Fault Diagnosis of Wind Turbine Drivetrain Based on Wasserstein Generative Adversarial Network-Gradient Penalty

The drivetrain is responsible for the energy transfer from rotor hub to generator in wind turbines. If any part of the drivetrain, such as gears and bearings, is abnormal, the wind turbine will face a huge safety hazard. Now most of the current wind turbine fault diagnosis based on the deep learning need to select target parameters artificially, and the identified fault has a close correlation with the selected variables, resulting in insufficient versatility. Wasserstein generative adversarial network-gradient penalty (WGAN-GP) uses Wasserstein distance between the generated data and the real data as a measurement for the cost function, which has the advantage of stable training results. This paper proposes a two-step data preprocessing method for data screening based on the supervisory control and data acquisition (SCADA) system, and designs anomaly state score of the wind turbine drivetrain based on the WGAN-GP model to identify the drivetrain faults. The proposed method uses common SCADA parameters, does not need to manually select target variables, and can stably identify non-specific faults in the wind turbine drivetrain, which has the advantages of accurate identification results and strong generalization ability. The status identification results of nine doubly-fed wind turbines verify the effectiveness of the proposed method, which can assist in guiding the operation and maintenance of wind farms. © 2021 Automation of Electric Power Systems Press.