Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter

被引:144
作者
Wang, Tianzhen [1 ,3 ]
Qi, Jie [1 ]
Xu, Hao [1 ]
Wang, Yide [2 ]
Liu, Lei [1 ]
Gao, Diju [1 ]
机构
[1] Shanghai Maritime Univ, Shanghai 201306, Peoples R China
[2] Univ Nantes, Ecole Polytech, Inst Elect & Telecommun Rennes, UMR CNRS 6164, F-44035 Nantes, France
[3] Shanghai Maritime Univ, Dept Elect Automat, Coll Logist Engn, Shanghai 201306, Peoples R China
关键词
Fault diagnosis; Fast Fourier Transform; Relative principal component analysis; Support Vector Machine; Cascaded-Multilevel Inverter; Wind turbine; CIRCUIT FAULT; TOLERANT; STRATEGY; SCHEME; PCA;
D O I
10.1016/j.isatra.2015.11.018
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. (C) 2015 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:156 / 163
页数:8
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