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Wind turbine condition monitoring by the approach of SCADA data analysis

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  1. Yang, Chunzhen & Liu, Jingquan & Zeng, Yuyun & Xie, Guangyao, 2019. "Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model," Renewable Energy, Elsevier, vol. 133(C), pages 433-441.
  2. 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.
  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. Xin Wu & Hong Wang & Guoqian Jiang & Ping Xie & Xiaoli Li, 2019. "Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data," Energies, MDPI, vol. 12(6), pages 1-19, March.
  5. Dao, Phong B., 2022. "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines," Applied Energy, Elsevier, vol. 318(C).
  6. Papatheou, Evangelos & Dervilis, Nikolaos & Maguire, Andrew E. & Campos, Carles & Antoniadou, Ifigeneia & Worden, Keith, 2017. "Performance monitoring of a wind turbine using extreme function theory," Renewable Energy, Elsevier, vol. 113(C), pages 1490-1502.
  7. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
  8. Raymond Byrne & Davide Astolfi & Francesco Castellani & Neil J. Hewitt, 2020. "A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
  9. 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.
  10. Mengnan Cao & Yingning Qiu & Yanhui Feng & Hao Wang & Dan Li, 2016. "Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data," Energies, MDPI, vol. 9(10), pages 1-18, October.
  11. Zhao, Hongshan & Liu, Huihai & Hu, Wenjing & Yan, Xihui, 2018. "Anomaly detection and fault analysis of wind turbine components based on deep learning network," Renewable Energy, Elsevier, vol. 127(C), pages 825-834.
  12. Jijian Lian & Ou Cai & Xiaofeng Dong & Qi Jiang & Yue Zhao, 2019. "Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
  13. Long, Lijia & Mai, Quang Anh & Morato, Pablo Gabriel & Sørensen, John Dalsgaard & Thöns, Sebastian, 2020. "Information value-based optimization of structural and environmental monitoring for offshore wind turbines support structures," Renewable Energy, Elsevier, vol. 159(C), pages 1036-1046.
  14. Xiaocong Xiao & Jianxun Liu & Deshun Liu & Yufei Tang & Fan Zhang, 2022. "Condition Monitoring of Wind Turbine Main Bearing Based on Multivariate Time Series Forecasting," Energies, MDPI, vol. 15(5), pages 1-23, March.
  15. Masabi, Sayed Nahiyan & Fu, Hailing & Flint, James A. & Theodossiades, Stephanos, 2024. "A pendulum-based rotational energy harvester for self-powered monitoring of rotating systems in the era of industrial digitization," Applied Energy, Elsevier, vol. 365(C).
  16. Gao, Linyue & Tao, Tao & Liu, Yongqian & Hu, Hui, 2021. "A field study of ice accretion and its effects on the power production of utility-scale wind turbines," Renewable Energy, Elsevier, vol. 167(C), pages 917-928.
  17. Fanghong Zhang & Yuze Zhu & Chuanjiang Zhang & Peng Yu & Qingan Li, 2023. "Abnormality Detection Method for Wind Turbine Bearings Based on CNN-LSTM," Energies, MDPI, vol. 16(7), pages 1-11, April.
  18. 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.
  19. Dao, Phong B., 2022. "Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data," Renewable Energy, Elsevier, vol. 185(C), pages 641-654.
  20. Chen, Wanqiu & Qiu, Yingning & Feng, Yanhui & Li, Ye & Kusiak, Andrew, 2021. "Diagnosis of wind turbine faults with transfer learning algorithms," Renewable Energy, Elsevier, vol. 163(C), pages 2053-2067.
  21. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
  22. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Eiriksson, Egill Thor, 2016. "Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes," Renewable Energy, Elsevier, vol. 91(C), pages 90-106.
  23. Sungmok Hwang & Cheol Yoo, 2021. "Health Monitoring and Diagnosis System for a Small H-Type Darrieus Vertical-Axis Wind Turbine," Energies, MDPI, vol. 14(21), pages 1-18, November.
  24. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
  25. Peng Sun & Jian Li & Junsheng Chen & Xiao Lei, 2016. "A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 9(11), pages 1-21, October.
  26. Liu, W.Y. & Tang, B.P. & Han, J.G. & Lu, X.N. & Hu, N.N. & He, Z.Z., 2015. "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 466-472.
  27. Chen, Jinglong & Pan, Jun & Zhang, Chunlin & Luo, Xiaoyu & Zhou, Zitong & Wang, Biao, 2017. "Specialization improved nonlocal means to detect periodic impulse feature for generator bearing fault identification," Renewable Energy, Elsevier, vol. 103(C), pages 448-467.
  28. Md Liton Hossain & Ahmed Abu-Siada & S. M. Muyeen, 2018. "Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review," Energies, MDPI, vol. 11(5), pages 1-14, May.
  29. Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.
  30. Seyed Abolfazl Mortazavizadeh & Reza Yazdanpanah & David Campos Gaona & Olimpo Anaya-Lara, 2023. "Fault Diagnosis and Condition Monitoring in Wave Energy Converters: A Review," Energies, MDPI, vol. 16(19), pages 1-16, September.
  31. Ruiming, Fang & Minling, Wu & xinhua, Guo & Rongyan, Shang & Pengfei, Shao, 2020. "Identifying early defects of wind turbine based on SCADA data and dynamical network marker," Renewable Energy, Elsevier, vol. 154(C), pages 625-635.
  32. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Dominic T. J. O’Sullivan, 2019. "Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses," Energies, MDPI, vol. 12(2), pages 1-22, January.
  33. Hang, Xinyu & Zhu, Xiaoxun & Gao, Xiaoxia & Wang, Yu & Liu, Longhu, 2024. "Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation," Renewable Energy, Elsevier, vol. 224(C).
  34. Usama Aziz & Sylvie Charbonnier & Christophe Berenguer & Alexis Lebranchu & Frederic Prevost, 2022. "A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods," Energies, MDPI, vol. 15(8), pages 1-21, April.
  35. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
  36. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
  37. Ruiz de la Hermosa González-Carrato, Raúl & García Márquez, Fausto Pedro & Dimlaye, Vichaar, 2015. "Maintenance management of wind turbines structures via MFCs and wavelet transforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 472-482.
  38. 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.
  39. Ruiz de la Hermosa González-Carrato, Raúl, 2018. "Wind farm monitoring using Mahalanobis distance and fuzzy clustering," Renewable Energy, Elsevier, vol. 123(C), pages 526-540.
  40. Jing, Bo & Qian, Zheng & Pei, Yan & Zhang, Lizhong & Yang, Tingyi, 2020. "Improving wind turbine efficiency through detection and calibration of yaw misalignment," Renewable Energy, Elsevier, vol. 160(C), pages 1217-1227.
  41. Alessandro Murgia & Robbert Verbeke & Elena Tsiporkova & Ludovico Terzi & Davide Astolfi, 2023. "Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis," Energies, MDPI, vol. 16(2), pages 1-20, January.
  42. Wymore, Mathew L. & Van Dam, Jeremy E. & Ceylan, Halil & Qiao, Daji, 2015. "A survey of health monitoring systems for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 976-990.
  43. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
  44. Abdul Ghani Olabi & Tabbi Wilberforce & Khaled Elsaid & Enas Taha Sayed & Tareq Salameh & Mohammad Ali Abdelkareem & Ahmad Baroutaji, 2021. "A Review on Failure Modes of Wind Turbine Components," Energies, MDPI, vol. 14(17), pages 1-44, August.
  45. 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.
  46. Cong Yang & Zheng Qian & Yan Pei & Lu Wei, 2018. "A Data-Driven Approach for Condition Monitoring of Wind Turbine Pitch Systems," Energies, MDPI, vol. 11(8), pages 1-17, August.
  47. Yonglong Yan & Jian Li & David Wenzhong Gao, 2014. "Condition Parameter Modeling for Anomaly Detection in Wind Turbines," Energies, MDPI, vol. 7(5), pages 1-17, May.
  48. Astolfi, Davide & Castellani, Francesco & Garinei, Alberto & Terzi, Ludovico, 2015. "Data mining techniques for performance analysis of onshore wind farms," Applied Energy, Elsevier, vol. 148(C), pages 220-233.
  49. Yingying Zhao & Dongsheng Li & Ao Dong & Dahai Kang & Qin Lv & Li Shang, 2017. "Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data," Energies, MDPI, vol. 10(8), pages 1-17, August.
  50. Mérigaud, Alexis & Ringwood, John V., 2016. "Condition-based maintenance methods for marine renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 53-78.
  51. Chen, Junsheng & Li, Jian & Chen, Weigen & Wang, Youyuan & Jiang, Tianyan, 2020. "Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders," Renewable Energy, Elsevier, vol. 147(P1), pages 1469-1480.
  52. Chengming Zuo & Juchuan Dai & Guo Li & Mimi Li & Fan Zhang, 2023. "Investigation of Data Pre-Processing Algorithms for Power Curve Modeling of Wind Turbines Based on ECC," Energies, MDPI, vol. 16(6), pages 1-24, March.
  53. Feng Gao & Xiaojiang Wu & Qiang Liu & Juncheng Liu & Xiyun Yang, 2019. "Fault Simulation and Online Diagnosis of Blade Damage of Large-Scale Wind Turbines," Energies, MDPI, vol. 12(3), pages 1-16, February.
  54. Tziavos, Nikolaos I. & Hemida, H. & Dirar, S. & Papaelias, M. & Metje, N. & Baniotopoulos, C., 2020. "Structural health monitoring of grouted connections for offshore wind turbines by means of acoustic emission: An experimental study," Renewable Energy, Elsevier, vol. 147(P1), pages 130-140.
  55. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Henningsen, Keld, 2015. "Performance assessment of wind turbine gearboxes using in-service data: Current approaches and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 144-159.
  56. Zhengnan Hou & Xiaoxiao Lv & Shengxian Zhuang, 2021. "Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects," Energies, MDPI, vol. 14(22), pages 1-15, November.
  57. Korkos, Panagiotis & Linjama, Matti & Kleemola, Jaakko & Lehtovaara, Arto, 2022. "Data annotation and feature extraction in fault detection in a wind turbine hydraulic pitch system," Renewable Energy, Elsevier, vol. 185(C), pages 692-703.
  58. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  59. 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.
  60. Aziz, Usama & Charbonnier, Sylvie & Bérenguer, Christophe & Lebranchu, Alexis & Prevost, Frederic, 2021. "Critical comparison of power-based wind turbine fault-detection methods using a realistic framework for SCADA data simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  61. Renström, Niklas & Bangalore, Pramod & Highcock, Edmund, 2020. "System-wide anomaly detection in wind turbines using deep autoencoders," Renewable Energy, Elsevier, vol. 157(C), pages 647-659.
  62. Lei Fu & Tiantian Zhu & Kai Zhu & Yiling Yang, 2019. "Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy," Energies, MDPI, vol. 12(16), pages 1-20, August.
  63. Teng, Wei & Ding, Xian & Zhang, Xiaolong & Liu, Yibing & Ma, Zhiyong, 2016. "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform," Renewable Energy, Elsevier, vol. 93(C), pages 591-598.
  64. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Ken Bruton & Dominic T. J. O’Sullivan, 2018. "A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study," Energies, MDPI, vol. 11(7), pages 1-21, July.
  65. Panagiotis Korkos & Jaakko Kleemola & Matti Linjama & Arto Lehtovaara, 2022. "Representation Learning for Detecting the Faults in a Wind Turbine Hydraulic Pitch System Using Deep Learning," Energies, MDPI, vol. 15(24), pages 1-17, December.
  66. de Azevedo, Henrique Dias Machado & Araújo, Alex Maurício & Bouchonneau, Nadège, 2016. "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 368-379.
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