| Summary: | Fault diagnostic techniques can reduce the requirements for the experience of maintenance crews, accelerate maintenance speed, reduce maintenance cost, and increase electric energy production profitability. In this paper, a new hybrid fault diagnosis method based on multivariate empirical mode decomposition (MEMD), fuzzy entropy (FE), and an artificial fish swarm algorithm (AFSA)-support vector machine (SVM) is proposed to identify the faults of a wind energy converter. Firstly, the measured three-phase output voltage signals are processed by MEMD to obtain three sets of intrinsic mode functions (IMFs). The multi-scale analysis tool MEMD is used to extract the common modes matching the timescale. It studies the multi-scale relationship between three-phase voltages, realizes their synchronous analysis, and ensures that the number and frequency of the modes match and align. Then, FE is calculated to describe the IMFs’ complexity, and the IMFs-FE information is taken as fault feature to increase the robustness to working conditions and noise. Finally, the AFSA algorithm is used to optimize SVM parameters, solving the difficulty in selecting the penalty factor and radial basis function kernel. The effectiveness of the proposed method is verified in a simulated wind energy system, and the results show that the diagnostic accuracy for 22 fault modes is 98.7% under different wind speeds, and the average accuracy of 30 running can be maintained above 84% for different noise levels. The maximum, minimum, average, and standard deviation are provided to prove the robust and stable performance. Compared with the other methods, the proposed hybrid method shows excellent performance in terms of high accuracy, strong robustness, and computational efficiency.
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