MULTILEVEL INVERTER SIMULATION
FAULT CLASSIFICATION AND DIAGNOSIS
SURYAKANT TRIPATHI
12117081
SUMAN KUMAR
12117080
1

AGENDA
• INTRODUCTION
• LITERATURE SURVEY
• PROPOSED WORK
• FUTURE WORK
• CONCLUSION
• REFERENCES
2

INTRODUCTION
 Nowadays most of the electrical projects are based on fault
identification and rectification as the society wants an
automated system that can not only run the plant smoothly but
also automatically rectifies the fault within it.
 Today’s demand is to run the system continuously, even at the
time of fault so that the production during a particular time
interval should be maximum to maximise the profit of any
industry.
3

LITERATURE SURVEY
 In December 1998 Raphael, Stephen and Jean produced a paper on fault
detection on three phase inverters by using Concordia transform or alpha
beta transform
 During switching fault conditions due to unbalance in the three phase
currents the relation between the alpha beta currents changes and the
type of fault can be classified. The relations for switching fault is given as-:
4

LITERATURE SURVEY
 In 2007 Surin Komfoi released his 22nd volume on fault detection technique
using neural networks. He implemented his theory on cascaded H-bridge
inverter.
 The basic steps for fault detection and remedies according to him is
 Step 1 - Feature extraction for different kind of faults(THD).
 Step 2 - Arranging these features in a matrix form and arrange
another matrix (target matrix) in which the type of fault are given.
 Step 3 - Arrange these data column wise feature extraction of n parameters
in first n columns and the target matrix in another column. This is called the
training data set.
 Step 4 - Fed this training data set to a neural network for training .
 Step 5 - Once the network is trained connect this network to a simulated
inverter in which feature extraction data has been taken and test for different
kind of faults.
5

LITERATURE SURVEY
PROPOSED MODEL
6

BASIC INVERTER
• A basic inverter is able to convert dc to pulsating form of ac
• These are basically of two types called CSI and VSI.
• CSI or current source inverters are those in which the source current
remains constant independent load, a VSI or voltage source inverter are
those in which voltage is kept constant.
• On the basis of construction inverter is classified as Cascaded H-Bridge,
flying capacitor type and diode clamped inverter
7

BASIC INVERTER 8

BASIC INVERTER 9

BASIC INVERTER 10
NO FAULT

BASIC INVERTER 11
FAULT

MULTI LEVEL INVERTER
 Unlike basic type inverters multi-level inverters have more than one
voltage levels
 They are meant to make the output voltage and current waveform more
sinusoidal.
 Actually we are getting a stair-case waveform, capacitive and inductive
filters are used to make the waveform smoothen and the resulting
waveform becomes sinusoidal.
 As the level of voltage level increases the size of the smoothening reactor
filter reduced to make the stair case waveform more sinusoidal.
 The main heart of inverter is its pulse sequence. PWM technique is
generally used for firing the IGBTS.
12

MULTILEVEL INVERTER
 If there are n level of inverter the no of PWM saw tooth waves required for
supplying the pulse in the IGBTs is P and the no of IGBTS are I then:-
P = I/2
I = 2(n – 1)
P = n-1
13

Neural Network
 Neural network is a highly interconnected sets of neurons which can be
trained and then can be used as a human brain .
 Its application is not only in engineering ,mathematics and science but also
in medicine ,business ,finance and literature as well.
 Most NNs have some sort of training rule. In other words, NNs learn from
examples (as children learn to recognize dogs from examples of dogs) and
exhibit some capability for generalization beyond the training data.
 Neural computing requires a number of neurons, to be connected together
into a neural network. neurons are arranged in layers.
14

Neural Network Architecture
Inputs Weights
Output
Bias
1
3p
2p
1p
f a
3w
2w
1w
     bwpfbwpwpwpfa ii332211
15

Learning Methods
 Supervised learning
 In supervised training, both the inputs and the outputs are provided.
 The network then processes the inputs and compares its resulting outputs
against the desired outputs.
 Examples-multi-layer perceptron
 Unsupervised learning
 In unsupervised training, the network is provided with inputs but not with
desired outputs.
 The system itself must then decide what features it will use to group the
input data.
 Examples-kohonen ,ART
16

THREE LEVEL INVERTER 17

VOLTAGE WAVEFORM 18

FIVE LEVEL INVERTER 19

VOLTAGE WAVEFORM 20

SEVEN LEVEL INVERTER 21

VOLTAGE WAVEFORM 22

NINE LEVEL INVERTER 23

VOLTAGE WAVEFORM 24

NINE LEVEL INVERTER WITH
CAPACITOR IN PARALLEL
 When a capacitor of suitable value is connected in parallel to the resistive
load It produces a sinusoidal voltage waveform. For a resistance of 1 ohm
0.1 F capacitor is required
25

ELEVEN LEVEL INVERTER 26

VOLTAGE WAVEFORM 27

THIRTEEN LEVEL INVERTER 28

VOLTAGE WAVEFORM 29

T TYPE INVERTER 30

T TYPE INVERTER VOLTAGE
WAVEFORM
31

T- TYPW INVERTER PULSES 32

CYCLOCONVERTER
 Cycloconverter i is an ac to ac converter by changing the input frequency.
 There are two types of cycloconverters step up and step down.
 The step up cycloconverter steps up the frequency of output waveform as
compared to input voltage waveform.
 The step down cycloconverter steps down the frequency of input voltage
waveform
33

CYCLOCONVERTER 34

CYCLOCONVERTER WAVEFORM
STEP DOWN 2:1
35

CYCLOCONVERTER WAVEFORM
STEP DOWN 2:1
36

INVERTER USED IN FAULT
IDENTIFICATION SYSTEM
37

FEATURE EXTRACTION
 For feature extraction process we have to separate the output waveform
in its frequency components.
 1st ,3rd,5th, ………19th harmonics are taken.
 This can be done by Fourier transformation block.
 Total Harmonic Distortion of each harmonic has been taken for each and
every switch fault conditions.
 For test purpose one switch at a time get faulted.
 The simulated results of fault condition of five level inverter is taken.
38

RESULTS (TRAINING DATA) 39
SWITCH/PARAMETER MI-1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
s1c 19.25 16.97 15.2 10.3 6.63 7.36 8.94 13.8 20.91 48.07
s1o 19.33 17.06 15.25 10.33 6.6 7.2 8.73 13.66 20.49 46.76
s2c 19.03 16.76 15.03 10.3 6.73 7.39 8.99 13.86 21.02 48.5
s2o 19.11 16.83 15.07 10.31 6.59 7.2 8.73 13.55 20.49 46.76
s3c 19.33 17.06 15.25 10.33 6.6 7.2 8.73 13.55 20.49 46.76
s3o 18.05 18.46 22 28.94 35 41.68 40.96 43.43 43.1 48.03
s4c 19.11 16.83 15.07 10.31 6.59 7.2 8.73 13.55 20.49 46.76
s4o 18.18 19.8 22.41 22.41 36.43 42.3 41.72 44.43 44.28 48.45
s5c 18.3 18.93 22.63 29.48 36.13 43.52 42.55 45.02 44.46 48.06
s5o 19.42 19.09 22.78 29.53 36.11 43.11 42.51 43.41 44.95 48.45
s6c 18.18 18.59 22.22 29.41 35.74 42.7 41.8 44.04 43.37 48.49
s6o 18.28 18.76 22.37 29.45 35.69 42.49 41.75 44.22 43.77 48.03
s7c 18.57 19.26 23.03 29.93 36.72 43.94 43.31 46.02 45.64 48.49
s7o 18.05 18.46 22 28.94 35 41.68 40.96 43.43 43.1 48.03
s8c 18.44 18.92 22.63 29.85 36.3 43.31 42.55 45.01 44.45 48.06
s8o 18.18 18.8 22.41 29.01 35.43 42.3 41.72 44.43 44.28 48.46
normal 3.2 3.36 4.53 4.4 5.07 7.2 8.73 13.55 20.49 46.76

TESTING DATA 40
SWITCH/PARAMETER 0.95 0.85 0.75 0.65 0.55 0.45 0.35 0.25 0.15 0.05
s1c 18.09 16.41 13.31 9.28 6.58 6.4 9.68 13.02 21.66 48.07
s1o 18.18 16.49 13.37 9.27 6.48 6.2 9.36 12.71 21.17 46.76
s2c 19.03 16.04 13.15 9.16 6.5 6.46 9.63 13.1 21.77 48.5
s2o 17.97 16.09 13.18 9.13 6.36 6.2 9.36 12.71 21.17 46.76
s3c 18.18 16.49 13.37 9.27 6.48 6.2 9.36 12.71 21.17 46.76
s3o 19.37 20.28 23.31 30.23 39.29 42.74 41.81 43.59 47.41 48.03
s4c 17.97 16.09 13.18 9.13 6.36 6.2 9.36 12.71 21.17 46.76
s4o 19.66 20.83 23.73 30.69 40.03 43.41 42.61 44.59 48.64 48.45
s5c 19.8 21 23.95 31.22 40.96 44.44 43.43 45.21 48.96 48.06
s5o 19.9 21.14 24.11 31.24 40.79 44.26 42.51 45.4 49.4 48.45
s6c 19.52 20.47 23.55 30.76 40.23 43.78 42.65 44.24 47.83 48.49
s6o 19.61 20.59 23.68 30.77 40.05 43.59 42.62 44.39 48.17 48.03
s7c 20.08 21.35 24.38 31.68 41.5 45.11 44.22 46.21 50.19 48.49
s7o 19.37 20.28 23.31 30.23 39.29 42.74 41.81 43.59 47.41 48.03
s8c 19.79 20.8 23.95 31.22 40.76 44.44 43.42 45.2 48.95 48.06
s8o 19.66 20.83 23.73 30.69 40.03 43.41 42.61 44.59 48.64 48.46
normal 2.74 3.25 4.14 3.58 5.93 6.2 9.36 12.71 21.17 46.76

TRAINING PROGRAM 41

TESTING PROGRAM 42

PROGRAM FOR CREATING P MATRIX 43

PROCEDURE FOLLOWED
 For fault diagnosis two neural networks are required for analysis, one for
open circuit fault and one for short circuit fault
 Total harmonic distortion of each case is taken for open circuit switch fault
classification.
 Neural network creation for open circuit switching faults.
 The THD matrix for open circuit switch fault classification is x
=[ 28.43 34.73 18.86 18.15 18.43 18.66 18.39 18.15 18.43]
 t = [0 1 2 37 48 5 6 37 48]
44

PROCEDURE FOLLOWED 45
Number of layers - 4(two hidden layers, one input
and one output layer)
Input layer - 10 neurons
Hidden layer 1- 8 neurons
Hidden layer 2 – 6 neurons
Output layer - 1 neuron
Function used – tangent sigmoid

PROCEDURE FOLL0WED 46

PROCEDURE FOLLOWED 47

PROCEDURE FOLLOWED 48

PROCEDURE FOLLOWED 49

PROCEDURE FOLLOWED
 Neural network creation for short circuit switching fault.
 the THD values are stored in the matrix x = [28.43 21.31 21.89 43.89
20.85 35.97 18.02 43.95 18.55]
 t = [0 1 2 3 4 5 6 7 8]
50

PROCEDURE FOLLOWED 51
Number of layers - 4(two hidden layers, one input
and one output layer)
Input layer - 11 neurons
Hidden layer 1- 6 neurons
Hidden layer 2 – 5 neurons
Output layer - 1 neuron
Function used – tangent sigmoid for input layer and
hidden layer 1
Pure linear for hidden layer 2 and output layer.

PROCEDURE FOLLOWED 52

PROCEDURE FOLLOWED 53

PROCEDURE FOLLOWED 54

PROCEDURE FOLLOWED 55

FUTURE WORK
 Simulation of T type inverter
 Fault diagnosis of both 5 level cascaded inverter and T type inverter
56

CONCLUSION
 Our project was the fault diagnosis in the multilevel inverter with the help
of ANN. We started with the study of neural network so that we would be
familiar with what we have exactly to do.
 In the neural network we studied about the basic neural network , its
biological interpretation, application, architecture, classification perceptron
neuron model, training rules and some examples.
 Then we move towards multilevel inverter we started with the basic
inverter knowledge and then we go towards multilevel inverter. In this we
studied about three level, five level, seven level and nine level inverter
and simulated these inverters to find out voltage waveforms
 We then found out voltage waveforms in the 1st, 3rd ,5th up to 19th
harmonics.
57

REFRENCES
 Fault diagnostic system for multilevel inverter using ANN by SURIN
KHOMFOI VOLUME 2 2007.
 Unique fault tolerant design for flying capacitor multilevel inverter by
XIAOMI N KUO
58