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

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improves the classification from PC-NN by about 2.5% point. The overall classification per<strong>for</strong>mance of the proposed networks is more than 90%. A reconfiguration technique is also developed. The effects of using the developed reconfiguration technique at high modulation index are addressed. The developed fault diagnostic system is validated with experimental results. The developed fault diagnostic system requires about 6 cycles at 60 Hz to clear an open circuit and about 9 cycles at 60 Hz to clear a short circuit fault. The experimental results show that the developed system per<strong>for</strong>ms satisfactorily to detect the fault type, fault location, and reconfiguration. vii
TABLE OF CONTENTS CHAPTER PAGE 1. INTRODUCTION............................................................................................................................... 1 1.1 BACKGROUND ............................................................................................................................. 1 1.2 MULTILEVEL INVERTER DRIVES (MLIDS) ................................................................................ 3 1.2.1 CASCADED H- BRIDGE MULTILEVEL INVERTER DRIVES ................................................................ 6 1.3 FAULTS AND THEIR CONSEQUENCES .......................................................................................... 8 1.4 RELIABILITY CONSIDERATIONS OF MLIDS............................................................................. 11 1.5 AI APPLICATIONS IN CONDITION MONITORING AND DIAGNOSIS............................................. 14 1.6 MAIN CONTRIBUTION OF THE DISSERTATION.......................................................................... 16 1.7 ORGANIZATION OF THE DISSERTATION ................................................................................... 18 2. SURVEY OF PREVIOUS WORKS................................................................................................ 19 2.1 INTRODUCTION ......................................................................................................................... 19 2.2 A GENERAL PROTECTION USED IN A CID SYSTEM................................................................... 19 2.3 FAULT DIAGNOSIS IN AN INVERTER OF A CID SYSTEM ........................................................... 24 2.3.1 PARK’S VECTOR APPROACH ........................................................................................................ 25 2.3.2 CONTROL SIGNAL OBSERVER APPROACH..................................................................................... 35 2.3.3 NORMALIZED DC CURRENT APPROACH ....................................................................................... 36 2.3.4 ARTIFICIAL INTELLIGENT BASED APPROACH............................................................................... 42 2.4 FAULT DIAGNOSIS IN AN INVERTER OF A MLID SYSTEM........................................................ 46 2.4.1 DIODE CLAMPED MLID .............................................................................................................. 46 2.4.2 FLYING CAPACITOR MLID.......................................................................................................... 48 2.4.3 CASCADED H-BRIDGES MLID .................................................................................................... 49 2.5 A PROMISING SUPPORT FOR AI-BASED FAULT PROTECTION IN MLID................................... 51 2.5.1 MODULAR POWER SWITCH FOR A MLID ..................................................................................... 51 2.5.2 HARDWARE IMPLEMENTATION OF AI-BASED DIAGNOSIS............................................................ 53 2.6 SUMMARY.................................................................................................................................. 56 3. AI-BASED MLID FAULT DIAGNOSIS ........................................................................................ 58 3.1 INTRODUCTION ......................................................................................................................... 58 3.2 STRUCTURE OF FAULT DIAGNOSTIC SYSTEM ........................................................................... 59 3.3 DIAGNOSTIC SIGNALS ............................................................................................................... 59 3.4 FEATURE EXTRACTION SYSTEM ............................................................................................... 69 3.5 PRINCIPAL COMPONENT ANALYSIS .......................................................................................... 73 3.5.1 DATA ANALYSIS USING PCA....................................................................................................... 75 3.5.2 PRINCIPAL COMPONENT SELECTION ............................................................................................ 80 3.5.3 GENETIC ALGORITHM.................................................................................................................. 80 3.5.4 PC SELECTION BY GENETIC ALGORITHM ..................................................................................... 82 3.5.5 DATA ANALYSIS USING GA AND PCA ........................................................................................ 85 3.6 NEURAL NETWORK CLASSIFICATION ....................................................................................... 89 3.6.1 NEURAL NETWORK ARCHITECTURE DESIGN ................................................................................ 89 3.6.2 INPUT/OUTPUT DATA................................................................................................................... 92 3.6.3 TRAINING PARADIGM .................................................................................................................. 92 viii
Page 1 and 2: To the Graduate Council: I am submi
Page 3 and 4: Copyright © by Surin Khomfoi All R
Page 5 and 6: ACKNOWLEDGEMENTS There are many peo
Page 7: ABSTRACT A fault diagnostic and rec
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
Page 55 and 56: As illustrated in Table 2.4, most d
Page 57 and 58: system or training a neural network
Page 59 and 60:
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
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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
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Table 5.3. Performance investigatio
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5mV/1A (2A/Div) Real open circuit f
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The Opal-RT system needs a few cycl
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6. CONCLUSIONS AND RECOMMENDATIONS
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their locations. Also, a genetic al
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6.2 Contributions This dissertation
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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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