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An approach combining data mining and control charts-based model for fault detection in wind turbines

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  • Yang, Hsu-Hao
  • Huang, Mei-Ling
  • Lai, Chun-Mei
  • Jin, Jhih-Rong

Abstract

Wind energy is growing to be one of main sources of renewable energy. As the operational and maintenance costs of wind turbines are adversely affected by the occurrence of faults, the early detection of potential faults can help reduce such costs. In this study, we propose a method for detecting potential faults sooner and identifying the probable variables contributing to the faults over a certain period as well as at a specific time. The proposed method uses data mining techniques to select the more important variables from the supervisory control and data acquisition (SCADA) systems of the turbine to improve the prediction accuracy and employs an exponentially weighted moving average (EWMA) model-based control chart to implement the residual approach, in order to remove the autocorrelation in the data. Both EWMA and multivariate EWMA (MEWMA) control charts are constructed so that their detection capabilities as well as the types of errors generated can be compared. We evaluated the proposed method by using both the SCADA data and the alarm log of a turbine. It was observed that the MEWMA chart is more suitable than the EWMA chart for the early detection and avoidance of errors.

Suggested Citation

  • Yang, Hsu-Hao & Huang, Mei-Ling & Lai, Chun-Mei & Jin, Jhih-Rong, 2018. "An approach combining data mining and control charts-based model for fault detection in wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 808-816.
    • Handle: RePEc:eee:renene:v:115:y:2018:i:c:p:808-816
    DOI: 10.1016/j.renene.2017.09.003
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    Cited by:

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    3. 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.
    4. 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.
    5. 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.
    6. Hu, Yang & Xi, Yunhua & Pan, Chenyang & Li, Gengda & Chen, Baowei, 2020. "Daily condition monitoring of grid-connected wind turbine via high-fidelity power curve and its comprehensive rating," Renewable Energy, Elsevier, vol. 146(C), pages 2095-2111.
    7. Cheng Xiao & Zuojun Liu & Tieling Zhang & Lei Zhang, 2019. "On Fault Prediction for Wind Turbine Pitch System Using Radar Chart and Support Vector Machine Approach," Energies, MDPI, vol. 12(14), pages 1-18, July.

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