Photovol ta ic Sy stem s using PSIM

Sol a r Cha rg e Controllll l er to im prove the effic ienc y of Sta nda l one

Desig n & Im pl em enta tion of va rious MPPT Al g orithm s for

A PROJECT REPORT

Submitted by

ARUMUGAM.R (97408105701)

KUTHALINGAM.C (97408105022)

PATTUSELVAM .S (97408105035)

THIRUVENGADAM.M (97408105059)

In partial fulfillment for the award of the degree

of

BACHELOR OF ENGINEERING

in

ELECTRICAL AND ELECTRONICS ENGINEERING

GOVERNMENT COLLEGE OF ENGINEERING,

TIRUNELVELI-627007

ANNA UNIVERSITY ::CHENNAI 600 025

APRIL 2012

a l one Photovolta ic Sy stem s using PSIM

MPPT a l g orithm s for Sol a r Cha rg e Controll l er to im prove the effic ienc y of Sta nd -

Desig n & Im pl em enta tion of va rious

BONAFIDE CERTIFICATE

Certified that this project report “

” is the Bonafide work of

ARUMUGAM.R (97408105701),KUTHALINGAM.C (97408105022),

PATTUSELVAM .S (97408105035),THIRUVENGADAM.M (97408105059) , who

carried out the project work under my supervision.

SUPERVISOR HEAD OF THE DEPARTMENT

Controlll l er to im prove the effic ienc y of Sta nd - a l one Photovol ta ic Sy stem s using PSIM

Desig n & Im pl em enta tion of va rious MPPT a l g orithm s for Sol a r Cha rg e

CERTIFICATE OF EVALUATION

Government College Of Engineering, Tirunelveli-7

Electrical and Electronics Engineering.

Submitted by

ARUMUGAM.R (97408105701)

KUTHALINGAM.C (97408105022)

PATTUSELVAM .S (97408105035)

THIRUVENGADAM.M (97408105059)

Done under the supervision of

Prof. INDRA GETZY DAVID, M.E.,

The reports of the project work submitted by the above students in partial

fulfillment for the award of Bachelor of Engineering Degree in Electrical and

Electronics Engineering, Anna University Chennai were confirmed and evaluated.

Submitted for the Anna University Examinations held at Government College

of Engineering, Tirunelveli - 7 on 18.04.2012

INTERNAL EXAMINER EXTERNAL EXAMINER

ACKNOWLEDGEMENT

Our first and foremost praises and thanks to God, the almighty for his valuable

grace upon us to complete this project.

We submit our sincere thanks to our Principal

Dr.V. LAKSHMI PRABHA., Head of the Institution, Government College of

Engineering, Tirunelveli who showed a deep solitude on all of us regarding this

project work. With great pride and immense pleasure we express our deep sense of

gratitude and profound thanks to Prof .INDRA GETZY DAVID, M.E., Head of the

Department, Electrical and Electronics Engineering, Government College of

Engineering, Tirunelveli, for encouraging us to undertake the project work and who

was an instrumental brain behind this project.

We extend our special thanks to Superintending Engineer Auto Substation

Muthaiyapuram,Tuticorin for permitting us for a visit during power apparatus testing.

Next we express our sincere thanks to our faculty advisor

Mrs.M.GNANA SUNDARI,M.E, Asst.professor of EEE for leading us in the way

of completion of this project. We also express our sincere thanks to all the faculty

members of Department of Electrical and Electronics Engineering for their co-

operation in completing this project.

We also offer special thanks to our parents who have sacrificed greatly in making

this project possible. We thank all those who have helped directly and indirectly in our

project.

TABLE OF CONTENTS

CHAPTER TITLE PAGE

NO

LIST OF TABLES (i)

LIST OF FIGURES

(ii)

1

2

3

INTRODUCTION

PV ARRAY MODELLING

2.1 Photovoltaic modules

2.2 Equivalent circuit of a solar cell

2.3 Open circuit voltage, short circuit current and

maximum power point

2.4 Fill Factor

2.5 Temperature and irradiance effects

SOLAR CHARGE CONTROLLER

3.1 MPPT based solar charge controller

3.2 Converter choice for MPPT

3.3 Boost converter

3.3.1 Mode1 operation of boost converter

3.3.2 Mode2 operation of boost converter

1

5

6

7

8

9

10

15

16

17

17

18

19

4

5

MAXIMUM POWER POINT TRACKING

ALGORITHM

4.1 An overview of maximum power point

tracking

4.2 Different MPPT techniques

4.2.1 Perturb and Observe

4.2.2 Incremental conductance method

SIMULATION AND EVALUATION

5.1 PSIM

5.2 Circuit structure

5.3 Solar cell models

5.4 Simulation model of Perturb & Observe

algorithm for MPPT

5.5 Limitations of Perturb & Observe method

5.6 Simulation model of incremental

conductance method for MPPT

5.7 Analysis & discussions of Simulation

results

5.8 Conclusion

21

22

24

24

28

34

35

36

37

39

41

42

43

47

This project describes a method for obtaining optimal power from a

ABSTRACT

small PV panel with maximum power point techniques. In an age of dwindling

fossil fuels and climate change a lot of attention is being been focused on

renewable forms of energy such as photovoltaic (PV) cells.

The Photovoltaic cell is

a semiconducting device that absorbs light and converts it into electrical energy in

the form of DC.

The

DC power extracted from the PV array is synthesized and

modulated

by the converter to suit the load requirements.

In

general, PV system

consists of a PV array, solar charge controller, rechargeable battery, solar inverter

and loads. The aim of this project work is to study the design of Solar Charge

Controller using various Maximum Power Point Tracking Algorithms. Further, the

various design techniques are simulated in

the Power SIM Software Environment

and their strengths and weakness are evaluated.

Solar Charge Controller to improve the efficiency of Standalone

Photovoltaic System using PSIM

Design & Implementation of various MPPT Algorithms for

CHAPTER – I

INTRODUCTION

“We believe that the clean and inexhaustible power of sunlight will be the most

promising resource in mankind's quest to develop sustainable energy in the 21st

century and beyond."

-Hirofumi Tezuka, director and general manager

Kyocera Corporation, Solar Division

1.1 Introduction

We have only one planet that we can call home. Yet, we are slowly destroying this

home with every litre of fossil fuel that we burn every day. No option, you say?

Of course there's an option. The Sun. India is one of the sunniest countries in the

world, with 250 – 300 sunny days every year. And we let this wonderful bounty of

nature go to waste.

Due to recent developments in photovoltaic technology, one can easily convert

solar energy to electrical power and store it for use whenever we need it. Solar

energy is free, virtually inexhaustible and does not pollute the planet. Surprisingly,

it is also very economical in the long run.

As people are much concerned with the fossil fuel exhaustion and the

environmental problems caused by the conventional power generation, renewable

energy sources and among them photovoltaic panels and wind-generators are

widely used now.

The efficiency of a PV plant is affected mainly by three factors:

the efficiency of the PV panel (in commercial PV panels it is between 8-15% )

the efficiency of the inverter (95-98 % )

the efficiency of the MPPT algorithm (which is over 98% )

Improving the efficiency of the PV panel and the inverter is not easy as it depends

on the technology available, it may require better components, which can increase

drastically the cost of the installation. Instead, improving the tracking of the

maximum power point (MPP) with new control algorithms is easier, not expensive

and can be done even in plants which are already in use by updating their control

algorithms, which would lead to an immediate increase in PV power generation

and consequently a reduction in its price.

MPPT algorithms are necessary because PV arrays have a nonlinear voltage-

current characteristic with a unique point where the power produced is maximum.

This point depends on the temperature of the panels and on the irradiance

conditions. Both conditions change during the day and are also different depending

on the season of the year. Furthermore, irradiation can change rapidly due to

changing atmospheric conditions such as clouds. It is very important to track the

MPP accurately under all possible conditions so that the maximum available power

is always obtained.

In this project, the perturb and observe (P&O) and incremental conductance

(InCond) algorithms are analyzed in depth and tested according to the standard

conditions mentioned above. After that, improvements to the P&O and the InCond

algorithms are suggested to succeed in the MPP tracking under conditions of

changing irradiance.

To test the MPPT algorithms according to the irradiation profiles proposed in the

standard, a simplified model was developed, because the simulation time required

in some of the cases cannot be reached with the detailed switching model of a

power converter in a normal desktop computer. The reason for that is that the

computer runs out of memory after simulating only a few seconds with the

complete model. Finally, each method is evaluated and their strengths and

weakness are identified.

CHAPTER – 2

PV ARRAY MODELLING

2.1 PHOTOVOLTAIC MODULES

Solar cells consist of a p-n junction fabricated in a thin wafer or layer of

semiconductor. In the dark, the I-V output characteristic of a solar cell has an

exponential characteristic similar to that of a diode.

Fig 2.1 Basic Solar Cell Construction

When exposed to light, photons with energy greater than the band gap energy of

the semiconductor are absorbed and create an electron-hole pair. These carriers are

swept apart under the influence of the internal electric fields of the p-n junction

and create a current proportional to the incident radiation. When the cell is short

circuited, this current flows in the external circuit; when open circuited, this

current is shunted internally by the intrinsic p-n junction diode. The characteristics

of this diode therefore set the open circuit voltage characteristics of the cell.

2.2 Equivalent circuit of a solar cell

The solar cell can be represented by the electrical model shown in Figure.

Fig 2.2 Electrical model of Solar cell

Its current voltage characteristic is expressed by the following equation:

[ ( )

]

(1)

where I and V are the solar cell output current and voltage respectively, I0is the

dark saturation current, q is the charge of an electron, A is the diode quality

(ideality) factor, k is the Boltzmann constant, T is the absolute temperature and RS

and RSH are the series and shunt resistances of the solar cell. RS is the resistance

offered by the contacts and the bulk semiconductor material of the solar cell. The

origin of the shunt resistance RSH is more difficult to explain. It is related to the

non-ideal nature of the p–n junction and the presence of impurities near the edges

of the cell that provide a short-circuit path around the junction. In an ideal case RS

would be zero and RSH infinite. However, this ideal scenario is not possible and

manufacturers try to minimize the effect of both resistances to improve their

products. Sometimes, to simplify the model, the effect of the shunt resistance is not

considered, i.e. RSH is infinite, so the last term in the above equation is neglected.

A PV panel is composed of many solar cells, which are connected in series and

parallel so the output current and voltage of the PV panel are high enough to the

requirements of the grid or equipment. Taking into account the simplification

mentioned above, the output current-voltage characteristic of a PV panel is

expressed by an equation, where np and ns are the number of solar cells in parallel

and series respectively.

[ ( )

] (2)

2.3 Open circuit voltage, short circuit current and maximum power point

Two important points of the current-voltage characteristic must be pointed out: the

open circuit voltage VOC and the short circuit current ISC. At both points the power

generated is zero. VOC can be approximated from (1) when the output current of the

cell is zero, i.e. I=0 and the shunt resistance RSH is neglected. It is represented by

equation (3). The short circuit current ISC is the current at V = 0 and is

approximately equal to the light generated current IL as shown in equation (4).

(

) (3)

(4)

The maximum power is generated by the solar cell at a point of the current-voltage

characteristics, where the product VI is maximum. This point is known as the MPP

and is unique, as can be seen in Figure 2.3, where the previous points are

represented.

Fig 2.3 Characteristics of Solar Cell

2.4 Fill factor

Using the MPP current and voltage, IMPP and VMPP, the open circuit voltage (VOC)

and the short circuit current (ISC), the fill factor (FF) can be defined as:

(5)

It is a widely used measure of the solar cell overall quality.

It is the ratio of the actual maximum power (IMPPVMPP) to the theoretical one

(ISCVOC), which is actually not obtainable. The reason for that is that the MPP

voltage and current are always below the open circuit voltage and the short circuit

current respectively, because of the series and shunt resistances and the diode

depicted in Figure 2.2. The typical fill factor for commercial solar cells is usually

over 0.70.

2.5 Temperature and irradiance effects

Two important factors that have to be taken into account are the irradiation and the

temperature. They strongly affect the characteristics of solar modules. As a result,

the MPP varies during the day and that is the main reason why the MPP must

constantly be tracked and ensure that the maximum available power is obtained

from the panel.

The effect of the irradiance on the voltage-current (V-I) and voltage-power (V-P)

characteristics is depicted in Figure 2.4, where the curves are shown in per unit, i.e.

the voltage and current are normalized using the VOC and the ISC respectively, in

order to illustrate better the effects of the irradiance on the V-I and V-P curves. As

was previously mentioned, the photon-generated current is directly proportional to

the irradiance level, so an increment in the irradiation leads to a higher photo-

generated current. Moreover, the short circuit current is directly proportional to the

photon- generated current; therefore it is directly proportional to the irradiance.

Fig 2.4V-I and V-P curves at constant temperature (25°C) and three different insolation values.

When the operating point is not the short circuit, in which no power is generated,

the photon generated current is also the main factor in the PV current, as is

expressed by equations (1) and (2). For this reason the voltage-current

characteristic varies with the irradiation.

In contrast, the effect in the open circuit voltage is relatively small, as the

dependence of the light generated current is logarithmic, as is shown in equation

(3).

Figure 2.4 shows that the change in the current is greater than in the voltage. In

practice, the voltage dependency on the irradiation is often neglected . As the effect

on both the current and voltage is positive, i.e. both increase when the irradiation

rises, the effect on the power is also positive: the more irradiation, the more power

is generated.

The temperature, on the other hand, affects mostly the voltage. The open circuit

voltage is linearly dependent on the temperature, as shown in the following

equation:

( )

( ) (6)

According to (6), the effect of the temperature on VOC is negative, because Kv is

negative, i.e. when the temperature raises, the voltage decreases. The current

increases with the temperature but very little and it does not compensate the

decrease in the voltage caused by a given temperature rise. That is why the power

also decreases. PV panel manufacturers provide in their data sheets the temperature

coefficients, which are the parameters that specify how the open circuit voltage,

the short circuit current and the maximum power vary when the temperature

changes. As the effect of the temperature on the current is really small, it is usually

neglected.

Figure 2.5 shows how the voltage-current and the voltage-power characteristics

change with temperature. The curves are again in per unit, as in the previous case.

Fig 2.5V-I and V-P curves at constant irradiation (1 kW/m2) and three different temperatures.

As was mentioned before, the temperature and the irradiation depend on the

atmospheric conditions, which are not constant during the year and not even during

a single day; they can vary rapidly due to fast changing conditions such as clouds.

This causes the MPP to move continuously, depending on the irradiation and

temperature conditions. If the operating point is not close to the MPP, great power

losses occur.

Hence it is essential to track the MPP in any conditions to assure that the

maximum available power is obtained from the PV panel. In a modern solar power

converter, this task is entrusted to the MPPT algorithms.

CHAPTER – 3

SOLAR CHARGE CONTROLLER

3.1 MPPT based Solar Charge Controller:

A maximum power point tracker (or MPPT) based Solar Charge Controller is a

high efficiency DC to DC converter which functions as an optimal electrical load

for a photovoltaic (PV) cell, most commonly for a solar panel or array, and

converts the power to a voltage or current level which is more suitable to whatever

load the system is designed to drive.

Fig 3.1 Schematic representation of MPPT charge Controller

Typically a charge controller performs the following basic functions:

Controls maximum power extraction from a panel by tracking the MPP and

ensuring that the panel operates at MPP.

Controls battery charging as defined in the battery charge cycle

specification to improve usable battery life and protect it against reverse

connection, over charging and deep discharging

Load protection against overloads and short-circuits

Display (LED or LCD) Status indications

3.2 Converter Choice for MPPT

Depending on the topology of the power electronics, an MPPT charge controller

cans be either:

• Buck only – the PV voltage must be higher than the battery voltage

• Boost only – the PV voltage must be lower than battery voltage

• Buck-boost – both the PV voltage and battery voltage can be variable values

with the system switching between buck and boost based on the relative voltages.

Fig 3.1 shows the block diagram of a MPPT Charge Controller

3.3 Boost Converter

The maximum power point tracking is basically a load matching problem. In order

to change the input resistance of the panel to match the load resistance (by varying

the duty cycle), a DC to DC converter is required.

It has been studied that the efficiency of the DC to DC converter is maximum for a

buck converter, then for a buck-boost converter and minimum for a boost converter

but as we intend to use our system either for tying to a grid or for a system which

requires 230 Vat the output end, so we use a boost converter. Fig 3.2 shows the

circuit diagram of Boost Converter.

Fig 3.2 Circuit diagram of Boost Converter

3.3.1 Mode 1 operation of the Boost Converter

When the switch is closed the inductor gets energized through the battery and

stores the energy. In this mode inductor current rises (exponentially) but for

simplicity we assume that the energizing and the de - energizing of the inductor are

linear. The diode blocks the current flowing and so the load current remains

constant which is being supplied due to the discharging of the capacitor. Fig 3.3

shows the Mode 1 Operation of Boost Converter.

Fig 3.3 Mode 1 Operation of Boost Converter

3.3.2 Mode 2 operation of the Boost Converter

In mode 2 the switch is open and so the diode becomes short circuited. The energy

stored in the inductor gets utilized through opposite polarities which charge the

capacitor. The load current remains constant throughout the operation. Fig 3.4

shows the Mode 1 Operation of Boost Converter.

The waveforms for a boost converter are shown in Figure 3.5

Fig 3.4 Mode 2 Operation of Boost Converter

Fig 3.5 Waveforms for a Boost Converter

CHAPTER - 4

MAXIMUM POWER POINT TRACKING ALGORITHMS

4.1 An overview of Maximum Power Point Tracking

The power output from the solar panel is a function of insolation level and

temperature. But for a given operating condition, we have a curve which gives the

voltage level maintained by the panel for a particular value of current. This plot is

known as the characteristics of the cell. From the characteristics plot, we will be

able to derive the power output with respect to the output current. We adopt the

method to find the current which has to be extracted so as to fix the operating point

of the cell at its maximum power.

Fig 4.1 PV Panel Characteristic curves

The operating point of any source sink mechanism is the intersection point of load

line with the source characteristic plot shown in fig 4.1. What we attempt here to

do is change the load angle theta (𝜃) to intersect the characteristics at maximum

power point i.e., nothing but the solution for impedance matching problem. The

principle is described below.

PV modules have a very low conversion efficiency of around 15% for the

manufactured ones. Besides, due to the temperature, radiation and load variations,

this efficiency can be highly reduced.

In fact, the efficiency of any semiconductor device drops steeply with the

temperature. In order to ensure that the photovoltaic modules always act supplying

the maximum power as possible and dictated by ambient operating conditions, a

specific circuit known as MPPT is employed.

In most common applications, the MPPT is a DC-DC converter controlled through

a strategy that allows imposing the photovoltaic module operation point on the

Maximum Power Point (MPP) or close to it. On the literature, many studies

describing techniques to improve MPP algorithms were published, permitting more

velocity and precision of tracking.

On the other hand, there is no theory to guide the designer to choose, among the

DC-DC converters family, the best one to operate as MPPT, thus, in most cases,

the designers are tempted to use the simplest DC-DC converters – namely buck

converter or boost converter.

4.2 Different MPPT techniques

There are different techniques used to track the maximum power point. Two of the

most popular techniques are:

Perturb & Observe Method

Incremental Conductance Method

The choice of the algorithm depends on the time complexity the algorithm takes to

track the MPP, implementation cost and the ease of implementation.

4.2.1 Perturb & Observe

Perturb & Observe (P&O) is the simplest method. Fig 4.2 shows the algorithmic

flowchart of Perturb & Observe method for MPPT. In this we use only one sensor,

that is the voltage sensor, to sense the PV array voltage and so the cost of

implementation is less and hence easy to implement. The time complexity of this

algorithm is very less but on reaching very close to the MPP it doesn’t stop at the

MPP and keeps on perturbing on both the directions. Fig 4.3 shows the Illustration

of MPPT Algorithm When this happens the algorithm has reached very close to the

MPP and we can set an appropriate error limit or can use a wait function which

ends up increasing the time complexity of the algorithm. However the method does

not take account of the rapid change of irradiation level (due to which MPPT

changes) and considers it as a change in MPP due to perturbation and ends up

calculating the wrong MPP. To avoid this problem we can use incremental

conductance method. The Perturb & Observe algorithm states that when the

operating voltage of the PV panel is perturbed by a small increment, if the resulting

changes in power ΔP is positive, then we are going in the direction of MPP and we

keep on perturbing in the same direction. If ΔP is negative, we are going away

from the direction of MPP and the sign of perturbation supplied has to be changed.

Fig 4.3 Illustration of P&O MPPT Algorithm

The flowchart for the P&O algorithm is shown in Figure 4.2

Fig 4.2 Algorithmic flow chart of Perturb & Observe method for MPPT

Start

Measure V(k)& I(k)

P(k)= V(k)* I(k)

ΔP = P(k)-P(k-1)

ΔP > 0

Decrease Array

Voltage

V(k) – V(k-1) > 0 V(k) – V(k-1) > 0

Increase Array

voltage

Update history

V(k-1)= V(k)

P(k-1)= P(k)

Decrease Array Voltage

Increase Array voltage

NO YES

YES YESNO NO

Figure 4.1 shows the plot of module output power versus module voltage for a

solar panel at a given irradiation. The point marked as MPP is the Maximum

Power Point, the theoretical maximum output obtainable from the PV panel.

Consider A and B as two operating points as shown in the figure above, the point

A is on the left hand side of the MPP. Therefore, we can move towards the MPP by

providing a positive perturbation to the voltage.

On the other hand, point B is on the right hand side of the MPP. When we give a

positive perturbation, the value of ΔP becomes negative, thus it is imperative to

change the direction of perturbation to achieve MPP.

4.2.2. Incremental Conductance Method

The theory of the incremental conductance method is to determine the variation

direction of the terminal voltage for PV modules by measuring and comparing the

incremental conductance and instantaneous conductance of PV modules. If the

value of incremental conductance is equal to that of instantaneous conductance, it

represents that the maximum power point is found.

The basic theory is illustrated with Fig. 4.4.

Fig 4.4 Illustration of InCond MPPT Algorithm

When the operating behavior of PV modules is within the constant current area,

the output power is proportional to the terminal voltage. That means the output

power increases linearly with the increasing terminal voltage of PV modules

(slope of the power curve is positive, dP/dV> 0). When the operating point of

PV modules passes through the maximum power point, its operating behavior is

similar to constant voltage. Therefore, the output power decreases linearly with the

increasing terminal voltage of PV modules (slope of the power curve is negative,

dP/dV< 0). When the operating point of PV modules is exactly on the maximum

power point, the slope of the power curve is zero (dP/dV= 0) and can be further

expressed as,

( )

( )

By the relationship of dP/dV= 0, (7) can be rearranged as follows,

( )

dI and dV represent the current error and voltage error before and after the

increment respectively. The static conductance (Gs) and the dynamic conductance

(Gd, incremental conductance) of PV modules are defined as follows,

( )

( )

The maximum power point (operating voltage is Vm) can be found When

(11)

When the equation in (8) comes in to existence, the maximum power point is

tracked by MPPT system. However, the following situations will happen while the

operating point is not on the maximum power point:

(

) ( )

(

) ( )

Equations (12) and (13) are used to determine the direction of voltage perturbation

when the operating point moves toward to the maximum power point.

In the process of tracking, the terminal voltage of PV modules will continuously

perturb until the condition of (8) comes into existence.

The main difference between incremental conductance and P&O algorithms is the

judgment on determining the direction of voltage perturbation. When static

conductance Gs is equal to dynamic conductance Gd, the maximum power point

is found.

From the flow diagram shown in Fig.4.5, it can be observed that the weather

conditions don’t change and the operating point is located on the maximum power

point when dV= 0 and dI= 0. If dV= 0 but dI> 0, it represents that the sun

irradiance increases and the voltage of the maximum power point rises.

Meanwhile, the maximum power point tracker has to raise the operating voltage of

PV modules in order to track the maximum power point.

On the contrary, the sun irradiance decreases and the voltage of the maximum

power point reduces if dI< 0. At this time the maximum power point tracker needs

to reduce the operating voltage of PV modules.

Furthermore, when the voltage and current of PV modules change during a

voltage perturbation and dI/dV>－I/V (dP/dV> 0), the operating voltage of PV

modules are located on the left side of the maximum power point in the P-V

diagram, and have to be raised in order to track the maximum power point.

If dI/dV<－I/V(dP/dV< 0), the operating voltage of PV modules will be located on

the right side of the maximum power point in the P-V diagram, and has to be

reduced in order to track the maximum power point. The advantage of the

incremental conductance method, which is superior to those of the other two

MPPT algorithms, is that it can calculate and find the exact perturbation direction

for the operating voltage of PV modules. In theory, when the maximum power

point is found by the judgment conditions (dI/dV= －I/V and dI= 0) of the

incremental conductance method, it can avoid the perturbation phenomenon near

the maximum power point which is usually happened for the other two MPPT

algorithms. The value of operating voltage is then fixed. However, it indicates that

perturbation phenomenon is still happened near the maximum power point under

stable weather conditions after doing some experiments. This is due to the reason

that the probability of meeting condition dI/dV=－I/V is extremely small.

Fig.4.5 Algorithmic flowchart of Incremental Conductance method for MPPT

Inputs: V(t),I(t),V(t-Δt),I(t-Δt)

P(t),P(t-Δt) calculated from the inputs

ΔV = V(t) –V(t-Δt) ΔP = P(t) – P(t -Δt)

ΔI = I(t) – I(t - Δt)

ΔV/ΔP = 0

ΔI/ΔP = 0

ΔV/ΔP >0

ΔI/ΔP < 0

Increase Vref

Decrease Vref

Return

NO

NO

YES

YES

Summarily, the incremental conductance algorithm is based on the fact that the

slope of the curve power vs. voltage (current) of the PV module is zero at the MPP,

positive (negative) on the left of it and negative (positive) on the right, as can be

seen in Figure 4.5:

ΔV/ΔP = 0 (ΔI /ΔP = 0) at the MPP

ΔV/ΔP > 0 (ΔI /ΔP < 0) on the left

ΔV/ ΔP < 0 (ΔI / ΔP > 0) on the right

By comparing the increment of the power vs. the increment of the voltage (current)

between two consecutives samples, the change in the MPP voltage can be

determined.

CHAPTER – 5

SIMULATION & EVALUATION

5.1PSIM:

PSIM is a simulation package specifically designed for power electronics and

motor control. With fast simulation, friendly user interface and waveform

processing, PSIM provides a powerful simulation environment for power converter

analysis, control loop design, and motor drive system studies.

The PSIM simulation package consists of three programs: circuit schematic editor

SIMCAD*, PSIM simulator, and waveform processing program SIMVIEW*. The

simulation environment is illustrated as follows.

5.2 Circuit Structure

A circuit is represented in PSIM in four blocks: power circuit, control circuit,

sensors, and switch controllers. The figure below shows the relationship between

these blocks.

The power circuit consists of switching devices, RLC branches, transformers, and

coupled inductors. The control circuit is represented in block diagram.

Components in s domain and z domain, logic components (such as logic gates and

flip flops), and nonlinear components (such as multipliers and dividers) can be

used in the control circuit. Sensors measure power circuit voltages and currents and

pass the values to the control circuit. Gating signals are then generated from the

control circuit and sent back to the power circuit through switch controllers to

control switches.

5.3 SOLAR CELL MODELS

Two types of solar cells models are provided. One is the functional model that

requires the minimum parameter inputs, and the other is the physical model that

can take into account the light intensity and ambient temperature variations.

Fig 5.1 Physical Model of Solar Cell

Fig 5.2 Characteristics of Solar cell (Physical Model)

Fig 5.3 Functional Model of Solar Cell

Fig 5.4 Characteristics of Solar Cell (Functional model)

5.4 Simulation Model of Perturb & Observe Algorithm for MPPT:

Fig.5.5 Simulation Model of Perturb & Observe Algorithm for MPPT

Fig 5.6 Sub circuit model of P&O MPPT

Simulation Output:

Fig 5.7 Simulation output of P&O MPPT Algorithm

5.5 Limitations of Perturb & Observe algorithm

Fig 5.8 Curve showing wrong tracking of MPP by P&O algorithm under rapidly varying

irradiance

In a situation where the irradiance changes rapidly, the MPP also moves on the

right hand side of the curve. The algorithm takes it as a change due to perturbation

and in the next iteration it changes the direction of perturbation and hence goes

away from the MPP as shown in the figure.

However, in this algorithm we use only one sensor, that is the voltage sensor, to

sense the PV array voltage and so the cost of implementation is less and hence easy

to implement. The time complexity of this algorithm is very less but on reaching

very close to the MPP it doesn’t stop at the MPP and keeps on perturbing in both

the directions. When this happens the algorithm has reached very close to the MPP

and we can set an appropriate error limit or can use a wait function which ends up

increasing the time complexity of the algorithm.

5.6 Simulation Model of Incremental Conductance Method for MPPT:

Fig 5.9Simulation Model Incremental Conductance MPPT Algorithm

Simulation Output:

Fig 5.10 Simulation Output of Incremental Conductance MPPT Algorithm

5.7 Analysis and discussion of Simulation results:

In order to compare the accuracy and efficiency of the two MPPT algorithms

selected in this project, PSIM Software package is used to implement the tasks of

modeling and simulation. The PV module used in the PV system is the product of

Solarex whose model is MX64. This kind of PV module is composed of 72 solar

cells in series, and the electrical specification tested by the factory under

1000W/m2, AM1.5 and 25oC conditions is listed in Table 1.

Fig. 5.11 is the block diagram of the PV simulation system used in this paper. The

hardware specification of the computer used for simulation is Intel Core i3

Processor M 370 @ 2.40GHz.

DC to DCConverter

MPPTController

Load

Ipv

Vpv

VpvIpv

Vout

pulses

Photovoltaic

Fig 5.11 Block diagram of PV Simulation system

Table 1: PV Panel Specifications

Power output Curve for P&O MPPT Algorithm:

Fig 5.12 MPPT Power output of Perturb and Observe Method

Power output Curve for Incremental Conductance MPPT Algorithm:

Fig 5.13 MPPT Power output of Incremental conductance MPPT Algorithm

The time response, the average power and the ripples amplitude of the output

power corresponding to the two treated MPPT controlled methods has been

obtained from the output graphs (above) and its comparison details are given

below table

And moreover, from the data points of the MPPT Power output curve, the

efficiency of an individual algorithm is determined.

The efficiency of two different methods under two different weather conditions are

determined and summarized in the table below.

Time

Response Average Power

Ripples

Amplitude

Perturb & Observe Method 4.08ms 53 watt 12 watt

Incremental Conductance

Method 4.37ms 59.52 watt 1.6 watt

Table 2: Comparison results

Weather P&O IncCond

Full sun 91.4% 94.6%

Partial Cloudy 95.6% 94.9%

Table 3: Comparison of efficiency

In general, the advantages of the ‘incremental conductance’ method over the

‘perturb and observe’ method are:

• Incremental method can calculate the direction, for which the array’s point

changed in order to reach the MPP,

• Incremental method should not oscillate about the MPP once it reaches it,

• Incremental method does not go on the wrong direction when conditions in the

system changed rapidly.

5.8 Conclusion

The sun is at the origin of the quasi-totality of the sources of energies used by the

humanity for its food, domestic and industrial needs. The solar energy is important

because it is non-pollutant energy. In this project, the conversion from solar energy

to electrical one is treated. In this case, the model of a photocell and a solar panel

are presented. The ‘Perturb and observe’ and the ‘incremental conductance’

methods are used to maximize the output power. The flow chart of each method

had been explained and discussed. With the incremental conductance method,

compared to the perturb and observe method, simulation results underline that the

time response is small, the existing ripples have low amplitude and the average

power is more important.