IDEAS home Printed from https://ideas.repec.org/r/eee/renene/v146y2020icp760-768.html

Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units

Citations

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


Cited by:

  1. Wu Wen & Yubao Liu & Rongfu Sun & Yuewei Liu, 2022. "Research on Anomaly Detection of Wind Farm SCADA Wind Speed Data," Energies, MDPI, vol. 15(16), pages 1-18, August.
  2. Adrian Stetco & Juan Melecio Ramirez & Anees Mohammed & Siniša Djurović & Goran Nenadic & John Keane, 2020. "An End-to-End, Real-Time Solution for Condition Monitoring of Wind Turbine Generators," Energies, MDPI, vol. 13(18), pages 1-18, September.
  3. 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.
  4. 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.
  5. Li, Jingmiao & Wang, Jun, 2020. "Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model," Energy, Elsevier, vol. 213(C).
  6. 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.
  7. 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).
  8. 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.
  9. Li, Yanting & Wu, Zhenyu, 2020. "A condition monitoring approach of multi-turbine based on VAR model at farm level," Renewable Energy, Elsevier, vol. 166(C), pages 66-80.
  10. 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).
  11. 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).
  12. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
  13. Liu, Tianhao & Shan, Linke & Jiang, Meihui & Li, Fangning & Kong, Fannie & Du, Pengcheng & Zhu, Hongyu & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Zhang, Dongdong, 2025. "Multi-dimensional data processing and intelligent forecasting technologies for renewable energy generation," Applied Energy, Elsevier, vol. 398(C).
  14. Wang, Shun & Vidal, Yolanda & Pozo, Francesc, 2026. "Recent advances in wind turbine condition monitoring using SCADA data: A state-of-the-art review," Reliability Engineering and System Safety, Elsevier, vol. 267(PA).
  15. Wu, Jie & Li, Na & Zhao, Yan & Wang, Jujie, 2022. "Usage of correlation analysis and hypothesis test in optimizing the gated recurrent unit network for wind speed forecasting," Energy, Elsevier, vol. 242(C).
  16. Qingquan Lv & Jialin Zhang & Jianmei Zhang & Zhenzhen Zhang & Qiang Zhou & Pengfei Gao & Haozhe Zhang, 2025. "Short-Term Wind Power Prediction Model Based on PSO-CNN-LSTM," Energies, MDPI, vol. 18(13), pages 1-18, June.
  17. Pang, Yanhua & He, Qun & Jiang, Guoqian & Xie, Ping, 2020. "Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 161(C), pages 510-524.
  18. Zhang, Wanwan & Vatn, Jørn & Rasheed, Adil, 2025. "Gearbox pump failure prognostics in offshore wind turbine by an integrated data-driven model," Applied Energy, Elsevier, vol. 380(C).
  19. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
  20. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  21. Li, Mingxin & Xu, Zifei & Li, Shen & Kikuchi, Yuka & Dong, You & Gryllias, Konstantinos C. & Baraldi, Piero & Zio, Enrico & Carroll, James, 2026. "Health prognostics and maintenance decision-making for wind energy: A comprehensive overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PA).
  22. Wu, Yueqi & Ma, Xiandong, 2022. "A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines," Renewable Energy, Elsevier, vol. 181(C), pages 554-566.
  23. 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.
  24. 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).
  25. Junshuai Yan & Yongqian Liu & Xiaoying Ren & Li Li, 2023. "Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network," Energies, MDPI, vol. 16(19), pages 1-22, September.
  26. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
  27. Afef Fekih & Hamed Habibi & Silvio Simani, 2022. "Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview," Energies, MDPI, vol. 15(19), pages 1-21, September.
  28. Shixian Dai & Shuang Han & Xinjian Bai & Zijian Kang & Yongqian Liu, 2025. "A Multivariate Spatiotemporal Feature Fusion Network for Wind Turbine Gearbox Condition Monitoring," Energies, MDPI, vol. 18(5), pages 1-22, March.
  29. Junshuai Yan & Yongqian Liu & Xiaoying Ren, 2023. "An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm," Energies, MDPI, vol. 16(10), pages 1-23, May.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.