Download The 27 level cascaded H-Bridge multilevel inverter

International Conference on Emerging Engineering Trends and Science (ICEETS – 2016 )
The 27 level cascaded H-Bridge multilevel inverter using neural network and
particle swarm optimization
1
Vasanthakumar.T, 2 Nafeena.R
PGScholor, 2 Assistant Professor,
Pandian saraswathi yadav Engineering College, Sivagangai India
1
Abstract
Over the past decades, depending upon the topologies and control strategies, numerous optimization
techniques have been proposed for desired output waveform. This paper presents are optimization
techniques used for multilevel inverters .Multilevel inverters have many advantages such as low cost,
good efficiency and etc, and some application such as PV panels and fuel cells. Cascaded H-bridge MLI
is one type of these MLIs. The Harmonic reduction techniques in multilevel inverters are considered very
important task .In this paper, minimization of THD by adjusting switching times of switches is used for a
27-level inverter. There are two optimization techniques are used: 1-Artificial Neural Network 2-Particle
Swarm Optimization after achieving these angles, a 27 -level inverter is simulated and a FFT analyses is
done.
Keywords— Multilevel inverter (MLI), 27-level inverter, Artificial Neural Network (ANN) , Particle
swarm optimization(PSO),Total harmonic distortion( THD) ,Distributed energy resources (DER).
I. Introduction
Recently multilevel inverters (MLIs) have been drawing growing attention especially in distributed
energy resources (DER) [1]. These MLIs can be applied for batteries, fuel cells, solar cells, wind and
micro turbine. Also MLIs can be used to feed a load or connected to ac grid without balancing problems.
An advantage of MLIs is that their switching frequency is lower than traditional inverters that means the
switching losses are decreased. These MLIs has increased the output voltage and introduced a solution to
limitation of classical semiconductor switches. Fig.1 illustrate some of advantage of the H-bridge MLI.
The technology of MLIs is based on production different DC voltage levels and composition of these
levels to obtain better output voltage waveform [2]. The output voltage waveform by adding step has
lower total harmonic distortion (THD) and reduced the harmonics in comparison to square wave
inverters.
MLIs can have one of three basic types: cascaded H-bridge, diode clamped and flying capacitor
convertors [5, 6]. A cascaded MLI has two or more separate DC voltage sources that can be batteries, fuel
cells or solar cells or other independent sources of DC voltage [7]. Cascaded MLI has a modular topology
and for this reason has higher reliability and can achieve higher output voltage. Cascaded H-bridge based
on DC input source applied, are classified in two classes: The Symmetric Multilevel Inverter (with equal
DC voltage source) and Asymmetric Multilevel Inverter (with unequal DC voltage source).
Figure 1. Some of advantages of H-bridge MLI
ISSN:2348 - 8379
www.internationaljournalssrg.org
Page 32
International Conference on Emerging Engineering Trends and Science (ICEETS – 2016 )
Different switching methods for decreasing harmonics and THD of output voltage waveform such as
sinusoidal pulse width modulation (PWM) and space vector PWM schemes are suggested in [3,4] .
Although these methods are useful but PWM schemes are complex and increase the switching frequency.
In [1], a method for tuning switching angles with Artificial Neural Network (ANN) is presented. Authors
in [8] used ANN for obtaining optimal switching angles for equal DC voltage sources. Analytical process
for solution in case of unequal DC sources is proposed in [9, 10] and in [11, 12], algorithms to solve for
the angles is presented.
In this paper, first the topology of asymmetrical MLI is described. Then switching table for a 27-level
inverter is stated. In third section of paper a review of optimization method used in paper is done. Two
algorithms 1- Artificial Neural network(ANN) 2- Particle swarm optimization(PSO)are used to optimize
THD in MLI. Theoretical and Simulated results are presented to compare two methods with other and
simulation results. Finally a conclusion of paper is presented.
II. Modeling of 27-level inverter
A 27-level is an asymmetrical MLI that has three H-bridge with unequal DC voltage source. Each Hbridge has four switches. Output voltage of each H-bridge is given as follows [13]:
V
oi = Vdc ( S1i - S2i )
(1)
Where i=1, 2, 3 (number of H-bridge inverter). Eventually for the 27- level of inverter is given by
V
oN

∑
3
V
(2)
oi
i 1
For 27 level inverter DC voltages are inverter of 1vdc,3vdc and 9vdc. Fig. 2 shows a simplified topology
of 27-level inverter.
.Fig .2 simplified topology of 27 level inverter
THD of waveform is expressed as
THD=√V orms 2 –va1
rms2 va1 rms2
THD of output voltage waveform and Angles obtained.
ISSN:2348 - 8379
www.internationaljournalssrg.org
Page 33
International Conference on Emerging Engineering Trends and Science (ICEETS – 2016 )
III. Optimization Techniques
Artificial neural networks:
The discussion in the last section is only an example of how important it is to define the primitive
functions and composition rules of the computational model. If we are computing with a conventional von
Neumann processor, a minimal set of machine instructions is needed in order to implement all
computable functions. In the case of artificial neural networks, the primitive functions re located in the
nodes of the network and the composition rules are contained implicitly in the interconnection pattern of
the nodes, in the synchrony or asynchrony of the transmission of information, and in the presence or
absence of cycles.
Networks of primitive functions:
Figure 3 shows the structure of an abstract neuron with n inputs. Each input channel i can
transmit a real value xi. The primitive function f computed in the body of the abstract neuron can be
selected arbitrarily. Usually the input channels have an associated weight, which means that the incoming
information xi is multiplied by the corresponding weight wi. The transmitted information is integrated at
the neuron (usually just by adding the different signals) and the primitive function is then evaluated.
Fig. 3. An abstract neuron
If we conceive of each node in an artificial neural network as a primitive function capable of
transforming its input in a precisely defined output, then artificial neural networks are nothing but
networks of primitive functions. Different models of artificial neural networks differ mainly in the
assumptions about the primitive functions used, the interconnection pattern, and the timing of the
transmission of information.
ISSN:2348 - 8379
www.internationaljournalssrg.org
Page 34
International Conference on Emerging Engineering Trends and Science (ICEETS – 2016 )
Fig. 4. Functional model of an artificial neural network
Typical artificial neural networks have the structure shown in Figure 4. The network can be thought of as
a function which is evaluated at the point (x, y, z). The nodes implement the primitive functions f1, f2,
f3, f4 which are combined to produce neurons. This function is implemented by a neural network .
Particle Swarm Optimization
Particle Swarm Optimization might sound complicated, but it's really a very simple algorithm. Over a
number of iterations, a group of variables have their values adjusted closer to the member whose value is
closest to the target at any given moment. Imagine a flock of birds circling over an area where they can
smell a hidden source of food. The one who is closest to the food chirps the loudest and the other birds
swing around in his direction. If any of the other circling birds comes closer to the target than the first, it
chirps louder and the others veer over toward him. This tightening pattern continues until one of the birds
happens upon the food. It's an algorithm that's simple and easy to implement.
The algorithm keeps track of three global variables:
1. Target value or condition.
2. Global best (gBest) value indicating which particle's data is currently closest to the Target.
3. Stopping value indicating when the algorithm should stop if the Target isn't found.
Each particle consists of:
1.Data representing a possible solution.
2.A Velocity value indicating how much the data can be changed.
ISSN:2348 - 8379
www.internationaljournalssrg.org
Page 35
International Conference on Emerging Engineering Trends and Science (ICEETS – 2016 )
3.A personal best (pBest) value indicating the closest the particle's Data has ever come to the
Target.
Fig. 5. Flowchart of particle swarm optimization
The particles' data could be anything. In the flocking birds example above, the data would be the X, Y, Z
coordinates of each bird. The individual coordinates of each bird would try to move closer to the
coordinates of the bird which is closer to the food's coordinates (gBest). If the data is a pattern or
sequence, then individual pieces of the data would be manipulated until the pattern matches the target
pattern. The velocity value is calculated according to how far an individual's data is from the target. The
further it is, the larger the velocity value. In the birds example, the individuals furthest from the food
would make an effort to keep up with the others by flying faster toward the gBest bird. If the data is a
pattern or sequence, the velocity would describe how different the pattern is from the target, and thus,
how much it needs to be changed to match the target. Each particle's pBest value only indicates the
closest the data has ever come to the target since the algorithm started. The gBest value only changes
when any particle's pBest value comes closer to the target than gBest. Through each iteration of the
algorithm, gBest gradually moves closer and closer to the target until one of the particles reaches the
target.
IV.Simulation result:
1) Results of ANN and PSO
ANN and PSO is run with objective of minimization THD for 27-level inverter. Number of iteration (N)
in ANN is 100 and it is in PSO. Output of each algorithm by trying different parameter is achieved and
ISSN:2348 - 8379
www.internationaljournalssrg.org
Page 36
International Conference on Emerging Engineering Trends and Science (ICEETS – 2016 )
finally switching angles that have minimum THD is choose as best solution. THD that achieved by ANN
and PSO is shown in Table I.
TABLE I. VALUE OF THD ACHIEVED FROM TWO METHODS
Method
Item
THD
ANN
PSO
3.95
3.06
From above mentioned results in Table I, it can be understand that PSO is better and more accurate
method for these problems.
Figure.6, 27 Level inverter of PSO applied MLI
ISSN:2348 - 8379
www.internationaljournalssrg.org
Page 37
International Conference on Emerging Engineering Trends and Science (ICEETS – 2016 )
Figure 7. FFT Analysis for ANN applied MLI.
Figure 8. FFT Analysis for PSO applied MLI.
V.Conclusion
In this work, an optimization method based on ANN and PSO has been presented. These techniques
has been used to have been proposed to minimize the total harmonic distortion in cascaded multilevel
inverters and Maintaining the desired level of fundamental output voltage. In order to evaluate the better
results , both methods has been compared with each other and the result show superiority of the PSO than
the other. The simulation results and FFT analysis results verified the proposed hybrid cascaded
multilevel inverter.
ISSN:2348 - 8379
www.internationaljournalssrg.org
Page 38
International Conference on Emerging Engineering Trends and Science (ICEETS – 2016 )
References
[1]A. A. Khodadoost Arani, H. R. Zaferani, M . J. Sanjari, G. B. Gharehpetian“Using Genetic Algorithm and Simulated
Annealing for 27-level PV Inverter THD M inimization,”IEEE., 2004.
[2]B. Ozpineci, L. M . Tolbert, and J. N. Chiasson, “Harmonic optimization of multilevel converters using genetic algorithms,”
in Proc. IEEE Power Electronics Specialists Conf., 2004, pp. 3911–3916.
[3] ] D.A.B Zambra, C,Rech, 1.R.Pinheiro, "Comparison of neutral point clamped symmetrical and hybrid asymmetrical
multilevel inverter ",IEEE Trans. Ind. Electron., Vo1.57, no. 7, pp 2297-2306, July 2010.
[4] L.M Tolbert, F. Z. Peng, T.G. Habetler, “M ultilevel PWM methods at low modulation indices,” IEEE Transactions on
Power Electronics,15(4), July 2000, pp. 719 – 725.
[5] L.M . Tolbert, T.G. Habetler, “Novel multilevel inverter carrier-based PWM method,” IEEE Transactions on Industry
Applications, 35(5), Sept.-Oct. 1999, pp. 1098 – 1107.
[6] Kouro S, M alinowski M , Gopakumar K, Pou J, Franquelo LG, Wu B, et al. Recentadvances and industrial applications of
multilevel converters. IEEE Trans IndElectron 2010;57(8):2553–80.
[7] Colak I, Kabalci E, Bayindir R. Review of multilevel voltage source invertertopologies and control schemes. Energy Convers
M anage 2011;52:1114–28.
[8] Al-Othman AK, Abdelhamid TH. Elimination of harmonics in multilevelinverters with non-equal dc sources using
PSO. Energy Convers M anage2009;50:756–64.
[9] B. Ozpineci, L. M . Tolbert, J. N. Chiasson, “Harmonic optimization of multilevel converters using Genetic Algorithms,”
IEEE Power Electronics Letters, vol. 3, no. 3, pp. 92-95, Sept. 2005.
[10] Z. Du, L. M . Tolbert, J. N. Chiasson, H. Li, “Low switching frequency active harmonic elimination in multilevel converters
with unequal DC voltages,” Annual M eeting of the IEEE Industry Applications Society, 2-6 Oct. 2005, pp. 92-98.
[11] Z. Du, L. M . Tolbert, J. N. Chiasson, “Active harmonic elimination for multilevel converters,” IEEE Transactions on
Power Electronics, vol. 21, no. 2, pp. 459-469, M arch 2006.
[12] M . G. H. Aghdam, S. H. Fathi, G. B. Gharehpetian, “Elimination of harmonics in a multi-level inverter with unequal
DC sources using the homotopy algorithm,” IEEE International Symposium on Industrial Electronics, June 2007, pp. 578583.
[13]T. Tang, J. Han, X. Tan, “Selective harmonic elimination for a cascade multilevel inverter,” IEEE International
Symposium on Industrial Electronics, July 2006, pp.977-981.
[14]Nathan, L.S. Karthik, S. Krishna, S.R,” The 27-level multilevel inverter for solar PV applications”, Power Electronics
(IICPE), 5th India International Conf. ,Delhi, 2012 ,pp 1-6.
[15]Palanivel, P .Dash, S.S,” Analysis of THD and output voltage performance forcascaded multilevel inverter using carrier
pulse width modulation techniques”, Power Electronics, IET, Vol4, pp951-958,Sep 2011.
[16] Farokhnia, N. Fathi, S.H. Vadizadeh, H. Toodeji, H,” Formulation of the Line Voltage THD of M ultilevel Inverter with
Unequal DC Sources”, Industrial Electronics and Applications (ICIEA) 5th IEEE Conf. Taichung,2010, pp 504-509.
[17] Kamlesh Kumar Vishwakarma,Hari M ohan Dubey , “Simulated Annealing Based Optimization for Solving Large Scale
Economic Load Dispatch Problems”, International Journal of Engineering Research & Technology,Vol.1, issue 3, M ay - 2012.
[18]Kamlesh Kumar Vishwakarma , Hari M ohan Dubey , M anjaree Pandit , B.K. Panigrahi , “Simulated annealing approach for
solving economic load dispatch problems with valve point loading effects” , IJEST,Vol4, No4,pp60-72, 2012.
ISSN:2348 - 8379
www.internationaljournalssrg.org
Page 39