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

Xuan-Kien Mai, Jun-Yeop Lee, Jae-In Lee, Byeong-Soo Go, Seok-Ju Lee and Minh-Chau Dinh ()
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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

Energies, 2025, vol. 18, issue 11, 1-19

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.

Keywords: deep neural network; DBSCAN algorithm; machine learning; operation and maintenance; principal component analysis; SCADA data; wind turbine gearbox (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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