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Fault Diagnosis of Wind Power Converter Based on Compressed Sensing Theory and Weight Constrained AdaBoost-SVM
Xiao-Xia Zheng* and Peng Peng¢Ó
Abstract As the core component of the transmission system, the converter is very prone to failure. In order to improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with wavelet and compressed sensing theory is proposed, and an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and processed by wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain the measurement signals based on the theory of compressive sensing. The 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 power transistors in converters and improve the precision of fault diagnosis.
Keyword Adaboost, Compressive sensing, Fault diagnosis, Support vector machine, Winder analysis, Wavelet power converter
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