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Design of an Efficient Deep Learning-Based Diagnostic Model for Wind Turbine Gearboxes Using SCADA Data

Author

Listed:
  • Xuan-Kien Mai

    (Department of Electrical Engineering, Changwon National University, Changwon 51140, Republic of Korea)

  • Jun-Yeop Lee

    (Department of Electrical Engineering, Changwon National University, Changwon 51140, Republic of Korea)

  • Jae-In Lee

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Republic of Korea)

  • Byeong-Soo Go

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Republic of Korea)

  • Seok-Ju Lee

    (School of Aerospace Engineering, Glocal Advanced Institute of Science & Technology, Changwon National University, Changwon 51140, Republic of Korea)

  • Minh-Chau Dinh

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Republic of Korea)

Abstract

Global efforts to address climate change have intensified the transition from fossil fuels to renewable energy sources, positioning wind power as a critical player due to its advanced technology, scalability, and environmental benefits. Despite their potential, the reliability of wind turbines, particularly their gearboxes, remains a persistent challenge. Gearbox failures lead to significant downtime, high maintenance costs, and reduced operational efficiency, threatening the economic competitiveness of wind energy. This study proposes an innovative condition monitoring model for wind turbine gearboxes, utilizing Supervisory Control and Data Acquisition systems and Deep Learning techniques. The model analyzes historical operating data from wind turbine to classify gearbox conditions into normal and abnormal states. Optimizing the dataset for deep neural networks through advanced data processing methods achieves an impressive fault detection accuracy of 98.8%. Designed for seamless integration into real-time monitoring systems, this approach enables early fault prediction and supports proactive maintenance strategies. By enhancing gearbox reliability, reducing unplanned downtime, and lowering maintenance expenses, the model improves the overall economic viability of wind farms. This advancement reinforces wind energy’s pivotal role in driving a sustainable, low-carbon future, aligning with global climate goals and renewable energy adoption.

Suggested Citation

  • Xuan-Kien Mai & Jun-Yeop Lee & Jae-In Lee & Byeong-Soo Go & Seok-Ju Lee & Minh-Chau Dinh, 2025. "Design of an Efficient Deep Learning-Based Diagnostic Model for Wind Turbine Gearboxes Using SCADA Data," Energies, MDPI, vol. 18(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2814-:d:1666801
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. A. G. Olabi & Khaled Obaideen & Mohammad Ali Abdelkareem & Maryam Nooman AlMallahi & Nabila Shehata & Abdul Hai Alami & Ayman Mdallal & Asma Ali Murah Hassan & Enas Taha Sayed, 2023. "Wind Energy Contribution to the Sustainable Development Goals: Case Study on London Array," Sustainability, MDPI, vol. 15(5), pages 1-22, March.
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