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논문 기본 정보

자료유형
학술저널
저자정보
Xiao-Xia Zheng (Shanghai University of Electric Power) Peng Peng (Shanghai University of Electric Power)
저널정보
전력전자학회 JOURNAL OF POWER ELECTRONICS JOURNAL OF POWER ELECTRONICS Vol.19 No.2
발행연도
2019.3
수록면
443 - 453 (11page)

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초록· 키워드

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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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Abstract
I. INTRODUCTION
II. RESEARCH ON THE POWER DEVICE FAULTS OF CONVERTERS
III. FEATURE EXTRACTION COMBINING WAVELET AND COMPRESSED SENSING
IV. FAULT DIAGNOSIS OF POWER CONVERTERS BASED ON AN IMPROVED ADABOOST – SVM MODEL
V. SIMULATION AND EXPERIMENTAL ANALYSIS
VI. CONCLUSION
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UCI(KEPA) : I410-ECN-0101-2019-560-000541326