Next Article in Journal
Multi-Objective Optimal Charging Method for Lithium-Ion Batteries
Previous Article in Journal
3-D FEM Analysis, Prototyping and Tests of an Axial Flux Permanent-Magnet Wind Generator
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Energies 2017, 10(9), 1273; https://doi.org/10.3390/en10091273

A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI

1
Department of Electrical Automation, Shanghai Maritime University, Shanghai 201306, China
2
Institut d’Electronique et Télécommunications de Rennes, UMR CNRS 6164, Polytech Nantes, Rue Christian Pauc, BP 50609, 44306 Nantes CEDEX 3, France
*
Author to whom correspondence should be addressed.
Academic Editor: Gabriele Grandi
Received: 26 July 2017 / Revised: 20 August 2017 / Accepted: 24 August 2017 / Published: 26 August 2017
(This article belongs to the Section Energy Fundamentals and Conversion)
Full-Text   |   PDF [2392 KB, uploaded 27 August 2017]   |  

Abstract

Cascaded H-bridge Multilevel Inverter (CHMI) is widely used in industrial applications thanks to its many advantages. However, the reliability of a CHMI is decreased with the increase of its levels. Fault diagnosis techniques play a key role in ensuring the reliability of a CHMI. The performance of a fault diagnosis method depends on the characteristics of the extracted features. In practice, some extracted features may be very similar to ensure a good diagnosis performance at some H-bridges of CHMI. The situation becomes even worse in the presence of noise. To fix these problems, in this paper, signal denoising and data preprocessing techniques are firstly developed. Then, a Principal Components Rearrangement method (PCR) is proposed to represent the different features sufficiently distinct from each other. Finally, a PCR-based fault diagnosis strategy is designed. The performance of the proposed strategy is compared with other fault diagnosis strategies, based on a 7-level CHMI hardware platform. View Full-Text
Keywords: fault diagnosis; feature representation; principal components rearrangement; cascaded H-bridge multilevel inverter fault diagnosis; feature representation; principal components rearrangement; cascaded H-bridge multilevel inverter
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liu, Z.; Wang, T.; Tang, T.; Wang, Y. A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI. Energies 2017, 10, 1273.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top