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Vol.11,February2013124

Elimination of Harmonics in Multilevel Inverters Connected to SolarPhotovoltaic Systems Using ANFIS: An Experimental Case Study

T.R.Sumithira*1, A.Nirmal Kumar2.

1K.S.R College of Engineering,Tiruchengode.TamilNadu.India.2Info Institute of Engineering

Annur.TamilNadu,India.*[email protected]

ABSTRACTIn this study, a multilevel inverter was designed and implemented to operate a stand-alone solar photovoltaicsystem. The proposed system uses pulse-width modulation (PWM) in the multilevel inverter to convert DC voltagefrom battery storage to supply AC loads. In the PWM method, the effectiveness of eliminating low-order harmonicsin the inverter output voltage is studied and compared to that of the sinusoidal PWM method. This work also usesadaptive neuro fuzzy inference (ANFIS) to predict the optimum modulation index and switch angles required for afive level cascaded H-bridge inverter with improved inverter output voltage. The data set for the ANFIS-basedanalysis was obtained with the Newton-Raphson (NR) method. The proposed predictive method is more convincingthan other techniques in providing all possible solutions with any random initial guess and for any number of levelsof a multilevel inverter. The simulation results prove that the lower-order harmonics are eliminated using theoptimum modulation index and switching angles. An experimental system was implemented to demonstrate theeffectiveness of the proposed system.

Keywords: multilevel inverters, harmonic elimination, adaptive neuro-fuzzy inference system, Newton-Raphsonmethod, genetic algorithm.

1. Introduction

Multilevel inverters play a major role in numerousapplications such as medium-voltage drives,renewable energy system, grid interfaces, andflexible AC transmission devices [1]. A multilevelinverter is a familiar power electronic converter thatcan synthesize a desired output voltage fromseveral levels of DC voltages as input source. Muchresearch work has been devoted to equal DCsources and little to unequal DC sources in themultilevel inverter circuit topologies. Based on theseidentical voltage levels and proper pulse-widthmodulation (PWM) control of the switching angles, astaircase waveform can be synthesized. Thereduction of switching losses in the inverter devicesdue to the on and off switch operation during one

fundamental cycle is one of the major benefits of thestaircase output waveform. Moreover, with reducedswitching frequencies, low-order frequencyharmonics can be observed in the staircase outputvoltage [2][8].

Although many approaches have focused oneliminating low-order frequency harmonics, theselective harmonic elimination (SHE) PWM method[9][10] is preferred widely in multilevel inverters[11]-[12]. The selective harmonic elimination pulse-width modulation switching (SHEPWM) techniquecan be used to compute switching angles by solvingthe nonlinear transcendental equationscharacterizing harmonics [13]-[14]. Many iterativetechniques have been reported to solve thenonlinear transcendental equations resulting in onlyone solution set or multiple solutions by assuming aproper initial guess [15]-[16]. Several traditionalmethods can be used to estimate switching angles;however, a few of them may be computationally

expensive. As soft computing techniques such asartificial neural networks (ANNs) and adaptiveneuro-fuzzy inference system (ANFIS) learn andadapt to a great diversity of data, it can be anexcellent option to estimate switching angles [17].

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EliminationofHarmonicsinMultilevelInvertersConnectedtoSolarPhotovoltaicSystemsUsingANFIS:AnExperimentalCaseStudy,T.R.Sumithira/124132

JournalofAppliedResearchandTechnology 125

Although microcontrollers are used for the solutionof applications of intermediate complexity, recentlyFPGA processors are preferred widely in areas withhigh integration as well as compact and flexibleextension of the system [18].

However, to the authors knowledge, the adaptiveneuro-fuzzy inference system (ANFIS)-basedprediction of switching angles in five-level inverterhas not been addressed so far. The case study isperformed to develop the stand-alone multilevelinverter system connected to solar panels. TheDC-DC converter was used to regulate the DCvoltages from solar panels, which varied becauseof changes in solar radiation. Previous results of astudy in prediction of solar radiation and sizing ofpanels for various regions of Tamilnadu, India [19]-[20] have now been utilized for the application of

the stand-alone multilevel inverter system. Anattempt has been made to train the ANFISprediction model to explore the prediction ofswitching angles that can produce the desiredfundamental voltage by eliminating the third-orderharmonics. Moreover, the experimental work hasbeen carried out to validate the proposed method.

The paper is organized as follows. The nextsection presents the structure of the multilevelinverter and the SHE-based modulation adopted toeliminate the higher-order harmonics. It alsodepicts the determination method for switching

angles that can be used to frame the data set forANFIS training. Section 3 describes the state-of-artANFIS predicition method. Section 4 presents theexperimental work and its results.

2. Multilevel inverter

In the family of multilevel inverters, the cascademultilevel inverter is one of the significanttopologies. It requires less components, a modularstructure with a simple switching strategy, and aneasily adjusted number of output voltage levels byadding or removing full-bridge cells [21]-[22]. The

cascade multilevel inverter is composed of anumber of H-bridge inverter units with a separateDC source for each unit and can be connected incascade to produce a near sinusoidal outputvoltage waveform using the proper modulationscheme.as shown in Figure 1.

Figure 1.Five-level cascaded Hbridge multilevel inverter.

The number of levels in the output phase voltage ismanipulated by 2s+1, where s is the number of H-

bridges units utilized. The system chosen in thisstudy bears two full-bridge inverters, eachconnected to 4 solar panels of 85 Wp capacity.Figure 2 shows the output voltage waveform ofthe five-level cascaded H-bridge inverter. Themagnitude of the AC output phase voltage is givenby vpn =vah1+ vah2 where vah1 and vah2 are thevoltages corresponding to the switching angles, 1and 2.

Figure 2.Generalized stepped voltage waveformof the multilevel inverter.

Although there are different switching strategies

available for minimization and elimination ofharmonics [23]-[24], in this work, the sinusoidalpulse-width modulation (SPWM) and selectiveharmonic elimination pulse-width modulation(SHEPWM) were developed to minimize andeliminate the low-order harmonics, respectively.

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2.1 Sinusoidal pulse-width modulation (SPWM)

The SPWM technique is one of the primitivetechniques; it is used to suppress harmonics presentin the quasi-square wave. In SPWM, a carrier wave

is compared with a reference wave [25]-[26]. Thereference wave corresponds to the desiredfundamental frequency at the output and thetriangular wave determines the frequency with whichvalues are switched. By changing the frequency ofthe carrier wave, higher switching frequencies can beobtained. In order to avoid the elimination of the evenharmonics, the carrier frequency should always be anodd multiple of three.

2.2 Selective harmonic elimination pulse-widthmodulation (SHEPWM)

Traditionally, the Fourier series expansion for anyoutput of the system can be expressed as inEquation (1):

V(t) = ( cost + sint) (1)As it is an odd symmetric function, the value of a0 =an= 0 (amplitude of the even harmonic component)and bn. The Fourier series expansion of thewaveform with the same magnitude than all the DCsources can be expressed as in Equation (2):

V(t) = V sin(nt) (2)where, Vn is the amplitude of the harmonics. Themagnitude of Vn becomes zero for even-orderharmonics because of an odd quarter-wavesymmetric characteristic:

Vn= 0 cos ; (3)Where is the switching angle limited betweenand Vdc is the voltage of DC sources which are ofthe same magnitude. Subsequently, Vnbecomes

V(t) = 4 (cos( )

,,+cos ) sin(mt)(4)

Where j is the number of switching angles and m isthe harmonic order.

Ideally, in the multilevel inverters, there can be knumber of switching angles, in which one switching

angle can be used for fundamental voltageselection and the remaining (k-1) switching anglescan be used to eliminate few prime low-orderharmonics. Moreover, the modulation index isdefined as the ratio fundamental output voltage V 1to the maximum obtainable voltage V max asshown in Equation (5):

= ; = * Vdc (5)The range of is considered as the fundamentallinear modulation component. For the five-levelcascade inverter ,k value is 2 or two degrees offreedom are available; one degree of freedom canbe used to control the magnitude of thefundamental voltage and the other can be used toeliminate the third-order harmonic component asit contributes more to the total harmonicdistortion. The following equations (6)(7) can beobtained from the above-stated conditions tomanipulate the switching angle for performingselective harmonic elimination:

[cos

+ cos

]= 2 * mi (6)

[cos 3 + cos 3 ] = 0 (7)2.2.1 Newton-Raphson Method

These nonlinear polynomial equations can besolved by the iterative Newton-Raphson method todetermine the switching angles, 1 and 2. Theinitial guess for switching angles (1, 2) lies inbetween 0 and /2. The flow chart for the Newton-Raphson method can be used to compute allpossible solutions without any computationalcomplexity as shown in Figure 3.

The NR method approach is capable of findinganalytical solutions for limited ranges. It gives amore approximate solution in providing a smoothdata set that is needed for the ANFIS training.

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Figure 3. Flow chart for the Newton-Raphson method.

3. Adaptive neuro-fuzzy inference system

3.1 State-of-The-Art : ANFIS theory

ANFIS is a hybrid soft computing model composed

of a neuro-fuzzy system in which a fuzzy inference(high-level reasoning capability) system can betrained by a neural network-learning (low-levelcomputational power) algorithm. ANFIS has theneural networks ability to classify data in groupsand find patterns and further develop a transparentfuzzy expert system. Moreover, ANFIS has theability to divide and adapt these groups to arrangea best membership function that can cluster anddeduce the desired output within a minimumnumber of epochs [27]-[28]. The fuzzy inferencesystem can be tuned by the learning mechanism.

A given input/output data set, ANFIS constructs a

fuzzy inference system (FIS) whose membershipfunction parameters can be tuned (adjusted) usingeither a back-propagation algorithm alone or incombination with a least squares type of method.Figure 4 shows the ANFIS architecture used in thiswork. This system has two inputs x and y and oneoutput, whose rule is.

Figure 4. ANFIS architecture.

Rule i:

; = + + ; = 1; 2 (8)where fi is the output and pi, qi and ri are theconsequent parameters of ith rule. Aiand Biare thelinguistic labels represented by fuzzy sets. Theoutput of each node in every layer can be denoted

by where i specifies the neuron number of thenext layer and lis the layer number [29]-[30] . In thefirst layer, called fuzzifying layer, the linguistic

labels are Ai and Bi. The output of the layer is themembership function of these linguistic labels andis expressed as.

= ()= ()where () and () are membershipfunctions that determine the degree to which thegiven x and y satisfy the quantifiers Ai and Bi. In thesecond layer, the firing strength for each rulequantifying the extent to which any input databelongs to that rule is calculated [31]. The output ofthe layer is the algebraic product of the inputsignals as can be given as

= () (); i = 1,2 (9)

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denotes a fuzzy norm operator which is afunction that describes a superset of fuzzyintersection (AND) operators, including minimum oralgebraic product. In this study, an algebraicproduct was used. In the third layer, called the

normalization layer, every node calculates the ratioof the ith rules firing strength to the sum of all rulesfiring strengths [32].

= ; i = 1,2 (10)In the fourth layer, the output of every node is

= ( + + ) (11) =

(12)

The ANFIS learning algorithm employs twomethods for updating membership functionparameters [34-35]:

A hybrid method consisting of back-propagation for the parameters associatedwith the input membership functions, and leastsquares estimation for the parametersassociated with the output membershipfunctions.

Back propagation for all parameters.

In order to improve the training efficiency, a hybridlearning algorithm can be applied to justify theparameters of the input and output membershipfunctions. The only user-specified information is thenumber of membership functions for each input andthe inputoutput training information. Hence, theoutput can be written as

f= w1f1+ w2f2 (13)

3.2 Design of the Proposed ANFIS Model

The design of the ANFIS model was developed byusing MATLAB 7.5.0.3402 (R2007) softwareversion. In this study, the ANFIS model was usedwith a hybrid learning algorithm to identifyparameters of Sugeno-type fuzzy inferencesystems. It applies a combination of the least

-squares method and the back-propagationgradient descent method for training (fuzzyinference system) FIS membership functions (MFs)parameters to estimate a given training data set.Three gbell-shaped membership function was

assigned for training FIS membership functions.The model was trained with data set framed byanalytical method and the genetic algorithmmethod. Figure 5 shows the proposed ANFISprediction model. In this work, the input variablesare array voltages from the solar panel and theswitching angles.

Figure 5. ANFIS architecture.

3.2 ANFIS simulation results.

The trained ANFIS network can be observed toexperience a negligible training error of 0.00066062as shown in Figure 6. The switching angles 1 and

2 along with the modulation index in the range of0.65 < mi< 1.1, which were obtained by theNewton-Raphson method for the five -level inverterwith third harmonic elimination is shown in Figure7. The values of the switching angle and themodulation index were used for the training andtesting phase. Figure 8 shows the comparativeresults of the study. From the results, it is clear thatthe optimum modulation index at which theswitching angles provide a more appropriatesolution of harmonic elimination can be obtainedfaster with negligible error. Table 1 shows acomparison between some of the other models

found in the literature and the proposed model inthis work. The results obtained show that ANFIS isa viable technique to predict the modulation indexand its switching angles at which the thirdharmonics is completely eliminated within lessnumber of iterations.

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Figure 6. Error during training phase.

Figure 7. Switching angle (1,2.) variation for differentmodulation indexes (0.1

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switches. A PIC microcontroller was used for firingpulse generation by the sinusoidal PWM andSHEPWM method. TOSHIBA 6N137 optocouplerswere used to afford an isolation between theboard and the power circuit. The ANFIS predicted

modulation index and switching angles with thirdharmonic elimination in the five-level inverter wereused experimentally. The simulated andexperimental results of the output voltage in thefive-level inverter can be observed as shown inFigure 10. In Figure 11, the results of the FFTharmonic analysis carried performed by the SPWMand SHEPWM method are presented.

(a)

(b)

(c)

Figure 10. Output voltage results of the case study. (a)Simulation results of the SPWM method. (b) Simulation

results of the SHEPWM method. (c) Experimental results

(a)

(b)

Figure 11. Harmonic Spectrum of (a) SPWMand (b) SHEPWM

5. Conclusions

The selective third harmonic elimination PWMmethod in a cascaded H-bridge five-level inverterwas observed to provide significant change in thepercentage of THD compared to the sinusoidalPWM method. The use of the ANFIS method toestimate the modulation index and switching angles

has been an additional advantage in claiming thatthe system provides better elimination of harmfulthird harmonics at the inverter output voltage. Thefeasibility of this technique is addressed and theresults through the harmonic spectrum arecompared, showing a significant difference. Resultsprove that the proposed method produces a fastand accurate response as well as minimum errorcompared to the NR method. Simulation andexperimental results show that the proposeddesign of a cascaded H-bridge five-level inverter isa viable option for any stand-alone solarphotovoltaic system with better output voltage.

0 2000 4000 6000 8000 10000 12000-150

-100

-50

0

50

100

150

Time(sec)

Voltage(volts)

0 2000 4000 6000 8000 10000 12000

-150

-100

-50

0

50

100

150

Time(sec)

Voltage

(volts)

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