong B. & Barszcz, Tomasz & Staszewski, Wieslaw J., 2024. "Anomaly detection of wind turbines based on stationarity analysis of SCADA data," Renewable Energy, Elsevier, vol. 232(C).
    9. Zheng, Minglei & Man, Junfeng & Wang, Dian & Chen, Yanan & Li, Qianqian & Liu, Yong, 2023. "Semi-supervised multivariate time series anomaly detection for wind turbines using generator SCADA data," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    10. Chen, Zhen & Zhou, Di & Zio, Enrico & Xia, Tangbin & Pan, Ershun, 2023. "Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    11. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    13. Liu, Hui & Duan, Zhu & Chen, Chao, 2020. "Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder," Applied Energy, Elsevier, vol. 280(C).
    14. 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).
    15. Zeqing Yang & Wenbo Zhang & Wei Cui & Lingxiao Gao & Yingshu Chen & Qiang Wei & Libing Liu, 2022. "Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks," Energies, MDPI, vol. 15(15), pages 1-22, August.
    16. Qian, XiaoYi & Sun, TianHe & Zhang, YuXian & Wang, BaoShi & Awad Gendeel, Mohammed Altayeb, 2023. "Wind turbine fault detection based on spatial-temporal feature and neighbor operation state," Renewable Energy, Elsevier, vol. 219(P1).
    17. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    18. Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
    19. Wang, Weicheng & Chen, Jinglong & Zhang, Tianci & Liu, Zijun & Wang, Jun & Zhang, Xinwei & He, Shuilong, 2023. "An asymmetrical graph Siamese network for one-classanomaly detection of engine equipment with multi-source fusion," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    20. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:7008-:d:1256242. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.