title: Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.
abstract: 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.
authors.affiliation: Shanghai Maritime University, China. Electronic address: wtz0@sina.com.
authors.forename: Tianzhen
authors.initials: T
authors.lastname: Wang
authors.affiliation: Shanghai Maritime University, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Qi
authors.affiliation: Shanghai Maritime University, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Xu
authors.affiliation: Ecole polytechnique Université de Nantes, L׳Institut d׳Electronique et de Télécommunications de Rennes, UMR CNRS 6164, France.
authors.forename: Yide
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Shanghai Maritime University, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Liu
authors.affiliation: Shanghai Maritime University, China.
authors.forename: Diju
authors.initials: D
authors.lastname: Gao
keywords: Cascaded-Multilevel Inverter; Fast Fourier Transform; Fault diagnosis; Relative principal component analysis; Support Vector Machine; Wind turbine