Development of battery charge controller for renewable energy systems

AIN SHAMS UNIVERSITY FACULTY OF ENGINEERING Electrical power and Machines Engineering Department

Development of battery charge controller for renewable energy systems A Thesis Submitted in partial fulfillment of the requirements of the degree of Master of Science in Electrical Engineering Submitted by Yasser Elhosseny Atyea Elsayed B.Sc. of Electrical Engineering AIN SHAMS July 2000

Supervised by Prof. Dr. Abdelhalim Abdelnaby Zekry Department of Electronics and Communications Engineering, Faculty of Engineering AIN SHAMS University Assoc. Prof. Dr. Naggar Hassan Saad Department of Electrical power and Machines Engineering Faculty of Engineering AIN SHAMS University Cairo, 2015

FACULTY OF ENGINEERING Electrical power and Machines Engineering Department

Development of battery charge controller for renewable energy systems By Yasser Elhosseny Atyea Elsayed B.Sc. of Electrical Engineering AIN SHAMS July 2000

Examiners Committee Name

Signature

Prof. Dr. Mohamed Abd El-Monem Abou El-Ela

.........................

Faculty of Engineering, Future University, Electronics and Communications Engineering Dept.

Prof. Dr. Ahmed Abd El-Sattar Abd El-Fattah

.........................

Faculty of Engineering, AIN SHAMS University, Electrical power and Machines Engineering Dept.

Prof. Dr. Abdelhalim Abdelnaby Zekry

.........................

Faculty of Engineering, AIN SHAMS University, Electronics and Communications Engineering Dept.

Assoc. Prof. Dr. Naggar Hassan Saad

.........................

Faculty of Engineering, AIN SHAMS University, Electrical power and Machines Engineering Dept.

Date : ….. / ….. / ………….

STATEMENT This dissertation is submitted to Faculty of Engineering, AIN SHAMS University for the degree of Master of Science in Electrical Engineering (Electrical power and Machines Engineering). The author carried out the work included in this thesis at Electrical power and Machines Engineering Department, Faculty of Engineering, AIN SHAMS University, Cairo, Egypt. No part of this thesis was submitted for a degree or a qualification at any other university or institution.

Name: Signature: Date:

Yasser Elhosseny Atyea Elsayed

Curriculum Vitae Name of Researcher

Yasser Elhosseny Atyea Elsayed

Date of Birth

11/10/1975

Place of Birth

Egypt

First University Degree B.Sc. in Electrical Engineering Name of University

AIN SHAMS

Date of Degree

July 2000

Current Job

Electrical Engineer

ABSTRACT Batteries are the power tank of renewable energy power systems. They play the role of power supply when the renewable energy power sources are not available. This thesis demonstrates the development of a new battery charge controller for renewable energy systems. A comprehensive review of the conventional controllers is conducted to collect their contributions, advantages, and disadvantages for comparison and validation. The new controller designed based on a novel maximum power point tracking (MPPT) technique. The new MPPT technique utilizes a smart algorithm and is optimized using the genetic neural algorithm. In addition, the proposed controller utilizes a smart multi stages charging algorithm in order to minimizing the charging time. The components of photovoltaic generator systems are introduced and mathematically modeled including the control and optimization methods. The charge controllers design aspects, functions, and charging control methods are discussed. Consequently, the new controller is modeled based on complete mathematical models. In order to determine the performance parameters and evaluate the validity and efficiency of the new controller, a complete PV system is modeled and simulated using MATLAB/SIMULINK. Moreover, a complete experimental prototype is implemented. The simulation and experimental results are compared for validation and clarification. They strongly agree on that, under all test conditions, the new controller tracks the target maximum power point (MPP) two hundred times faster than the traditional methods. In addition, it can tracks successfully the global maximum power point (GMPP) in all partial shading conditions. Moreover, the charging time is significantly reduced.

The thesis is divided into six chapters organized as follows: Chapter One introduces the components of a general photovoltaic system. Moreover, it reviews the recent publications and contributions in the scope of maximum power point tracking and charging control methods for renewable energy systems. In addition, it lists the thesis objectives and outlines. Chapter Two covers the mathematical model of general photovoltaic systems and the control and optimization methods. Chapter Three demonstrates the theory, design aspects, and control methods of the battery charge controllers. Chapter Four shows the complete modeling, simulation, and validation of the proposed system. Chapter Five presents a complete experimental version for the proposed system including all test cases and validation. Chapter Six concludes the work and states the recommendations for future work.

ACKNOWLEDGEMENT ‫الحوذ هلل رب العالويي‬

I would like to take the opportunity to acknowledge the direct and indirect help of many people who made this thesis possible. Without the great support of my supervisors, it would be not possible to complete this work. I would like to express my appreciation to Prof. Dr. Abdel Haleem Zekry and Dr. Naggar Hassan for their continuous support and guidance. Finally, I would like to thank my wife Dr. Marwa abdellah for her support, encouragement, and quiet patience.

Yasser Elhosseny January 2015

Table of Contents List of figures List of tables List of symbols List of abbreviations Chapter 1 Introduction 1.1. General 1.2. Photovoltaic (PV) 1.3. DC-DC converter 1.4. Battery charging controller 1.5. Literature survey 1.6. Thesis objectives and outlines 1.7. Summary Chapter 2 Photovoltaic Systems 2.1. Introduction 2.2. PV modeling 2.2.1. Variation of Solar Irradiation (G) 2.2.2. Variation of ambient Temperature (T) 2.3. PV Control methods 2.3.1. Constant voltage method (CV) 2.3.2. Short current pulse method (SC) 2.3.3. Open circuit method 2.3.4. Perturb and Observe Methods (P&O) 2.3.5. Incremental Conductance Methods (IC) 2.4. PV optimization methods 2.4.1. Neural network optimization techniques: 2.5. Effects of Partial Shading on PV Array Characteristics 2.6. Summary Chapter 3 Battery charge controllers 3.1. Introduction 3.2. The functions of Battery charge controllers. 3.3. Charge controllers basics and theory 3.3.1. Voltage regulation set point (VR) 3.3.2. Low-Voltage load disconnect set point (LVD) 3.3.3. Voltage regulation hysteresis (VRH) 3.3.4. Low voltage disconnect hysteresis (LVDH) 3.4. Charging control methods 3.4.1. Shunt Controller 3.4.2. Shunt-Interrupting controller 3.4.3. Shunt-Linear controller 3.4.4. Series Controller 3.4.5. Series-Interrupting controller 3.4.6. Series-Interrupting, 2-step, Constant-Current controller 3.4.7. Series-Interrupting, 2-Step, Dual Set Point controller 3.4.8. Series-Linear, Constant-Voltage controller i

IIII IX X XII 1 1 1 2 3 3 6 7 8 8 8 9 10 11 11 11 11 12 12 13 13 16 18 19 19 19 19 19 19 20 20 20 20 21 21 21 22 22 22 23

Table of Contents 3.4.9. Series-Interrupting, Pulse Width Modulated (PWM) controller 3.4.10. MPPT charge controller

23 23

3.5. Battery charging control algorithms 3.6. Battery charge controller structure 3.7. Summary Chapter 4 Modeling and simulation 4.1. Introduction 4.2. System description 4.3. System structure and components 4.3.1. The PV model 4.3.2. The DC-DC converter model 4.3.3. The inverter model 4.3.4. The MPPT controller 4.3.5. The proposed new MPPT technique 4.3.6. Optimization of the new MPPT method using the genetic neural network 4.3.7. PI controller model 4.3.8. The battery model and charging algorithm. 4.4. System operation 4.5. Test cases and results 4.5.1. Test case I, temperature and irradiance are fixed 4.5.2. Test case II, temperature and/or irradiance are changing with time 4.5.3. Test case III, the new MPPT test 4.5.4. Validation 4.6. Summary Chapter 5 System implementation 5.1. Introduction 5.2. System description and operation 5.3. System hardware structure 5.3.1. The Power Circuit of the charge controller 5.3.2. The Control Circuit 5.3.3. The sensor circuit board 5.3.4 Computer serial interface board 5.4. I-V and P-V characteristics of the PV module 5.5. System software 5.6. Experiment results 5.6.1. Test case I, fixed temperature, and irradiance 5.6.2. Test case II, varying temperature, and/or irradiance with time 5.6.3. Test case III, the case of partial shading 5.6.4. Test case IV, the new MPPT test 5.6.5. Charging Process test 5.7. Validation 5.8. Summary

23 24 25 26 26 26 26 27 27 27 27 27 28 31 31 32 32 32 35 37 40 41 42 42 42 43 44 45 46 47 49 52 53 53 55 56 58 62 63 64

ii

Table of Contents 65 65 66 67 71 72 88

Chapter 6 Conclusions, Recommendation, 6.1. Conclusion and recommendation 6.2. Future work References Extracted Paper Appendix A Appendix B

iii

List of Figures List of figures Fig. 1.1. Solar cells are connected in parallel and in series.

1

Fig. 1.2. Modules are connected in parallel and in series.

2

Fig. 1.3. The Buck DC-DC converter.

2

Fig. 1.4. The Boost DC-DC converter.

2

Fig. 1.5. The Buck-Boost DC-DC converter.

3

Fig. 2.1. Equivalent circuit model of PV Cell.

8

Fig. 2.2. Power -Voltage and Current-Voltage curve of a solar cell at given T and G.

9

Fig. 2.3. I-V characteristics of a PV module at various irradiance, constant temperature.

10

Fig. 2.4. I-V characteristics of a PV module at various temperature, constant irradiance.

10

Fig. 2.5. CV block diagram.

11

Fig. 2.6 SC block diagram.

11

Fig. 2.7. OV block diagram.

12

Fig. 2.8. P&O block diagram.

12

Fig. 2.9. ICb block diagram.

13

Fig. 2.10. MPPT methods comparison regarding cost and efficiency.

13

Fig. 2.11. Search techniques classification.

14

Fig. 2.12. GA reproduction cycle and stages.

15

Fig. 2.13. GA Crossover.

15

Fig. 2.14. GA mutation.

16

Fig 2.15. The shading effects on solar cells.

17

Fig 2.16. I-V characteristic of PV array under partial shading condition.

17

iii

List of Figures Fig 2.17. P-V characteristic of PV array under partial shading condition.

17

Fig. 3.1. The charge controller set points.

20

Fig. 3.2. The shunt charge controller.

21

Fig. 3.3. The series charge controller.

22

Fig. 3.4. The MPPT charge controller.

23

Fig. 3.5. Three stages charging process (CCCV).

24

Fig. 3.6. The MPPT charge controller block diagram.

24

Fig. 4.1. Complete system model Block diagram.

26

Fig. 4.2. Flowchart of the new MPPT method.

28

Fig. 4.3. GA interfaces with MPPT.

29

Fig. 4.4. Chart of the GA reproduction cycle and stages.

30

Fig. 4.5. MPP location changed with irradiance change.

30

Fig. 4.6. PI controller model.

31

Fig. 4.7. The battery model.

31

Fig. 4.8. The PV current of GA controller based, PI controller based and MPPT with no control.

32

Fig. 4.9. The PV Voltage of GA controller based, PI controller based and MPPT with no control.

33

Fig. 4.10. The PV power of GA controller based, PI controller based and MPPT with no control.

33

Fig. 4.11. The PV power of GA controller based, PI controller based and MPPT with no control through the first 10 samples. Fig. 4.12. The inverter output before and after LC filter is applied.

33

Fig. 4.13. The line to line three phase output voltage.

34

Fig. 4.14. PV current changes due to irradiance change of MPPT with no control, GA controller based MPPT and PI controller based MPPT. Fig. 4.15. PV voltage changes due to irradiance change of MPPT with no control, GA controller based MPPT and PI controller based MPPT.

35

iv

34

35

List of Figures Fig. 4.16. PV power changes due to irradiance change of MPPT with no control, GA controller based MPPT and PI controller based MPPT. Fig. 4.17. PV power changes due to irradiance change in a period of one sample.

35

Fig. 4.18. P-V curve changes due to irradiance change of MPPT with no control, GA controller based MPPT and PI controller based MPPT. Fig. 4.19. I-V curve changes due to irradiance change of MPPT with no control, GA controller based MPPT and PI controller based MPPT. Fig. 4.20. The voltage of the new MPPT technique compared to the traditional MPPT techniques.

36

Fig. 4.21. The first 100 samples of the voltage of the new MPPT technique versus the traditional MPPT techniques. Fig. 4.22. Current curve of the new MPPT technique versus the traditional MPPT techniques.

37

Fig. 4.23. The first 100 samples of the current curve of the new MPPT technique compared to the traditional MPPT techniques. Fig. 4.24. The Power curve of the new MPPT technique compared to the traditional MPPT technique.

38

Fig. 4.25. The first 100 samples of the power curve of the new MPPT technique versus the traditional MPPT techniques. Fig. 4.26. Efficiency of the new MPPT technique versus the traditional techniques.

39

Fig. 4.27. The first 100 samples of efficiency of the new MPPT technique compared to the traditional MPPT techniques. Fig. 4.28. The P-V curve of the new MPPT technique compared to the traditional technique.

39

Fig. 5.1. The charger controller block diagram.

42

Fig. 5.2. Charging characteristic.

43

Fig. 5.3. Circuit block diagram of the charge controller.

43

Fig. 5.4. The power circuit of the proposed controller.

45

Fig. 5.5. The prototype board of the control circuit.

45

Fig. 5.6. The circuit diagram of the sensor board.

46

Fig. 5.7. The circuit diagram of the battery current sensor board.

46

Fig. 5.8. The calibration test of the sensor circuit board.

47

Fig. 5.9. The voltage divider circuit.

47

v

36

36 37

38

38

39

40

List of Figures Fig. 5.10. The serial interface circuit diagram.

48

Fig. 5.11. The output signals at different position.

48

Fig. 5.12. The proposed controller prototype.

49

Fig. 5.13. The I-V characteristic curves at fixed temperature and different irradiance.

50

Fig. 5.14. The I-V characteristic curves at fixed irradiance and different temperature.

50

Fig. 5.15. The P-V characteristic curves at fixed irradiance and different temperature.

51

Fig. 5.16. The P-V characteristic curves at fixed temperature and different irradiance.

51

Fig. 5.17. The software flowchart of the proposed controller.

52

Fig. 5.18. The PV current of MPPT versus the current of MPPT optimized by GA.

53

Fig. 5.19. The PV voltage of MPPT versus the voltage of MPPT optimized by GA.

54

Fig. 5.20. The PV power of MPPT versus the power of MPPT optimized by GA.

54

Fig. 5.21. The PV power curve when irradiance changes from 1000 Wm2to 750 Wm2.

55

Fig. 5.22. The PV current curve in partial shading case for MPPT and GA based MPPT controller.

56

Fig. 5.23. The PV voltage curve in partial shading case for MPPT and GA based MPPT controller.

57

Fig. 5.24. The PV power curve in partial shading case for MPPT and GA based MPPT controller.

57

Fig. 5.25. The P-V curve in partial shading case for MPPT and GA based MPPT controller.

58

Fig. 5.26. The PV current curve of the new MPPT versus the traditional MPPT.

58

Fig. 5.27. The PV voltage curve of the new MPPT versus the traditional MPPT.

59

Fig. 5.28. The PV power curve of the new MPPT versus the traditional MPPT.

59

Fig. 5.29. The PV efficiency curve of the new MPPT versus the traditional MPPT.

60

Fig. 5.30. The first 10 seconds of the PV current curve of the new MPPT versus the traditional MPPT.

60

Fig. 5.31. The first 10 seconds of the PV voltage curve of the new MPPT versus the traditional MPPT.

61

vi

List of Figures Fig.5.32. The first 10 seconds of the PV power curve of the new MPPT versus the traditional MPPT.

61

Fig. 5.33. The P-V curve of the new MPPT technique compared to the traditional technique.

62

Fig. 5.34. The three stage constant current constant voltage charging process.

62

Fig B.1. The Simulink of the complete model.

88

Fig B.2. The PV model Simulink.

89

Fig. B.3. The PV model block diagram.

89

Fig. B.4. The DC-DC boost converter.

89

Fig. B.5. The converter PWM generator.

90

Fig. B.6 The Inverter model.

90

Fig. B.7 MPPT controller Block Diagram.

91

Fig B.8. MPPT controller Simulink model.

91

Fig. B.9. PI controller model.

91

Fig. B.10. The GA block diagram.

92

Fig. B.11 GA Simulink model.

92

Fig. B.12 the battery model.

93

Fig. B.13. The proposed controller prototype.

93

Fig. B.14. The prototype board of the power circuit.

94

Fig. B.15. The prototype board of the control circuit.

94

Fig. B.16. The prototype of sensor circuit board.

95

Fig. B.17. The prototype of the battery current sensor board.

95

Fig. B.18. The charger controller PCB.

96

Fig. B.19, Snapshot of system operation.

96

vii

List of Tables

List of tables Table 1.1 MPPT algorithm efficiency.

4

Table 1.2 Characteristics of different MPPT techniques.

4

Table 4.1. Samples of the training data, which obtained from the PV model.

30

Table 4.2. A comparison between a representative calculated values.

31

Table 4.3 A comparison between a representative calculated values and the model obtained results of PV array.

40

Table 4.4. A comparison between a representative the module datasheet parameter values and the GA model obtained values.

41

Table 4.5. The efficiency of the new method for MPPT compared to previous methods.

41

Table 5.1 The input design parameters of the proposed boost converter.

44

Table 5.2 The output design parameters of the proposed boost converter.

44

Table 5.3 The practical values that are used in the proposed boost converter.

44

Table 5.4 The SOLARA S320P36 ULTRA module parameters.

44

Table 5.5 Sample records of the system log showing the case of MPPT with GA controller and irradiance varying from 1000 Wm2to 750 Wm2.

56

Table 5.6 A sample of the system log showing the battery charging process transition from constant current to constant voltage.

63

Table 5.7 Sample of system log showing the third stage is finished

63

Table 5.8 Comparison between the PV datasheet parameters and the practical values of the experiments.

64

ix

List of Symbols List of symbols I

: Cell output current (A)

V

: Cell output voltage (V) : Cell reverse saturation current (A) : Reference cell reverse saturation current at

(A)

: Shunt current (A) : Parallel resistance (Ω) : Series resistance (Ω) ⁄

: Boltzmann constant T

: Solar cell temperature (°C) : Charge of electron =

C

: Short circuit current temperature coefficient at : Short circuit current at 25°C G

⁄

: Irradiance intensity (

)

: Reference irradiance intensity 1000 (

⁄

: Ambient temperature (°C) : Local wind speed (

⁄

D : duty cycle : the optimum current for MPP for specific irradiance G :proportional constant is estimated to be approximately 92% : the PV short circuit current : the optimum voltage for MPP for specific irradiance G the PV open circuit voltage for specific irradiance G : the PV array voltage

x

List of Symbols : the PV array power : Short-circuit current of the PV module at reference solar radiation (A) : Open-circuit voltage at the reference temperature (V) : The temperature coefficient of : The temperature coefficient of : Proportional factor, with typical values in the ranges of (0.75-0.85). Proportional factor, with typical values in the ranges of (0.9-0.92). : optimum value of voltage : optimum value of current : optimum value of power (W) : the previous scan cycle power value (V) : the previous scan cycle voltage value (°C) : battery temperature (V) : battery voltage (A) : battery charging current (V) : converter output voltage (A) : converter output current : converter output power : the starting limit of voltage : the starting limit of power : the ending limit of voltage : the ending limit of power : the current value of voltage : the current value of power

xi

List of Abbreviations List of abbreviations PV

Photovoltaic

INC

incremental conductance

P&O

Perturb and Observe

MPPT

Maximum power point tracking

MPP

Maximum power point

ANN

Artificial neural network

GA

Genetic algorithm

AI

Artificial intelligence

PHEV

the plug-in hybrid electric vehicle

SPTLE

Solar Powered Traffic Light Equipment

EPP

Estimate-Perturb-Perturb

CV

Constant voltage MPPT method

SC

Short current pulse method

CTC

constant trickle current

CCCV

The constant current constant voltage

CC

constant current

SOC

Stat of charge

VLL

Line to line voltage

Vph

Phase voltage

PWM

Pulse width modulation

MIPS

Mega instruction per second

CCP

Capture,Compare and PWM

PSP

Parallel Slave Port

MSSP

Master Synchronous Serial Port

ADC

Analog to digital converter

GUI

graphical user interface xii

List of Abbreviations UART

Universal Asynchronous Receiver/Transmitter

MOSFET

Metal Oxide Semiconductor Field Effect Transistor

DSP

Digital Signal Processor

DC

Direct Current

AC

Alternate Current

PC

Personal Computer

PCB

Printed Circuit Board

PI

Proportional Integral

xiii

Chapter 1

Introduction

Chapter 1

Introduction

1.1. General The conventional energy sources become not sufficient to meet the day-to-day increasing power demand. So, renewable sources of energy are utilized along with conventional systems to meet that demand. Wind and solar energy are the prime energy sources that are being utilized in this regard. Photovoltaic generation systems have two major problems. The conversion efficiency of electric power generation is low (9-17%) especially under low irradiation conditions, and the amount of electric power generated by the solar arrays, changes continuously according to the differences in weather conditions and daytime [1].

1.2. Photovoltaic (PV) A photovoltaic (PV), or solar electric system, is composed of several solar cells. These cells are connected in series to produce specific voltage (V) and in parallel to output specific current (A) forming a PV module as shown in Fig. 1.1. Similarly, these modules are connected in parallel and in series forming array of modules to produce the designed current and voltage as depicted in Fig. 1.2. However, the PV output current to voltage (I-V) characteristic is nonlinearly changed based on the ambient temperature and irradiance [1]. In general, there is a unique point on the current-voltage I-V or power-voltage P-V curve of a photovoltaic array where the output power from the array has a maximum value. To extract the maximum power from the array, it must be operated continuously at this maximum power point (MPP). Because the nature of the photovoltaic generator, the maximum output power changes with the incident solar radiation and weather conditions especially the temperature. The location of the MPP on the I-V curve of the array is not known, so it must be determined either through calculation models or by search algorithms [2].

Fig. 1.1. Solar cells are connected in parallel and in series. 1

Chapter 1

Introduction

Fig. 1.2. Modules are connected in parallel and in series.

1.3. DC-DC converter The DC-DC converter adjusts the PV voltage to the required output DC voltage. There are three types of DC/DC converter. Step up converter where the output voltage is larger than the input voltage like a Boost converter, step down converter where the output voltage is lower than the input voltage like a Buck converter, and step Up/Down converter where it has the ability to step up and step down like a Buck-Boost converter. Fig. 1.3, 1.4, and 1.5 show the simplest power circuits of different type of the DCDC converters.

Fig. 1.3. The Buck DC-DC converter.

Fig. 1.4. The Boost DC-DC converter.

2

Chapter 1

Introduction

Fig. 1.5. The Buck-Boost DC-DC converter.

1.4. Battery charging controller A battery storage plays the role of backup power supply when there is no output power of the PV. Consequently, a battery-charging controller is needed to control the battery charging process and protect the battery from overcharging. In a simple charge controller such as series charge controller, it disables further current flow into the batteries when they are full. Moreover, in a shunt charge controller, it diverts excess electricity to an auxiliary load when the batteries are fully charged. In addition, there are more sophisticated charge controller such as pulse width modulation (PWM) and maximum power point tracking (MPPT) controller. In these controllers, the charging rate is adjusted according to the battery state of charge (SOC). In the next section, a literature survey will be conducted in order to review the previous contributions in the scope of development and implementation of the maximum power point tracking techniques and the battery charging controllers.

1.5. Literature survey There are many researches on the maximum power point tracking to improve the performance parameters continuously. The target is to improve the efficiency, and maximize the extracted power from a photovoltaic system. The incremental conductance (INC) and perturb and observe (P&O), are the most efficient MPPT method [3, 4]. Furthermore, the MPPT technique, which is based on the PI controller or the artificial intelligence controller, has an improvement in efficiency [5, 6]. Most commonly used techniques of MPPT are, Perturb and Observe (P&O), Hill-climbing method, Constant Voltage and Current, Incremental Conductance, Parasitic Capacitance, Swarm chasing along with some DSP based methods [7, 8]. P&O method is simple, but the steady state error is large. Moreover, it has oscillated at steady state operation at the vicinity of maximum power point [7 - 12]. The parasitic capacitance method gets stuck at local maxima or mina [13]. The modified P&O technique has improved convergence problem in rapidly changing weather pattern, but has not improved the efficiency [14,15]. Hill climbing method is widely applied in the MPPT controllers due to its simplicity and easy implementation. Nevertheless, steady-state oscillations always appear due to the perturbation. Thus, the power loss may be increased [9,10]. Incremental conductance has proved to be better in terms of efficiency, but one major problem is that power of solar panel is a non-linear function of duty cycle [8,9,11,16,17,18,19]. Fractional open-circuit voltage and short-circuit current methods provide a simple and effective way to acquire the maximum power. However, they require periodical disconnection or short-circuit of the PV modules to measure the open-circuit voltage or short-circuit current for reference, resulting in more power loss. Also, these methods cannot always produce the maximum power available from PV 3

Chapter 1

Introduction

arrays due to the use of the predefined PV curves, that often cannot effectively reflect the real-time situation, due to PV nonlinear characteristics and weather conditions. [11,20,21]. Moreover, fuzzy and neural network methods that focus on the nonlinear characteristics of the PV array, provide a good alternative for the MPPT control. Since the output characteristics of the PV array should be well ascertained to create the MPPT control rules, the versatility of these methods is limited [22, 23]. The neural network based controllers can track the maximum power point online; but they have a high cost of implementation owing to complex algorithms that usually need a DSP as their computing platform [11]. In addition, they face a trade-off between settling time and steady state error; furthermore, their response is very slow [18]. The P&O and the INC algorithms are the most common techniques. These techniques have the advantage of an easy implementation, but they also have their drawbacks. Other techniques based on different principles are fuzzy control, neural circuit, fractional open circuit voltage or short circuit current, current sweep, etc. Most of these methods yield a local maximum and some, like the fractional open circuit voltage or short circuit current, give an approximated MPP, not the exact one [24]. Table 1.1 shows the efficiency of each MPPT algorithm. Table 1.1 MPPT algorithm efficiency. Ref. P&O

Reported MPPT Efficiency Incremental Constant Voltage Conductance

[18] [25] [26]

AI Techniques 98%

(PSO)

98.7%

(ANN -P&O) (Fuzzy Logic)

99.2% [18] [13] [13] [27] [28] [19] [12] [27]

99% 98% 98.2% 95% 98.7% 98.1%

It is clear from the table that the efficiencies of the different algorithms are sufficiently high. Table 1.2 outlines the main features of the different MPPT techniques [24]. For each algorithm, there exist few drawbacks such as being caught in maxima, high steady state error, and inability to follow the direction of change in perturbation. Table 1.2 Characteristics of different MPPT techniques. MPPT techniques Perturb & observe Incremental conductance Fractional open circuit voltage Fractional short circuit current Fuzzy logic control Neural network

Convergence speed Varies Varies Medium Medium Fast Fast

Complexity Low Medium Low Medium High High

Periodic tuning No No Yes Yes Yes Yes

On the other hand, there are intensive and continuous research efforts on the design and implementation of the solar charge regulators to improve their performance parameters. The targets are, improving their efficiency, increasing their speed of maximum power point tracking and reducing the charging period of the battery. In this section, the recent publications in the scope of the design and

4

Chapter 1

Introduction

implementation of charger controllers will be reviewed to show their main features and drawbacks. This would assist us to achieve advancements in the solar charger controller. F. Sani, H.N Yahya, M. Momoh, I.G. Saidu and D.O. Akpootu presented the design and construction of microcontroller based charge controller for photovoltaic application [29].They concentrated on the charging process. The controller has no contribution regarding the maximum power point tracking. The charging process takes 6.5 hours. Wallies Thounaojam, V. Ebenezer and Avinash Balekundri introduced a design and development of a microcontroller based solar charge controller [30], the controller does not track the maximum power point, but it concentrates on the charging process and protection functionality. However, the charging time exceeds 9 hours. Jaya N. Ingole,Madhuri A. Choudhary and R.D. Kanphade presented a PIC based solar charging controller for battery [31]. The controller has the most charging and protection functions, but it has no maximum power point tracking features. It takes over 9 hours in charging process. S. K. Patil and D. K. Mahadik presented a design of maximum power point tracking (MPPT) based PV charger [32]. It is based on the perturb and observe (P&O) tracking algorithm. So, its tracking speed and accuracy are low. J Jian-Long Kuo, Kai-Lun Chao, and Li-Shiang Lee presented a dual mechatronic MPPT controller with control algorithms for the Rotatable Solar Panel, which increases the efficiency of the plug-in hybrid electric vehicle (PHEV) by using rotatable solar panels [33]. The proposed dual mechatronic MPPT controller is suitable for the PHEV system. The drawback of this system is the slow tracking speed and transient response because it is a mechanical based tracking system. Roger Gules , Juliano De Pellegrin Pacheco and HélioLeães Hey introduced the analysis, design, and implementation of a parallel connected maximum power point tracking (MPPT) system for standalone PV power generation [34]. The parallel connection of the MPPT system reduces the negative influence of power converter losses on the overall efficiency. However, the tracking speed and accuracy are slow. Also, the charging period was too long due to using constant current charging method. S. G. Tesfahunegn and O. Ulleberg designed a new solar battery charge controller that combines both MPPT and over-voltage controls as a single control function [35]. The designed controller was demonstrated to have good transient response with only small voltage overshoot. However, it has a low tracking speed and long charging time. An adjustable Self-Organizing Fuzzy Logic controller (SOFLC), for a Solar Powered Traffic Light Equipment (SPTLE), with an integrated MPPT system, on a low cost microcontroller, has been presented by Noppadol Khaehintung and Phaophak Sirisuk [36]. It comprises of the boost converter for high performance SPTLE. It utilizes a classical O&P MPPT method, so it is not accurate and slow in transient response. Yuncong Jiang, Ahmed Hassan, Emad Abdelkarem and Mohamed Orabi Presented an analogue Maximum Power Point Tracking (MPPT) controller for a Photovoltaic (PV) solar system that utilizes the load current to achieve maximum output power from the solar panel [37]. They reduced the cost and size of the proposed circuit. However, the tracking speed is low and charging time is more than 9 hours. A new MPPT system has been developed by Eftichios Koutroulis, Kostas Kalaitzakis and Nicholas C. Voulgarisl [38]. It consists of a Buck-type DC/DC converter, which was controlled by a Microcontroller. The resulting system has high-efficiency, lower-cost and can be easily modified to handle more energy sources (e.g., wind-generators). However, it is restricted to specific size of battery because it uses a buck converter. Zheng Shicheng and Liu Wei presented a research and implementation of photovoltaic charging system with maximum power point tracking [39]. The implemented system was verified to be virtues as simple configuration and high efficiency. But, it has a low tracking accuracy due to utilizing the constant voltage MPPT technique. Chamnan Ratsame presented an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic powered water pump system for long tailed boat in Thailand. This system 5

Chapter 1

Introduction

consisted of a solar array, a switching battery charger based on boost DC-DC converter, a battery, and a small water pump [40]. It utilizes P & O MPPT tracking method, so the tracking speed was moderate and not accurate. A novel cost effective, more accurate and efficient microcontroller based MPPT system has been proposed by Siwakoti and Yam Prasad [41]. It uses the PWM technique to regulate the power output of boost DC/DC converter at its maximum possible value. Moreover, simultaneously controls the charging process of the battery. It has a moderate tracking speed and long charging time. The modeling and control design of the PV charger system using a Buck-Boost converter was presented by B. SreeManju, R. Ramaprabha and Dr. B. L. Mathur [42]. The controller was designed to balance the power flow from PV module to the battery and the load such that PV power was utilized effectively and the battery was charged in three charging steps. The system is flexible in tracking. However, the tracking speed is not high. A. Yafaoui, B. Wu and R. Cheung implemented a real-time Estimate-Perturb-Perturb (EPP) algorithm for maximum power point tracking (MPPT) control in a PV system [43]. They demonstrated that the EPP method is a promising MPPT control scheme for residential PV systems. They focused on the function of MPPT. However, the tracking speed is moderate. A. Harish and M. V. D. Prasad proposed a technique for extracting maximum power from a photovoltaic panel to charge the battery [44]. The proposed MPPT charge controllers can be used to utilize the maximum power output of solar panels instead of investing in number of solar panels. In addition, there is an RS485 interface is included for monitoring purpose. It uses the P&O MPPT method, so it is not accurate and its tracking speed is low. Zaki Majeed AbduAllah , Omar Talal Mahmood and Ahmed M. T. Ibraheem AL-Naib implemented practical buck-type power converter for Photovoltaic (PV) system for energy storage application based on constant voltage maximum power point tracking (MPPT) algorithm [45]. Simulation and experimental results demonstrate the effectiveness and validity of the proposed system. The tracking speed is high, but the accuracy is low due to using the constant voltage-tracking algorithm. Dr. Anil S. Hiwale, Mugdha V. Patil and Hemangi Vinchurkar presented An Efficient MPPT solar charge controller [46]. They showed an increase in the efficiency of power transfer in comparison to systems with direct connection. Thus, reducing the size and the cost of the PV panel. The tracking speed and accuracy are very low. Masudul Haider Imtiaz, Mst. Rumana Aktar Sumi and Kazi Rizwana Mehzabeen presented a design and implementation of an intelligent solar hybrid inverter in grid oriented system for utilizing PV energy [47]. Computer simulations and experimental results prove the high power conversion efficiency and low harmonic distortions. Nevertheless, it has a normal tracking speed. Chihchiang Hua, Jongrong Lin, and Chihming Shen implemented a DSP-Controlled Photovoltaic System with Peak Power Tracking [48]. It overcomes the problem of mismatch between the solar arrays and the given load. However, the tracking speed is low and not accurate. From the preceding survey, for each controller, there exist a few drawbacks such as slow tracking response, low accuracy due to oscillation around the MPP and long charging time.

1.6. Thesis objectives and outlines The basic objective would be to study the battery charge controller and successfully implement a new controller based on a novel developed control technique. The new technique is optimized using a genetic neural algorithm in order to have a competitive controller in its fast tracking and efficiency. Firstly, a complete mathematical model is created based on this model. The simulation model is built, tested and validated. Finally, a complete experimental prototype of the proposed controller is built, tested, and validated. This thesis has been broadly divided into six chapters. The first one is a general introduction and thesis objectives and outlines. Chapter 2 introduce the different components and control methods of photovoltaic systems. Chapter 3 covers the theory, design and types of battery charge 6

Chapter 1

Introduction

controllers. Chapter 4 presents the modeling and simulation of the proposed system including testing and validation. Chapter 5 shows the complete system experimental implementation. Finally, the conclusions, recommendation, and further work are presented in chapter 6. It is worth mentioning that, all source code programs, SIMULINK/MATLAB models, and photographs of the circuit‟s prototype are included in the appendixes.

1.7. Summary A brief review of the photovoltaic, DC-DC converter, and battery charging controllers are presented. In addition, a comprehensive literature surveys are discussed including the recent contributions in the scope of maximum power point tracking and charging control methods. Finally, the thesis objectives and outlines are listed.

7

Chapter 2

Photovoltaic Systems

Chapter 2

Photovoltaic Systems

2.1. Introduction The photovoltaic is the basic component in photovoltaic energy power systems. Therefore, the mathematical model of photovoltaic should be studied and analyzed. This chapter deals with the photovoltaic. Firstly, it shows the mathematical model. Moreover, studies the effects of temperature and irradiance variation on the photovoltaic output power efficiency. Secondly, it presents the control and optimization methods that are utilized to maximize the output power. Finally, it shows the partial shading effects on the output power.

2.2. PV modeling A PV module is formed by connecting solar cells in series and parallel, depending on the required output current and voltage. A typical model for a solar cell is shown in Fig. 2.1 [49].

Fig. 2.1. Equivalent circuit model of PV Cell. The standard model equations are given by: (

) ⁄















 

 

 

  

  

  

  

⁄ Where I : Cell output current (A) V : Cell output voltage (V) : Photo current (A) : Cell reverse saturation current (A) : Reference cell reverse saturation current at : Shunt current (A) : Parallel resistance (Ω) : Series resistance (Ω)

(A)

Chapter 2

Photovoltaic Systems

⁄ : Boltzmann constant : Solar cell temperature (°C) : Charge of electron = C : Short circuit current temperature coefficient at : Short circuit current at 25°C G : Irradiance intensity ( ⁄ ) : Reference irradiance intensity 1000 ( ⁄ : Ambient temperature (°C) : Local wind speed ( ⁄ The characteristic equation of a solar module, is dependent on the number of cells in parallel ( ), and the number of cells in series ( ). The current variation is less dependent on the shunt resistance, and is more dependent on the series resistance [49]. T

(

)

(

)













The characteristic equation of a solar module is dependent on the number of cells in parallel and number of cells in series. It is observed that, the current variation is less dependent on the shunt resistance and is more dependent on the series resistance [49]. Based on the above mathematical model equations, the PV module are modeled using MATLAB/SIMULINK. The I-V and P-V characteristics are depicted in Fig. 2.2.

Fig. 2.2. Power -Voltage and Current-Voltage curve of a solar cell at given T and G.

2.2.1. Variation of Solar Irradiation (G) Fig. 2.3 shows that, the I-V characteristics are highly affected by changing the solar irradiation values. The solar irradiation changes with the changes in environmental conditions, the Higher is the solar irradiation, the higher solar input would be in the solar cell, and consequentially power magnitude would increase for the same voltage value. The open circuit voltage increases with an increase in the solar irradiation. This is because, when sunlight that incident on to the solar cell increases, the electrons are supplied with higher excitation energy, thereby the electron mobility increases and the generated power increases [49].

9

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Photovoltaic Systems

Fig. 2.3. I-V characteristics of a PV module at various irradiance, constant temperature.

2.2.2. Variation of ambient Temperature (T) On the contrary, Fig. 2.4 shows that, the increase of the ambient temperature of the solar cell decreases the open circuit voltage value. Consequently, that decreases the power generation capability. As a result, solar cell efficiency is reduced [49].

Fig. 2.4. I-V characteristics of a PV module at various temperature, constant irradiance.

10

Chapter 2

Photovoltaic Systems

2.3. PV Control methods Appropriate maximum power point tracking, MPPT, technique is needed to maintain the operating point of the PV array at the MPP under any operating conditions. There are many MPPT methods available in the literature. The most widely used techniques are described in the following sections, starting with the simplest method.

2.3.1. Constant voltage method (CV) In this method, the PV voltage is adjusted to reference voltage, which is the closest value to the maximum power point voltage [50]. Fig. 2.5 shows the block diagram of the constant voltage method.

Fig. 2.5. CV block diagram.

2.3.2. Short current pulse method (SC) It is clear from Fig. 2.6 that, the optimum current for the maximum power point is proportional to the short circuit current [51]. (2.6) Where : The optimum current of MPPT for specific irradiance G : Proportional constant is estimated to be approximately 92% : The PV short circuit current

Fig. 2.6 SC block diagram.

2.3.3. Open circuit method Similarly, Fig. 2.7 shows that, this method is based on the fact that, the optimum voltage for the maximum power point is proportional to the open circuit voltage [52]. (2.7) Where : The optimum voltage for MPPT for specific irradiance G : Proportional constant is estimated to be approximately 76% : The PV open circuit voltage for specific irradiance G 11

Chapter 2

Photovoltaic Systems

Fig. 2.7. OV block diagram.

2.3.4. Perturb and Observe Methods (P&O) The value of

is observed periodically to determine the perturbation direction of the voltage

and or current until reaching the maximum power point. The common problem of this method is the oscillation around the maximum power point. Furthermore, when the atmosphere is changing rapidly the P&O could be failed. There are three types of P&O methods. The first is the classic P&O (P&Oa), in this method, it perturbs by a fixed amount. The second technique is the optimized P&O technique (P&Ob). It uses an average of several samples to adjust dynamically the perturbation magnitude. The third technique is the three-point weight (P&Oc). In this technique, the perturbation direction is decided by comparing the PV output power on three points of the P-V curve. These three points are the current operating point (A), a point B perturbed from point A, and a point C doubly perturbed in the opposite direction from point B [4]. All three algorithms require two measurements, the voltage ( ) and the current ( ) as it is shown in Fig. 2.8.

Fig. 2.8. P&O block diagram.

2.3.5. Incremental Conductance Methods (IC) This method is based on the fact that, at the maximum power point the below equation is valid [53]. (2.8) Where

are the PV array current and voltage respectively. Therefore, the perturbation direction can

be determined based on the sign of the value of {

when it is positive, the optimum operating

point in the P-V plane is to the left of the MPP. Therefore, an increment perturbation is required. However, if it is negative, the optimum operating point is to the right of the MPP. Therefore, a decrement perturbation is required and so on until reach the maximum power point. There are two types of IC methods. The first one is the classical method (ICa). In this method, the perturbation is determined based on the sign of {

. The second method is (ICb). It is a

combination of the constant voltage CV and the IC methods. When the irradiance (G) is less than 30% of the nominal value, it uses the CV method. It realizes results that are more efficient. In other cases, it uses

12

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Photovoltaic Systems

the (ICa) method. Therefore, additional measurement of irradiance is required for these methods as shown in Fig. 2.9.

Fig. 2.9. ICb block diagram. As mentioned above, there are many methods for obtaining the maximum power point. Studies show that, the P&O and IC algorithms are the most efficient MPPT techniques as shown in Fig.2.10. [4].

Fig. 2.10. MPPT methods comparison regarding cost and efficiency.

2.4. PV optimization methods The process of searching the maximum power point is very important to maximize the efficiency of the PV system. In this section, the optimization methods are discussed and compared.

2.4.1. Neural network optimization techniques: In order to search for the optimum solutions, optimization techniques are used. They can be divided into three classes [54], as shown in Fig. 2.11

13

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Photovoltaic Systems

Fig. 2.11. Search techniques classification.

2.4.1.1. Numerical techniques They use a set of conditions to be satisfied by the solutions of an optimization problem. Newton method is an example of this technique.

2.4.1.2. Enumerative techniques They are searching all points related to the function's domain space, one point at a time. Although these are very simple techniques to be implemented. However, they usually require significant computation, and are not suitable for applications with large domain spaces. Dynamic programming is a good example of this technique.

2.4.1.3. Guided random search techniques They are enumerative search techniques. Simulated annealing and evolutionary algorithms are the two major subclasses for this technique. Genetic Algorithms (GAs) are a good example of this technique.

2.4.1.3.1. Genetic Algorithms (GA) Genetic algorithms are a class of computational models that mimics natural evolution to solve problems in a wide variety of domains. Genetic algorithms are particularly suitable for solving complex optimization problems. It is formed by a set of individual elements (the population) and a set of biologically inspired operators that can change these individuals. Moreover, according to evolutionary theory the most suited individuals in the population are likely to survive and to generate offspring, and transmitting their biological heredity to the new generations. Strings of numbers mapped to each potential solution. Each solution represents an individual in the population. The genetic algorithm then manipulates the most promising strings in its search for an improved solution. The algorithm operates as follows [54]: 1. Creation of a population of strings. 14

Chapter 2

Photovoltaic Systems

2. Evaluation of each string. 3. Selection of the best strings based on the evaluation result. 4. Create a new population of strings by genetic manipulation. Fig. 2.12 shows how these four stages are interconnected. At the first stage, a population of possible solutions is created. Each population individual is encoded into a string (the chromosome) in order to be manipulated by the genetic operators. In the next stage, the individuals are evaluated by applying it in the cognitive function. This determines the fitness of each individual in the population. A selection mechanism chooses the best pairs for the genetic manipulation process based on each individual fitness. In this manner, the survival of the fittest individuals is guaranteed.

Fig. 2.12. GA reproduction cycle and stages. The manipulation process applies genetic operators to produce a new population of individuals. The two used operators are crossover and mutation. The offspring generated by this process occupies the place of the older population, and the cycle continues until a termination condition such as a desired level of fitness determined or specific number of cycles is reached [54]. A) Crossover Crossover is one of the genetic operators. It is used to produce a new population. Simply it swaps part of two chromosomes and genetic information to produce new chromosomes. Fig. 2.13 shows that, after the crossover point has been randomly chosen, portions of the parent's chromosome (strings) Parent 1 and Parent 2 are combined to produce the new offspring Son.

Fig. 2.13. GA Crossover.

15

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Photovoltaic Systems

B) Mutation The mutation operator introduces new genetic structures in the population by randomly changing some of its building blocks, in this way the modification is very random and thus not related to any previous genetic structures. It creates different structures related to other sections of the search space. Mutation is implemented by altering a random bit from a chromosome (string) occasionally, as shown in fig. 2.14 the operator is being applied to the fifth element of the chromosome.

Fig. 2.14. GA mutation.

C) Problem dependent parameters A real implementation takes account of a number of problem-dependent parameters. For instance, the offspring produced by the genetic manipulation (the next population to be evaluated) can replace either the whole population (generational approach) or just its less fitted members (steady-state approach). Problem constraints will prescribe the best option. Other parameters to be adjusted are the population size, crossover and mutation rates, evaluation method, and convergence criteria. D) Encoding The choice of underlying encoding for the individuals of the population has its effects on algorithm performance. Binary encodings have been used traditionally because they are easy to implement. The crossover and mutation operators described earlier are specific to binary encodings. E) The evaluation step The evaluation step is the closest one related to the actual system, which the algorithm is trying to optimize. It takes individual representation strings, and creates from them the actual individuals to be tested. The resultant strings should be of the right size to represent well the characteristics to be optimized. After the actual individuals have been created, they have to be tested by the evaluation or cognitive function and scored. These two tasks again are much related to the actual system being optimized.

2.5. Effects of Partial Shading on PV Array Characteristics Frequently, the PV arrays get shadowed, partially or wholly, by the moving clouds. Fig. 2.15 shows the shading effects on solar cells. Consequently, the PV characteristics get more complex with more than one peak. Fig. 2.16 and 2.17 show the I-V and P-V characteristics of PV array under partial shading condition. 16

Chapter 2

Photovoltaic Systems

Fig 2.15. The shading effects on solar cells.

Fig 2.16. I-V characteristic of PV array under partial shading condition.

17

Chapter 2

Photovoltaic Systems

Fig 2.17. P-V characteristic of PV array under partial shading condition.

2.6. Summary This chapter concentrated on the photovoltaic. It presented the mathematical model of photovoltaic. It showed that, the temperature variation has a negative correlation to the output power of the photovoltaic. Nevertheless, solar irradiance has a positive correlation to the PV output power. Moreover, the PV control and optimization methods are discussed and compared. It shows that, the maximum power point tracking is the best control method. Neural network optimization techniques are the most efficient technique to search the optimum values for tracking the maximum power point. Finally, the partial shading effects on the PV output power are listed.

18

Chapter 3

Chapter 3

Battery Charge Controllers

Battery charge controllers

3.1. Introduction After showing the construction of photovoltaic energy power systems, the battery charging control system is one of the most important part in photovoltaic systems. This chapter presents the battery charging control systems. It shows their main functions, structure, conceptual theories, types, control methods, and charging algorithms.

3.2. The functions of Battery charge controllers. The battery charger in renewable energy power systems has two main control duties. The first one is the tracking control of the maximum power point. The second one is the charging control system. It controls the battery charging process in order to protect the battery from overcharging and over discharging. In addition, the charging process should be as fast as possible in order to be ready to back up the PV array. Moreover, providing the ordinary functions of any charger like protecting the battery from over temperature, overvoltage, overloading... etc.

3.3. Charge controllers basics and theory Each Charge controller has a specific control method and algorithm. However, all of them have basic parameters and characteristics. Manufacturer's data generally provide the limits of controller application such as PV and load currents, operating temperatures, losses, set points, and set point hysteresis values. In some cases, the set points may be intentionally dependent upon the temperature of the battery and/or controller, and the magnitude of the battery current [55]. Fig. 3.1. shows that, the basic charge controller set points are:

3.3.1. Voltage regulation set point (VR) The voltage at which the battery should be disconnected to prevent overcharging. At this point, a controller will either discontinue battery charging or begin to regulate the amount of current delivered to the battery.

3.3.2. Low-Voltage load disconnect set point (LVD) The voltage at which the load is disconnected from the battery to prevent over discharge. The LVD defines the actual allowable maximum depth-of-discharge and available capacity of the battery. The available capacity must be carefully estimated in the system design and sizing process. The proper LVD set point will maintain good battery health while providing the maximum available battery capacity to the system.

Chapter 3

Battery Charge Controllers

3.3.3. Voltage regulation hysteresis (VRH) It is the difference between the VR set point and the voltage when the full array current is reapplied. The greater this voltage span, the longer the array current is interrupted from charging the battery. If the VRH is too small, then the control element will oscillate, inducing noise and possibly harming the switching element. The VRH is an important factor in determining the charging effectiveness of a controller.

3.3.4. Low voltage disconnect hysteresis (LVDH) It is the difference between the LVD set point and the voltage at which the load is reconnected to the battery. If the LVDH is too small, the load may cycle on and off rapidly at low battery state-of-charge, possibly damaging the load and/or controller. If the LVDH is too large, the load may remain off for extended periods until the array fully recharges the battery. With a large LVDH, battery health may be improved due to reduced battery cycling, but this will reduce load availability.

Fig. 3.1. The charge controller set points.

3.4. Charging control methods The series and shunt regulation are the two basic methods for charging control. The algorithm or control strategy of a battery charge controller determines the effectiveness of battery charging and PV array utilization, and ultimately the ability of the system to meet the electrical load demands. Some of the more common design approaches for charge controllers are described in this section.

3.4.1. Shunt Controller Since photovoltaic cells are current-limited by design. Therefore, they can be short-circuited without any harm. The ability to short-circuit modules or an array is the basis of operation for shunt controllers. The shunt controller regulates the charging of a battery from the PV array by short-circuiting the array internal to the controller. Fig. 3.2. shows the simple scheme of the shunt charging controller. A blocking diode must be used in series between the battery and the shunt element to prevent the battery 20

Chapter 3

Battery Charge Controllers

from short-circuiting when the array is regulating. Because there is some voltage drop between the array and controller and due to wiring and resistance of the shunt element, the array is never entirely short circuited, resulting in some power dissipation within the controller. For this reason, most shunt controllers require a heat sink. They are generally limited to use in PV systems with array currents less than 20 amps. The regulation element in shunt controllers is typically a power transistor or MOSFET, depending on the specific design. There are a couple of variations of the shunt controller design. The first is a simple interrupting, or on-off type controller design. The second type limits the array current in a gradual manner, by increasing the resistance of the shunt element as the battery reaches a full state of charge. These two variations of the shunt controller are discussed next.

Fig. 3.2. The shunt charge controller.

3.4.2. Shunt-Interrupting controller The shunt-interrupting controller completely disconnects the array current in an interrupting or on-off fashion when the battery reaches the voltage regulation set point. When the battery voltage decreases to the array reconnect voltage, the controller connects the array to resume charging the battery. This cycling between the regulation voltage and array reconnect voltage is why these controllers are often called „on-off‟ or „pulsing‟ controllers. Shunt-interrupting controllers are widely available and are low cost, however they are generally limited to use in systems with array currents less than 20 amps due to heat dissipation requirements. In general, on-off shunt controllers consume less power than series type controllers that use relays, so they are best suited for small systems.

3.4.3. Shunt-Linear controller Once a battery becomes nearly fully charged, a shunt-linear controller maintains the battery nearly at a fixed voltage by gradually shunting the array through a semiconductor regulation element. In some designs, a comparator circuit in the controller senses the battery voltage, and makes corresponding adjustments to the impedance of the shunt element, thus regulating the array current. In other designs, simple zener power diodes are used, which are the limiting factor in the cost and power ratings for these controllers. There is generally more heat dissipation in shunt-linear controllers than in shunt-interrupting types.

3.4.4. Series Controller The simple scheme of series charging controller is shown in Fig. 3.3. It works in series between the array and battery, rather than in parallel as for the shunt controller. There are several variations to the series type controller, all of which use some type of control or regulation element in series between the array and the battery. While this type of controller is commonly used in small PV systems, it is also the 21

Chapter 3

Battery Charge Controllers

practical choice for larger systems due to the current limitations of shunt controllers. In a series controller design, a relay or solid-state switch either opens the circuit between the array and the battery to discontinue charging, or limit the current in a series-linear manner to hold the battery voltage at a high value. In the simpler series interrupting design, the controller reconnects the array to the battery once the battery falls to the array reconnect voltage set point. As these on-off charge cycles continue, the „on‟ time becoming shorter and shorter as the battery becomes fully charged.

Fig. 3.3. The series charge controller.

3.4.5. Series-Interrupting controller It is the most simple series controller. The charge controller constantly monitors battery voltage, and disconnects or open-circuits the array in series once the battery reaches the regulation voltage set point. After a pre-set period, or when battery voltage drops to the array reconnect voltage set point, the array and battery are reconnected, and the cycle repeats. As the battery becomes more fully charged, the time for the battery voltage to reach the regulation voltage becomes shorter each cycle, so the amount of array current passed through to the battery becomes less each time. In this way, full charge is approached gradually in small steps or pulses, similar in operation to the shunt-interrupting type controller. The principle difference is the series or shunt mode by which the array is regulated.

3.4.6. Series-Interrupting, 2-step, Constant-Current controller This type of controller is similar to the series-interrupting type, however, when the voltage regulation set point is reached, instead of totally interrupting the array current, a limited constant current remains applied to the battery. This „trickle charging‟ continues either for a pre-set period of time, or until the voltage drops to the array reconnect voltage due to load demand. Then full array current is once again allowed to flow, and the cycle repeats. Full charge is approached in a continuous fashion, instead of smaller steps as described above for the on-off type controllers.

3.4.7. Series-Interrupting, 2-Step, Dual Set Point controller This type of controller operates similar to the series-interrupting type; however, there are two distinct voltage regulation set points. During the first charge cycle of the day, the controller uses a higher regulation voltage provides some equalization charge to the battery. Once the array is disconnected from the battery at the higher regulation set point, the voltage drops to the array reconnect voltage and the array is again connected to the battery. However, on the second and subsequent cycles of the day, a lower regulation voltage set point is used to limit battery overcharge.

22

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Battery Charge Controllers

3.4.8. Series-Linear, Constant-Voltage controller In a series-linear, constant-voltage controller design, the controller maintains the battery voltage at the voltage regulation set point. The series regulation element acts like a variable resistor, controlled by the controller battery voltage sensing circuit of the controller. The series element dissipates the balance of the power that is not used to charge the battery, and generally requires heat sinking. The series element and the voltage drop inherently control the current across it.

3.4.9. Series-Interrupting, Pulse Width Modulated (PWM) controller This algorithm uses a semiconductor-switching element between the array and battery, which is switched on/off at a variable frequency with a variable duty cycle to maintain the battery at or very close to the voltage regulation set point. Although a series type PWM design is discussed here, shunt-type PWM designs are also popular and perform battery charging in similar ways. Similar to the series-linear, constant-voltage algorithm in performance, power dissipation within the controller is considerably lower in the series interrupting PWM design. The PWM charge controller is a good low cost solution for small systems only, when the solar cell temperature is moderate to high (between 45°C and 75°C).

3.4.10. MPPT charge controller Fig. 3.4. shows the block diagram of an MPPT charge controller. It incorporates a DC-to-DC converter such that the PV array can operate at the maximum power point at the prevailing solar irradiance.

Fig. 3.4. The MPPT charge controller.

3.5. Battery charging control algorithms The battery charging control methods are classified into two classes, single stage, and multi-stage method. The constant current is a good example for single stage method and the constant current, constant voltage (CCCV) is a good example for multistage charging method. In the Constant trickle current (CTC) method, a small constant current is provided. The charging period is extremely long. In order to reduce this period, it needs to increase the constant charge current [56]. In the constant current constant voltage (CCCV) method, it overcomes the disadvantages of the long charging period, overcharging and over temperature. This method is divided into three stages, the first stage is the bulk stage, a constant current 23

Chapter 3

Battery Charge Controllers

(CC) is applied until the charging voltage reaches a predetermined value. In addition, the charging process goes into the second stage (absorption stage). In this stage, a constant voltage (CV) is applied for a while. The charging current will be automatically reduced along with the increase of the state of charge (SOC) until it reaches 3% of the nominal current value. At this time the third and last stage is started and continues until the charging process is completed [56] , all these stages are depicted in Fig. 3.5.

Fig. 3.5. Three stages charging process (CCCV). Studies show that, the multi stages charging method is the most efficient for battery charging regardless the battery type [56].

3.6. Battery charge controller structure The structure of battery charge controllers depends on the type of the controller. In the series and shunt controllers, it simply consists of a switching element, such as a relay that is switched on/off based on the value of a predefined set point. In a PWM and MPPT controllers, the circuits are more sophisticated. A PWM generator circuitry or microcontrollers are needed in order to drive the switches of a DC-DC converter. Fig. 3.6. shows the block diagram of an MPPT controller. It consists of a controller that manages the maximum power point tracking process and DC-DC converter. The MPPT controller is the solution of choice for higher power systems. The MPPT controller will also harvest substantially more power when the solar cell temperature is low below 45°C, or very high, above 75°C, or when irradiance is very low.

24

Chapter 3

Battery Charge Controllers

Fig. 3.6. The MPPT charge controller block diagram.

3.7. Summary The battery-charging controllers are discussed showing their functions, design considerations, types, control algorithms, and structure. It was clear that, the MPPT based battery charging controllers are the most efficient controller and the multi stage-charging algorithm is the most safe and efficient for battery charging.

25

Chapter 3

Battery Charge Controllers

26

Chapter 4

Chapter 4

Modeling and Simulation

Modeling and simulation Of The proposed system

4.1. Introduction This chapter covers the modeling of the proposed controller based on the mathematical models that are described in the previous chapter. Not only that, but also it presents the simulation process and its results, and also proves the validity of the modeled system. This chapter is divided into three major parts: Firstly, the overall system description and modes of operation are discussed. In addition, the system structure, components, and models are described. Secondly, the simulation test cases and test case results are shown and discussed. Finally, the system validation is conducted in order to verify and validate the proposed model. All Simulation and MATLAB/SIMULINK models are included in appendix B. All MATLAB scripts are included in appendix A.

4.2. System description In this section, the model of a general photovoltaic generator is described in full details. This model is required for the calculation of the system performance and the determination of its performance parameters. As shown in Fig. 4.1, the model of a photovoltaic system is mainly composed of the PV array of panels, the MPPT Controller, the DC-DC converter and the grid connected inverter.

Fig. 4.1. Complete system model Block diagram.

4.3. System structure and components The proposed system consists of the following components:

Chapter 4 Simulation

Modeling and

4.3.1. The PV model It is clear in the model block diagram that, the PV model inputs are the ambient temperature and irradiance. The outputs are the current, voltage, and power of the solar panel. The model is built according to the PV mathematical model equations [49].

4.3.2. The DC-DC converter model Using the DC/DC boost converter model, the PV voltage is boosted to the desired output. The pulse width modulation (PWM ) model, produces the required pulses to drive the converter switches. That is based on the duty cycle. The PWM model generates a 100 KHZ signal with pulse width depends on the duty cycle (D).

4.3.3. The inverter model It is composed of a universal bridge that inverts the converter output DC voltage to AC voltage. It is filtered by LC filter and transformed to be matched to the grid voltage using a three-phase transformer.

4.3.4. The MPPT controller In the previous chapter, the maximum power point methods are studied and compared. As a result, the perturb and observation (P&O) is selected for the proposed system model. The MPPT controller model shows that there are three modes of operation. In the first mode, the traditional MPPT method is used, in the second mode of operation, the PI controller is used, and the third mode is for the GA based MPPT controller.

4.3.5. The proposed new MPPT technique The new maximum power point tracking method makes the full use of the fact; there is only one maximum power point in the whole search range from zero voltage to the open circuit voltage Voc. To the left of this point the slope dP/dV is positive while it is negative on the right side. This method does not scan every point, like the traditional methods [4]; but every time the search region is divided into two parts. Based on the observation results of the slope dP/dV, one part is selected to be the new search region and the other part is discarded. For example, when taking /2) as the starting region from [0 to /2] ⁄ ⁄ or on the right region from [ /2 to ], according to the observation of . If is greater than zero, all points of the left region will be discarded, and the new search region is divided into two parts, also the new starting point will be updated to /4). Again, the MPP is located either on the left region from [ /2 to or in the right region from [ /4 to ], and so on. Consequently, for every observation time, a half of the search region is discarded and consequently, the search time is appreciably reduced compared to the conventional search method. The advantages of the new method are that, the search is very fast as compared to the traditional methods; it reaches the MPP in less than a 100 sample times. It reaches MPP directly with initial limited swing around the target point. Fig. 4.2 shows the flow chart of the new MPPT method. In initializing section, the starting points and ( ) are initialized, also the ending points ( ) and are initialized, then the operating point ( ) is moving according to the prescribed logic until it reaches the target point which is the peak power point.

28

Chapter 4 Simulation

Modeling and

Start 𝑷𝒔𝒕𝒂𝒓𝒕

𝑷𝑽𝒐𝒄 𝟐 , 𝑽𝒔𝒕𝒂𝒓𝒕

𝑷𝒆𝒏𝒅

𝑷𝑽𝒐𝒄 𝑽𝒆𝒏𝒅

𝑽𝒐𝒄 𝟐 𝑽𝒐𝒄

𝑷𝒄𝒖𝒓𝒓𝒆𝒏𝒕

𝑷𝒆𝒏𝒅 – 𝑷𝒔𝒕𝒂𝒓𝒕 𝟐

𝑽𝒄𝒖𝒓𝒓𝒆𝒏𝒕

𝑽𝒆𝒏𝒅 – 𝑽𝒔𝒕𝒂𝒓𝒕 𝟐

𝒅𝑷

𝑷𝒄𝒖𝒓𝒓𝒆𝒏𝒕 – 𝑷𝒔𝒕𝒂𝒓𝒕

𝒅𝑽

𝑽𝒄𝒖𝒓𝒓𝒆𝒏𝒕

𝑷𝒔𝒕𝒂𝒓𝒕 𝑷𝒄𝒖𝒓𝒓𝒆𝒏𝒕

𝑽𝒔𝒕𝒂𝒓𝒕 Yes

𝑽𝒔𝒕𝒂𝒓𝒕 𝑽𝒄𝒖𝒓𝒓𝒆𝒏𝒕

Yes

𝑷𝒆𝒏𝒅 𝑷𝒄𝒖𝒓𝒓𝒆𝒏𝒕

𝒅𝑷 𝒅𝑽 > No 𝒅𝑷 𝒅𝑽 =Pold ) if (P(2)>=Vold) Iref=Iref-Increment*DeltaI; else Iref=Iref+Increment*DeltaI; end else if (P(2)>=Vold) Iref=Iref+Increment*DeltaI; else Iref=Iref-Increment*DeltaI; end end; if (Iref > IrefH) Iref = IrefH; end % check for lower limit if (Iref < IrefL) Iref = IrefL; end % save power value Pold = P(1); Vold=P(2); if (P(4)==1) Iref=P(3); end; y = Iref; % Modification log % File name InitializeMPPtrackIref_new.m % Description : the new maximum power point tracking intiatization % Developer Yasser E Abueldahab % Date 01-04-2014 % ECEN2060 % Initialize MPPtrackIref % %global Ioptimum; global Iref; global Pold; 78

Appendix A

Software Source Codes

global Vold; global istart global iend clc; %clear; %Ts=1e-5; Vold=0; Pold = 0; % initial value for the sensed power istart=5.45; %Isc iend=5.45/2; Iref =5.45; % initial value for the current reference %Ioptimum=0; % Modification log % File name MPPtrackIref_new.m % Description : the new maximum power point tracking program % Developer Yasser E Abueldahab % Date 01-04-2014 % Output: reference current %p(1) is the input instantinous power Ppv %p(2) is the input intantinous voltage Vpv %p(3) is the Ioptimum at specific T and G come from GA %P(4) is the triger flage to update Iref = Iopt function y = MPPtrackIref(P) global Pold; global Vold; global Iref; global istart; global iend; IrefH = 5.45; % upper limit for the reference current IrefL = 0; % lower limit for the reference current %inputs : p(1)=Power,p(2)=Volt,p(3)=Ioptimum,P(4)=flage if its true then set Iref to Ioptimum to %display (P(1)); %display (P(2)); %display (Iref); if (P(1)>Pold ) if (P(2)>Vold) istart = Iref; %Iref=Iref-Increment*DeltaI; elseif (P(2) if (0) then discharging if (1) charging % rated voltage =12v , rated current=1A , battery capacity 10 Ah will be % set as parameters P_rated_current P_rated_voltage P_capacity % Ibat=Irated ,Vbat=11.80 , SOC= 0% %CCCV %Irated=1 pu = 1A; %Vrated=1 pu = 12v; %SOC=0; %Vc=.25 %R=(Vt-Vr)/Ibat global Irated; %pu = 1A; global Vc; global SOC; global Tsample; global Ibat; global period; period=.1; Tsample=.00003; si=-50; %slope of i sv=9.375; %slope v if x(2)==1 if SOC.08 && SOC ppv_old Then If vpv > vpv_old Then 'dp / dv > 0 D-'ftemp = vpv * 100 - 5 duty = duty - 5 Else 'dp / dv < 0 D++ 'ftemp = vpv * 100 + 5 duty = duty + 5 End If End If If ppv < ppv_old Then If vpv > vpv_old Then 'dp / dv < 0 D++

90

Appendix A

Software Source Codes

'ftemp = vpv * 100 + 5 duty = duty + 5 'ftemp = ftemp / 100 Else 'dp / dv > 0 D-'ftemp = vpv * 100 - 5 duty = duty - 5 End If End If 'duty = 100 - ftemp / 40 If scancycle >= 5 Then If temp tprev Or alumn gprev Then 'SEARCH \GA FOR OPTIMUM 'duty = 50 duty = duty End If End If 'duty range 66-83 If duty = 83 Then duty = 83 End If vpv_old = vpv ppv_old = ppv If vbat > 40 Then 'PWMoff 1 'Else

91

Appendix A

Software Source Codes

'PWMon 1, 6 duty = 100 * vpv duty = duty / 40 duty = 100 - duty 'duty = vpv * 100 'duty = duty / 40 'duty = 100 - duty Lcdcmdout LcdClear 'clear LCD display WaitMs 1 Lcdcmdout LcdLine1Home Lcdout "vbat>=", #vbat End If duty1 = duty * 255 / 100 PWMduty 1, duty1 tprev = temp gprev = alumn Goto loop 'loop forever

92

Appendix B

The Simulink Models

Appendix B The Simulink Models This section lists the MATLAB/SIMULINK simulation models of the proposed charge controller and snapshot of the implemented system.

Fig B.1. The Simulink of the complete model.

Appendix B

The Simulink Models

Fig B.2. The PV model Simulink.

Fig. B.3. The PV model block diagram.

VPV

Fig. B.4. The DC-DC boost converter.

94

Appendix B

The Simulink Models

Fig. B.5. The converter PWM generator.

Fig. B.6 The Inverter model.

95

Appendix B

The Simulink Models

Fig. B.7 MPPT controller Block Diagram.

Fig B.8. MPPT controller Simulink model.

Fig. B.9. PI controller model.

96

Appendix B

The Simulink Models

Fig. B.10. The GA block diagram.

Fig. B.11 GA Simulink model.

97

Appendix B

The Simulink Models

Fig. B.12 the battery model.

Fig. B.13. The proposed controller prototype.

98

Appendix B

The Simulink Models

Fig. B.14. The prototype board of the power circuit.

Fig. B.15. The prototype board of the control circuit.

99

Appendix B

The Simulink Models

Fig. B.16. The prototype of sensor circuit board.

Fig. B.17. The prototype of the battery current sensor board.

100

Appendix B

The Simulink Models

Fig. B.18. The charger controller PCB.

Fig. B.19, Snapshot of system operation.

101

Appendix B

The Simulink Models

Appendix B

The Simulink Models

‫كلية الهندسة‬ ‫قسم هندسة القوى واألالت الكهربائية‬

‫تطوير جهاز شحن البطارية ألنظمة الطاقة المتجددة‬ ‫رسالة‬ ‫مقدمة للحصول على درجة الماجستير فى الهندسة الكهربائية‬ ‫(قسم هندسة القوى واألالت الكهربائية)‬ ‫مقدمة من‬

‫ياسر الحسيني عطية السيد‬ ‫بكالوريوس الهندسة الكهربائية‬ ‫جامعة عين شمس يوليو ‪0222‬‬ ‫تحت أشــــراف‬

‫أ‪.‬د‪ .‬عبد الحميم عبد النبى ذكرى‬

‫قسم هندسة اإلليكترونيات واالتصاالت‬ ‫كمية الهندسة جامعة عين شمس‬

‫د‪ .‬نجار حســـن ســــعد‬

‫قسم هندسة القوى واآلالت الكهربائية‬ ‫كمية الهندسة جامعة عين شمس‬

‫القاهرة – ‪0202‬‬

‫كلية الهندسة‬ ‫قسم هندسة القوى واألالت الكهربائية‬ ‫الموافقة على المنح‬ ‫تطوير جهاز شحن البطارية ألنظمة الطاقة المتجددة‬ ‫مقدمة من‬

‫ياسر الحسيني عطية السيد‬ ‫لجنة الحكم‬ ‫اإلســــــــــم‬ ‫أ‪.‬د‪ .‬محمد عبد المنعم أبو العال‬

‫التوقيع‬ ‫‪------------------‬‬

‫أستاذ بقسم هندسة اإلليكترونيات واالتصاالت‬ ‫كمية الهندسة جامعة المستقبل‬ ‫أ‪.‬د‪ .‬أحمد عبد الستار عبد الفتاح‬

‫‪------------------‬‬

‫أستاذ بقسم هندسة القوى واآلالت الكهربائية‬ ‫كمية الهندسة جامعة عين شمس‬ ‫أ‪.‬د‪ .‬عبد الحميم عبد النبى ذكرى‬ ‫أستاذ بقسم هندسة اإلليكترونيات واالتصاالت‬

‫‪------------------‬‬

‫كمية الهندسة جامعة عين شمس‬ ‫د‪ .‬نجار حسن سعد‬ ‫أستاذ مساعد بقسم هندسة القوى واآلالت الكهربائية‬ ‫كمية الهندسة جامعة عين شمس‬

‫‪------------------‬‬

‫كلية الهٌذسة‬ ‫قسن القىي واآلالت الكهرتائية‬ ‫رسالة هاجستير‬ ‫‪ :‬ياسر الحسيىي عطيت السيد‬

‫اسن الطالة‬

‫عٌىاى الرسالة ‪ :‬تطىير جهاز شحه البطاريت ألوظمت الطاقت المتجددة‬ ‫إسن الذرجة‬ ‫القسن‬

‫‪ :‬ماجستير‬ ‫‪:‬‬

‫هىدست القىي واألالث الكهربائيت‬

‫لجٌة اإلشراف‬ ‫‪ -0‬أ‪.‬د‪ .‬عبد الحميم عبد النبى ذكرى‬ ‫أستاذ بقسم هندسة اإلليكترونيات واالتصاالت‪ -‬كمية الهندسة‪ -‬جامعة عين شمس‬

‫‪ -2‬د‪ .‬نجار حسن سعد‬

‫أستاذ مساعد بقسم القوى واآلالت الكهربائية – كمية الهندسة‪ -‬جامعة عين شمس‬

‫الدراسات العميا ‪:‬‬

‫ختم اإلجازة ‪ :‬أُجيزت الرسالة بتاريخ‬

‫‪.........../.../ ...‬‬

‫تاريخ البحث ‪........ / ... / ... :‬‬ ‫‪.........../.../ ...‬‬ ‫موافقة مجمس القسم‬

‫‪.........../.../ ...‬‬

‫موافقة مجمس الجامعة‬

‫التعريف توقذم الرسالة‬ ‫اسن الثاحث‬

‫‪ :‬ياسر الحسيني عطية السيد‬

‫تاريخ الويالد‬

‫‪0791/01/11 :‬‬

‫‪U‬‬

‫هحل الويالد ‪ :‬القاهرة‬ ‫الذرجة العلوية االولي ‪ :‬بكالىريىس الهىدست الكهربيت‬ ‫الجهة الواًحة للذرجة العلوية االولي ‪ :‬كليت الهىدست – جامعت عيه شمس‬ ‫تاريخ الوٌح ‪ :‬يىليى ‪0222‬‬ ‫الىظيفة الحالية ‪ :‬مهىدس كهرباء – المقاولىن العرب‬

‫الولخص‬ ‫يرجع اهمية استخدام البطاريات فى انظمة الطاقة الجديدة والمتجددة مثل الطاقة الشمسية الى انها‬ ‫تلعب دور مصدر القدرة الكهربائية االحتياطي فى حالة غياب الشمس‪ .‬تقدم لنا هذه الرسالة تطوير لمتحكم‬ ‫فى شحن البطاريات ألنظمة الطاقة المتجددة‪.‬‬ ‫اوال تقدم الرسالة نبذة عن أنظمة الطاقة الشمسية ومكوناتها بما فى ذلك أنظمة التحكم فى شحن‬ ‫البطاريات‪ .‬ثم يأتى استعراض للنشرات واإلنجازات الحديثة فى هذا السياق وجمع المزايا والعيوب من أجل‬ ‫المقارنة والتحقق من صالحية النظام المقترح‪ .‬ثانيا يتم اشتقاق جميع النماذج الرياضية ألنظمة توليد الطاقة‬ ‫الكهربائية من الشمس ومن ثم تكون اساس لبناء نموذج المحاكاة المستخدم فى اختبار النظام فى جميع‬ ‫الظروف المختلفة للتشغيل وبعد ذلك يتم التحقق من صالحية نموذج المحاكاة‪ .‬وجدير بالذكر انه تم ابتكار‬ ‫طريقة جديدة لتتبع نقطة خرج القدرة العظمى ‪ .‬هذه الطريقة تعتمد على إحدى تقنيات الذكاء االصطناعى‬ ‫وهى الخوارزميات الجينية وذلك للبحث على النقطة المثلى والتى تكون أقرب مايكون لنقطة خرج القدرة‬ ‫العظمى وبناءا عليه تقصر مسافة المسار للوصول للنقطة المرجوة ومن ثم يقل وقت البحث‪.‬‬ ‫ثم بعد ذلك يأتي دور اختبار نظام المحاكاة مرة فى حالة تشغيل الطرق التقليدية لتتبع نقطة خرج‬ ‫القدرة العظمى ‪ .‬ومرة أخرى فى حالة استخدام الطريقة المبتكرة ثم المقارنة بين النتائج ‪ .‬نتائج المحاكاة‬ ‫اث بتت ان الطريقة المبتكرة اسرع عشرة آالف مرة من الطرق التقليدية ليس ذلك وحسب بل ايضا الطريقة‬ ‫الجديدة تصل إلى الهدف مباشرة وبدون تردد حول الهدف كما هو الحال فى الطرق التقليدية مما يحقق‬ ‫المزيد من االستقرار والكفاءة فى استخالص الطاقة‪ .‬وتقدم الرسالة أيضا استعرض للنموذج العملى والدوائر‬ ‫الكهربائية سواء كانت دوائر القدرة أو التحكم‪ .‬وتم إجراء االختبارات العملية لجميع ظروف التشغيل‬ ‫والمقارنة بين النتائج العملية ونتائج نموذج المحاكاة وذلك من أجل التحقق من صالحية النموذج العملي‬ ‫المبتكر‪.‬‬ ‫ولقد جاءت النتائج العملية موافقة لنتائج نموذج المحاكاة ‪ .‬حيث ان جميع النتائج اثبتت صالحية‬ ‫النظام المقترح في تتبع نقطة خرج القدرة العظمى وبسرعة تفوق الطرق التقليدية مئات المرات وبدقة‬ ‫وكفاءة ملحوظة‪ .‬أما بالنسبة لعملية الشحن فلقد جاءت النتائج لتقر ان وقت الشحن أقل بكثير من الطرق‬ ‫المعتادة ويرجع الفضل الستخدام خوارزم متعدد المراحل فى عملية الشحن‪ .‬يعتبر الظل الجزئي أو الكلي‬ ‫واحد من أعظم المشاكل التى تواجة أنظمة الطاقة الشمسية لذلك كان واحدا من حاالت االختبار فى نماذج‬ ‫المحاكاة والنماذج العملية وكانت النتيجة هي نجاح النظام المقترح فى اجتياز هذه المشكلة وبكفائة عالية‪.‬‬ ‫وتتكون هذه الرسالة من ستة فصول كالتالي ‪:‬‬ ‫الفصل األول ‪-:‬‬

‫يعرض بإيجاز مكونات أنظمة توليد الطاقة الكهربائية من الطاقة الشمسية متضمنا متحكمات عملية‬ ‫شحن البطاريات ‪ .‬كذلك يقوم بمراجعة أحدث النشرات والدوريات فى هذا السياق مجمعا لإلنجازات‬ ‫وا لمزايا والعيوب لكل منها كي يتم االستفادة منها فى عملية التطوير المقترحة‪ .‬وأخيرا يعرض الغرض من‬ ‫الرسالة ومحتوياتها‪.‬‬ ‫الفصل الثانى ‪-:‬‬ ‫يقدم هذا الفصل ما تم اشتقاقة من نماذج رياضية ألنظمة الطاقة الشمسية ومكوناتها من خاليا‬ ‫شمسية وطرق التشغيل والتحكم وانظمة التحكم فى عمليات الشحن والتى ستكون أساس بناء نموذج‬ ‫المحاكاة‪.‬‬ ‫الفصل الثالث ‪-:‬‬ ‫يقدم النظريات واألسس التى يجب مراعتها فى عمليات التصميم لمتحكمات الشحن متضمننا طرق‬ ‫التحكم المختلفة والمقارنة بينها وكذلك انواع متحكمات الشحن‪.‬‬ ‫الفصل الرابع ‪-:‬‬ ‫يعرض هذا الفصل نموذج المحاكاة لنظام الطاقة الشمسية متضممنا متحكم شحن البطارية ودراسة‬ ‫مكونات هذا النموذج ووصف طرق التشغيل وأجراء جميع االختبارات المحاكية لظروف التشغيل المختلفة ‪.‬‬ ‫ثم بعد ذلك يتم استعراض النتائج وتحليلها والتعليق عليها ‪ .‬وأخيرا يتم التحقق من صالحية النموذج المقترح‬ ‫عن طريق مقارنة نتائج نموذج المحاكاة مع النتائج المحسوبة من المعادالت الرياضية‪.‬‬ ‫الفصل الخامس ‪-:‬‬ ‫يعتبر هذا الفصل من أهم الفصول حيث انه يقدم المنتج النهائي من كل األعمال السابقة ‪ .‬وفيه يتم‬ ‫عرض النموذج العملي المقترح ودوائر القدرة والتحكم الخاصة به ‪ .‬ثم يتم استعراض االختبارات العملية‬ ‫لجميع ظروف التشغيل المختلفة متضمنا اختبارات الشحن للبطاريات واالختبارات الخاصة بمشكلة الظل‬ ‫الجزئي أو الكلى وهى من أعظم المشاكل التى تواجه انظمة الطاقة الشمسية‪ .‬وبعد ذلك يم مقارنة النتائج‬ ‫العملية مع نتائج المحاكاة للمقارنة والتحقق من صالحية النموذج العملي المقترح‪.‬‬ ‫الفصل السادس ‪-:‬‬ ‫يقدم هذا الفصل جميع االستنتاجات من التجارب العملية واختبارات المحاكاة والمقارنة بينها وبين‬ ‫األنظمة التقليدية من ناحية الصالحية والكفاءة والمزايا والعيوب‪ .‬باألضافة إلى ذلك يقدم هذا الفصل‬ ‫التوصيات والمقترحات من أجل التطويرات المستقبلية للنموذج المقترح‪.‬‬