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Anomaly detection and fault analysis of wind turbine components based on deep learning network

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  1. Deng, Ziwei & Li, Yuxuan & Zhu, Hongqiu & Huang, Keke & Tang, Zhaohui & Wang, Zhen, 2020. "Sparse stacked autoencoder network for complex system monitoring with industrial applications," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
  2. 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).
  3. Marc-Alexander Lutz & Stephan Vogt & Volker Berkhout & Stefan Faulstich & Steffen Dienst & Urs Steinmetz & Christian Gück & Andres Ortega, 2020. "Evaluation of Anomaly Detection of an Autoencoder Based on Maintenace Information and Scada-Data," Energies, MDPI, vol. 13(5), pages 1-18, February.
  4. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & David Infield & Conaill Soraghan, 2020. "Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data," Energies, MDPI, vol. 13(19), pages 1-19, October.
  5. 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.
  6. 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.
  7. Aleksandra Grzesiek & Radosław Zimroz & Paweł Śliwiński & Norbert Gomolla & Agnieszka Wyłomańska, 2021. "A Method for Structure Breaking Point Detection in Engine Oil Pressure Data," Energies, MDPI, vol. 14(17), pages 1-24, September.
  8. 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.
  9. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
  10. Majdi Mansouri & Khaled Dhibi & Hazem Nounou & Mohamed Nounou, 2022. "An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization," Sustainability, MDPI, vol. 14(18), pages 1-11, September.
  11. 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.
  12. Peng Qian & Xiange Tian & Jamil Kanfoud & Joash Lap Yan Lee & Tat-Hean Gan, 2019. "A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network," Energies, MDPI, vol. 12(18), pages 1-15, September.
  13. 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.
  14. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2021. "Increasing resiliency against information vulnerability of renewable resources in the operation of smart multi-area microgrid," Energy, Elsevier, vol. 220(C).
  15. 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.
  16. Zhang, Chen & Gao, Wei & Yang, Tao & Guo, Sheng, 2019. "Opportunistic maintenance strategy for wind turbines considering weather conditions and spare parts inventory management," Renewable Energy, Elsevier, vol. 133(C), pages 703-711.
  17. Choe, Do-Eun & Kim, Hyoung-Chul & Kim, Moo-Hyun, 2021. "Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades," Renewable Energy, Elsevier, vol. 174(C), pages 218-235.
  18. Zhang, Chen & Yang, Tao, 2021. "Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ," Renewable Energy, Elsevier, vol. 164(C), pages 1540-1549.
  19. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & Charlie Plumley, 2021. "Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data," Energies, MDPI, vol. 14(20), pages 1-20, October.
  20. 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).
  21. 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.
  22. Shao, Kaixuan & He, Yigang & Xing, Zhikai & Du, Bolun, 2023. "Detecting wind turbine anomalies using nonlinear dynamic parameters-assisted machine learning with normal samples," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
  23. Matthew Boeding & Kelly Boswell & Michael Hempel & Hamid Sharif & Juan Lopez & Kalyan Perumalla, 2022. "Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid," Energies, MDPI, vol. 15(22), pages 1-22, November.
  24. Meng Li & Shuangxin Wang, 2019. "Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks," Energies, MDPI, vol. 12(17), pages 1-20, August.
  25. Jia Tian & Xingqin Zhang & Shuangqing Zheng & Zhiyong Liu & Changshu Zhan, 2024. "Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems," Mathematics, MDPI, vol. 12(9), pages 1-25, April.
  26. Rogerio Adriano da Fonseca Santiago & Natasha Benjamim Barbosa & Henrique Gomes Mergulhão & Tassio Farias de Carvalho & Alex Alisson Bandeira Santos & Ricardo Cerqueira Medrado & Jose Bione de Melo Fi, 2024. "Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis," Energies, MDPI, vol. 17(5), pages 1-24, February.
  27. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
  28. 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.
  29. Kouadri, Abdelmalek & Hajji, Mansour & Harkat, Mohamed-Faouzi & Abodayeh, Kamaleldin & Mansouri, Majdi & Nounou, Hazem & Nounou, Mohamed, 2020. "Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 150(C), pages 598-606.
  30. Xu, Qifa & Fan, Zhenhua & Jia, Weiyin & Jiang, Cuixia, 2020. "Fault detection of wind turbines via multivariate process monitoring based on vine copulas," Renewable Energy, Elsevier, vol. 161(C), pages 939-955.
  31. Yening Lai & Ling Zhu & Xueping Pan & Jinpeng Guo & Dazhuang He & Wei Liang, 2022. "Online Estimation of the Mechanical Parameters of a Wind Turbine with Doubly Fed Induction Generator by Utilizing Turbulence Excitations," Energies, MDPI, vol. 15(6), pages 1-14, March.
  32. 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.
  33. Konstantina Fotiadou & Terpsichori Helen Velivassaki & Artemis Voulkidis & Dimitrios Skias & Corrado De Santis & Theodore Zahariadis, 2020. "Proactive Critical Energy Infrastructure Protection via Deep Feature Learning," Energies, MDPI, vol. 13(10), pages 1-19, May.
  34. Rami Al-Hajj & Ali Assi & Bilel Neji & Raymond Ghandour & Zaher Al Barakeh, 2023. "Transfer Learning for Renewable Energy Systems: A Survey," Sustainability, MDPI, vol. 15(11), pages 1-28, June.
  35. Adaiton Oliveira-Filho & Ryad Zemouri & Philippe Cambron & Antoine Tahan, 2023. "Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model," Energies, MDPI, vol. 16(12), pages 1-21, June.
  36. 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.
  37. 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).
  38. Ahmad Y. Al Rashdan & Hany S. Abdel-Khalik & Kellen M. Giraud & Daniel G. Cole & Jacob A. Farber & William W. Clark & Abenezer Alemu & Marcus C. Allen & Ryan M. Spangler & Athi Varuttamaseni, 2022. "A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection," Energies, MDPI, vol. 15(15), pages 1-20, August.
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