Fault detection in wind turbine based on machine learning
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https://doi.org/10.54939/1859-1043.j.mst.94.2024.3-10Keywords:
Renewable energy; Wind turbine; Fault; SCADA; Machine learning.Abstract
Renewable energy in general and wind energy in particular are receiving increasing attention with the goal of reducing greenhouse gas emissions and producing clean energy. In recent years, wind farms and plants have significantly increased, driving wind energy to become an immensely potential energy source. However, due to the unpredictable nature of wind energy, ensuring the safe operation of wind turbine systems, minimizing downtime due to malfunctions plays a crucial role in optimizing production costs and enhancing the system's reliability. In this study, the authors propose using machine learning models to detect issues occurring in wind turbine systems. Operational parameters measured from SCADA are used as input data for the machine learning models. The results indicate that machine learning models can detect issues in the wind turbine system with an accuracy of over 99%, with model training taking only tens of milliseconds.
References
[1]. McMillan, D.; Ault, G. “Quantification of condition monitoring benefit for offshore wind turbines,” Wind Energy, vol. 31, pp. 267-285, (2007). DOI: https://doi.org/10.1260/030952407783123060
[2]. Drewry, M.; Georgiou, G. “A review of NDT techniques for wind turbines,” Insight, vol. 49, pp. 137-141, (2007). DOI: https://doi.org/10.1784/insi.2007.49.3.137
[3]. Aval, S.; Ahadi, A. “Wind turbine fault diagnosis techniques and related algorithms,” Int. J. Renew. Energy Res., vol. 6, pp. 80-89, (2016).
[4]. Hameed, Z.; Hong, Y.; Cho, Y.; Ahn, S.; Song, C. “Condition monitoring and fault detection of wind turbines and related algorithms: A review,” Renew. Sustain. Energy Rev., vol. 13, pp. 1-39, (2009). DOI: https://doi.org/10.1016/j.rser.2007.05.008
[5]. Lu, B.; Li, Y.; Wu, X.; Yang, Z. “A review of recent advances in wind turbine condition monitoring and fault diagnosis,” In Proceedings of the IEEE Power Electronics and Machines in Wind Applications, Lincoln, NE, USA, pp. 1-7, (2009). DOI: https://doi.org/10.1109/PEMWA.2009.5208325
[6]. Y. Shi, Y. Liu and X. Gao, “Study of Wind Turbine Fault Diagnosis and Early Warning Based on SCADA Data,” in IEEE Access, vol. 9, pp. 124600-124615, (2021). DOI: https://doi.org/10.1109/ACCESS.2021.3110909
[7]. María Peco Chacón and F. Pedro García Márquez, “SCADA data analytics for fault detection and diagnosis of wind turbines,” 7th International Conference on Control, Instrumentation and Automation (ICCIA), Tabriz, Iran, pp. 1-6, (2021). DOI: https://doi.org/10.1109/ICCIA52082.2021.9403538
[8]. Karadayi, Y. Kuvvetli and S. Ural, “Fault-related Alarm Detection of a Wind Turbine SCADA System,” 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey, pp. 1-5, (2021). DOI: https://doi.org/10.1109/HORA52670.2021.9461331
[9]. S. Zhang, Y. Wang, M. Liu and Z. Bao, “Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM,” in IEEE Access, vol. 6, pp. 7675-7686, (2018). DOI: https://doi.org/10.1109/ACCESS.2017.2785763
[10]. C. Ren, H. Yuan, Q. Li, R. Zhang and Y. Xu, “Pre-Fault Dynamic Security Assessment of Power Systems for Multiple Different Faults via Multi-Label Learning,” in IEEE Transactions on Power Systems, vol. 38, no. 6, pp. 5501-5511, Nov. (2023). DOI: https://doi.org/10.1109/TPWRS.2022.3223166
