Fault Diagnostic System for Cascaded H-Bridge Multilevel Inverter ...

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FAULT DIAGNOSTIC SYSTEM FOR CASCADED H-BRIDGE MULTILEVEL INVERTER DRIVES BASED ON ARTIFICIAL INTELLIGENT APPROACHES INCORPORATING A RECONFIGURATION TECHNIQUE A Dissertation Presented <strong>for</strong> the Doctor of Philosophy Degree The University of Tennessee, Knoxville Surin Khomfoi May 2007
Copyright © by Surin Khomfoi All Rights Reserved ii
Page 1: To the Graduate Council: I am submi
Page 5 and 6: ACKNOWLEDGEMENTS There are many peo
Page 7 and 8: ABSTRACT A fault diagnostic and rec
Page 9 and 10: TABLE OF CONTENTS CHAPTER PAGE 1. I
Page 11 and 12: LIST OF FIGURES FIGURE 1.1. SINGLE-
Page 13 and 14: FIGURE 5.21. SIMULATION RESULTS OF
Page 15 and 16: 1. INTRODUCTION 1.1 Background In r
Page 17 and 18: Multilevel inverters provide more p
Page 19 and 20: • Common-mode (CM) voltage: Multi
Page 21 and 22: Advantages: • The number of possi
Page 23 and 24: VCA C C5 C4 C3 C2 120 o C1 A A5 A4
Page 25 and 26: times the level of an unbalanced vo
Page 27 and 28: then, if the probability of a singl
Page 29 and 30: It is possible that AI-based techni
Page 31 and 32: Va n v a2 v a1 A + S A - S B- S A +
Page 33 and 34: 2. SURVEY OF PREVIOUS WORKS 2.1 Int
Page 35 and 36: vehicle. It would be better if one
Page 37 and 38: Several research papers on fault de
Page 39 and 40: technology would reduce the impleme
Page 41 and 42: The vector in the d-q frame can als
Page 43 and 44: iβ iβ iβ Fault @ S 1 iα phase a
Page 45 and 46: As can be seen in Figure 2.5, the t
Page 47 and 48: Current beta (A) Current beta (A) 1
Page 49 and 50: 2.3.2 Control signal observer appro
Page 51 and 52: Current phase A (A) 25 20 15 10 5 0
Page 53 and 54:
As can be seen, two binary bits are
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As illustrated in Table 2.4, most d
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system or training a neural network
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Murphy [33] presented a fault diagn
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Vdc Vdc Current (A) n 15 10 5 0 -5
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2.4.3 Cascaded H-bridges MLID Fault
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2.5 A promising support for AI-base
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waveforms. It is evident that the u
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Figure 2.16 (b) shows an example of
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technique methodology of fault diag
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3.2 Structure of fault diagnostic s
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The modulation index (ma) is the ra
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The simulation model is illustrated
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Normal Fault A+ Fault A- Fault B+ F
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(a) (c) Figure 3.8. Experiment of o
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3.4 Feature extraction system Simul
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Normal Fault A+ Fault A- Fault B+ F
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X1 X2 X3 X4 X5 Xn Data PCA Scores t
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Selecting a reduced subset (PCs kep
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The collected data from both simula
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Score PC1 Score PC1 -2 -4 -6 4 6 4
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population created from multiple in
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• Encoded input PCs: the PCs to b
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andomly selects a crossover point w
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Loadings on PC#13 Scores on PC#13 L
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3.6 Neural network classification A
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As a comparison among transformatio
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Reselect training set Table 3.2. Ta
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[42], is used to simulate the targe
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The second category of testing resu
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4. RECONFIGURATION TECHNIQUE 4.1 In
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4.3 Reconfiguration method The reco
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Amplitude (pu.) 1 0.8 0.6 0.4 0.2 0
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traction motor only at half load co
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% Relative to fundamental component
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Van Vbn Vcn Vab Vbc Vca 100 0 -100
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4.5 Summary The reconfiguration tec
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5.2 Fault Diagnostic technique for
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will be trained with short circuit
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5.2.3 Principal component selection
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5.3 Simulation validation Several S
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The principal component analysis (P
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1 p{1} a{1} Input 1 p{1} Subsystem
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INV_A INV_B INV_C Showing the sub-s
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Fault creating circuit RT-LAB comma
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Figure 5.13. Obviously, the loss of
Page 145 and 146:
For the short circuit validation, t
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Starting current Fault start (a) Fa
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As can be seen, the simulation and
Page 151 and 152:
Loss of gate drive signal fault Rea
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Starting current Fault start Fault
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Fault start Fault clear Fault start
Page 157 and 158:
Table 5.3. Performance investigatio
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5mV/1A (2A/Div) Real open circuit f
Page 161 and 162:
The Opal-RT system needs a few cycl
Page 163 and 164:
6. CONCLUSIONS AND RECOMMENDATIONS
Page 165 and 166:
their locations. Also, a genetic al
Page 167 and 168:
6.2 Contributions This dissertation
Page 169 and 170:
technique for tracking the current
Page 171 and 172:
• S. Khomfoi, L. M. Tolbert, “A
Page 173 and 174:
Chapter 1 [1] L. M. Tolbert, F. Z.
Page 175 and 176:
Characteristics,” IEEE Transactio
Page 177 and 178:
[28] S. Nandi, H. A. Toliyat, X. Li
Page 179 and 180:
[43] D. Zhang, H. Li, “A Low Cost
Page 181 and 182:
[58] J. Fieres, A. Grubl, S. Philip
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