Research on Wind Turbine Fault Detection Based on CNN-LSTM

Lin Qi, Qianqian Zhang, Yunjie Xie, Jian Zhang () and Jinran Ke ()
Additional contact information
Lin Qi: School of Management Science and Engineering, Beijing Information Science & Technology University, Beijing 102206, China
Qianqian Zhang: School of Management Science and Engineering, Beijing Information Science & Technology University, Beijing 102206, China
Yunjie Xie: School of Management Science and Engineering, Beijing Information Science & Technology University, Beijing 102206, China
Jian Zhang: School of Management Science and Engineering, Beijing Information Science & Technology University, Beijing 102206, China
Jinran Ke: Beijing World Urban Circular Economy System (Industry) Collaborative Innovation Center, Beijing 100192, China

Energies, 2024, vol. 17, issue 17, 1-21

Abstract: With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

Keywords: fault detection; wind turbine fault; neural network; CNN-LSTM; deep learning (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/17/4497/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/17/4497/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:17:p:4497-:d:1473525

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().