An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring
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DOI: 10.1016/j.renene.2018.04.059
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- Qu, Fuming & Liu, Jinhai & Zhu, Hongfei & Zhou, Bowen, 2020. "Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic," Applied Energy, Elsevier, vol. 262(C).
- Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
- Ren, He & Liu, Wenyi & Shan, Mengchen & Wang, Xin & Wang, Zhengfeng, 2021. "A novel wind turbine health condition monitoring method based on composite variational mode entropy and weighted distribution adaptation," Renewable Energy, Elsevier, vol. 168(C), pages 972-980.
- Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
- 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.
- Jianjun Chen & Weihao Hu & Di Cao & Bin Zhang & Qi Huang & Zhe Chen & Frede Blaabjerg, 2019. "An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach," Energies, MDPI, vol. 12(14), pages 1-15, July.
- Conor McKinnon & Alan Turnbull & Sofia Koukoura & James Carroll & Alasdair McDonald, 2020. "Effect of Time History on Normal Behaviour Modelling Using SCADA Data to Predict Wind Turbine Failures," Energies, MDPI, vol. 13(18), pages 1-19, September.
- 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.
- Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
- 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.
- Jastrzebska, Agnieszka & Morales Hernández, Alejandro & Nápoles, Gonzalo & Salgueiro, Yamisleydi & Vanhoof, Koen, 2022. "Measuring wind turbine health using fuzzy-concept-based drifting models," Renewable Energy, Elsevier, vol. 190(C), pages 730-740.
- 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).
- Moghaddass, Ramin & Sheng, Shuangwen, 2019. "An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework," Applied Energy, Elsevier, vol. 240(C), pages 561-582.
- 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.
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Keywords
Wind turbine; Unsupervised condition monitoring; Spatiotemporal graphical modeling;All these keywords.
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