Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/65289
Title: Model-based fault diagnosis for voltage source inverters
Authors: Seyedeh Marjan Alavi
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
DRNTU::Engineering::Electrical and electronic engineering::Power electronics
Issue Date: 2015
Source: Seyedeh Marjan Alavi. (2015). Model-based fault diagnosis for voltage source inverters. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: This thesis introduces a fault detection and isolation (FDI) method for diagnosis faulty semiconductor switches of a pulsewidth modulated (PWM) voltage source inverter (VSI). This method analyses the pole voltage signals in a time-free domain called voltage space. For a healthy inverter, the projection of the state transitions in the voltage space results in a cubic pattern. It shows that each switch fault changes the voltage space pattern (VSP) uniquely that allows isolating the faulty switch. The mathematical model of the inverter can predict these patterns. Monitoring the transition vectors in the voltage space and comparing to the fault signatures can diagnose the faults. An experimental set-up has been designed and implemented to simulate the switch faults in a three-phase inverter. Simulations and experimental results validate the efficiency and consistency of the VSP-based FDI. The fault detection time is within only one PWM carrier period which is significantly faster than current-based conventional FDI methods. It shows that the VSP-based FDI method can overcome the FDI problem of multilevel multiphase inverters with only one voltage detector per phase leg. It is sufficiency fast to be integrated with a fault tolerant control to maintain maximum efficiency of the inverter in the presence of a faulty switch.
URI: http://hdl.handle.net/10356/65289
metadata.item.grantfulltext: restricted
metadata.item.fulltext: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Marjan_thesis_Binding_10_06_2015.pdfPhD Thesis5.6 MBAdobe PDFThumbnail
View/Open

Page view(s)

376
checked on Jan 11, 2020

Download(s)

549
checked on Jan 11, 2020

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.