1 MULTILEVEL INVERTER SIMULATION

FAULT CLASSIFICATION AND DIAGNOSIS

SURYAKANT TRIPATHI

12117081

SUMAN KUMAR

12117080

2AGENDA

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

3INTRODUCTION

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.

4LITERATURE 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-:

5LITERATURE 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.

6LITERATURE SURVEYPROPOSED MODEL

7BASIC 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

8BASIC INVERTER

9BASIC INVERTER

10BASIC INVERTER

NO FAULT

11BASIC INVERTER

FAULT

12MULTI 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.

13MULTILEVEL 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

14Neural 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.

15Neural Network Architecture Inputs Weights

Output

B ias1

3p

2p

1p

f a3w

2w

1w

bwpfbwpwpwpfa ii332211

16Learning 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

17THREE LEVEL INVERTER

18VOLTAGE WAVEFORM

19FIVE LEVEL INVERTER

20VOLTAGE WAVEFORM

21SEVEN LEVEL INVERTER

22VOLTAGE WAVEFORM

23NINE LEVEL INVERTER

24VOLTAGE WAVEFORM

25NINE 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

26ELEVEN LEVEL INVERTER

27VOLTAGE WAVEFORM

28THIRTEEN LEVEL INVERTER

29VOLTAGE WAVEFORM

30T TYPE INVERTER

31T TYPE INVERTER VOLTAGE WAVEFORM

32T- TYPW INVERTER PULSES

33CYCLOCONVERTER

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

34CYCLOCONVERTER

35CYCLOCONVERTER WAVEFORM STEP DOWN 2:1

36CYCLOCONVERTER WAVEFORM STEP DOWN 2:1

37INVERTER USED IN FAULT IDENTIFICATION SYSTEM

38FEATURE 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.

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

40TESTING DATA

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

41TRAINING PROGRAM

42TESTING PROGRAM

43PROGRAM FOR CREATING P MATRIX

44PROCEDURE 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 18.3918.15 18.43]

t = [0 1 2 37 48 5 6 37 48]

45PROCEDURE FOLLOWED

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

46PROCEDURE FOLL0WED

47PROCEDURE FOLLOWED

48PROCEDURE FOLLOWED

49PROCEDURE FOLLOWED

50PROCEDURE 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]

51PROCEDURE FOLLOWED

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 1Pure linear for hidden layer 2 and output layer.

52PROCEDURE FOLLOWED

53PROCEDURE FOLLOWED

54PROCEDURE FOLLOWED

55PROCEDURE FOLLOWED

56FUTURE WORK

Simulation of T type inverter Fault diagnosis of both 5 level cascaded inverter and T type

inverter

57CONCLUSION

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.

58REFRENCES

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