References
- Alvarez, E. J., and A. P. Ribaric. 2018. An improved-accuracy method for fatigue load analysis of wind turbine gearbox based on SCADA. Renewable Energy 115:391–99. doi:10.1016/j.renene.2017.08.040.
- Borchersen, A. B., and M. Kinnaert. 2016. Model-based fault detection for generator cooling system in wind turbines using SCADA data. Wind Energy 19:593–606. doi:10.1002/we.v19.4.
- Cao, M., Y. Qiu, Y. Feng, H. Wang, and D. Li. 2016. Study of wind turbine fault diagnosis based on unscented Kalman filter and SCADA data. Energies 9:847. doi:10.3390/en9100847.
- Dao, P. B., W. J. Staszewski, T. Barszcz, and T. Uhl. 2018. Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data. Renewable Energy 116:107–22. doi:10.1016/j.renene.2017.06.089.
- Du, M., J. Yi, P. Mazidi, L. Cheng, and J. B. Guo. 2017. A parameter selection method for wind turbine health management through SCADA data. Energies 10:253. doi:10.3390/en10020253.
- Global Wind Report 2016. 2016.
- Kusiak, A., and W. Li. 2011. The prediction and diagnosis of wind turbine faults. Renewable Energy 36:16–23. doi:10.1016/j.renene.2010.05.014.
- Liu, W. Y., B. P. Tang, J. G. Han, X. N. Lu, N. N. Hu, and Z. Z. He. 2015a. The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. Renewable and Sustainable Energy Reviews 44:466–72. doi:10.1016/j.rser.2014.12.005.
- Liu, X., M. Li, S. Y. Qin, X. J. Ma, and W. Z. Wang. 2015b. A predictive fault diagnose method of wind turbine based on K-means clustering and neural networks. China National Computer Congress 1521–528. doi:10.6138/JIT.2016.17.7.20151027i.
- Marti-Puig, P., A. Blanco-M, J. J. Cárdenas, J. Cusidó, and S.-C. Jordi. 2019. Feature selection algorithms for wind turbine failure prediction. Energies 12:453. doi:10.3390/en12030453.
- Qiu, Y., Y. H. Feng, J. Sun, W. Zhang, and D. Infield. 2016. Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data. IET Renewable Power Generation 10:661–68. doi:10.1049/iet-rpg.2015.0160.
- Santos, P., L. F. Villa, A. Reñones, A. Bustillo, and J. Maudes. 2015. An SVM-based solution for fault detection in wind turbines. Sensors (Basel, Switzerland) 15:5627–48. doi:10.3390/s150305627.
- Schlechtingen, M., I. F. Santos, and S. E. Achiche. 2013. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description. Applied Soft Computing 13:259–70. doi:10.1016/j.asoc.2012.08.033.
- Sun, P., J. Li, C. Wang, and X. Lei. 2016. A generalized model for wind turbine anomaly identification based on SCADA data. Applied Energy 168:550–67. doi:10.1016/j.apenergy.2016.01.133.
- Tautz-Weinert, J., and S. J. Watson. 2017. Using SCADA data for wind turbine condition monitoring - a review. IET Renewable Power Generation 11 (4):382–94. doi:10.1049/iet-rpg.2016.0248.
- Tchakoua, P., R. Wamkeue, M. Ouhrouche, F. S. Hasnaoui, T. A. Tameghe, and G. Ekemb. 2014. Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies 7:2595–630. doi:10.3390/en7042595.
- Yampikulsakul, N., E. Byon, S. Huang, S. W. Sheng, and M. You. 2014. Condition monitoring of wind power system with nonparametric regression analysis. IEEE Transactions on Energy Conversion 29:288–99. doi:10.1109/TEC.2013.2295301.
- Zhang, Z.-Y., and K.-S. Wang. 2014. Wind turbine fault detection based on SCADA data analysis using ANN. Advances in Manufacturing 2 (1):70–78. doi:10.1007/s40436-014-0061-6.
- Zhao, Y. Y., D. S. Li, A. Dong, D. Kang, Q. Lu, and L. Shang. 2017. Fault prediction and diagnosis of wind turbine generators using SCADA data. Energies 10:1210. doi:10.3390/en10081210.