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Classification of Highly Imbalanced Supervisory Control and Data Acquisition Data for Fault Detection of Wind Turbine Generators

Author

Listed:
  • Jorge Maldonado-Correa

    (Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain
    Technological and Energy Research Center (CITE), National University of Loja, Loja 110150, Ecuador)

  • Marcelo Valdiviezo-Condolo

    (Technological and Energy Research Center (CITE), National University of Loja, Loja 110150, Ecuador)

  • Estefanía Artigao

    (Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain)

  • Sergio Martín-Martínez

    (Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain)

  • Emilio Gómez-Lázaro

    (Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain)

Abstract

It is common knowledge that wind energy is a crucial, strategic component of the mix needed to create a green economy. In this regard, optimizing the operations and maintenance (O&M) of wind turbines (WTs) is key, as it will serve to reduce the levelized cost of electricity (LCOE) of wind energy. Since most modern WTs are equipped with a Supervisory Control and Data Acquisition (SCADA) system for remote monitoring and control, condition-based maintenance using SCADA data is considered a promising solution, although certain drawbacks still exist. Typically, large amounts of normal-operating SCADA data are generated against small amounts of fault-related data. In this study, we use high-frequency SCADA data from an operating WT with a significant imbalance between normal and fault classes. We implement several resampling techniques to address this challenge and generate synthetic generator fault data. In addition, several machine learning (ML) algorithms are proposed for processing the resampled data and WT generator fault classification. Experimental results show that ADASYN + Random Forest obtained the best performance, providing promising results toward wind farm O&M optimization.

Suggested Citation

  • Jorge Maldonado-Correa & Marcelo Valdiviezo-Condolo & Estefanía Artigao & Sergio Martín-Martínez & Emilio Gómez-Lázaro, 2024. "Classification of Highly Imbalanced Supervisory Control and Data Acquisition Data for Fault Detection of Wind Turbine Generators," Energies, MDPI, vol. 17(7), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1590-:d:1364307
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    References listed on IDEAS

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    1. 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.
    2. Qiu, Yingning & Feng, Yanhui & Infield, David, 2020. "Fault diagnosis of wind turbine with SCADA alarms based multidimensional information processing method," Renewable Energy, Elsevier, vol. 145(C), pages 1923-1931.
    3. 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.
    4. Pedro Santos & Jesús Maudes & Andres Bustillo, 2018. "Identifying maximum imbalance in datasets for fault diagnosis of gearboxes," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 333-351, February.
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    Cited by:

    1. Bita Ghasemkhani & Recep Alp Kut & Derya Birant & Reyat Yilmaz, 2025. "Balanced Hoeffding Tree Forest (BHTF): A Novel Multi-Label Classification with Oversampling and Undersampling Techniques for Failure Mode Diagnosis in Predictive Maintenance," Mathematics, MDPI, vol. 13(18), pages 1-45, September.
    2. Adaiton Oliveira-Filho & Monelle Comeau & James Cave & Charbel Nasr & Pavel Côté & Antoine Tahan, 2024. "Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies," Energies, MDPI, vol. 18(1), pages 1-23, December.

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