A Robust Fault Diagnosis Scheme for Converter in Wind Turbine Systems

Fault diagnosis is a powerful tool to reduce downtime and improve maintenance efficiency; thus, the low management cost of wind turbine systems and effective utilization of wind energy can be obtained. However, the accuracy of fault diagnosis is extremely susceptible to the nonlinearity and noise in the measured signals and the varying operating conditions. This paper proposes a robust fault diagnosis scheme based on ensemble empirical mode decomposition (EEMD), intrinsic mode function (IMF), and permutation entropy (PE) to diagnose faults in the converter in wind turbine systems. Three-phase voltage signals output by the converter are used as the input of the fault diagnosis model and each signal is decomposed into a set of IMFs by EEMD. Then, the PE is calculated to estimate the complexity of the IMFs. Finally, the IMF-PE information is taken as the feature of the classifier. The EEMD addresses nonlinear signal processing and mitigates the effects of mode mixing and noise. The PE increases the robustness against variations in the operating conditions and signal noise. The effectiveness and reliability of the method are verified by simulation. The results show that the accuracy for 22 faults reaches about 98.30% with a standard deviation of approximately 2% under different wind speeds. In addition, the average accuracy of 30 runs for different noises is higher than approximately 76%, and the precision, recall, specificity, and F1-Score all exceed 88% at 10 dB. The standard deviation of all the evaluation indicators is lower than about 1.7%; this proves the stable diagnostic performance. The comparison with different methods demonstrates that this method has outstanding performance in terms of its high accuracy, strong robustness, and computational efficiency.