Research on Wind Turbine Fault Detection Based on CNN-LSTM

• Published in: Energies (Basel) 2024-09, Vol.17 (17), p.4497
• Main Authors: Qi, Lin, Zhang, Qianqian, Xie, Yunjie, Zhang, Jian, Ke, Jinran
• Format: Article
• Language: English
• Subjects: Alternative energy sources; CNN-LSTM; deep learning; Failure; fault detection; Fault diagnosis; Fourier transforms; Methods; neural network; Neural networks; Preventive maintenance; Sensors; Turbines; Wind power; wind turbine fault
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17174497

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.