Anand b and balasubramonian m

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International Journal of Advanced Information Science and Technology (IJAIST) Vol.22, No.22, February 2014

ISSN: 2319:2682

Generalized Hopfield Neural Network Based SHEPWM in A Single Phase Inverter Anand.B

Balasubramonian.M

PG Scholar/Department of EEE AlagappaChettiar College of Engineering & Technology, Karaikudi, India anandha08ece@gmail.com

Assistant Professor/Department of EEE AlagappaChettiar College of Engineering & Technology, Karaikudi, India balu94rec@gmail.com

Abstract— In this paper, a Generalized Hopfield Neural Network based Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) in a single phase inverter is designed and implemented. The objective of this paper is to eliminate 5, 7, 11, 13th order harmonics in the output voltage waveform of the single phase inverter while retaining fundamental component to the desired value. The switching angles corresponds to the above objective are obtained by solving a set of non-linear algebraic transcendental equations. The problem is redrafted as an optimization problem and it is solved by using GHNN. An energy function is formulated for the above problem and set of differential equations describing the behavior of GHNN were formed by using the derived energy function. These set of differential equations are explicit in nature and it is numerically solved by Runge-Kutta fourth order (RK4) method with suitable initial conditions. A MATLAB simulation was carried out and the FFT analysis of the simulated output voltage waveform and confirms the effectiveness of the proposed method. Hence, the proposed method proves that it is much applicable in the industrial applications by virtue of its suitability in real time applications. Index terms -SHEPWM- Selective Harmonic Elimination Pulse Width Modulation, GHNN-Generalized Hopfield Neural Network.

I. INTRODUCTION The switching strategy is the heart of wide power electronic applications in order to control and adjust the voltage and current waveforms. Such applications include electric drives, AC power supply at variable voltage and variable frequency, UPS,DC to AC or AC to DC power conversions, STATCOM, Active filters, HVDC, FACTS, SMPS, handling energy sources etc. However irrespective of the application and the level of sophistication, a suitable pwm strategy is achieved electronically using PWM generating circuit. In SHEPWM techniques, the PWM signal can be programmed as a pattern, and this pattern is driving power-switching devices in order to perform certain required operation on analog voltage or current waveforms suitable for required application. Therefore, PWM strategies have been developed in various approaches relating to the chronologically progressingtechnology and the types of applications. Starting from using analog electronic circuits, through TTL electronics, programmable integrated circuits, microprocessor,

microcomputer, microcontrollers, and more recently DSP and FPGA are gaining popularity due their higher advanced features while their cost are decreasing [1]. These PWM methods are categorized as either off-line or on-line generation. Since the PWM pattern generation, using SHEPWM can eliminate certain lower order harmonic components and the fundamental component value is retained at any desired value with low switching frequency, also high modulation index is obtained and more utilization of DC source compared with other methods[2-7]. But due their nonlinearity of HEM equations, it is normally generated in offline and stored as a look up table using ROM. Therefore huge tables of data are needed to be stored to obtain variable magnitudes of the fundamental components. In order to overcome these problems neural networks both feed forward[8-17] and feedback are used to replace the tables and then neural network is realized in hardware by using an FPGA.FPGA allows very complex and high speed designs to be implemented. The objective of this paper is to show how to realize selective Harmonic elimination pulse width modulation (SHEPWM) in an FPGA based feedback neural network, such that high speed of response for the determination of switching angles associated with the specified modulation index for the elimination of certain lower order harmonics while retaining the required fundamental is achieved. For that the problem of finding the solutions of a set of nonlinear algebraic transcendental equations is redrafted as an optimization problem and it is solved by using single layer recurrent neural network GHNN [18-22]. In this paper FPGA based real time implementation of selective harmonic elimination (SHEPWM) using Generalized Hopfield Neural Network (GHNN) to minimize 5th, 7th, 11th and 13th order harmonics while retaining the desired fundamental in a single phase inverter is discussed. Figure.1 shows the block diagram of the proposed method. For the given Modulation Index the GHNN block is used to calculate the switching instants online. These switching instants are given to the PWM generator which generates the required gating signals to the inverter.

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International Journal of Advanced Information Science and Technology (IJAIST) Vol.22, No.22, February 2014

Figure.1 Block diagram of the proposed method.

The paper has been organized as follows: Chapter II gives an overview of GHNN and its application to the solution of nonlinear optimization problems. Chapter III describes the proposed method of application of GHNN to SHEPWM as applied to single-phase inverter to minimize 5th, 7th, 11th and 13th order harmonics while retaining the desired fundamental. Chapter IV explains the MATLAB simulation and its real time implementation using the FPGA. Presentation, verification and discussions on the results of simulation as well as the results of experimental endeavors are given in Chapter V. Finally, summarization of this work is presented in Chapter VI.

II.GENERALIZED HOPFIELD NEURAL NETWORK AND ITS APPLICATION TO THE SOLUTION OF NONLINEAR EQUATIONS The continuous time single layer feedback networks are called “gradient type networks� in which time is assumed a continuous variable. Gradient type neural networks are the Generalized Hopfield Neural Networks (GHNN) in which the computational energy decreases continuously with time. The evolution of the system is in the general direction of the negative gradient of the energy function. The transients of the GHNN converge to one of the stable minima in the state space [23-26]. The generalized electrical architecture of the GHNN is shown as in Figure.2.

ISSN: 2319:2682

It consisting of an interconnection of n’ neurons, each one of which is assumed to have the same mathematical model described in equations (1) and (2). Where Rj represents leakage resistance, Cjrepresents leakage capacitance andđ?œ‘ (.) represents activation function. In physical terms, the synaptic weights Wj1, Wj2,‌Wjnrepresent conductanceâ€&#x;s, and the respective inputs x1(t),x2(t),‌., xn(t) represent potentials; n is the number of inputs. These inputs are applied to a current summing junction characterized by low input resistance, unity current gain and high output resistance. It thus acts as a summing node for the input currents. In the generalized Hopfield neural network the inputs to each neuron of the network not only come from the outputs of other neurons but also from the product of several outputs. The total current flowing toward the input node of the nonlinear element(activation function) in Figure.2 is therefore where the first term is due to the stimuli x1(t),x2(t),‌., xn(t) acting on the synaptic weights (conductance) Wj1, Wj2,‌Wjn respectively, and the second term is due to the current source Ijrepresentingan externally applied bias. đ?‘ (1) đ?‘–=1 đ?‘¤đ?‘—đ?‘– đ?‘Ľđ?‘– đ?‘Ą +Ij Let uj(t) denote the induced local field at the input of the nonlinear activation function. We may then express the total current flowing away from the input node of the nonlinear element as follows: đ?‘˘đ?‘— đ?‘Ą đ?‘…đ?‘—

�� � � ��

+Cj

(2)

Where the first term is due to the leakage resistance Rj and the second term is due to leakage capacitance Cj. From Kirchoffâ€&#x;s current law, we know that the total current flowing toward any node of an electrical circuit is zero. By applying Kirchoffâ€&#x;s current law to the input node of the nonlinearity. We may define the dynamics of the network by the following system of coupled first-order differential equations by ignoring interneuron propagation time delays. đ?‘‘đ?‘˘ đ?‘— (đ?‘Ą)

Cj

đ?‘‘đ?‘Ą

=−

� � (�) ��

+

đ?‘ đ?‘–=1 đ?‘¤đ?‘—đ?‘–

�� � +Ij’,

(3)

An assumption is made that the activation function relating the output xj(t) of neuron „j’ to its induced local field uj(t) is a continuous function and therefore differentiable. A commonly used activation function is the logistic function. 1 đ?œ‘ (uj)= , j=1,2,‌n (4) 1+đ?‘’đ?‘Ľđ?‘? −đ?‘˘ đ?‘—

The energy function for the proposed model is given by đ??¸=(

đ?‘Ľ đ?‘– đ?œ‘ −1 đ?‘ đ?‘– 0 đ?‘…đ?‘–

đ?‘‘đ?‘

đ?‘–

đ?‘—

đ?‘¤đ?‘–đ?‘— đ?‘“đ?‘–đ?‘— đ?‘Ľ1, đ?‘Ľ2, ‌ . , đ?‘Ľđ?‘ đ?‘Ľđ?‘– +

đ??źđ?‘– đ?‘Ľđ?‘– )

(5) By using conventional methodology one can see that the derivative for the proposed energy function is always less than đ?‘‘đ??¸ or equal to zero, i.e. đ?‘‘đ?‘Ą < 0 and energy of the system is thus bounded.

Figure.2.Generalized Architecture of GHNN.

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International Journal of Advanced Information Science and Technology (IJAIST) Vol.22, No.22, February 2014

ISSN: 2319:2682

A.

Application of GHNN to the solution of nonlinear equations Let us consider a set of nonlinear algebraic equations given below: đ?‘“1 đ?‘Ľ1 , đ?‘Ľ2 , ‌ , đ?‘Ľđ?‘— ‌ đ?‘Ľđ?‘› = đ?‘ƒ1 đ?‘“2 đ?‘Ľ1 , đ?‘Ľ2 , ‌ , đ?‘Ľđ?‘— ‌ đ?‘Ľđ?‘› = đ?‘ƒ2 . . đ?‘“đ?‘— đ?‘Ľ1 , đ?‘Ľ2 , ‌ , đ?‘Ľđ?‘— ‌ đ?‘Ľđ?‘› = đ?‘ƒđ?‘— . . đ?‘“đ?‘› (đ?‘Ľ1 , đ?‘Ľ2 , ‌ , đ?‘Ľđ?‘— . . . đ?‘Ľđ?‘› ) = đ?‘ƒđ?‘› (6) In the above equations đ?‘“đ?‘— (. )is a function of variablesđ?‘Ľ1 , đ?‘Ľ2 , ‌ , đ?‘Ľđ?‘— . . . đ?‘Ľđ?‘› đ?œ–â„œ and đ?œ–â„œ is a real constant. Our objective is to find the values for variables đ?‘Ľ1 , đ?‘Ľ2 , ‌ , đ?‘Ľđ?‘— . . . đ?‘Ľđ?‘› such that it satisfies the equation (6). To obtain the solution using proposed approach an energy function has to be formulated. The energy function for the above set of equations is derived as follows: đ??¸=

� � ((��

2

. )

where, đ?‘”đ?‘— . = (đ?‘“đ?‘— đ?‘Ľ1 , đ?‘Ľ2 , ‌ , đ?‘Ľđ?‘— . . . đ?‘Ľđ?‘› − đ?‘ƒđ?‘— )

(7)

(8)

Equations (7) and (8) have been used for designing the proposed network. The number of neurons in network is equal to the number of variables whose value is to be determined. In the given problem we have „nâ€&#x; number of variables and hence the network should have „nâ€&#x; number of neurons. The network dynamics are governed by the following differential equations: đ?‘‘đ?‘˘ đ?‘— đ?‘‘đ?‘Ą

đ?œ•đ??¸

= − đ?œ•đ?‘Ľ (9) đ?‘—

đ?‘Ľđ?‘— = đ?œ‘ đ?‘˘đ?‘— , đ?‘— = 1 ‌ đ?‘› (10) where, uj is the net input to the jth neuron in the network and is its output. In this application the function đ?œ‘đ?‘— (. )linear input – outputtransfer function for the jthneuron. Calculating the partial derivatives of equation (8) with respect to unknown variablesđ?‘Ľ1 , đ?‘Ľ2 , ‌ , đ?‘Ľđ?‘— . . . đ?‘Ľđ?‘› and collecting the terms of identical order will result in Hopfield equations like form. The coefficients and constants in the available expression give the weights and bias values for the network respectively. III. APPLICATION OF GHNN TO SHEPWM A standard H bridge single phase inverter shown in Figure. 3 is used to generate the unipolar waveform shown as in Figure. 4.There are four switches in the circuit; which provide 24=16different possible switching combinations. Only four of these combinations are useful to get an alternating waveform across the load. Hence there are only three possible states for the load voltage Va-b. A number of periodic waveforms can be generated using three states.

Figure.3 Single phase full bridge inverter. Figure.4 Inverter output voltage waveform.

A. Formulation of the Transcendental Equations The Fourier series expansion of the unipolar waveform shown in Fig. 4 is given as đ?‘‰ đ?œ”đ?‘Ą = 4đ?‘‰đ?‘‘đ?‘? đ?œ‹

∞

�=1,3,5‌

sin đ?‘›đ?œ”đ?‘Ą đ?‘›

cos đ?‘›đ?œƒ1 − cos đ?‘›đ?œƒ2 + cos đ?‘›đ?œƒ3 − cos đ?‘›đ?œƒ4 + cos đ?‘›đ?œƒ5

(11) Given a desired fundamental voltage V1 and for the elimination 5,7,11 and 13th harmonics, the problem here is to determine the switching angles đ?œƒ1 , đ?œƒ2 , đ?œƒ3 , đ?œƒ4 &đ?œƒ5 such that đ?‘?đ?‘œđ?‘ đ?œƒ1 − đ?‘?đ?‘œđ?‘ đ?œƒ2 + đ?‘?đ?‘œđ?‘ đ?œƒ3 − đ?‘?đ?‘œđ?‘ đ?œƒ4 + đ?‘?đ?‘œđ?‘ đ?œƒ5 = đ?‘€ đ?‘?đ?‘œđ?‘ 5đ?œƒ1 − đ?‘?đ?‘œđ?‘ 5đ?œƒ2 + đ?‘?đ?‘œđ?‘ 5đ?œƒ3 − đ?‘?đ?‘œđ?‘ 5đ?œƒ4 + đ?‘?đ?‘œđ?‘ 5đ?œƒ5 = 0 đ?‘?đ?‘œđ?‘ 7đ?œƒ1 − đ?‘?đ?‘œđ?‘ 7đ?œƒ2 + đ?‘?đ?‘œđ?‘ 7đ?œƒ3 − đ?‘?đ?‘œđ?‘ 7đ?œƒ4 + đ?‘?đ?‘œđ?‘ 7đ?œƒ5 = 0 đ?‘?đ?‘œđ?‘ 11đ?œƒ1 − đ?‘?đ?‘œđ?‘ 11đ?œƒ2 + đ?‘?đ?‘œđ?‘ 11đ?œƒ3 − đ?‘?đ?‘œđ?‘ 11đ?œƒ4 + đ?‘?đ?‘œđ?‘ 11đ?œƒ5 = 0 đ?‘?đ?‘œđ?‘ 13đ?œƒ1 − đ?‘?đ?‘œđ?‘ 13đ?œƒ2 + đ?‘?đ?‘œđ?‘ 13đ?œƒ3 − đ?‘?đ?‘œđ?‘ 13đ?œƒ4 + đ?‘?đ?‘œđ?‘ 13đ?œƒ5 = 0 (12) 4đ?‘‰đ?‘‘đ?‘? where, M = V1 / ( đ?œ‹)This is a system of five nonlinear algebraic transcendental equations in the unknowns đ?œƒ1 , đ?œƒ2 , đ?œƒ3 , đ?œƒ4 & đ?œƒ5 . B. Formulation of an Energy function The energy function for the above system of equations is given by đ??¸ = −0.5(đ?‘?đ?‘œđ?‘ đ?œƒ1 − đ?‘?đ?‘œđ?‘ đ?œƒ2 + đ?‘?đ?‘œđ?‘ đ?œƒ3 − đ?‘?đ?‘œđ?‘ đ?œƒ4 + đ?‘?đ?‘œđ?‘ đ?œƒ5 − đ?‘€)2 +

2

(đ?‘?đ?‘œđ?‘ 5đ?œƒ1 − đ?‘?đ?‘œđ?‘ 5đ?œƒ2 + đ?‘?đ?‘œđ?‘ 5đ?œƒ3 − đ?‘?đ?‘œđ?‘ 5đ?œƒ4 + đ?‘?đ?‘œđ?‘ 5đ?œƒ5 )

2

+

(đ?‘?đ?‘œđ?‘ 7đ?œƒ1 − đ?‘?đ?‘œđ?‘ 7đ?œƒ2 + đ?‘?đ?‘œđ?‘ 7đ?œƒ3 − đ?‘?đ?‘œđ?‘ 7đ?œƒ4 + đ?‘?đ?‘œđ?‘ 7đ?œƒ5 ) 2 + đ?‘?đ?‘œđ?‘ 11đ?œƒ1 − đ?‘?đ?‘œđ?‘ 11đ?œƒ2 + đ?‘?đ?‘œđ?‘ 11đ?œƒ3 − đ?‘?đ?‘œđ?‘ 11đ?œƒ4 + đ?‘?đ?‘œđ?‘ 11đ?œƒ 2 5 + (13) (đ?‘?đ?‘œđ?‘ 13đ?œƒ1 − đ?‘?đ?‘œđ?‘ 13đ?œƒ2 + đ?‘?đ?‘œđ?‘ 13đ?œƒ3 − đ?‘?đ?‘œđ?‘ 13đ?œƒ4 + đ?‘?đ?‘œđ?‘ 13đ?œƒ5 )

The differential equation governing the behavior of the network dynamics is calculated using energy function and is given as follows: đ?‘‘đ?œƒ1 đ?œ•đ??¸ =− đ?‘‘đ?‘Ą đ?œ•đ?œƒ1

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International Journal of Advanced Information Science and Technology (IJAIST) Vol.22, No.22, February 2014

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đ?‘‘đ?œƒ2 đ?œ•đ??¸ =− đ?‘‘đ?‘Ą đ?œ•đ?œƒ2 đ?‘‘đ?œƒ3 đ?œ•đ??¸ =− đ?‘‘đ?‘Ą đ?œ•đ?œƒ3 đ?‘‘đ?œƒ4 đ?œ•đ??¸ =− đ?‘‘đ?‘Ą đ?œ•đ?œƒ4 đ?‘‘đ?œƒ5 đ?œ•đ??¸ =− đ?‘‘đ?‘Ą đ?œ•đ?œƒ5

(14)

C. Solution of ODEs by RungeKutta 4th order method The five differential equations are describing the behavior of the GHNN form a system of explicit differential equations and are solved numerically by RK4 method. The flow chart of the RK4 algorithm to solve a set of five nonlinear ODEs is shown as in Figure.5. START GET THE VALUE OF M func1(t,y1,y2,y3,y4,y5,M), func2(t,y1,y2,y3,y4,y5,M), func3(t,y1,y2,y3,y4,y5,M),func4(t,y1,y2,y3,y4,y5,M), func5(t,y1,y2,y3,y4,y5,M),

Figure.5 Flowchart for RK4 algorithm to solve a set of five nonlinear ODEâ€&#x;s Figure.6 MATLAB SIMULINK Simulation diagram of the proposed system.

Input y10, y20,y30,y40,y50

IV. SIMULATION AND IMPLEMENTATION Initialize t=0, n=0, h=.00012 Calculate the constants k11,k12,k13,k14,k15,k21,k22,k23,k24,k25,k31,k32,k34,k35,k41,k42,k43,k 44,k45 k1=(k11+2*(k21+k31)+k41)/6.0; k2=(k12+2*(k22+k32)+k42)/6.0; k3=(k13+2*(k23+k33)+k43)/6.0; k4=(k14+2*(k24+k34)+k44)/6.0; k5=(k15+2*(k25+k35)+k45)/6.0; y1new=y10+k1;y2new=y20+k2;y3new=y30+k3;y4new=y40+k4; y5new=y50+k5;

Is y1new=y1old,y2new=y2old, y3new=y3old,y4new=y4old,y 5new=y5old No Yes

t=t+h n=n+1

No Is n>3 Yes y1=y1new; y2=y1new;y3=y1new;y4=y4new;y5=y5new;

In this section the MATLAB simulation and real time implementation using mathematical model are explained. The proposed system is simulated in MATLAB /SIMULINK environment. The simulation diagram of the proposed system is shown as in Figure. 6. A full bridge topologyhas been formed using four MOSFETS. The switching signals are derived from the switching sub system. The switching subsystem accepts the modulation index that lies in between 0.05 and 0.95continuously with a resolution of Âą0.01. The switching section generates the two switchingpulses for the two cross arms of the inverter. The switching subsystem consists of further two sub systems. One of these twosubsystems is a MATLAB embedded function that containedthe RK4 algorithm. This subsystem accepts differentmodulation indices and gives the solution sets comprising ofthe 5 switching instants namelyđ?œƒ1 , đ?œƒ2 , đ?œƒ3 , đ?œƒ4 &đ?œƒ5 .The switchinginstants derived are in radians. The second subsystem consistsof a triangular wave generator and a gating system. The triangularwave generator generates triangular wave that rises linearly in 5 ms from 0 to 1.57radians and falls to zero at the same rate to zeroto the period of half of the inverter output frequency. A comparisonof this triangular wave against the switching instants produced theembedded MATLAB function block

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International Journal of Advanced Information Science and Technology (IJAIST) Vol.22, No.22, February 2014 gives the „on and off‟ pulsesrequired to drive the MOSFET switches. The same train of pulses justdelayed by 10 milliseconds drives the second cross arm of theinverter.

moves towards a convergence. Getting repeatedly the same solution set for three consecutive iterations is taken as stopping criteria. With a clock frequency of 20 MHz the average calculation period is around 7.6 milliseconds which is lesser than half cycle period of the required 50 Hz output. In order to get more precise results, if the step size „h‟ of theRK4 algorithm can be reduced, it takes more time for convergence. The inverter wasstudied with a DC supply of 48 volts and checked with a RLload of R=50Ω and L=150.0mH. V. RESULTS AND DISCUSSION

p/2

2p

p

q1q2 q3 q4 q5

Gate Signal for S2 and S3

Gate Signal for S1 and S4

Carrier Signal

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3p

5p/2

The MATLAB simulated and inverter output voltage waveform and load current for low, medium and high

Carrier Signal

p

3p/2

2p q5 in radians q4 in radians q3 in radians q2 in radians q1 in radians

Figure. 7 The triangular wave, switching instants and the generated gate signals. Modulati The look on index

method up table contains theGAsolution sets for modulation indices0.1 rad. through 0.9rad.with a step increment of 0.1. rad. These rad. rad. solutions are obtained by minimizing the energy function with 0.1 given 0.85885 1.19611 1.24554 Algorithm 1.53728 the constraints0.88505 using MATLAB Genetic toolbox. The results are shown as1.1686 in table I. 1.2671 0.2 0.84386 0.89548 1.50258 0.3

0.82772

0.90299

1.13857

1.28484

1.4647

0.4

0.80997

0.90536

1.1042

1.29348

1.4184

0.5

0.78677

0.89268

1.05559

1.26324

1.33748

0.6

0.59844

0.65929

0.87342

1.0356

1.12408

0.7

0.44135

0.53398

0.7124

0.85099

0.97754

0.8

0.32025

0.43605

0.5903

0.81134

0.90838

0.9

0.24094

0.37876

0.49373

0.75063

0.78331

modulation indices and the corresponding FFTs are shown from as in Figure. 8 to Figure. 10. From the FFT spectrum it is inferred that the 5, 7, 11,13th harmonics are minimized while

retaining the required fundamental. For a given modulation index the RK4 iterative method selects the suitable initial guess from the “look up table” and

Figure. 8 Inverter output voltage waveform and FFT for modulation index of M=0.1.

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International Journal of Advanced Information Science and Technology (IJAIST) Vol.22, No.22, February 2014

Figure.9 Inverter output voltage waveform and FFT for modulation index of M=0.4.

Figure. 10 Inverter output voltage waveform and FFT for modulation index of M=0.8.

VI.CONCLUSION A reliable on line system to generate pulse width modulation (PWM) signals with selected harmonic elimination (SHE) by using FPGA for a single phase inverter is designed and synthesized in this paper.The utilization of FPGA to ANN becomes very attractive since it allows fast hardware design and modification at low costs. This work realizes fully digital PWM pattern generation and greatly improves the performance of PWM generator without increasing switching losses. The circuit design is based on the advance of FPGA technology, which provides much faster processing capability, and performs multiple functions including control and system I/O in a single chip. The proposed PWM pattern generator is a viable industrial solution for high switching frequency power converters, and it is also extendable to multi-level cascaded VSI converter applications. REFERENCES [1]. ConcettinaBuccella, Carlo Cecati, and HamedLatafat, Digital Control of Power Converters—A Survey, IEEE Transactions on Industrial Informatics 08/2012; 8(3):437 447. · 2.99. [2]. Hasmukh S. Patel and Richard G Hoft ―Generalized techniques of Harmonic Elimination and Voltage Control in thruster Inverters: I Harmonic elimination‖IEEE Transactions on Industry Applications, vol.IA-9, no.3, pp. 310-317, June 1973. [3]. Ramkumar, S. ;Kamaraj, V. ; Thamizharasan, S, "GA based optimization and critical evaluation SHE methods for three-level inverter‖ , 1st International Conference on

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Electrical Energy Systems (ICEES), 2011, Page(s): 115 – 121. [4]. Kavousi, A. ;Farokhnia, N. ; Fathi, S.H. ; Vahabzadeh, A."Implementation of Selective Harmonic Elimination PWM switching strategy on DSTATCOM‖ ,11th International Conference on Electrical Power Quality and Utilisation (EPQU), 2011 , Page(s): 1 – 6. [5]. Gourisetti, S.N.G. ;Patangia, H. ; Bhadra, S."Comparison based selective harmonic elimination using a modified reference signal‖IEEE International Conference on Industrial Technology (ICIT), 2013 , Page(s): 487 – 492. [6]. Imarazene, K. ;Berkouk, E.M. ; Chekireb, H."Selective harmonics elimination: PWM method versus optimized total harmonic distortion‖Fourth International Conference on Power Engineering, Energy and Electrical Drives (POWERENG),2013, Page(s): 295 – 298. [7]. Ghoreishy, Hoda ;Varjani, Ali Yazdian ; Mohamadian, Mustafa ; Farhangi, Shahrokh ; Zhe Zhang. "A new selective harmonic elimination pulse-width and amplitude modulation (SHE-PWAM) for drive applications‖, 39th Annual Conference of the IEEE Industrial Electronics Society, IECON 2013, 2013, Page(s): 234 – 239. [8]. Zhang, Wenyi ,‖ Analyzing of voltage-source selective harmonic elimination inverter‖, International Conference on Mechatronics and Automation (ICMA2011), 2011, pp. 1888-1892. [9]. Ahmadi, D. ―A Universal Selective Harmonic Elimination Method for High-Power Inverters‖, IEEE Transactions on Power Electronics, vol. 26, no. 10, pp. 2743 – 2752, Oct. 2011. [10]. V.G. Agelidis, A.I Balouktsis and C. Cossar, ―On Attaining the Multiple Solutions of Selective Harmonics elimination PWM three Level Waveforms Through Function Minimization‖IEEE Transactions on Industrial Electronics, vol.55, no.3, pp. 996-1003, Mar. 2008. [11]. V. Jegathesan, Jovitha Jerome ―Elimination of lower order harmonics in VoltageSource Inverter feeding an induction motor drive using Evolutionary Algorithms‖Expert Systems with Applications, Vol.38, no.1, pp. 692-699,Jan. 2011. [12]. Jin Ping Chen, Yong FengJu, Yu Yao He,‖ Selective Harmonics Elimination for PWM Inverters Using the PSO Optimization Technique‖, Applied Mechanics and Materials, Vol. 135 - 136, pp. 879 885, online on Oct.2011. [13]. Kavousi, A. ;Vahidi, B. ; Salehi, R. ; Bakhshizadeh, M. ; Farokhnia, N.; Fathi, S.S."Application of the Bee Algorithm for Selective Harmonic Elimination Strategy in Multilevel Inverters ―,, IEEE Transactions on Power Electronics, Vol: 27 , Issue: 4 ,2012. [14]. Keerthivasan, K. ; Narayanan, M. ; Deve, V.S. ; Jerome, J. "Estimation of optimal single phase inverter's switching angles using Pattern Search Method‖, International Conference on Computer Communication and Informatics (ICCCI), 2012, Page(s): 1 – 6. [15]. Mohammadi, Hamid Reza ;Falahati, S. ; Zeraati, M."A new method for Selective Harmonic Elimination in Voltage Source Inverter using Imperialist Competitive Algorithm‖,

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International Journal of Advanced Information Science and Technology (IJAIST) Vol.22, No.22, February 2014 Power Electronics and Drive Systems Technology (PEDSTC),2012. [16]. Rosu, S.G. ; Radoi, C. ; Florescu, A. ; Guglielmi, P. ; Pastorelli, M, "The analysis of the solutions for harmonic elimination PWM bipolar waveform with a specialized differential evolution algorithm‖ , 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), 2012, Page(s): 814 – 821. [17]. ConcettinaBuccella, Carlo Cecati, and HamedLatafat, Digital Control of Power Converters—A Survey, IEEE Transactions on Industrial Informatics 08/2012; 8(3):437 447. · 2.99. [18]. Saied, Basil M.‖ FPGA based selective harmonic elimination pulse width modulation technique‖, 4th International Workshop on Design and Test (IDT), 2009, pp. 1 – 6 and 15-17. [19]. Filho, F., Tolbert, L.M.; Yue Cao; Ozpineci, B., ―Real-Time Selective Harmonic Minimization for Multilevel Inverters Connected to Solar Panels Using Artificial Neural Network Angle Generation‖, IEEE Transaction on Industry Applications, vol. 47 , no. 5, pp.2117 – 2124, Oct. 2011. [20]. Shubhangi S. Ambekar and Madhuri A. Chaudhari, ―Artificial Neural Network Controller for Performance Optimization of Single phase inverter‖, IJAIA, vol. 3, no.1, pp.53-64 Jan. 2012. [21]. Nisha, G.K. ;Ushakumari, S. ; Lakaparampil, Z.V."CFT based optimal PWM strategy for three phase inverter‖ , 2nd International Conference on Power, Control and Embedded Systems (ICPCES),2012,Page(s):1 –6. [22]. Mohan, V. ; Jeevananthan, S. ; Raja, J. ―An on-line adaptive filtering for selective elimination of dominant harmonics from line currents of a VSI fed drive using recursive least square algorithm‖, International Conference on Advances in Engineering, Science and Management (ICAESM), 2012, Page(s): 773 – 778. [23]. JacekM.Zurada, ―Introduction to Artificial Neural Systems‖, JAICO PUBLISHING HOUSE, Mumbai-1, pp. 251-312. [24]. J.J.Hopfield and D.W.tank., ―Neural computation of decision in Optimization problem‖, Biological Cybernetics, vol.6.no.52, pp. 141-152, 1985. [25]. Kennedy MP, Chua LO, ―Neural networks for nonlinear programming‖, IEEE Transactions on Circuit System, vol.35, no.5, pp. 554-562,1986. [26]. Deepak Mishra, PremK.Kalra, ―Modified Hopfield Neural Network Approach for Solving Nonlinear Algebraic equations‖, Engineering Letters, 14.1, EL_14_1_23, Advance online

ISSN: 2319:2682 Authors Profile

M. Balasubramonian received B.E in Electrical and Electronics Engineering from Regional Engineering College, Bharathidasan University, Tiruchirappalli, Tamilnadu, India, in the year 1994, Post graduate degree in M.E. Power Electronics and Drives from Govt. College of Enginering, Anna University, Tirunelveli, Tamilnadu, India in the year 2005 and pursuing Ph.D. degree from Anna University ,Chennai, Tamilnadu, India.. He started his academic carrier in the year 1994 as Lecturer. Currently, he is working as a Assistant Professor in Electrical and Electronics Engineering Dept. in A.C.College of Engg.& Tech., Karaikudi, Tamilnadu, India. His area of interest includes Power Electronics and Drives, Neural Networks and Control Systems. He is the life member of ISTE, New Delhi, India. B.Anandreceived the B.E. degree in electronics and communication engineering from the Vickram College of Engineering, Sivaganga, Anna University, Chennai, India, in 2009.Currently doing M.E. in electrical and electronics engineering (Power Electronics and Drives) in AlagappaChettiar College of Engineering & Technology, Karaikudi India. His research interest includes wireless communication (WiFi,WiMax), Mobile Ad hoc networks ,Sensor Networks ,Neural Networks and fuzzy logic, Communication networks

publication Feb. 2007.

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