grid connected photovoltaic using multi level inverter · 2. 3- multi level inverter fig .5 basic...

DOI:10.23883/IJRTER.2018.4378.PYX33 5

Grid Connected Photovoltaic using multi level inverter

3Amr Mohamed Saleh ,2Ahmed Abdel Sttar,1Naggar H. Saad l Power and Machines DepartmentlectricaE2,1

Faculty of Engineering, Ain Shams University,Cairo, Egypt. 3Faculty of Engineering, Ain Shams University,

Cairo, Egypt.M. Sc. Student

Abstract : This paper presents Photovoltaic (PV) connected to single phase grid based on maximum power point tracking .paper related with how to solve problems caused by unconformity

phenomena as like partial shading condition (PSC) which the photovoltaic always be in most time .In

order to enhancing the efficiency of tracking global maximum power point under partial shading

condition (PSC) particle swarm optimization MPPT technique is approached.The paper studys

photovoltaic (PV) cell which is modeled as two diodes under the variation of irradiation and temperature

(partial shading conditions) by using two different MPPT methods called particle swarm optimization

(PSO) and incremental conductance method (IC) .The particle swarm optimization is one of the

artificial intelligence method used to detect MPPT. The efficiency of PSO is higher than any traditional

method such as IC. The efficiency of each method are shown in matlab simulation to show the

advantage of detecting the optimal power point in all cases. The particle swarm optimization represent

lower energy losses and reach max power point more quicker than incremental conductance method

specially in the partially shading conditions. Both of two MPPT methods feed the input of the grid tied

inverter with the determined DC bus voltage.

Keywords: photovoltaic (pv) ,Maximum power point (MPPT) ,particle swarm optimization

(PSO),Incremental conductance (IC),Partial shading condition(PSC).

I. INTRODUCTION

The solar cell radiation seems to be one of the most promising renewable energy sources and

can be directly converted into electricity using the photovoltaic (PV) devices, This subject

causes to make a great deal of investments in different energy solutions to enhance the energy

performance and power quality problems .The renewable energy cost are lower than oil

energy and have a good environment affect.In orde to get the maximum benfit of this energy

many researchers try to reach maximum power point of PV cells by many methods .

The PV array connect to the utility grid not directly but through few devices to be ready to

connect [1-13]. The output voltage from PV is not enough and not stable to enter to the

inverter DC/AC which delivery power produced by PV into the grid [5-8].To overcome this

problem a DC/DC chopper (Boost converter) is used [7-13].Then the pv array connected to

the utility grid through a boost convereter and a full bridge inverter . Choppers are mainly employed in regulated switch mode power supplies where the input voltage of these

converters changes in wide range especially in the Photovoltaic (PV) supply source due to

sudden and unpredictable changes in the solar Irradiation level like the cell operating

temperature. The dependency of the solar system characteristics to the temperature and

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 04, Issue 09; September- 2018 [ISSN: 2455-1457]

@IJRTER-2018, All Rights Reserved 6

insolation level variations gets the issue worse. Hence, the maximum power point (MPP)

and operating point of the solar cells get mismatch. To achieve the maximum power from

the PV panel source it should be capable to track the solar panel unique maximum power

point which varies with temperature and irradiance. To do this, MPPT can be employed by

using any one of the algorithms proposed in [14].One of the oldest MPPT methods is

Icremental Conductance [15,16] ,

In the recent years the artificial intelligence techniques (soft computing methods) for MPPT

are proposed in [17-20].Particle swarm optimization is one of advanced MPPT methods

proposed in [21-23] which can solve problems of partial shading condition. In this paper

theoretical comparison between Incremental conductance MPPT method and particle swarm

optimization (optimized MPPT algorithm) under two different solar irradiation functions

[24-25]. The presented MPPT algorithm based on PSO is used to tracking the global

maximum power point (GMPP) of the photovoltaic cells. On the contrary of the incremental

conductance method (IC) which depend onthe local maximum power point (LMPP). So

that the PSO algorithm reduce the shadow condition affect and improve the performance of

the whole system. The PSO algorithm function enhance the dynamic response of the system

under the sudden radiation changes and the other enviromental effects. Also the PSO

function is used to find the duty cycle for the boost converter.The PSO method needs

iterations to get the global peak (GB). It is based on a lagrange interpolation (LI) formula.

Once it finds the MPP the steady state oscillation are reduced to be approxmaitly zero.The

tracking speed is being faster and the efficiency of the presented method is increased.This

paper is arranged as following section 2 presents the pv system description. Section 3

presents comparison between MPPT algorithms IC & PSO. The performance and feasibility

of the grid-tied PV system are evaluated by means of simulations, as well as by using a

laboratory prototype for experimental validation in section 4. Finally, the conclusions are

presented in Section 5.

II. PV System Description

PV ArrayDC / DC

Converter

DC / AC

Inverter

MPPT & Control

PWM & Gate drive

GRID

Synchronization

Transformer

Fig.1 Block Diagram of the PV System and Tepology .



2.1 Main Photovoltaic Technologies

2.1.1-PV array

The principle of Photovoltaic System is simple: it is a direct conversion of light to

electricity. Since the 1950's, and the first crystalline silicon PV , many material have been

put into solar cells as crystalline silicon and amorphous silicon, Cadmium telluride (CdTe)

and cadmium sulphide (CdS).

This large variety of PV technologies provides great opportunities as each material has a

different light absorption capability, especially regarding the incident light spectrum.

The solar energy conversion into electricity takes place in a semiconductor device that is

called a solar cell. The solar cell is the basic unit of a PV system.

In order to use solar electricity for practical devices, which require a particular voltage or

current for their operation, a number of solar cells have to be connected together (in

series/parallel combinations) to form a PV module , also called a PV panel . To increase the

voltage, modules are connected in series and to increase the current they are connected in

parallel.

For large-scale generation of solar electricity the solar panels are connected together into a

solar array.

So the PV array consists of a number of PV modules or panels and the PV module is an

assembly of a large number of interconnected PV cells

Cell

Module

String

Array

Fig.2 Describtion of PV Cell , PV Module,PV String and PV Array.

2.1.2-PV Cell Equivalent Circuit

For simplicity in analyzing characteristics of solar cells, electrical equivalent circuits are

realized and are hence modelled using simulation softwares. It helps in predicting behavior

under various environmental conditions, and further in obtaining (I-V) and (P-V)

characteristic curves.

The common approach is to utilize the electrical equivalent circuit, which is primarily based

on a light generated current source connected in parallel to a p-n junction diode. Many

models have been proposed for the simulation of a solar cell or for a complete photovoltaic

(PV) system at various solar intensities and temperature conditions .



The most commonly used models are single diode and two diode model.

2.1.2.2 Double-Diode PV Cell

RS

RPID2ID1IPV

V

Fig.3 Electrical model describtion of double diode PV cell .

Double Diode PV Equations

𝐈 = 𝐈𝐩𝐯 − 𝐈𝐃𝟏 − 𝐈𝐃𝟐 (1)

Where ID1= IO1[EXP[V/A1VT]-1] (2)

ID2 = IO2[EXP[V2/AVT]-1] (3)

The Inclusion of additional parameters RS,RSH the Eq 1 becomes :

I =IPV- IO1[EXP[V+RS*I/A1VT]-1] – IO2[EXP[V+RS*I/A2VT]-1] –[V+RS*I/RSH] (4)

2.2-Boost converter

The Boost converter is a simple power electronic converter and basically consists of a

voltage source, an inductor, a high frequency switch (usually a MOS- FET or an IGBT) and

a diode. Boost converter steps up the input Voltage magnitude to a required output voltage

magnitude without the use of a transformer . The control strategy lies in the manipulation of

the duty cycle of the switch which causes the voltage change.

Fig.4 Electrical circuit schemes describtion of boost converter .

Boost converter consists of input voltage source, switch, inductor, diode, capacitor and

resistor which acts as a load. The switch can be closed or open depends on the output

requirement. The output voltage across the load or resistor is always greater than that of

input voltage. Due to a single switch, it has a high efficiency. The input current is

continuous.

If the switch is turned on and off repeatedly at very high frequencies and assuming that in

the steady state the output will basically be DC (large capacitor).



MODES OF OPERATION

There are two modes of operation of a boost converter. Those are based on the closing and

opening of the switch. The first mode is when the switch is closed; this is known as the

charging mode of operation. The second mode is when the switch is open; this is known as

the discharging mode of operation.

Charging Mode

In this mode of operation; the switch is closed and the inductor is charged by the source

through the switch. The charging current is exponential in nature but for simplicity is

assumed to be linearly varying [11]. The diode restricts the flow of current from the source

to the load and the demand of the load is met by the discharging of the capacitor.

Discharging Mode

In this mode of operation; the switch is open and the diode is forward biased [11]. The

inductor now discharges and together with the source charges the capacitor and meets the

load demands. The load current variation is very small and in many cases is assumed

constant throughout the operation.

2. 3- MULTI LEVEL INVERTER

Fig .5 Basic Digram of Three Phase Inverter .

Fig.5 showed Inverters are power electronic circuit which converts the DC voltage into AC

voltage. The DC source is normally a battery or output of the controlled rectifier. The output

voltage waveform of the inverter can be square wave , quasi-square wave or low distorted

sine wave .The output voltage can be controlled with the help of drives of the switches. The

pulse width modulation techniques are most commonly used to control the output.

Multilevel inverters are a a source of high power, often used in industrial applications and

can use either sine or modified sine waves. Instead of using one converter to convert an AC

current into a DC current, a multilevel inverter uses a series of semiconductor power

converters (usually two to three) thus generating higher voltage. Therefore multilevel

inverters are the preferred choice in industry for the application in high voltage and high

power application.



2.3.1 TOPOLOGY OF MULTILEVEL INVERTERS

Multilevel inverters have an arrangement of power switching devices and capacitor voltage

sources. Multilevel inverters are suitable for high-voltage applications because of their

ability to synthesize output voltage waveforms with a better harmonic spectrum and attain

higher voltages with a limited maximum device rating.

2.3.2 Types of Multilevel Inverters: The classification of multilevel inverter is described in ( Fig.6)

Fig .6 Classification of multilevel Inverter Topologies.

III. MPPT TECHNIQUES

In this section the descriptions of MPPT techniques based on PSO and IC methods are

presented. In addition, the mechanism for determining the DC-bus voltage reference are also

presented.

3.1-MPPT based on Incremental conductance method (IC):

Maximum power point is obtained [9,10] when dP/dV=0 where P= V*I

d(V*I)/dV = I + V*dI/dV = 0 (5)

dI/dV = -I/V (6)



dI, dV = fundamental components of I and V ripples measured with a sliding time window

T_MPPT

I , V = mean values of V and I measured with a sliding time window T_MPPT

The integral regulator minimizes the error (dI/dV + I/V)

Regulator output = Duty cycle correction

This method needs many sensors to work so it is not cheap.

3.2-MPPT based on PSO :

PSO is one of well known search techniques which be famous in the engineering fields recently.PSO

was proposed first time in 1995 by Kennedy and Ebrahat . PSO is quoted from the birds swarms

behavior.The idea of PSO algorithm is tracking the MPP and improve the efficiency of the MPPT

controller under PSCs.[16,17].

The PSO theority depend on finding a large solution area where the solution of the problem may be exist

in any place in this area.The PSO starts procedure with intial random particles. In this method there are

three main steps called initialization,movement and evaluation.

In the Intialization step PSO start with a population which take random values and the algorithm

generates random particles which are considered sloutions based on the reference voltage value which is

closed to the MPP value.

In the Movement step each particle has a velocity and search to the better position by the influence of

neighboring particle position.

Inthe Evaluation step each particle fitness is evaluated in the new position and be saved to improve the

future particle movements in the next iterations until a particle reach to the best position which in our

case the voltage of the gretest power generated (Fitness function) all particles will be influenced by it.

That is will done after number of iterations are predefined and the determination of the MPP still is not

ensured.The stopping condition can be set to ensure the determination of the MPP and not related to the

predefined iterations.

The process of MPP based on PSO is applied to the PV output power conversion and represented in

(Fig.7) .



Start

Particles numbers np

For i = 0...2numran = rand ([0:1])

Vpv,i=Voc*numran

i=0

measure ipv,i(vpv,i) Pi(k) = vpv,i(k) ipv,i(k)ibest = position max(P

[1...np])

σ(vpv,0...np )

i = i + 1

Si(k + 1) = w Si(k) + Λ1R1(vbest,i vpv,i(k))

+Λ2R2(ttbest vpv,i(k)) vpv,i(k + 1) = vpv,i(k)

vbest,i = vpv,i(k)

I > 2?

Pi(k) > Pi(k 1)?

σ < T olerance?

Power change?

YES

YES

NO

NO

YES

YES

NO

NO

Fig .7 Flow chart describtion for PSO technique .

IV. RESULTS

Table1 shows the simulation parameters used in PSO. PV represented as electrical circuit

with a series resistance and a shunt resistance. The PV output voltage and current depend on

the input irradiation and temperature. Temperature is used as 25 C and irradiation is a



function presents sudden variation shape (1000 W/m2and decreased to600 W/m2 at istant 1

sec then be 950 W/m2 at instant 7 sec) .Boost converter parameters as capacitance and

inductance equal 100 micro frad and henry. All the MPPT algorithms have a perturbation

period of 400 µs, while the perturbation amplitude is 1 V for the IC algorithm and the initial

perturbation amplitudes is 0.8 V for the PSO algorithm.

The chooper voltage incrase rapidly until 0.1 sec to reach steady state (S.S) value 160 volt

then be constant value until 1 sec it began to decrase slowly to reach 158 volt and continue

until 7 sec increase again to reach steady state value 160 V and continue .Thats as result of

affect of partial shading condition (PSC) and the variation of the presented radiation.

By using particle swarm optimization MPPT technique can reduce the affect of partial

shading condition (PSC) and make the output voltage more smooth and approximitly pure

DC which presented to the full bridge inverter .

Table 1. Simulation Parameters.

4.1-SIMULINK RESULTS FOR INCREMENTAL CONDUCTANCE METHOD;

The simulation of MPPT algorithm is done based on incremental conductance method with

perturbation amplitudes of 1 V.

Fig.8 and Fig.9 show the performance of the IC algorithm with a perturbation amplitude of

1 V. The time to reach the maximum power point is close to 50 ms, while the steady-state

error is 1.6% and energy losses during 100 ms are 150 mJ. The zoom of the voltage sub-

figure shows the settling time designed

Figure 8 Profile of the chooper voltage .(IC MPPT)

PV panel Boost Converter MPPT

Vpv = 120 V C = 100 µF Ta = 400 µs

Ipv = 390 A , Rsh = 10000 Ω L = 100 µH ∆V = 1 V (IC)

Rs = 0.001 Ω. VConverter = 300 ∆V0 = 0.8 V (10 p.) LPF@5 kHz



Figure 9 power extraction from Boost Converter.(IC MPPT)

Fig10.and Fig.11 show voltage and power output from PV cell.

Figure 10. Profile of PV Panel Voltage .(IC MPPT)

Figure 11. Profile of PV Panel Power Extraction. .(IC MPPT)

4.2- SIMULINK RSULTS FOR PARTICLE SWARM OPTMIZATION ALGORITHM:

The simulation of PSO MPPT method of two iterations with 10 particles were made.This

method does not guarantee the best solution. Fig12 and Fig.13 show the performance of the



PSO algorithm with a perturbation amplitude of 0.8 V. The time to reach the maximum

power point is close to 50 ms, while the steady-state error is 0.11% and energy losses during

100 ms are 95 mJ. The zoom of the voltage sub-figure shows the settling time designed

.Fig.14 and Fig.15 show voltage and power output from PV cell.

Fig. 12 MPPT based on PSO. Profile of the chooper voltage

Fig. 13 MPPT based on PSO. Profile of the chooper power extraction.

Fig.14 MPPT based on PSO. Profile of the pv panel voltage



Fig.15 MPPT based on PSO. Profile of the pv panel power extraction.

Table 2. Main simulation results used to compare the performance of the MPPT techniques based on IC

and PSO.

With partial

shading

IC PSO

time to reach the MPP (s) 150 15

power oscillation in steady state (%) 0.06 0.008

PV power extracted at MPP (W) 1384 1939

tracking efficiency (%) 71.93 99.95

V. CONCLUSION

This paper proposes on the effect of the sudden change in input radiation for photovoltaic PV on the

output current and voltage and how to solve it by two different MPPT method.We find that the particle

swarm optimization PSO is faster in response than incremental conductance IC.This is due to that

paticles search to the best position automatically in the different weather changes when particles are

initialized correctly around the MPP. This make the tracking time which particles waste in the wrong

direction is reduced as result the system tracking effieciency is raised and the fluctuation around the

MPP is reduced.The power loss in steady state oscillation in incremental conductance method IC is

higher than its by the particle swarm optimization PSO.From the obtained results it was found that the

PSO based MPPT exhibits superior performance compared to incremental conductance IC.Tracking the

global MPP using PSO algorithm is making the controllers system independent ,robust and fast under

partial shading conditions (PSC).

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