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Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks

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  1. 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.
  2. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
  3. 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.
  4. Dao, Phong B., 2022. "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines," Applied Energy, Elsevier, vol. 318(C).
  5. Xiaoxun, Zhu & Xinyu, Hang & Xiaoxia, Gao & Xing, Yang & Zixu, Xu & Yu, Wang & Huaxin, Liu, 2022. "Research on crack detection method of wind turbine blade based on a deep learning method," Applied Energy, Elsevier, vol. 328(C).
  6. Zhou, Guangzhao & Guo, Zanquan & Sun, Simin & Jin, Qingsheng, 2023. "A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction," Applied Energy, Elsevier, vol. 344(C).
  7. Lv, Zhihan & Wang, Nana & Lou, Ranran & Tian, Yajun & Guizani, Mohsen, 2023. "Towards carbon Neutrality: Prediction of wave energy based on improved GRU in Maritime transportation," Applied Energy, Elsevier, vol. 331(C).
  8. Zhang, Chen & Hu, Di & Yang, Tao, 2024. "Research of artificial intelligence operations for wind turbines considering anomaly detection, root cause analysis, and incremental training," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
  9. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy," Applied Energy, Elsevier, vol. 313(C).
  10. 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.
  11. Li, Ding & Zhang, Yufei & Yang, Zheng & Jin, Yaohui & Xu, Yanyan, 2024. "Sensing anomaly of photovoltaic systems with sequential conditional variational autoencoder," Applied Energy, Elsevier, vol. 353(PA).
  12. Phong B. Dao, 2023. "On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data," Energies, MDPI, vol. 16(5), pages 1-17, March.
  13. 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).
  14. Wang, Yu & Wei, Shanbi & Yang, Wei & Chai, Yi, 2023. "Adaptive economic predictive control for offshore wind farm active yaw considering generation uncertainty," Applied Energy, Elsevier, vol. 351(C).
  15. 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.
  16. Xiaocong Xiao & Jianxun Liu & Deshun Liu & Yufei Tang & Shigang Qin & Fan Zhang, 2022. "A Normal Behavior-Based Condition Monitoring Method for Wind Turbine Main Bearing Using Dual Attention Mechanism and Bi-LSTM," Energies, MDPI, vol. 15(22), pages 1-17, November.
  17. Xu, Zifei & Bashir, Musa & Yang, Yang & Wang, Xinyu & Wang, Jin & Ekere, Nduka & Li, Chun, 2022. "Multisensory collaborative damage diagnosis of a 10 MW floating offshore wind turbine tendons using multi-scale convolutional neural network with attention mechanism," Renewable Energy, Elsevier, vol. 199(C), pages 21-34.
  18. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
  19. 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.
  20. Alan Turnbull & Conor McKinnon & James Carrol & Alasdair McDonald, 2022. "On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
  21. Xiang, Ling & Fu, Xiaomengting & Yao, Qingtao & Zhu, Guopeng & Hu, Aijun, 2024. "A novel model for ultra-short term wind power prediction based on Vision Transformer," Energy, Elsevier, vol. 294(C).
  22. 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).
  23. 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.
  24. Wei Wang & Shiyou Yang & Yankun Yang, 2022. "An Improved Data-Efficiency Algorithm Based on Combining Isolation Forest and Mean Shift for Anomaly Data Filtering in Wind Power Curve," Energies, MDPI, vol. 15(13), pages 1-12, July.
  25. Mirza, Adeel Feroz & Shu, Zhaokun & Usman, Muhammad & Mansoor, Majad & Ling, Qiang, 2024. "Quantile-transformed multi-attention residual framework (QT-MARF) for medium-term PV and wind power prediction," Renewable Energy, Elsevier, vol. 220(C).
  26. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.
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