relative approach of firing angle optimization scheme in ... · generation of inexhaustible power...

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 7 (2018) pp. 5057-5064

© Research India Publications. http://www.ripublication.com

5057

Relative Approach of Firing Angle Optimization Scheme in Grid Connected

7 Level CHB-MLI

Shazma Khan1, Balvinder Singh2 and Pratibha Singh3

1M.Tech Scholar, Power System 2,3Assistant Professor, Department of Electrical Engineering, Govt. Women Engineering College Ajmer, India.

Abstract

This paper presents the execution of two Artificial Intelligence

(AI) techniques- Artificial Neural Network (ANN) and Fuzzy

Logic (FL) in the PV fed Cascaded Multi-Level Inverter

(CMLI). Condition of voltage variance are arises in the PV

system owing to variable irradiance or temperature. In this

paper ANN and FL are implemented to determine the

optimized value of firing angles for unequal and equal dc

voltages of seven level CMLI such that harmonics will be

eliminated in the system. ANN and FL are technologies of

almost same age. In this paper we have tried to train our AI

controller from the data obtain by using Genetic Algorithm.

Due to partial shading voltage variation of about 10-20% is

observed and here approximately 20% voltage variance are

taken. THD attained by implementing ANN and FL are

displayed in the look-up table and MATLAB/SIMULINK

domain is used to validate the simulation result.

Keywords: Artificial Neural Network (ANN); Fuzzy Logic

(FL); Genetic Algorithm (GA); Multi-Level Inverter (MLI);

Total Harmonics Distortion (THD).

INTRODUCTION

In present scenario, there is an immediate requirement to

accelerate the evolution of leading energy skills in order to

address the universal deviations of sustainable expansion,

variation in climate and green energy. Generation of

inexhaustible power helps the nations to come across these

challenges and hence re-organize the prevailing system.

Photovoltaic energy is a commercially feasible and consistent

tool with huge potential in the upcoming years.

Recently, AI techniques are gaining importance as an

alternative to conventional practices [1]. These are engaged to

resolve complex issues of real-world in numerous regions for

example in handwriting recognition, engineering zones,

robotics, computer numerical control etc. Nowadays these

techniques gaining importance due to their attractive features.

These are - capacity to handle noisy data as well as incomplete

data; it can learn from samples; it can handle non-linearity;

handle complex issues; and when trained can do estimation and

simplification quickly.

AI implemented system are established and deployed globally

in various purpose as mentioned. In this paper, AI-techniques

are employed in order to size the PV linked Cascaded H-Bridge

Multi Level Inverter (CHB-MLI) systems. Foremost benefit of

applying an AI technique in the sizing of PV fed systems is its

noble optimization, wherever data are not all the time

accessible [2][3].

AI methods are classified as: Fuzzy Logic, Artificial Neural

Network, Genetic Algorithm, wavelets, hybrid for example

ANN with FL or ANN with GA etc. [7]. Other techniques

includes PSO-MPPT, ANN-MPPT, FL-MPPT and GA-MPPT

etc., for photovoltaic linked system working in mismatch

circumstances [8].

AI techniques are used to obtain Maximum Power Point

(MPPT) [4] such that maximum voltage and resultant

maximum current [5].Many shading mismatch conditions are

discussed and new topology namely DPP means Differential

Power Processing is suggested [6]. From results it has been

perceived that around 10%-20% drop in dc voltage of the

bridges in CHB can be expressed and AI techniques are

employed in order to predict the optimized value of firing

angles for bridges of CMLI.

The paper is structured as: Section I provides the introduction

of work. Section II defines the multilevel inverter particularly

CHB-MLI. Section III deliberates the modelling of PV

system.AI techniques such as ANN and FL and their working

are discussed in section IV. In section V, the implementation of

these techniques (ANN and FL) are done in PV fed CHB-MLI

system via look-up table. Simulation results, conclusion and

references are discussed and shown in the subsequent sections.

MULTILEVEL INVERTER

MLI offers several gains over a conventional two-level

inverter. It produce staircase output and as the number of levels

increase, output has more steps and therefore the harmonics in

the output voltage and therefore THD are minimized [9]. The

circuit diagram of n level CMLI is shown in Figure 1.

For n=7, 3 H-bridges are required and are connected in series

in order to obtain the desired output. The main benefit of

Cascaded H-Bridge MLI over others i.e. Diode Clamp and

Flying Capacitor is that it has simple switching and minimum

losses. Moreover, it is cheaper as it does not require extra

diodes and capacitors. Cascaded MLI is chiefly employed in

PV fed grid system as-

It provides reliability and energy whenever the PV panel

function under mismatch condition.

It performs transformer less action therefore cost effective.

The CMLI requires separate dc sources which can be found



5058

from batteries, solar cells or fuel cells.

A

S11

S41 S21

S31

A

S42 S22

S32

S1n

S4n S2n

S3n

S12

Vout

PV

PANEL 1V1

V2

Vn

PV

PANEL 2

PV

PANEL N

Bridge 1

Bridge 2

Bridge N

Figure 1. Single Phase Configuration of N Level MLI

In seven level Cascaded H-Bridge MLI, three H-bridges are

employed and voltage levels are from +3Vdc to -3Vdc and the

output voltage is the summation of the separate converter

output.

Voltage level (m) is given by -

m = 2s+1 (1)

where,

s- number of dc sources

Cascaded MLI are classified as – Symmetric and Asymmetric.

In symmetric CMLI values of DC sources are same whereas in

asymmetric type these values are unequal. Major benefit is that

it employs less switches. Moreover higher number of levels

are obtained and the levels increases proportionality in

comparison to the symmetrical type. The main fault in this

configuration is the loss of modularity such that the level upto

which the system constituents can be detached and associated

is lost. Acquired ac output voltage is the summation of output

derived from individual h-bridge.

The output voltage equation is given by-

V0 (θ) = ∑4𝑉𝑠

𝑛𝜋sin 𝑛𝜔𝑡∞

𝑛=1,3,5 (2)

THD equation is expressed as -

F(z) = % THD = √𝑉𝑜,𝑟𝑚𝑠

2 − 𝑉1,𝑟𝑚𝑠2

𝑉1,𝑟𝑚𝑠2 X 100 (3)

α1 α2 α3

+nVdc

-nVdc

0

π/2 3π/2π ωt

Figure 2. Output voltage waveform of seven level CMLI

MODELLING OF SOLAR PV SYSTEM

The solar array is formed by the combination of series and

parallel solar cells which provides the desired output voltage

and current under regular conditions. Terminal current of cell

is shown as the function of diode current, photo-generated and

shunt current. Figure 3 shows the equivalent circuit diagram of

PV Module.

Iph Id Ip

Rp

Rs

V

I

Figure 3. PV Module Equivalent Circuit

The photo-generated current (Iph) depends on temperature and

irradiance, calculated at standard conditions of irradiation Gref ,



5059

temperatureTc,ref and photocurrent Iph,ref as

Iph =G

Gref(Iph,ref + Iscc(Tc – Tc,ref)) (4)

Where,

Tc -actual working temperature of cell (K)

G - actual solar irradiance (W/m2)

Iscc-manufactured temperature coefficient of short-circuit

current (A/K)

Using the Shockley equation, diode current is -

Id = I0[expeVc

nKTc− 1] (5)

Where,

I0 - reverse saturation current (A)

Vc-voltage across diode (V)

e - electron charge (1.602x10-19 C)

η - quality or ideality factor

Rs- series resistance (Ω)

K - Boltzmann constant (1.38x10-23 J/k)

Reverse saturation current (I0) is given as-

I0 = I0,ref (Tc

Tc,ref)

3

exp ((eEg

nK) (

1

Tc,ref−

1

Tc)) (6)

Shunt current Ip is given as-

Ip = Vc

Rp (7)

where,

Rp- shunt resistance

The equation that depicts the I-V characteristics of PV

module is given by-

I = Iph – I0 (exp (e(V+IRs)

nKTc) − 1) −

V+IRs

Rp (8)

Figure 4. Grid Connected PV system

ARTIFICIAL NEURAL NETWORK IN CHB-MLI

The basic structural design of ANN is shown in Figure 5. It

comprises of three neuron layers namely - input, hidden, and

output layer. For feed-forward systems, the signal varies from

input to output via feedforward path. ANN is trained so that it

perform the task by modifying the values of the weights

amongst the elements. It is adjusted, on the basis of comparison

between output and target, unless the output of network meets

the desired target [10].

Figure 5. Architecture of Artificial Neural Network

In CMLI, each H bridge operate at different angle and the

output of CMLI is to be contingent on these angles. Usually,

these angles are computed off-line and put in the look-up table

for the functioning of CHB-MLI.

Figure 6. Block Diagram of Firing Angle Optimization System

For this work ‘Levenberg-Marquardt back propagation’

technique is used using NN Toolbox present in the MATLAB.

Using this, model of NN is made and is incorporated with the

seven level CHB inverter structure. Now the whole system is

employed in eliminating the harmonic in multilevel inverter.



5060

FUZZY LOGIC IN CHB-MLI

They have the advantage to be robust and relatively simple in

designing because they does not require the knowledge of the

exact model [11]. Fuzzy Logic (FL) method is built on the

fuzzy set theory and correlated method discovered by Lotfi

Zadeh. FL is a non-linear technique, tries to implement the

proficient knowledge of an expert in the designing of the

controller. Figure 7 shows block diagram of FLC. It comprises

of four components:

a) Fuzzification- It plans the crisp values into input fuzzy

sets in order to initiate the instructions or rules.

b) Rule Base-Instructions that describe the operation of

controller by employing a group of IF-THEN statements.

c) Inference Engine- It maps the input sets into the output by

applying the rules.

d) Defuzzification-It maps the output values of fuzzy into the

crisp values.

FU

ZZ

IFIC

AT

ION INTERFERNCE

ENGINE

DE

FU

ZZ

IFIC

AT

ION

RULE BASE

Input Output

Figure 7. Fuzzy Logic Interference System

GENETIC ALGORITHM

GA is one of the intelligence method and is built on the

mutative procedure. It is the presumptive, population based

optimization method, which is employed widely for

optimization purpose in various fields such as in engineering,

medical etc.

Figure 8. Flowchart of Genetic Algorithm



5061

In GA [12] prior to initialization the size of population and

iteration are fixed. Favorable solution of situation is termed as

chromosome. It is represented in binary or floating point

sequence.

𝑐ℎ𝑟𝑜𝑚𝑜𝑠𝑜𝑚𝑒 = (𝜃1, 𝜃2, 𝜃3) (9)

Now, conformity level of individual solution is determined then

on its basis individual is multiplied in subsequent iterations.

SIMULATION RESULTS AND DISCUSSION

The proposed seven level CHB-MLI structure is implemented

in solar fed system. As of look-up table, it is observed that

when H-bridges voltage are unequal then the angles

corresponding to it are predicted. A lookup table has been

made which comprises of diverse dc voltage with the voltage

deviation of about 20% means 5 V and resultant firing angles

acquired from the ANN and FLC.

A. Output obtain from proposed system by applying ANN technique:-

Figure 9. Case-I Output voltage waveform

Figure 10. Case-I Harmonic Spectrum

Figure 11. Case-II Output voltage waveform

Figure 12. Case-II Harmonic Spectrum

Figure 13. Case-III Output voltage waveform

Figure 14. Case-III Harmonic Spectrum

Figure 15. Case-IV Output voltage waveform

Figure 16. Case-IV Harmonic Spectrum



5062

Figure 17. Performance plot of ANN

B. Output obtain from proposed system by applying FL technique:-


Figure 19. Case-I Harmonic Spectrum


Figure 21. Case-II Harmonic Spectrum

Figure 22. Case-III Output voltage waveform

Figure 23. Case-III Harmonic Spectrum

Figure 24. Case-IV Output voltage waveform



5063

Figure 25. Case-IV Harmonic Spectrum

Figure 26. Control surface for the fuzzy model found by the GA

Table I. Lookup table of optimized switching angles and voltages

Cases Actual

Voltages (V)

Approximated

Voltages (V)

Firing angle obtained

from the lookup

table(0)

%THD

w.r.t

actual

values

(From

GA)

%THD w.r.t

approximated

values

(From ANN)

%THD w.r.t

approximated

values (From FL)

V1 V2 V3 V1 V2 V3 α1 α2 α3

I 88 89.5 90.5 90 90 95 100 200 280 14.39 11.39 9.59

II 90.2 92 93 95 95 95 60 170 100 14.02 11.58 9.63

III 95 93.4 96.6 95 95 100 90 150 300 13.69 11.91 9.64

IV 97.2 88.6 95.4 100 90 100 100 170 380 16.09 11.80 9.79

V 92.6 85 89.8 95 85 90 70 130 320 14.52 11.77 9.60

VI 99.8 89.7 96.3 100 90 100 90 190 390 15.59 11.63 9.61

Table II. Error Between GA-ANN and GA-FL

Cases % Error (GA-ANN) % Error (GA-FL)

I 3 4.8

II 2.44 4.39

III 1.78 4.05

IV 4.29 6.3

V 2.75 4.92

VI 3.96 5.98



5064

As shown in the table firing angle for PV connected 7 level

CHB-MLI system are optimized during actual as well as in

approximated condition. It has been observed that error

comes out to be lesser in case of GA optimized ANN system

as compared to GA optimized FL system. Therefore we can

say that GA optimized ANN system is superior in

performance from FLC system.

CONCLUSION

In this paper two AI techniques- ANN and FL are used and

Lookup Table employed angle optimization schemes are

implemented for PV fed seven level Cascaded H- Bridge

MLI system. These method are implemented and they gives

the results of lower harmonics and of higher accuracy. All

these results are justified in MATLAB by taking voltage

variation of about 20%. It is detected that the techniques

worked suitably for intermediate values of voltage and as

well as with very less difference in the harmonic. Whenever

there is a small difference in approximate voltages and

actual voltage, ANN and FL- using GA optimized lookup

table methodology can be applied. From results, it can be

seen that error is lesser in case of GA-ANN than that of GA-

FL. Further these techniques are extended to hybrid system

like ANFIS that is a combination of ANN and FL. The work

done in this paper is first step prior to field installation in

order to experimentally authorise the effectiveness of the

proposed system.

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relative approach of firing angle optimization scheme in ... · generation of inexhaustible power...

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