Hopfield neural network based selective harmonic elimination for h bridge

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME
32
HOPFIELD NEURAL NETWORK BASED SELECTIVE HARMONIC
ELIMINATION FOR H-BRIDGE INVERTER
N. Veeramuthulingam1
, S. Sivajanani @ Santhoshma 2
and Thebinaa Venugopal 3
1
Department of Electrical and Electronics Engineering, Surya Group of Institution/ School of
Engineering & Technology, Villupuram, India
2
Department of Electrical and Electronics Engineering, Manakula Vinayagar Institute of Technology,
Pondicherry, India
3
Department of Electrical and Electronics Engineering, Manakula Vinayagar Institute of Technology,
Pondicherry, India
ABSTRACT
The major consequence of the paper is about the selective harmonic elimination (SHE) of
harmonics for the H-Bridge inverter output voltage waveform using artificial neural network (ANN).
PWM inverters are of wide and great impact in no end of engineering disciplines. Its plays for an
implant role in the domain of power electronics and many. These papers clarify the use of artificial
neural network in gate signals control in Pulse width modulation voltage source inverter. In the SHE
stand on inverter, the fundamental voltage and the harmonics chosen for deletion are unmistakable,
using a neural network. For the SHE technique, the results of achieve switching angle patterns, using
the ANN, for step on H-bridge inverter, show nearly accurate resemblance, when compared to those
obtained using conventional methods. Also this technique has many advantages like quick response
in choosing and generating the PWM patterns, bush delay time etc., which are vital to shape up the
inverter output voltage.
Keywords: H-bridge inverter, SHE-PWM, Hopfield Neural Network
I. INTRODUCTION
Power electronic converters are widely used in industrial power conversion systems both for
utility and drives applications. The Power Quality and Reliability has become a major concern for
electrical engineers. These harmonics are to be kept below a safe limit to avoid their detrimental
effects and for maintaining power factor of the system. One of these systems is the Voltage source
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING &
TECHNOLOGY (IJEET)
ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 4, Issue 4, July-August (2013), pp. 32-41
© IAEME: www.iaeme.com/ijeet.asp
Journal Impact Factor (2013): 5.5028 (Calculated by GISI)
www.jifactor.com
IJEET
© I A E M E

33
inverter, is used in various fields of power converters such as SMPS, HVDC, UPS, drives and
tractions [1]. Voltage Source Inverter it’s nearly now to convert DC to AC supply at needed number
of phases and values of voltage and frequency. Various PWM methods were used to generate the
gate signals for switching the power switching devices in the inverter. The ultimate efficient method
is the PWM that has ability to shape up the magnitude and frequency of inverter output voltage, and
also eliminating serious harmonic components of the output voltage. This nature of Voltage Source
Inverter, based on Pulse Width Modulation method, can be performed different types of approach.
The fundamental, types are; natural sampling, suboptimal, optimal and harmonic elimination
strategies. Every one of this type depends on an algorithm that generates the Gate Pulse which are
used to drive the inverter switching power devices [2]. The most effective type is SHE technique,
which eliminates low order harmonics from the spectrum and also make less the total harmonic
distortion (THD) [3]. In order to obtain the PWM pattern, first the non linear equations of ANN
technique should be solved. These non linear equations are formed by the unknown switching angles
which are in turn decided by the number of harmonic components to be eliminated. These can be
reach a goal in off line and stored as look up tables. Due to the description of nonlinearity between
the switching angles and the fundamental of the PWM, a large number of look up tables are vital.
These look up tables are stored in a programmable memory and using for example,
microcomputer or microcontroller board which has been programmed to obtain the value of
modulation index and generate the conformable switching angles. The problems of the predominant
methods are the off line look up tables calculations, the choosing values of PWM pattern , or using
the analogy and digital hardware because the solution of the equations for solving switching angles is
challenging in on-line [4,9]. Therefore a propose techniques are applied by using the artificial neural
network. The neural network methods are effective for this case because they imitate nonlinear and
complex models and apply this theory by simulating it with MATLAB/SIMULINK program.
Section II deals with the problem formulation. Section III comprises of the theoretical
analysis of gate pulse generation using conventional Newton Rapshson algorithm. Section IV
depicts gate pulse generation and the steps involved in the proposed Hopfield Neural network
algorithm. Section V deals with the simulation of the H-bridge inverter using both the conventional
and proposed algorithm along with the comparison of the simulated results. Section VI deals with the
conclusion of the paper based on the comparison.
II. PROBLEM FORMULATION
The SHE-PWM technique is forthwith used to arrange an output voltage waveform of a full-
bridge inverter. In this study, a three-level Selective Harmonic Elimination pulse pattern generated
by an H-bridge inverter is considered. A H-bridge voltage source inverter, which involves four
switches and a dc source, is depicted in Fig. 1. And, Fig. 2 shows a discover three-level SHE-PWM
waveform, which was synthesized using the inverter circuit. The output waveform is chopped N
number of times per quarter cycle. Every switch is therefore switched 2N times per half cycle to
generate such a voltage waveform.
The H-bridge inverter operates on Positive, Negative and zero. Consider the discover three –
level SHE – PWM waveform shown in Fig. 2.

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July
Figure 1. A Full
‘
Figure 2. Three
By applying Fourier series to the above
out
1
V ( ) sin( )n
n
t a n tω ω
∞
=
= ∑
1
1
4
( 1) cos( ),
N
k
n k
k
E
a n for odd n
n
α+
=
= −
Π
∑
N is the number of the switching angles per quarter.
the following condition:
1 2 3....
2
kα α α α
∏
< < < <
E is the amplitude of the dc source
method is applied to solve the SHE PWM switching angles
III. ANALYSIS OF GATE PULSE GENERATION USING NEWTON RAPHSON
ALGORITHM
1) The switching angle matrix
αj
= [ α1
j
, α2
j
, α3
j
, α4
j
, α
6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME
34
Figure 1. A Full – Bridge Voltage Source Inverter
Figure 2. Three – Level SHE PWM Waveform
By applying Fourier series to the above model waveform, the output voltage is given by,
a n for odd n
N is the number of the switching angles per quarter. αk is the switching angles, which must satisfy
and N is the harmonic order. In Section III, Newton Raphson
method is applied to solve the SHE PWM switching angles.
II. ANALYSIS OF GATE PULSE GENERATION USING NEWTON RAPHSON
The switching angle matrix
, α5
j
] T
(1)
August (2013), © IAEME
model waveform, the output voltage is given by,
is the switching angles, which must satisfy
and N is the harmonic order. In Section III, Newton Raphson
II. ANALYSIS OF GATE PULSE GENERATION USING NEWTON RAPHSON
(1)

35
2) The nonlinear matrix
Cos(α1
j
)-Cos(α2
j
)+Cos(α3
j
)-Cos(α4
j
)+Cos(α5
j
)
Cos(3α1
j
)-Cos(3α2
j
)+Cos(3α3
j
)-Cos(3α4
j
) +Cos(3α5
j
)
F = Cos(5α1
j
)-Cos(5α2
j
)+Cos(5α3
j
)-Cos(5α4
j
)+Cos(5α5
j
) (2)
Cos(7α1
j
)-Cos(7α2
j
)+Cos(7α3
j
)-Cos(7α4
j
)+Cos(7α5
j
)
Cos(9α1
j
)-Cos(9α2
j
)+Cos(9α3
j
)-Cos(9α4
j
)+Cos(9α5
j
)
Sin(α1
j
)-Sin(α2
j
)+Sin(α3
j
)-Sin(α4
j
)+Sin(α5
j
)
Sin(3α1
j
)-Sin(3α2
j
)+Sin(3α3
j
)-Sin(3α4
j
)+Sin(3α5
j
)
[
డ௙
డఈ
] = Sin (5α1
j
)-Sin(5α2
j
) +Sin(5α3
j
)-Sin(5α4
j
)+Sin(5α5
j
) (3)
Sin(7α1
j
)-Sin(7α2
j
)+Sin(7α3
j
)-Sin (7α4
j
)+Sin(7α5
j
)
Sin (9α1
j
)-Sin(9α2
j
)+Sin (9α3
j
)-Sin(9α4
j
)+Sin(9α5
j
)
3) The harmonic amplitude matrix
T= [
ሺ଴.଼ହሻగ
ସ
0 0 0 0] T
(4)
Thus, equation (1) to (4) can be rewritten in the following matrix format:
F (α) =T (5)
By using matrices (1) to (4) and the Newton-Raphson method, the statement of algorithm is shown
as follows:
1) Guess a set of initial values for αJ
with j=o;
Assume
αj
= [ α1
0
, α2
0
, α3
0
, α4
0
, α5
0
] T
(i)
2) Calculate the value of F (α0
) = F0
(ii)
3) Linearize equation (4) about α0
F0
- [
డ௙
డఈ
] T
dα0
= T (iii)
dα0
= [ dα1
0
, dα2
0
, dα3
0
, dα4
0
, dα5
0
] (iv)
4) Solve dα0
from equation (3), i.e.
dα0
=INV [
డ௙
డఈ
] 0
(T-F0
) (v)
Where INV [
డ௙
డఈ
] 0
is the inverse matrix of [
డ௙
డఈ
] 0
5) As updated the initial data
αj+1
= αj
+ dαj
(vi)
6) Repeat the above process for equations (2) to (6) until dαj
is satisfied the condition:
α1< α2< α3< α4< α5<
௽
ଶ
(vii)
VI. ANALYSIS OF GATE PULSE GENERATION USING HOPFIELD NEURAL
NETWORK ALGORITHM
The implementation of the Hopfield neural network based SHE in single phase voltage
source inverter is entertaining and it makes the neural network controls the magnitude of
fundamentals harmonic (H1) easier in cases like even its value changes, this technique would make
it returns to the desired value. A continuous Hopfield Neural Network is designed for the

6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July
optimization of a set of non-linear transcendental equations given by (1) and (2). Fig. 5 shows the
Hopfield network corresponding to five pulse
converted into an optimization equation.
Figure 3. Typical Processing unit of Artificial Neuron
Min (Fk) =
Where, Fk = Output to be optimized the network
constraint
Bpk(t+1) =
For 0 Bk
Where Bpk(t) represents an array of pattern
Bpk(t) = activation of the j
Bk = self-bias
Wxy = connection
Wxy= Wyx= for continuous Hopfield network for Optimal function;
-0.7 ≤ Wxy
N = learning
Sgn = 1/(1-exp(
6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME
36
linear transcendental equations given by (1) and (2). Fig. 5 shows the
Hopfield network corresponding to five pulse-positions. The set of equation mentioned above are
converted into an optimization equation.
Figure 3. Typical Processing unit of Artificial Neuron
K
Where, Fk = Output to be optimized the network equation is represented by (2)
kjBpj(t)+Bk )
,
represents an array of pattern
= activation of the jth
neuron at t
= connection to the weight between neuron X and neuron
= for continuous Hopfield network for Optimal function;
xy ≤ 0.7
rate of the network
exp(-B)), sigmoidal function
August (2013), © IAEME
linear transcendental equations given by (1) and (2). Fig. 5 shows the
positions. The set of equation mentioned above are
equation is represented by (2) subject to given
and neuron Y
= for continuous Hopfield network for Optimal function;

37
Figure 4. Hopfield Neural Network
Hopfield neural network algorithm
Initialize the population P (0);
while condition is not satisfied,
Do
Determine potential parents from current population P(t);
Apply evolutionary operators, yielding offspring O(t);
Obtain P(t +1) from P(t) U O(t);
end-while;
Return best candidate solution from current population.
Figure 5. Block Diagram of Inverter
The neural network is used to obtain pulse-positions such that the value of function Fk will be
minimized. The node values are refreshing based on its immediate net weighted input. Fig. 6 shows
the algorithm for Evolution Program. An Evolution Program is used to obtain appropriate connection
weights between various nodes. The connection weights are optimized using the Evolution Program.
V. SIMULATION RESULTS
The MATLAB simulation results of the H-Bridge inverter employed with the gate pulse
generated by the conventional Newton Raphson method and the proposed Hopfield Neural method
are enlisted in this section. The parameters employed in the simulation are listed in Table 1. Pulse-
positions given by the conventional Newton-Raphson methods are listed in Table 2. Table 3 lists a

38
few pulse-positions given by Hopfield neural network. Trigger pulses produced at the pulse-
positions given by both the techniques resulted in variation of harmonic components and Total
Harmonic Distortion. It is inferred that the ANN pulse –position give better harmonic reduction as
compared to the NR pulse positions. The output obtained from the ANN controlled Inverter is fed to
a single phase induction motor (IM). The parameters of IM are also enlisted in the Table 1.
TABLE 1. SIMULATION PARAMETERS
Parameters Values
Input Voltage of inverter
Output frequency of inverter
Power rating of the IM
Motor input frequency
Motor input voltage
230V
50Hz
200W
50Hz
230V
Table 2. Pulse Positions using ANN method
Table 3. Pulse Positions using N-R method
The gate pulse waveform obtained from the simulation of ANN controlled inverter is
depicted in Fig. 6. The Fig. 7. Shows the output voltage waveform of the inverter which is
followed by the FFT analysis of the Voltage waveform in bar and listed mode in Fig. 8.
Figure 6. Trigger pulses produced by ANN
Harmonics 1st
3nd 5th
7th
9th
Switching Angle 22.58 33.60 46.64 68.49 75.09
Eliminate % 100 0.07 0.12 0.15 0.08
Harmonics 1st
3nd
5th
7th
9th
Switching Angle 22.38 33.68 46.74 68.99 75.28
Eliminate % 100 0.7 0.5 0.9 0.8

39
Figure 7. Output Voltage of ANN controlled Inverter
Figure 8. FFT analysis of the voltage waveform in Bar & listed mode

40
VI. CONCLUSION
It is observed that the trigger pulse-positions given by SHE controller produces better quality
of voltage at the Inverter output obtained from MATLAB simulation when compared to that of the
conventional numerical technique of Newton-Raphson method. The Intelligent SHE controller can
handle the complex non-linear transcendental equation set in a better manner producing the optimum
trigger pulse-positions. It is further observed that the Induction motor load is also delivering better
performance when driven by Intelligent SHE controlled Inverter. Thus this paper provides a detailed
analysis of SHE by using ANN technique.
REFERENCE
[1] N.Mohan, T.M. Undelanad and W.P Robbins, “Power Electronics: Converter, Applications
and Design, 2nd
Edition, New York; Wiley, 1995.
[2] Zainal Salam, “DC to AC conversion (inverters)”, Power Electronics and Drives (Version 2)
2002.
[3] Mohammed H. Rashid, “Power Electronics”, Prentice –Hall of India Pvt. Ltd, 2nd
Edition,
1994.
[4] Bimal. K. Bose, Bernard Goodwin, Michelle Vincenti, “Modern power electronics and AC
Drives”, United States of America, 2008.
[5] Akagi H, “New Trends in active filters for power Conditioning” IEEE Transaction on India
Applications, Vol. 2, No. 6, Nov-Dec.’96.
[6] L. Gyugy, E. C. Strycula, “Active power Filters", IEEE / IAS Annual Conference
Proceedings, 1976, pp 529-535.
[7] H. Fujita, H. Akagi, “A Practical Approach to Harmonic Compensation in Power Systems -
Series Compensation of Passive and Active Filters”, IEEE/IAS Conference. Proceedings,
1990, pp 1107-1112.
[8] B.E. Kushare, A.M. Jain, “ Overview of Harmonics Injected in Power Systems Network,
their Effects & Elimination Techniques by Using Filters”, 4th International Seminar on
Power Electronics & Automation, Jan. 1999, Mumbai.
[9] Do-Hyun Jang, Gyu-Ha Choe, M. Ehsani, “Asymmetrical PWM Technique with Harmonic
Elimination and Power Factor Control in AC Choppers", IEEE Transactions on Power
Electronics, Volume. 10, No. 2, March 1995, pp 175-184.
[10] A.Oliva, H. Chiacchiarini, A. Aymonino and P. Mandolesi, “Reduction of Total Harmonic
Distortion in Power Inverter”, Latin American Applied Research, Vol.35, No.2, June 2005.
[11] Albert N.S. and M.M. Beno, “An Intelligent Controller for a non-linear Power Electronic
Boost Converter”, IEE Proc. On Int. Conference on Energy, Information Tech., Power
Sector, Kolkata, pp 232-235.
[12] S. S. Ambekar, M.A. Chaudhari, “Artificial Neural Network Controller for Performance
Optimization of Single Phase Inverter”, Int. Journal of Artificial Intelligence and
Applications, Vol. 3, No.1, Jan.2012, pp 53-64.
[13] Vinesh Kapadia and Dr. Hina Chandwani, “Comparison of Modulation Techniques for
Cascaded H-Bridge Type Multilevel Current Source Inverter”, International Journal of
Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 2, 2013,
pp. 181 - 190, ISSN Print: 0976-6480, ISSN Online: 0976-6499.
[14] Anuradha Tomar and Dr. Yog Raj Sood, “All About Harmonics in Non-Linear PWM AC
Drives”, International Journal of Electrical Engineering & Technology (IJEET), Volume 3,
Issue 1, 2012, pp. 138 - 144, ISSN Print : 0976-6545, ISSN Online: 0976-6553.

41
BIOGRAPHIES
N.Veeramuthulingam was born on 16th
Feb 1988, in, Pondicherry
India. He received his B.Tech degree in Electrical and Electronics
Engineering from Pondicherry University in 2009 and M.Tech Degree
in Electric Drives and Control from Pondicherry Engineering College
in 2011. He presently works as Assistant Professor in the Department
of Electrical Engineering at, Surya Group of Institutions/ School of
Engineering & Technology, Tamilnadu, India. His research area
includes Harmonics Analysis in power converter, efficient pulse width
modulation strategies.
S.Sivajanani @ Santhoshma received her B.Tech in Electrical
and Electronics Engineering from Pondicherry University in 2010 and
also she completed her M.Tech in Electrical Drives & Control in
Pondicherry Engineering College in 2012. Presently, she is working as
Assistant Professor in the Department of Electrical and Electronics
Engineering, Manakula Vinayagar Institute of Technology,
Pondicherry, India. Her area of interest includes power electronics,
harmonic analysis.
V. Thebinaa obtained her Bachelor degree in Electrical and
Electronics and Master Degree in Power Systems Engineering from
Annamalai University, Chidambaram. She is currently working as
Assistant Professor in the Department of Electrical and Electronics
Engineering, Manakula Vinayagar Institute of Technology,
Pondicherry. Her area of interests is power system, power quality.

Hopfield neural network based selective harmonic elimination for h bridge

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (19)

Destaque

Destaque (14)

Semelhante a Hopfield neural network based selective harmonic elimination for h bridge

Semelhante a Hopfield neural network based selective harmonic elimination for h bridge (20)

Mais de IAEME Publication

Mais de IAEME Publication (20)

Último

Último (20)

Hopfield neural network based selective harmonic elimination for h bridge