libro pv simulation

8/12/2019 Libro PV Simulation

1/146

-

F

ap -

t

f a young

Henry For

odern inveord, Firesto

e are lik hen we shnd tide. ...ower!

Thomas E

e book: Un

an makes

, during a

tions offer e Radio Ta

tenant fauld be usiI'd put m

ison, in co

common Fri

up his min

hort radio

d young mlk ).

mers, chong Nature'y money o

versation

ends (1987)

to work, t

roadcast a

en during t

ping downs inexhausn the sun

ith Henry

by James

heres no li

out the exc

e late 192

the fenceible sourcand solar

ord and Ha

. Newton.

mit to wha

iting oppor

s (YouTub

around ous of energenergy. W

rvey Firest

he can do

unities that

e Video: E

house for sun ,

hat a sour

ne; as quot

new,

ison,

fuel,ind,

ce of

ed in


2/146


3/146

Copyright by

Ayedh H A S Alqahtani

2013


4/146

ii

AbstractThe aim of this dissertation is to conduct performance analysis for the modeling

and control practices of photovoltaic (PV) systems. Different modeling techniques of PVsystems are considered. The current existing modeling techniques are examined withfurther study, and the desired operating characteristics are achieved through the proposaof a simplified and accurate modeling process that estimates the PV model parameters fo

a PV module. This is done by taking a new approach that replaces the typical modelin practice in the literature. The main objective of this new approach is to avoid the need focomplex calculation and tedious combination of equations to extract the PV systemmodel parameters. The PV system behavior is studied under different environmentaconditions.

In addition, most PV system models in the literature have internal parameters thatare not provided by the manufacturers. These parameters are not given in the PV moduldata sheet and require numerical methods for their determination due to the nonlineanature of the PV systems output characteristics. This research presents an improved andcomprehensive PV system characterization method that relies only on the values provided by the manufacturer. New improvements and modifications to some of thexisting PV models are also presented.

A control design strategy for control of the power generated by PV systems issuggested to provide effective energy extraction. The controller design ensures trackinof the maximum power point (MPP) for the PV system using a sliding mode contro

method of self-optimization. It offers fast and accurate convergence to the MPP in steadstate and during varying weather conditions.

Published research results and discussion are provided. The simulation in thisresearch proposal is carried out using a Matlab/Simulink environment. Experimenta


5/146

iii

verification of the simulated results are included to demonstrate the validity of the proposed controller design.


6/146

iv

To my mother, Nora Ali.


7/146

v

AcknowledgmentsFirst and foremost, I wish to express my gratitude and thanks to my advisor, Prof

Vadim Utkin. His astute mentorship, patience, inspired guidance, and unwavering

support is deeply appreciated. He implanted in me the ability to look at complex

problems with simplicity, and taught me how to be happy when I felt so desperate. I am

indebted to him for teaching me to approach research with maturity. He has a unique

aptitude to see the big picture, link concepts, and scrutinize results. I have never seen

professor who treats his students with kindness and gentleness as he does.

My thanks also go to my committee members, Prof. Longya Xu and Prof. Donald

Kasten, for their support and helpful advice throughout my Ph.D. study. I am thankful t

Prof. Stephen Sebo and Prof. Jin Wang; I am extremely fortunate to have taken thei

exceptional course: High Voltage Engineering and Laboratory.

I am grateful to many who have had a part: Shoghig Sahakyan, PAAET director

from Kuwait Embassy, who, at the most difficult times, with her swift assistance and

warm-hearted support, has been always prompt; to Muthanna Abuhamdeh, for the

constant exchange of information and fruitful discussion over the past five years; to

Carlos Osorio from MathWorks, for his helpful MATLAB answers; to Prof. Fusun

Ozguner, the ECE department graduate studies chair, for addressing all the problems

had during my Ph.D. study; to Patricia Toothman, the graduate academic counselor in th


8/146

vi

ECE department, for her unceasing warm support; to my wife Khuloud: I could not hav

asked for more; thank you for putting up with me.


9/146

vii

Vita

May 2000 ......................................................B.Sc. Electrical Engineering, University o

North Carolina at Charlotte, USA

October 2000 May 2004 ............................Laboratory Engineer, Kuwait University

Kuwait

May 2004 ......................................................M.Sc. Electrical Engineering, Kuwai

University, Kuwait

December 2005 .............................................M.Sc. Electrical Engineering, University o

Southern California, USA

December 2005 December 2008 ................Faculty Member, Public Authority fo

Applied Education & Training, Kuwait


10/146

viii

PublicationsA. Alqahtani, "A simplified and accurate photovoltaic module parameters extractionapproach using Matlab," 2012 IEEE International Symposium on Industrial Electronics(ISIE), vol., no., pp.1748-1753, 28-31 May 2012.

A. Alqahtani, M. Abuhamdeh, and Y. Alsmadi, "A simplified and comprehensiveapproach to characterize photovoltaic system performance," Energytech, 2012 IEEE,vol., no., pp.1-6, 29-31 May 2012.

A. Alqahtani, and V. Utkin, "Self-optimization of photovoltaic system power generation based on sliding mode control," IECON 2012 - 38th Annual Conference on IEEEIndustrial Electronics Society, vol., no., 25-28 October 2012.

A. Alqahtani, and V. Utkin, "Control of photovoltaic system power generation usingsliding mode control," International Conference on Power System Technology(POWERCON 2012), November 2012.

A. Alqahtani, M. Abuhamdeh, Y. Alsmadi, and V. Utkin "Photovoltaic poweroptimization using sliding mode control with two axis tracking system," Energytech,2013 IEEE, vol., no., pp.1-6, 21-23 May 2013.

A. Alqahtani, R. Giral, E. Vidal-Idiarte, L. Martinez-Salamero, and Vadim Utkin Self-optimization of photovoltaic system power generation based on sliding mode control formicrogrids," IEEE Transaction on Control Systems Technology, , vol., no., pp., 05 Sep.2013.Submitted .

Fields of Study

Major Field: Electrical and Computer Engineering


11/146

ix

Table of ContentsAbstract ...............................................................................................................................

Dedication ..........................................................................................................................

Acknowledgments...............................................................................................................

Vita .................................................................................................................................... v

Publications ...................................................................................................................... v

List of Tables .................................................................................................................... x

List of Figures .................................................................................................................. xi

CHAPTER 1: INTRODUCTION ......................................................................................

1.1 Background ..........................................................................................................

1.2. How PV Cells Work ............................................................................................. 2 1.3 PV Cells, Modules, and Arrays ............................................................................ 5

1.4 Types of PV Systems ........................................................................................... 8

1.4.1 Stand-alone PV Systems ............................................................................... 8 1.4.2 Hybrid PV Systems ....................................................................................... 9

1.4.3 Grid-connected PV Systems ....................................................................... 10

1.5 Advantages and Disadvantages of PV Systems ................................................. 11

1.5.1 Advantages of PV Systems............................................................................... 1

1.5.2 Disadvantages of PV Systems .......................................................................... 1 CHAPTER 2: LITERATURE REVIEW ......................................................................... 1

2.1 Problem Definition ............................................................................................. 1

2.2 Relevant Research .............................................................................................. 14


12/146

x

2.3 Summary ............................................................................................................ 19

CHAPTER 3: MODELING AND PARAMETER EXTRACTION OF PV SYSTEMS 2

3.1 PV System Parameter Extraction ....................................................................... 20

3.1.1 Parameter-extraction Procedures ................................................................ 23 3.1.2 Initialization ................................................................................................ 23

3.1.3 Optimization Algorithm .............................................................................. 24

3.1.4 Case Study .................................................................................................. 26

3.2 Modeling PV Array ............................................................................................ 30

3.3 Temperature Impact ........................................................................................... 33

3.4 Irradiance Impact ................................................................................................ 36

CHAPTER 4: ANALYTICAL MODEL FOR PV SYSTEMS ....................................... 3

4.1 PV System Analytical Model ............................................................................. 39 4.2 Analytical Model Extension ............................................................................... 40

4.3 Further Addition to the Analytical Model .......................................................... 41

4.3.1 Temperature Dependence ........................................................................... 41

4.3.2 Irradiance Dependence................................................................................ 42 4.4 PV Module Analytical Model Results ............................................................... 43

4.5 PV Array Analytical Model Results ................................................................... 49

CHAPTER 5: PV SYSTEM POWER GENERATION CONTROL ............................... 5

5.1 PV System Power Control ................................................................................. 53 5.2 Self-optimization Sliding Mode Control ............................................................ 54

5.3 Control Design Strategy ..................................................................................... 61

5.3.1 Adapted PV System .................................................................................... 63

5.3.2 Control Algorithm ....................................................................................... 65

5.3.3 DC/DC Boost Converter ............................................................................. 68

5.3.4 Results and Discussion ............................................................................... 68

5.4 Experimental Verification .................................................................................. 73

CHAPTER 6: PV SYSTEM POWER GENERATION CONTROL CASE STUDY .. 7

6.1 The PV System Characteristic............................................................................ 77

6.2 Preliminary Simulation ...................................................................................... 79

6.3 Experimental Results.......................................................................................... 82


13/146

xi

CHAPTER 7: MECHANICAL TRACKING OF PV SYSTEM POWER ...................... 8

7.1 PV System Physical Tracking ............................................................................ 87

7.2 Suggested Physical Control Strategy ................................................................. 89

7.2.1 Mechanical Control Algorithm ................................................................... 94

7.2.2 PV Module Mechanical Tracker Realization .............................................. 99 7.3 Results and Discussion ..................................................................................... 100

CHAPTER 8: CONCLUSION AND FUTURE WORK ............................................... 10

8.1 Conclusion ........................................................................................................ 10

8.2 Future Work ..................................................................................................... 105

REFERENCES ............................................................................................................... 10

APPENDIX A: Data sheets information......................................................................... 112

APPENDIX B: Simulation Testbed ................................................................................ 12


14/146

xii

List of Tables

Table 3.1 BP MSX60 PV Module Electrical Characteristics under STC ( 25C, AM 1.5,and 1000 W/m2). ............................................................................................................... 26 Table 3.2 Comparison between values found in [3] and values using proposed simplifiedmethod in this study for the KC200GT PV modules under STC. .................................... 3 Table 3.3 Array data sheet related to modules under STC. ............................................. 3

Table 4.1 BP MSX60 PV Module Electrical Characteristics under STC. ....................... 4 Table 4.2 PV-MF170EB3 PV Module Electrical Characteristics under STC. ................ 5

Table 5.1 Mitsubishi PV-MF170EB3 PV Module Electrical Characteristics under STC............................................................................................................................................ 6

Table 6.1 BP585 Solar Photovoltaic Module Electrical Characteristics at 25C, AM 1.5,and 1000 W/m2 ................................................................................................................. 77

Table 7.1 Mitsubishi PV Module Electrical Characteristics at 25C, AM1.5, and1000W/m2. ........................................................................................................................ 90


15/146

xiii

List of Figures

Figure 1.1 Principle of the operation of PV cell [2]. .........................................................

Figure 1.2 I-V and P-V characteristics for a PV device. .................................................... 3

Figure 1.3 Equivalent circuit model for a PV device. ........................................................

Figure 1.4 Generic PV array structure. ..............................................................................

Figure 1.5 I-V curve for three PV modules connected in series. ....................................... 7

Figure 1.6 I-V curve for three PV modules connected in parallel. .................................... 7

Figure 1.7 Stand-alone PV system. ....................................................................................

Figure 1.8 Hybrid PV system.............................................................................................

Figure 1.9 Grid-connected PV system. ............................................................................ 1

Figure 1.10 Timeline of PV cell energy conversion efficiencies [7]. .............................. 1

Figure 2.1 PV module equivalent circuit (a) ideal model, (b) single-diode model, (c)double-diode model. ......................................................................................................... 1

Figure 2.2 A typical I-V curve for a PV module. ............................................................. 18 Figure 2.3 A typical P-V curve for a PV module. ............................................................ 18

Figure 3.1 A typical P-V curve for a PV module. ............................................................. 22

Figure 3.2 Flowchart for determination of the PV models five parameters. .................. 2

Figure 3.3 I-V characteristic for the BP MSX60 PV module under STC. ....................... 27

Figure 3.4 P-V characteristic for the BP MSX60 PV module under STC. ...................... 27

Figure 3.5 Absolute errors of the model data regarding the experimental data for the BP

MSX60 PV module under STC. ....................................................................................... 2 Figure 3.6 I-V characteristic for the KC200GT PV module under STC. ......................... 29

Figure 3.7 P-V characteristic for the KC200GT PV module under STC. ........................ 29

Figure 3.8 PV array equivalent circuit. ............................................................................ 3


16/146

xiv

Figure 3.9 I-V characteristic for the designed PV array under STC. ............................... 32

Figure 3.10 P-V characteristic for the designed PV array under STC. ............................ 33

Figure 3.11 I-V characteristics for the BP MSX60 PV module at various temperatures.Modeled (line) and experimental (circles). ....................................................................... 3

Figure 3.12 P-V characteristics for the BP MSX60 PV module at various temperatures.Modeled (line) and experimental (circles). ....................................................................... 3

Figure 3.13 I-V-T three-dimensional characteristic for the BP MSX60 PV module atvarious temperatures and constant level of irradiance at 1000 W/m2. .............................. 36

Figure 3.14 I-V characteristics for the BP MSX60 PV module at various irradiances. ... 37

Figure 3.15 P-V characteristics for the BP MSX60 PV module at various irradiances. .. 37

Figure 3.16 Predicted P-I-V characteristics for the BP MSX60 PV module at various

irradiances and constant temperature 25 C. ...................................................................... 38

Figure 4.1 Modeled and experimental I-V characteristic for the BP MSX60 PV moduleunder STC. ........................................................................................................................ 4

Figure 4.2 Modeled and experimental P-V characteristic for the BP MSX60 PV moduleunder STC. ........................................................................................................................ 4

Figure 4.3 Absolute errors of the modeled data with respect to the experimental data forthe BP MSX60 PV module under STC............................................................................. 45

Figure 4.4 Modeled (line) and experimental (circles) I-V characteristics for the BPMSX60 PV module at various temperatures. ................................................................... 4

Figure 4.5 Modeled (line) and experimental (circles) P-V characteristics for the BPMSX60 PV module at various temperatures. ................................................................... 4

Figure 4.6 I-V-T three dimensional characteristic for BP MSX60 PV module at varioustemperatures and constant level of irradiance 1000W/m2. ............................................... 47

Figure 4.7 Predicted I-V characteristics for the BP MSX60 PV module at variousirradiances. ........................................................................................................................ 4

Figure 4.8 Predicted P-V characteristics for the BP MSX60 PV module at variousirradiances. ........................................................................................................................ 4


17/146

xv

Figure 4.9 Predicted P-I-V characteristics for the BP MSX60 PV module at variousirradiances. ........................................................................................................................ 4

Figure 4.10 Predicted P-V characteristic for the designed PV array under STC. ............ 50

Figure 4.11 Predicted I-V characteristics for the designed PV array at varioustemperatures. ..................................................................................................................... 5

Figure 4.12 Predicted P-V characteristics for the designed PV array at varioustemperatures. ..................................................................................................................... 5

Figure 4.13 Predicted I-V characteristics for the designed PV array at various irradiances............................................................................................................................................ 5

Figure 4.14 Predicted P-V characteristics for the designed PV array at variousirradiances. ........................................................................................................................ 5

Figure 5.1 PV system, DC/DC converter, and MPPT control. ........................................ 5

Figure 5.2 Optimization system. ...................................................................................... 5

Figure 5.3 Self-optimization control scheme. .................................................................. 5

Figure 5.4 Control inputu. ............................................................................................... 57

Figure 5.5 Relay control functionv. ................................................................................ 60

Figure 5.6 PV system, DC/DC boost converter, and the MPPT controller. .................... 6

Figure 5.7 Block diagram for the controller design. ........................................................ 6

Figure 5.8 I-V characteristic for the PV-MF170EB3 module by Mitsubishi Electric atvarious irradiances. ........................................................................................................... 6

Figure 5.9 P-V characteristic for the PV-MF170EB3 module by Mitsubishi Electric atvarious irradiances. ........................................................................................................... 6

Figure 5.10 The functionsu and v used in the control algorithm. ................................ 67

Figure 5.11 The PV system power, voltage, and current time response under the STCwhere the irradiance is 1000W/m2. ................................................................................... 69

Figure 5.12 The PV system power, voltage, and current time response under irradiancechange from 1000W/m2 to 400W/m2. ............................................................................... 71

Figure 5.13 The PV system power, voltage, and current time response under irradiancechange from 1000W/m2 to 600W/m2 at 0.5s and back to 1000W/m2 at 1s. ..................... 72


18/146

xvi

Figure 5.14 Experimental setup for the PV system, DC/DC converter, and MPPTcontroller. .......................................................................................................................... 7

Figure 5.15 Diagram for the hardware arrangement. ....................................................... 7

Figure 5.16 Power under steady state condition. ............................................................. 7

Figure 5.17 Power under varying weather conditions. .................................................... 7

Figure 6.1 BP585 PV array (installed on the roof of the Electrical EngineeringLaboratory at the Rovira i Virgili University in Tarragona, Spain). ................................ 76

Figure 6.2 I-V characteristic for the BP585 modules at various irradiances. .................. 78

Figure 6.3 P-V characteristic for the BP585 modules at various irradiances. ................. 78

Figure 6.4 PV output power time response using the proposed MPP tracking controlassuming steady state under STC for the BP585 modules. .............................................. 8

Figure 6.5 PV output power time response using the proposed MPP tracking controlunder irradiance change from 1000 W/m2 to 400 W/m2 for the BP585 modules. ............ 81

Figure 6.6 Hardware arrangement for the entire PV system. .......................................... 8

Figure 6.7 Experimental results for the BP585 modules from lower to higher irradiance............................................................................................................................................ 8

Figure 6.8 Experimental results for the BP585 modules from higher to lower irradiance............................................................................................................................................ 8

Figure 6.9 Experimental results for the BP585 modules from lower to higher irradianceafter modifying the control algorithm for improved transition. ........................................ 8

Figure 6.10 Experimental results for the BP585 modules from higher to lower irradianceafter modifying the control algorithm for improved transition. ........................................ 8

Figure 7.1 Diagram of a PV systems major components with electrical and mechanicalMPPT. ............................................................................................................................... 8

Figure 7.2 Variation of Sun path during the day and seasons. ........................................ 8

Figure 7.3 Diagram of PV system Azimuth angle () and Elevation angle (). .............. 88 Figure 7.4 PV system, DC/DC converter, electrical MPPT, and the proposed mechanicalsliding mode controller. .................................................................................................... 9


19/146

xvii

Figure 7.5 P-V characteristic for the PV-MF170EB3 module by Mitsubishi Electric atvarious irradiances. ........................................................................................................... 9

Figure 7.6 PV-MF170EB3 module power profile with respect to the azimuth angle. .... 9

Figure 7.7 PV-MF170EB3 module power profile with respect to the elevation angle. .. 9

Figure 7.8 Power output for various combinations of azimuth and elevation angles. ..... 9

Figure 7.9 Block diagram for the controller design for both Azimuth and Elevationangles. ............................................................................................................................... 9

Figure 7.10 The functionsu andv used in the control algorithm. ................................... 96

Figure 7.11 View of the PV module tracker mechanism (vertical and horizontal). ........ 9

Figure 7.12 Power, optimization variable (azimuth angle:), and control inputu. ...... 101

Figure 7.13 Power, optimization variable (elevation angle:), and control inputu. .... 102

Figure B.1 PV system, DC/DC converter, and the MPPT controller. ........................... 12

Figure B.2 The MPPT controller. .................................................................................. 12


20/146


21/146

2

laboratories around the world are rapidly accelerating PV systems manufacturing

capacity and commercialization.

1.2. How PV Cells Work

Figure 1.1 shows that when a p-n junction is exposed to sunlight, the electrons

flow from the n-side contact, through the load, and back to the p-side, where they

recombine with holes. This forms a voltage that can be exploited to deliver current to

load.

P-type

N-type

Electrical Contacts

Bottom Contacts

Photons

Load

-

V

+

Electrons

I

Figure 1.1 Principle of the operation of PV cell [2].

PV cells are made of special materials called semiconductors. Semiconductors are

useful because their performance can be manipulated by the addition of impurity

elements. This process is known as doping. Silicon is the most commonly used

semiconductor material to create a p-n junction. It is the same material used for diode


22/146

3

and transistors, and it is extremely plentiful; it is the second most abundant element afte

oxygen. Silicon makes up 27.7 percent of the Earths crust.

A PV cell can be considered as a constant current source as well as a voltage

source, which makes it a nonlinear device. The current and voltage output characteristic

of a PV device are shown in Figure 1.2. Depending on the movement of the operatin

point along the curve, the current and voltage can be changed electrically to the desire

value.

Figure 1.2 I-V and P-V characteristics for a PV device.

When the current is zero (no load), the voltage is called the open-circuit voltage

VOC. As the current increases due to a decreasing load resistance, the voltage decrease


23/146

4

slightly, up to the maximum power point (MPP), which occurs at the knee of the I-V

curve. At MPP, the voltage is called VMP and the current is called IMP. Next, the voltage

drops significantly with a slight increase in current, where the current is approaching

load resistance of zero (short-circuit). The voltage is zero in this case and the current i

called the short-circuit current ISC.

The P-V curve is complementary to the I-V curve. The power available from a PV

device at any operating point along the I-V curve is the product of the current and voltage

at that point and is expressed in watts. To operate at any particular operating point of th

I-V curve, one needs to connect the necessary load resistance to the PV device. Howeverwhen a PV system is directly connected to a load, the operating point is seldom the MPP

A power converter (power conditioning and control) is needed to adjust the energy flow

from the PV to the load or battery. This type of power conditioning and control is calle

maximum power point tracking (MPPT).

Figure 1.3 Equivalent circuit model for a PV cell.


24/146

5

Figure 1.3 shows the equivalent electrical circuit model to describe the behavior

of a PV cell. The current source is used to represent the incident solar irradiance and th

diode to characterize the polarization phenomena. The ideal PV model in the figure doe

not include the parasitic resistances accounting for the cells power loss. This model i

named the single-diode model and it is the traditional PV model known as the five

parameter model. R p is introduced in the practical model to absorb the leakage around the

edge of the PV cell. R s involves the contact resistance between the metallic contacts,

semiconductors, and the resistance of the semiconductor material of the PV cell. In

Figure 1.3, I ph is the photovolatic current, Id is the diode current, I is the PV device outputcurrent, and V is the PV device output voltage.

1.3 PV Cells, Modules, and Arrays

A PV system directly converts sunlight into electricity, and the basic device of a

PV system is the PV cell [3]. The basic building block for PV systems is a PV module

consisting of a number of pre-wired cells in series. Modules are then connected in serie

to increase the voltage and in parallel to increase the current; the product is power. A PV

array is formed by series and parallel combinations of PV modules [4]. The power

available at the terminal of a PV system can provide electricity to either small loads, suc

as calculators, or utility-scale PV systems in the range of 10 MW and more. Figure 1.4

shows a generic PV array structure.


25/146

6

Figure 1.4 Generic PV array structure.

For modules connected in series to create higher voltage, at any current value the

voltages are additive, while the current remains constant as suggested in Figure 1.5

whereas, for modules connected in parallel, the voltage remains constant, and the curren

increases as suggested in Figure 1.6. Depending on the application, several PV module

are interconnected in order to achieve higher power. The performance of a PV system i

normally evaluated under the standard test condition (STC), which uses an average sola

spectrum (air mass) at AM1.5, an irradiance (solar intensity) normalized to 1000 W/m2,

and a cell temperature of 25C [5]. Special testing equipment is needed to guarantee the

requirement of temperature and irradiance for STC. Under real-world operating

conditions that involve varying irradiances as well as significant temperature changes, a

commercial modules behave quite differently according to the location, time of the day

and season of the year [6].


26/146


27/146

8

1.4 Types of PV Systems

PV systems can be categorized as:

1) Stand-alone PV systems.

2) Hybrid PV systems.

3) Grid-connected PV systems.

1.4.1 Stand-alone PV Systems

A stand-alone PV system is connected to the DC loads via a power conditioning

unit and to the AC loads through an inverter. An energy storage system such as a batter

bank is essential in this kind of configuration since this type of PV system has no accesto a utility grid. The storage system ensures energy storage when an excess is availabl

and provides it when it is required. Figure 1.7 shows a diagram of a stand-alone PV

system, with battery storage, that powers both DC and AC loads.

Figure 1.7 Stand-alone PV system.


28/146

9

1.4.2 Hybrid PV Systems

Remote area users distant from utility grids often depend on diesel generators,

operating continuously, or for designated hours, to supply power. Hybrid PV systems

combine multiple sources of energy with storage technology to create power generatio

systems. The components of this type of system are usually a PV array with energy

storage (typically a rechargeable battery) and a fossil fuel engine generator (Genset). Th

genset could be replaced by a natural gas genset or any renewable energy source such a

wind turbines. Applications for hybrid PV systems range from supplying power to remot

huts for lighting and essential electrical appliances, to village electrification for remotcommunities. See Figure 1.8.

PV System

Storage System

PowerConditioning

DCLoad

Rectifier Inverter

ACLoad

Figure 1.8 Hybrid PV system.


29/146

10

1.4.3 Grid-connected PV Systems

Figure 1.9 depicts the main components associated with grid-connected PV

systems. The inverters in these types of PV systems must be synchronized with the grid

voltage and frequency to safely operate. The PV grid-connected systems may be withou

batteries; however, for optimum reliability the system could include battery storage as

backup. A standby backup can deal with lasting power failures due to tornadoes

hurricanes, ice storms, or any other serious weather conditions. Surplus energy from th

PV array can be injected into the grid for net metering and renewable energy incentives

Many utility companies are encouraging this scheme in many parts of the world. It iworth mentioning that the interaction between the customer and the utility company

should encompass a predefined agreement detailing safety standards to be followed

during the connection.

Figure 1.9 Grid-connected PV system.


30/146

11

1.5 Advantages and Disadvantages of PV Systems

As more consumers take advantage of renewables (green energy) in general, and

PV systems in particular, the question of whether PV systems are worth the investmen

needs to be answered. According to the latest PV best research cell efficiencies char

from the National Center for Photovoltaics at the National Renewable Energy Laborator

(NREL), the best efficiency on record is around 44 percent. The chart demonstrates th

best laboratory efficiencies obtained for various technologies on small cells; large

commercial PV systems efficiencies are certainly significantly lower. See Figure 1.10.

Figure 1.10 Timeline of PV cell energy conversion efficiencies [7].


31/146

12

1.5.1 Advantages of PV Systems

PV energy is one of the most abundant, non-polluting, and renewable

source of energy.

PV modules have long lifespans (up to 25 years).

PV systems do not incorporate moving mechanical parts and involve no

disturbance.

The operating and maintenance cost is relatively low.

PV systems are cost effective for electrification in remote areas where the

cost of grid extension is prohibitive. When connected with the grid, PV system power can uniquely contribute

to the peak energy demand since PV system peak power generation

coincides with the peak demand during daytime and summer season.

PV system price and deployment, along with the installation apparatuses,

have undergone a remarkable decrease in the recent past. This is due to

support from governments in the form of tax incentives.

1.5.2 Disadvantages of PV Systems

Although the technology is improving to enhance PV system efficiency

and performance, the efficiency levels are still relatively limited compared

to other renewable energy sources.

PV systems are extremely dependent on weather conditions. In addition, afluctuation in environmental conditions, such as cloud shading,

considerably affects the PV efficiency.


32/146

13

CHAPTER 2: LITERATURE REVIEW

The traditional central power stations with their pollution-related problems will

likely be replaced with cleaner and smaller power plants closer to the loads. These

smaller power plants may be realized via microgrid power systems. A microgrid powe

system can be defined as a system with at least one distributed generation source and

associated loads with storage systems, and should be able to operate in grid-connectedand island modes [8][12]. Microgrids integrate renewable resources on the distributio

level close to customers. Utilizing microgrids includes, but is not limited to, service

reliability improvement, more environmentally friendly impact with a lower carbon

footprint, and more manageable energy generation and distribution [13][15]. The

advancement in PV module manufacturing technology, as well as the relative increase i

their efficiency, has resulted in a recent growing demand for PV power generator

installations [16].

2.1 Problem Definition

PV models in the literature comprise a set of transcendental equations that add

complexity to PV system modeling and simulation. Therefore, the growing demand fo

PV modules requires the development of reliable and simplified methods for accurat

extraction of the modules internal parameters. Not all the modules parameters are

provided by the manufacturers; thus, evaluation of these parameters with robus


33/146

14

estimation methods is crucial in order to predict the behavior of any PV module

accurately. This work attempts to introduce a comprehensive and improved methodolog

for PV system characterization, relying only on the values given by the manufacturer

The methodology serves as a complete guide for PV system designers and researchers fo

the prediction of PV system performance over a wide range of temperatures and sola

irradiances.

In addition, a design strategy for control of the power generated by PV systems is

investigated for effective energy extraction. Most of the existing conventional contro

methods currently used to track the PV MPP have drawbacks. These drawbacks may bsummarized as:

a) Slow convergence to the MPP because of periodic measuring and evaluating of

gradient.

b) Oscillation around the MPP, which causes power losses and low accuracy.

c) When irradiance changes quickly, the operating point moves away from the

MPP on cloudy days.

2.2 Relevant Research

Over the years, studies in the literature have presented many PV system models

that describe the behavior of the PV system. Some models are mathematical model

based on the theoretical equations that describe the function of the PV system usin

equivalent circuits [17][22]. Other models are empirically based and acquire thei

accuracy from the fact that the individual equations used in the models are derived from


34/146

15

individual PV system characteristics, such as the one developed at the Sandia Nationa

Laboratories [23].

Figure 2.1 PV module equivalent circuit (a) ideal model, (b) single-diode model, (c)double-diode model.

Figure 2.1 (a)(c) shows the equivalent electrical circuit models that describe the

behavior of a PV module. The current source is used to represent the incident sola

irradiance and the diode to characterize the polarization phenomena. The ideal model iFigure 2.1 (a) does not include the parasitic resistances accounting for the cells powe

loss. The single-diode model in Figure 2.1 (b) is the traditional PV model known as th


35/146


36/146


37/146

18

Maximum power point ( MPP ): the point at the knee of the I-V curve and the

maximum of the P-V curve. See Figure 2.3.

Figure 2.2 A typical I-V curve for a PV module.

Figure 2.3 A typical P-V curve for a PV module.


38/146

19

On the other hand, concerning controlling the power generated by PV systems

the literature covering MPP tracking is extremely wide. Esram et al. [24] provided a

review paper that contains most MPP tracking techniques with a comprehensive

comparative analysis.

Most conventional methods listed in the paper depend on searching for MPP

based on periodic measuring, or estimating the gradient. The sliding mode contro

approach mentioned in this paper is implemented assuming that the exact model i

available and the gradient is estimated, which is not realistic in some applications. Som

authors proposed modified approaches of the existing conventional controlling methodFor example, the authors in [25] introduced a modified hill-climbing method to track th

MPP using a fuzzy-logic controller, while [26] presented an improved particle swarm

optimization (PSO) technique. The existing control methods will be revisited in greate

detail in chapter 5.

2.3 Summary

The nonlinear nature of the PV system output makes the modeling of PV systems a

challenge. Dealing with transcendental equations during the modeling and simulatio

processes of PV systems adds further complexity to the process. These issues associate

with modeling the PV systems and the shortcomings related to controlling the outpu

power extracted from the PV are addressed in the subsequent chapters.


39/146

20

CHAPTER 3: MODELING AND PARAMETER EXTRACTION OF PV SYSTEMS

This chapter presents a novel approach for accurate extraction of the PV module

single-diode models five parameters: I ph, I sat , A, R s, and R p. The parameter extraction

approach is simplified and unlike the approaches described in the previous literature, i

does not require any complex calculations or tedious combination of other equations textract the five parameters for a PV module. The presented methodology is also extende

to design a PV array. The PV module behavior at different temperature and irradiance i

also forecasted.

3.1 PV System Parameter Extraction

Consider the five-parameters model equation in Eq.(2.2). Unfortunately, this

model equation is, by itself, a transcendental equation for which there is no explici

solution for either the voltage, or the current of the PV module. To find a solution to

transcendental equation, one must use graphical or numerical methods. Transcendenta

equations also require the use of implicit differentiation, which makes it harder to

perform this type of modeling task. Thus, the following modeling procedures are

developed.

Eq.(2.2) will be evaluated at the short-circuit operating point whenV = 0 . This

leads to the following:


40/146

21

1 sc s

s T

I R N AV sc s

sc ph sat p

I R I I I e

R

(3.1)

At the open-circuit operating point,V = V oc and I = 0 .

0 1oc

s T

V N AV oc

ph sat p

V I I e

R

(3.2)

At the MPP,V = V mp and I = I mp.

1mp mp s

s T

V I R

mp mp s N AV mp ph sat

p

V I R I I I e

R

(3.3)

At the MPP, the derivative of the power with respect to voltage is zero as depicted in

Figure 3.1. Using this fact, it can be deduced that

0mp

mp

V I

d IV dP dP dV dI dI I V I V

dV dV dV dV dV dV (3.4)


41/146

22

0dP

dV

Figure 3.1 A typical P-V curve for a PV module.

So far, we have four equations but we have five unknowns, viz.,, , , ph sat s I I A R

and p R . The fifth equation can be obtained by using the widely accepted assumption tha

sc ph I I . This approximation is based on the fact that for a good PV module, p R is very

high and s R is very low. See Eq.(3.5).

1 sc s

s T sc

I R N A

ph sV sc

ph s

a p

t I I e R I

I R I

(3.5)


42/146

23

3.1.1 Parameter-extraction Procedures

The parameter-extraction process takes into consideration the equivalent circuit o

the model and identifies all of its parameters [27]. Eqs.(3.1)(3.5) are used to extract th

unknown but key parameters, , , , ph sat s I I A R and p R . These unknowns are not given by

the module data sheet. Eq.(3.4), which involves lengthy differentiation, is simplified an

prearranged using the MATLAB Symbolic Math toolbox. All the unknowns are then

found using an optimization algorithm that solves the system of nonlinear equations wit

the fsolve command.

3.1.2 Initialization

When nonlinear optimization algorithms are used to solve mathematical programs

initialization is often required [28]. The determination of the initial values for the fiv

unknowns is not a trivial task because inappropriate selection of initial values will resul

in non-convergence of the algorithm. Thus, an educated guess for the initial value

should be as follows:

For ph I , it has been shown in [29] that this can be approximated as sc I .

For sat I , the initial guess is made using the nominal saturation current as

demonstrated by [3] using the following equation1

oc

s T

sc sat V

N AV

I I

e

.


43/146

24

For A , the diode ideality factor, many authors have discussed ways to choose

the correct value for this constant, but it is usually selected as (1 A 1.5)

according to [3], [30], and [31]

For R p, the initial value may be found by evaluating the model at the open-

circuit condition in Eq.(3.2).

For R s, the initial value can be obtained by using Eq.(3.1) and assuming the

parallel resistance is very high.

3.1.3 Optimization Algorithm

Three optimization algorithms have been chosen for their robustness and iterative

efficiency. The first one is trust-region-dogleg because it is one of few algorithms that i

designed to solve nonlinear equations [32][33]. The MATLAB script subsequently

works with other algorithms in order to find an algorithm that works best for the problem

Once the function converges to a solution, it produces the values for the five unknowns

A detailed flowchart for this parameter-extraction method is depicted in Figure 3.2.


44/146


45/146

26

3.1.4 Case Study

A case study is carried out to find the five parameters of the BP MSX60 PV

module [34]. The five parameters are extracted using the electrical specifications

provided by the data sheet information. The parameter extraction is conducted for th

electrical performance under the STC. This method could be extended under varying

environmental conditions by using the temperature coefficients of the open-circui

voltage and short-circuit current included in the data sheet.

Table 3.1 BP MSX60 PV Module Electrical Characteristics under STC ( 25C, AM 1.5,and 1000 W/m2).

Data SheetSpecification

ExtractedParameters

I sc 3.8 A I ph 3.8021 AV oc 21.1 V I sat 10.22e-8 A I mp 3.5 A A 1.3094V mp 17.1 V R s 0.214 N s 36 R p 389.02

Table 3.1 lists the values from the data sheet and the extracted parameters using

the procedure presented in sections 3.1.1 through 3.1.3. The results show that sc ph I I as

explained in the previous sections. Additionally, the value of the series resistance is smal

and the parallel resistance is large for a good PV device. The diode ideality factor A is inthe expected range 1 A 1.5. This method is clearly more convenient and accurate

because it does not involve model adjustment at the three operating points, as in [3]. I


46/146

27

addition, using this method, one does not depend on the experimental I-V curve to

determine R p. Figure 3.3 and Figure 3.4 show that the I-V and P-V characteristics match

the experimental data for the BP MSX60 PV module.

Figure 3.3 I-V characteristic for the BP MSX60 PV module under STC.

Figure 3.4 P-V characteristic for the BP MSX60 PV module under STC.

0 5 10 15 20 250

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Voltage (V)

C u r r e n t

( A )

Proposed MethodExp. Data

0 5 10 15 20 250

10

20

30

40

50

60

70

Voltage (V)

P o w e r

( W )



47/146

28

Figure 3.3 and Figure 3.4 are plotted using MATLAB, solving Eq.(2.2) for the

intervals I [0 , I sc] andV [0 , V oc]. As mentioned earlier, Eq.(2.2) is a transcendental

equation and does not have a direct solution because I = f (V, I) and V = f (V, I) . A

numerical solution is acquired by solving the function g (V, I) = I f (V, I) = 0 for a set

of V values and then obtaining the corresponding I values. In Figure 3.5, the absolute

errors of the model data with respect to the experimental data are calculated, showing th

difference between the measured and modeled data graphically.

Figure 3.5 Absolute errors of the model data with respect to the experimental data for thBP MSX60 PV module under STC.

The validity of the proposed method was tested further by comparing it on the

KC200GT PV module with the technique proposed by [3]. Figure 3.6 and Figure 3.

illustrate an excellent matching between the model data using the method in this stud

and the experimental data. The extracted parameters found by [3] were then compare

0 5 10 15 20 250

0.05

0.1

0.15

0.2

0.25

0.3

0.35

A b s o l u t e e r r o r

( A )

Voltage (V)

Absolute errors for BP MSX60 PV module


48/146

29

with the extracted parameters using the simplified proposed method in this research, an

they appear comparable. See Table 3.2.

Figure 3.6 I-V characteristic for the KC200GT PV module under STC.

Figure 3.7 P-V characteristic for the KC200GT PV module under STC.

0 5 10 15 20 25 30 350

1

2

3

4

5

6

7

8

9

Voltage (V)

C u r r e n t

( A )


0 5 10 15 20 25 30 350

20

40

60

80

100

120

140

160

180

200

Voltage (V)

P o w e r

( W )



49/146

30

Table 3.2 Comparison between values found in [3] and values found using the simplifiemethod proposed in this study for the KC200GT PV modules under STC.

Extracted ParametersProposed by [3]

Extracted ParametersUsing Proposed Method

I ph 8.214 A I ph 8.213 A I sat 9.825e-8 A I sat 10.89e-8 A A 1.3 A 1.3074 R s 0.221 R s 0.228 R p 415.405 R p 420.88

Figure 3.8 PV array equivalent circuit.

N s: cells connected in series; N p: cells connected in parallel.

3.2 Modeling PV Array

PV modules are generally composed of cells connected in series. These modules

are connected in series to increase voltage and in parallel to increase current. It is

important in PV system design to decide how many modules should be connected in

series and in parallel in order to deliver the energy required. Figure 3.8 shows the


50/146

31

equivalent circuit for a PV array arranged in N p for the number of modules connected in

parallel and N s for the number of modules connected in series. The modified single-diode

mathematical equation for the arrays current and voltage becomes

1

1 s

T s p

IRV AV N N p s

p ph p sat p s p

N IRV I N I N I e

R N N

(3.6)

According to [35], N p I ph corresponds to the short-circuit current of the PV array.

Assuming matching characteristics of each module under identical operating conditions

as demonstrated in [36], the array parameters can be estimated from data sheet values o

the module and from the number of modules in series-parallel combination in the array. I

the number of modules connected in series in a string is N ss and the number of strings

connected in parallel to form an array is N pp, then the specifications of the array will be as

shown in Table 3.3. Utilizing Table 3.3 and the data sheet information given in Table 3.1

one can find the five parameters for an array using the method introduced earlier in

section 3.1. Hence, Figure 3.9 and Figure 3.10 can be generated to represent the I-V and

P-V characteristics for PV array, assuming N ss=15 and N pp=2 . The modules used to

design this array are the BP MSX60 PV modules. The series connection increased th

open-circuit voltage by a factor of fifteen, whereas the parallel connection increased th

short-circuit current by a factor of two.


51/146

32

Table 3.3 Array data sheet related to modules.

Module Data SheetValues

Equivalent ArrayValues

I sc I sc N pp V oc V oc N ss I mp I mp N pp V mp V mp N ss N s N s N ss I ph I ph N pp I sat I sat N A A R s R s N ss / N R p R p N ss / N pp

Figure 3.9 I-V characteristic for the designed PV array under STC.

0 50 100 150 200 250 300 3500

1

2

3

4

5

6

7

8

9

Voltage (V)

C u r r e n t

( A )

Designed PV array15 modules in series02 strings in parallelModule: BP MSX60


52/146

33

Figure 3.10 P-V characteristic for the designed PV array under STC.

3.3 Temperature Impact

In a PV system, high operating temperatures significantly reduce the voltage

output. On the other hand, the current increases with the temperature, but only slightly, s

the net result is a decrease in power and efficiency [37]. Exposing the modules to high

temperatures for prolonged periods may cause early degradation of the module

encapsulation. PV systems generally perform the best on normal, clear days rather tha

hot days. Prediction of the PV module I-V and P-V characteristics for temperatures other

than under the STC requires one to have the temperature coefficients (K V and K I) from

the data sheet for the module being used. A change in temperature has an effect on th

performance of the PV module according to the following equations [2][3], and [37]

[38],

0 50 100 150 200 250 300 3500

200

400

600

800

1000

1200

1400

1600

1800

2000

Voltage (V)

P o w e r

( W )

Designed PV array15 modules in series02 strings in parallelModule: BP MSX60


53/146

34

oc oc stc v stcV T V T K T T (3.7)

mp mp stc V stcV T V T K T T (3.8)

1 sc sc stc I stc I T I T K T T (3.9)

1mp mp stc I stc I T I T K T T (3.10)

Figure 3.11 and Figure 3.12 demonstrate the results of the generated curves atdifferent temperature values related to the data under STC (T = 25 C). K I and K V are

obtained from the BP MSX60 PV module data sheet as (0.065 0.015) %/C and (80

10) mV/oC, respectively. See Appendix A.

Figure 3.11 I-V characteristics for the BP MSX60 PV module at various temperatures.Modeled (line) and experimental (circles).

0 5 10 15 20 250

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Voltage (V)

C u r r e n t

( A )

25 C50 C

75 C

0 C


54/146

35

Figure 3.12 P-V characteristics for the BP MSX60 PV module at various temperatures.Modeled (line) and experimental (circles).

The curves representing current and voltage for a PV module with temperature ar

in reality three-dimensional curves. For this reason, a MATLAB script is developed to

create a surface fit for the BP MSX60 module data ( I-V-T ). The nearest neighbor

interpolation method for the module curves with four temperature values can be used t

create a surface that not only represents the points with the four different temperature

but also characterizes all the points in between. See Figure 3.13.

0 5 10 15 20 250

10

20

30

40

50

60

70

Voltage (V)

P o w e r

( W )

25 C

50 C

75 C

0 C


55/146

36

Figure 3.13 I-V-T three-dimensional characteristic for the BP MSX60 PV module undervarious temperatures and constant level of irradiance at 1000 W/m2.

3.4 Irradiance Impact

It is documented [2] that short-circuit current I sc is directly proportional to the

solar insolationG (irradiance). This relationship may be described as

sc sc stc stc

G I G I G

G

(3.11)

Cutting the irradianceG in half, for instance, leads to a drop in I sc by half.

Decreasing irradiance also reducesV oc, but it does so following a logarithmic relationship

that results in a relatively modest change ofV oc. This relationship may be described as

/oc oc stc s T stcV G V G N V ln G G (3.12)

05

1015

200

20

40

600

1

2

3

4

Temperature(C)Voltage(V)

C u r r e n t

( A )


56/146

37

Figure 3.14 and Figure 3.15 depict the results of the generated curves at differen

irradiance values related to the data under STC (1000 W/m2). Figure 3.16 also provides a

better view of the significant decrease of the PV module MPP when the current decrease

because of the irradiance drop.

Figure 3.14 I-V characteristics for the BP MSX60 PV module at various irradiances.

Figure 3.15 P-V characteristics for the BP MSX60 PV module at various irradiances.

0 5 10 15 20 250

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Voltage (V)

C u r r e n t

( A )

1000 W/m 2

800 W/m 2

600 W/m 2

400 W/m 2

200 W/m 2

0 5 10 15 20 250

10

20

30

40

50

60

70

Voltage (V)

P o w e r

( W )

1000 W/m 2

800 W/m 2

600 W/m 2

400 W/m 2

200 W/m 2


57/146

38

Figure 3.16 Predicted P-I-V characteristics for the BP MSX60 PV module under variousirradiances and constant temperature 25 C.

05

1015

2025

01

23

4

0

10

20

30

40

50

60

Voltage (V)

200 W/m 2

400 W/m 2

600 W/m 2

800 W/m 2

1000 W/m 2

Current (A)

P o w e r

( W )

MPP


58/146

39

CHAPTER 4: ANALYTICAL MODEL FOR PV SYSTEMS

Modeling in engineering is basically an appropriate simplification of reality to

solve physical problems. It can be divided into two essential parts: physical/empirica

modeling and theoretical/analytical modeling. Analytical expressions of the I-V curve for

a PV system can be derived from the PV module described in section 2.2. Among thmodels reported in literature is the analytical model in [39]. The equation in this

analytical model is altered so that the computer can derive its own curve-fitting constant

from the experimental PV system test data input.

4.1 PV System Analytical Model

The analytical model given in [39] is represented by the equation:

121 1oc

V C V

sc I I C e

(4.1)

where

11 2

11

1

mpoc

mpV

mpoc C V

mp sc

sc

V

I V C C e I I

ln I

(4.2)


59/146

40

The model equation given by Eq.(4.1) can be utilized to model only the PV

module, which is generally the building block of any PV system. PV modules are

commercially available with data sheet information that has typical electrical

characteristics such as:

Maximum power (Pmax)

Maximum power voltage (Vmp)

Maximum power current (Imp)

Open-circuit voltage (Voc)

Short-circuit current (Isc) Temperature coefficient of the open-circuit voltage (K V)

Temperature coefficient of the short-circuit current (K I)

4.2 Analytical Model Extension

The above electrical parameters are given for STC and can be used to generate the

current-voltage ( I-V ) and power-voltage ( P-V ) characteristics of any PV module under

STC. A general model that can describe the characteristics of a PV array is essential

Consequently, Eq.(4.1) can be extended to represent a PV array with N ss modules

connected in series and N pp modules connected in parallel by the following modification:

121 1 ss oc

V N C V

pp sc I N I C e

(4.3)


60/146


61/146


62/146

43

4.4 PV Module Analytical Model Results

A simulation was carried out employing the proposed comprehensive approach

characterizing any PV system using Eq.(4.1) and Eq.(4.2). The module selected for th

simulation is the BP MSX60 solar module [34], with the module data sheet specification

listed in Table 4.1. Figure 4.1 and Figure 4.2 show that the model matches well with th

experimental data obtained from the manufacturer. In Figure 4.3, the absolute error of th

model with respect to the experimental data is calculated, and it shows the difference

between the measured and the modeled data. Using the temperature coefficient of th

short-circuit current and the temperature coefficient of the open-circuit voltage given inthe data sheet, Figure 4.4 and Figure 4.5 illustrate the generated curves at differen

temperature values (0oC, 50oC, and 75oC) related to the temperature under STC

(T=25oC).

Table 4.1 BP MSX60 PV Module Electrical Characteristics under STC.Data sheet Specification I sc 3.8AV oc 21.1V I mp 3.5AV m 17.1V N s 36

Temp. Coeff. of I sc , K I (0.0650.015)%/CTemp. Coeff. of V oc , K V -(8010)mV/C


63/146


64/146

45

Figure 4.3 Absolute errors of the modeled data with respect to the experimental data fothe BP MSX60 PV module under STC.

Figure 4.4 Modeled (line) and experimental (circles) I-V characteristics for the BPMSX60 PV module at various temperatures.

0 5 10 15 20 250

0.05

0.1

0.15

0.2

0.25

0.3

0.35

A b s o l u t e e r r o r ( A

)

Voltage(V)

Absolute errors for BP MSX60 PV module

0 5 10 15 20 250

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Voltage(V)

C u r r e n t

( A )

25 C50 C

75 C

0 C


65/146

46

Figure 4.5 Modeled (line) and experimental (circles) P-V characteristics for the BPMSX60 PV module at various temperatures.

A MATLAB script is used to create a surface fit for the BP MSX60 solar module

experimental data ( I,V,T ). The curves representing current and voltage for any PV

cell/module/array with temperature are, in reality, three-dimensional curves. As shown in

Figure 4.6, a cubic interpolation for the I-V curves with four temperature values creates a

surface and mathematical equation that represent not only the points with the fou

different temperature values, but also all points in between.

0 5 10 15 20 250

10

20

30

40

50

60

70

Voltage(V)

P o w e r

( W )

25 C

50 C

75 C

0 C


66/146

47

Figure 4.6 I-V-T three dimensional characteristic for BP MSX60 PV module at varioustemperatures and constant level of irradiance 1000W/m2.

Forecasting the PV module behavior at irradianceG other than the STC

(1000W/m2) requires that the temperature be fixed at 25C and the set of equations in

Eq.(4.84.11) can then be used to generate the curves representing the I-V and P-V

characteristics of the PV module at different irradiance levels. Figure 4.7 and Figure 4.

demonstrate the irradiance impact on the I-V and P-V characteristics of the selected

module. The generated curves are plotted at different irradiance values (800W/m2,

600W/m2, 400W/m2, and 200W/m2) related to the irradiance under STC (G=1000W/m2).

As mentioned earlier, decreasing irradiance reducesV oc, but it does so following a

logarithmic relationship that results in a relatively modest change ofV oc. Figure 4.9

shows that the PV module maximum power point is decreasing significantly when

current decreases due to the irradiance drop.

05

1015

200

20

40

600

1

2

3

4

Temperature(C)Voltage(V)

C u r r e n t

( A )


67/146

48

Figure 4.7 Predicted I-V characteristics for the BP MSX60 PV module at variousirradiances.

Figure 4.8 Predicted P-V characteristics for the BP MSX60 PV module at variousirradiances.

0 5 10 15 20 250

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Voltage(V)

C u r r e n t

( A )

1000 W/m 2

800 W/m 2

600 W/m 2

400 W/m 2

200 W/m 2

0 5 10 15 20 250

10

20

30

40

50

60

70

Voltage(V)

P o w e r

( W )

1000 W/m 2

800 W/m 2

600 W/m 2

400 W/m 2

200 W/m 2


68/146


69/146

50

Table 4.2 PV-MF170EB3 PV Module Electrical Characteristics under STC.

Data sheet Specification I sc 7.38AV oc 30.7V I

mp 6.93A

V mp 24.6V N s 50

Temp. Coeff. of I sc , K I 0.057%/CTemp. Coeff. of V oc , K V -0.346%/C

Figure 4.10 Predicted P-V characteristic for the designed PV array under STC.

The temperature and irradiance impact on the designed PV array can be evaluated

by applying the approach with the BP MSX60 solar module, considering the new dat

sheet specification in Table 4.2 for the module PV-MF170EB3. Figure 4.11 and Figure

4.12 represent the forecasted temperature effect on the designed PV array with the

generated curves at different temperature values (0oC, 50oC, 75oC, and 100oC) related to

the temperature under STC (T=25oC). In Figure 4.13 and Figure 4.14, the irradiance

0 50 100 150 200 250 300 350 400 450 5000

2000

4000

6000

8000

10000

12000

14000

16000

18000

Voltage(V)

P o w e r

( W )

Designed Array15 Modules in series 7 Strings in parallelModule: MF170EB3 170 Watt


70/146

51

effect is also shown for irradiance change (800W/m2, 600W/m2, 400W/m2, and

200W/m2) related to the irradiance under STC (G=1000W/m2) for the designed PV array.

Figure 4.11 Predicted I-V characteristics for the designed PV array at varioustemperatures.

Figure 4.12 Predicted P-V characteristics for the designed PV array at varioustemperatures.

0 50 100 150 200 250 300 350 400 450 5000

5

10

15

20

25

30

35

40

45

50

55

Voltage(V)

C u r r e n t

( A ) 25 C

50 C

75 C

100 C

0 C

0 50 100 150 200 250 300 350 400 450 5000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

4

Voltage(V)

P o w e r

( W )

25 C

50 C

75 C

100 C

0 C


71/146

52

Figure 4.13 Predicted I-V characteristics for the designed PV array at various irradiances.

Figure 4.14 Predicted P-V characteristics for the designed PV array at variousirradiances.

0 50 100 150 200 250 300 350 400 450 5000

10

20

30

40

50

60

Voltage(V)

C u r r e n t

( A )

1000 W/m 2

800 W/m 2

600 W/m 2

400 W/m 2

200 W/m 2

0 50 100 150 200 250 300 350 400 450 5000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

4

Voltage(V)

P o w e r

( W )

1000 W/m 2

800 W/m 2

600 W/m 2

400 W/m 2

200 W/m 2


72/146

53

CHAPTER 5: PV SYSTEM POWER GENERATION CONTROL

PV systems generally suffer from low efficiency and high cost. To deal with this

shortcoming, a maximum power point tracking system, frequently referred to as MPPT

is used electronically in a manner that steers the PV modules operating point to delive

the maximum available power. Generally, when a PV system is connected directly to aDC load that needs power, the PV system must be oversized to guarantee a sufficien

power supply. This obviously leads to an oversized and expensive PV system. Thus, th

PV system needs to be operated continuously at its MPP for optimum operation.

5.1 PV System Power Control

Figure 5.1 depicts a diagram of a PV system, DC/DC converter, and MPPT to

track the MPP of the PV system using controlling algorithm. Depending on the

application, the DC/DC converter could be a buck converter, boost converter, or buck

boost converter. Several control algorithms have been proposed in different publications

Perturbation and observation (P&O) or the hill climbing algorithm is the most commonl

employed method in commercial PV MPPTs [41][45]. Among other control

mechanisms for MPPT are fuzzy logic [46][50], neural network [51][55], and

incremental conductance (IncCond) control [56][60].


73/146

54

Figure 5.1 PV system, DC/DC converter, and MPPT control.

Figure 5.2 Optimization system.

5.2 Self-optimization Sliding Mode Control

Generally, an optimization system consists of a controller generating a control

inputu, which is fed to an integrator. Figure 5.2 shows the block diagram for this systemThe problem is to organize a search that will minimize/maximize the plant output. As

result, the search proceeds, following the optimization procedure. The essence of th


74/146


75/146

56

where x and y are input and output signals, respectively, and the function, f x , is

differentiable and reaches a maximum at a certain unknown value of the inputo x and

0df dx with o x . A system should be developed in which the output y tracks some

monotonically increasing function and thus approaches the extremum.

The purpose of the control is to make the difference g t y t vanish,

where g t is a monotonically increasing function, as shown in Figure 5.3. The motion

within the system is described by the equations

Figure 5.3 Self-optimization control scheme.

1 2

,

, o

y f x g v

x u u u sign s s

(5.2)

where and ou are constant; ou > 0, > 0, 1 s , 2 s , is a small positive

value. The purpose of the additional signalv will be described below.


76/146

57

Normally, in relay systems, the control function is of the form ou u sign , but

the use of this type of control in our tracking system in one branch of the extremal-typ

curve will result in negative feedback and in the other, in positive feedback.

Consequently, in such systems (provided, of course, that the gradient value is not

measured), the quantity y to be optimized cannot follow the setpoint g t . Let us show

that this problem can be solved by using the control in Eq.(5.2), which is a relay functio

of two arguments. See Figure 5.4.

2 0 s 1 0 s

ou

ou

Figure 5.4 Control inputu.

Let the initial conditions be such that 1 2o o s t s t < 0 , and odf

u dx > . In

addition, assume for a moment that the functionv is equal to zero. Let us consider the


77/146

58

behavior of the system under these initial conditions by using the equation of motion wit

respect to the error coordinate; by virtue of Eq.(5.1) and Eq.(5.2), this equation is of th

form:

1 2odf

u sign s sdx

(5.3)

Since 1 2 s s under the initial conditions 1 2o o s t s t < 0 for one function,1 s

or 2 s , the inequality 1 1 s s < 0 or 2 2 s s < 0 holds. Therefore,1 s (or 2 s ) will change its sign

and so will the derivative1 s (or 2 s ). In other words, the inequality1 1 s s < 0 (or 2 2 s s < 0)

holds in the vicinity of the point1 0 s (or 2 0 s ).

Now, let us suppose that the conditionodf

udx

> is not valid only in a certain

vicinity of the extremum. Then, because either y g or y g , and since g is a

monotonically increasing function ( g < 0), the plant output will reach this vicinity ina finite time period. Now we select the signalv that, first, insures the initial conditions of

the type 1 2o o s t s t < 0 in the system and, second, makes it possible to stabilize the

system motion in the vicinity of the extremum.

The need to solving these problems is dictated by the fact that the above reasoning

applies only to initial conditions of the type 1 2o o s t s t < 0, and once the extremumvicinity is reached, there is no point in further decrease of g t . Therefore, the function

v should be selected with a magnitude large enough, positive with1 s < 0, 2 s < 0, negative


78/146

59

with 1 s > 0, 2 s > 0, and equal to zero with1 2 s s < 0. Then caused by the fast change of

the setpoint g , the initial conditions will appear in the system. Since the integrator

generating the setpoint g t is an element of the controller, this quantity may be varied

as fast as described and further discussion will assume that the initial conditions appear i

the system instantaneously.

A sliding mode starts in the system, which brings the system to the extremum

with v = 0. During this motion, either1 s or 2 s is of a constant sign, while the other is a

variable sign varying at a high frequency and magnitude. With the law for variation of th

function v and with the identity of the signs of1 s and 2 s during sliding mode,v is

nonzero, the function g at these times is not equal to , and the setpoint g t may be

found to be a function other than a monotonically increasing one. This phenomenon ma

be eliminated if the switches implementing the functionv have symmetrical hysteresis

loops whose width exceeds the double amplitude of oscillation of the1 s or 2 s in sliding

mode. In this case, the functionv is of the form

1 2

1 2

1 2

0 00 0

0 0

with ,

with

with , , ,

M s s

v s s

M s s M const M

(5.4)

where 2 is the width of the hysteresis loop in Figure 5.5. Over the interval1 s and

2 s , v maintains the value it had before the describing point reached the intervals.

With this way of generating the functionv , the describing point at the initial time is


79/146

60

tossed in the region 1 2 0 s s rather than into the region ; then, as before, the

sliding mode takes the plant output into a certain domain E containing an extremum, and

where the conditionodf

u dx > is not met, or odf

u dx < .

2 0 s 1 0 s

2

2

Figure 5.5 Relay control functionv.

The condition odf

udx

> identifies a domain where the rate of motion to the

extremum is constant. As for the motion in the domain E given by odf

udx

< , it can be

proved that once in this domain the quantity y to be optimized continues increasing in

oscillating mode with the rate of this motion gradually falling, and at the end of thetransient process the maximal value of y does not exceed min y .


80/146

61

Let us take up the motion of the system in the domain E . For v = 0, the equality

0odf

udx

holds. Consequently, no matter whether it was1 s or 2 s that was zero

during motion in sliding mode, the describing point reaches the point2 s in further

motion; then, according to Eq.(5.4), the signalv instantaneously restores the condition

2 s and oscillation starts. While moving in the interval 20 s , the plant output

y decreases with 0df dx

and increase with 0df dx

.

In summary, the method of solution described here relies on a system that is

capable of controlling a plant with sign-varying and unknown gain. The derivative of th

function to be maximized acts as this gain, steering the plant output to a maximal possibl

point.

5.3 Control Design Strategy

After detailing the essence of the sliding mode control method of self-optimizingone needs to design a strategy to apply the described control method to a PV system

Figure 5.6 shows the PV system, DC/DC boost converter, and the sliding mode controlle

for tracking the MPP of the PV system using the method of self-optimization. In Figur

5.7, the block diagram for the controller is given with the duty ratio as a control input an

the PV system output power as the plant output.


81/146

62

Figure 5.6 PV system, DC/DC boost converter, and the MPPT controller.

Figure 5.7 Block diagram for the controller design.


82/146

63

5.3.1 Adapted PV System

The PV system selected to realize the sliding mode controller is the PV-

MF170EB3 module by Mitsubishi Electric Corporation studied in chapter 4. Table 5.

provides the module electrical characteristics provided by the manufacturer data shee

and the internal parameters used for the simulation employing the modeling methodolog

presented in chapter 3. Figure 5.8 and Figure 5.9 illustrate the I-V and P-V profiles of the

selected PV module at different irradiances.

Table 5.1 Mitsubishi PV-MF170EB3 PV Module Electrical Characteristics under STC.

Data sheetSpecification

InternalParameters

I sc 7.38A I h 7.378AV oc 30.6V I sat 8.58e-8A I m 6.93A A 1.3V mp 24.6V R s 0.211 N s 50 R p 10.57k


83/146

64

Figure 5.8 I-V characteristic for the PV-MF170EB3 module by Mitsubishi Electric atvarious irradiances.

Figure 5.9 P-V characteristic for the PV-MF170EB3 module by Mitsubishi Electric atvarious irradiances.

0 5 10 15 20 25 30 350

1

2

3

4

5

6

7

8

Voltage(V)

C u r r e n t

( A )

1000W/m 2

800W/m2

600W/m 2

400W/m 2

200W/m 2

0 5 10 15 20 25 30 350

20

40

60

80

100

120

140

160

180

Voltage(V)

P o w e r (

W )

1000W/m 2

800W/m 2

600W/m 2

400W/m 2

200W/m 2


84/146


85/146

66

If 0dP dD

then: 1 1 2, 0o odP dP

s u sign s s udD dD

, and 1 s is steered to zero in

finite time under the condition odP

udD

.

If 0dP dD

then: 2 1 0odP

s s udD

and 2 s is steered to zero in finite time under the

condition odP udD .

In sliding mode, either 0 P g or P g , the output P follows the

reference g to increase. When the sliding condition odP

udD

does not hold, P

continues to increase while oscillating. After the transient settles, the maximum value o

P does not exceed max P .

The functionsu and v are plotted in Figure 5.10. The parametersou , , and

are positive constants. is positive value that is constant or varying depending on the

particular search technique. The hysteresis width2 should not exceed , and the

inequality odP

M udD

should be satisfied for . Generally, suitable selection of

the controller parameters is a tuning process. However, the mentioned guidelines ar

extremely valuable for effective calibration of the controller.


86/146

67

Figure 5.10 The functionsu and v used in the control algorithm.


87/146


88/146

libro pv simulation

Documents