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Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network

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Cited by:

  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. 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.
  3. 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.
  4. 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.
  5. Li, Tong & Wang, Zhaohua & Zhao, Wenhui, 2022. "Comparison and application potential analysis of autoencoder-based electricity pattern mining algorithms for large-scale demand response," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
  6. 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).
  7. Mohammad Barooni & Turaj Ashuri & Deniz Velioglu Sogut & Stephen Wood & Shiva Ghaderpour Taleghani, 2022. "Floating Offshore Wind Turbines: Current Status and Future Prospects," Energies, MDPI, vol. 16(1), pages 1-28, December.
  8. Meng, Jinyu & Dong, Zengchuan & Zhu, Shengnan, 2025. "A copula-based wind-solar complementarity coefficient: Case study of two clean energy bases, China," Energy, Elsevier, vol. 318(C).
  9. 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).
  10. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
  11. 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.
  12. 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.
  13. Urmeneta, Jon & Izquierdo, Juan & Leturiondo, Urko, 2023. "A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 281-292.
  14. Li, Yanting & Wang, Peng & Wu, Zhenyu & Su, Yan, 2024. "Collaborative monitoring of wind turbine performance based on probabilistic power curve comparison," Renewable Energy, Elsevier, vol. 231(C).
  15. 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.
  16. Christian Gück & Cyriana M. A. Roelofs & Stefan Faulstich, 2024. "CARE to Compare: A Real-World Benchmark Dataset for Early Fault Detection in Wind Turbine Data," Data, MDPI, vol. 9(12), pages 1-16, November.
  17. Oh, So Young & Joung, Chanwoo & Lee, Seonghwan & Shim, Yoon-Bo & Lee, Dahun & Cho, Gyu-Eun & Jang, Juhyeong & Lee, In Yong & Park, Young-Bin, 2024. "Condition-based maintenance of wind turbine structures: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
  18. Peivand, Ali & Azad Farsani, Ehsan & Abdolmohammadi, Hamid Reza, 2024. "Accelerating optimal scheduling prediction in power system: A multi-faceted GAN-assisted prediction framework," Renewable Energy, Elsevier, vol. 230(C).
  19. 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).
  20. Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
  21. Cezary Banaszak & Andrzej Gawlik & Paweł Szcześniak & Marcin Rabe & Katarzyna Widera & Yuriy Bilan & Agnieszka Łopatka & Ewelina Gutowska, 2023. "Economic and Energy Analysis of the Construction of a Wind Farm with Infrastructure in the Baltic Sea," Energies, MDPI, vol. 16(16), pages 1-20, August.
  22. Martin Geibel & Galih Bangga, 2022. "Data Reduction and Reconstruction of Wind Turbine Wake Employing Data Driven Approaches," Energies, MDPI, vol. 15(10), pages 1-40, May.
  23. 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).
  24. 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.
  25. Mohammad Barooni & Deniz Velioglu Sogut & Parviz Sedigh & Masoumeh Bahrami, 2025. "Novel Hybrid Deep Learning Model for Forecasting FOWT Power Output," Energies, MDPI, vol. 18(13), pages 1-17, July.
  26. Fanjie Yang & Yun Zeng & Jing Qian & Youtao Li & Shihao Xie, 2023. "Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm," Energies, MDPI, vol. 16(3), pages 1-19, January.
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