Fault Diagnosis of Wind Power Converters Based on Compressed Sensing Theory and Weight Constrained AdaBoost-SVM

Xiao-Xia; Zheng Peng Peng

Fault Diagnosis of Wind Power Converters Based on Compressed Sensing Theory and Weight Constrained AdaBoost-SVM

Xiao-Xia

Zheng Peng Peng

Vol. 19, No. 2, pp. 443-453, Mar. 2019

10.6113/JPE.2019.19.2.443

Adaboost

Compressive sensing

Fault diagnosis

Support Vector Machine

Wavelet power converter

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Abstract

As the core component of transmission systems, converters are very prone to failure. To improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed. In addition, an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain measurement signals based on the theory of compressive sensing. A sparse vector is obtained by the orthogonal matching pursuit algorithm, and then the fault feature vector is extracted. The fault feature vectors are input to the improved AdaBoost-SVM classifier to realize fault diagnosis. Simulation results show that this method can effectively realize the fault diagnosis of the power transistors in converters and improve the precision of fault diagnosis.

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Cite this article

[IEEE Style]

Xiao-Xia and Z. P. Peng, "Fault Diagnosis of Wind Power Converters Based on Compressed Sensing Theory and Weight Constrained AdaBoost-SVM," Journal of Power Electronics, vol. 19, no. 2, pp. 443-453, 2019. DOI: 10.6113/JPE.2019.19.2.443.

[ACM Style]

Xiao-Xia and Zheng Peng Peng. 2019. Fault Diagnosis of Wind Power Converters Based on Compressed Sensing Theory and Weight Constrained AdaBoost-SVM. Journal of Power Electronics, 19, 2, (2019), 443-453. DOI: 10.6113/JPE.2019.19.2.443.