AMERICAN UNIVERSITY OF BEIRUT MAXIMUM POWER POINT TRACKING SYSTEM: AN ADAPTIVE ALGORITHM FOR SOLAR PANELS by MOHAMMED ALI SERHAN A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering to the Department of Electrical and Computer Engineering of the Faculty of Engineering and Architecture at the American University of Beirut Beirut, Lebanon January 2005
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MAXIMUM POWER POINT TRACKING SYSTEM of solar panel
PV modules used to be expensive, but in recent years, their price has been slowly dropping, and as they become increasingly economical, they will be used in more applications. In the U.S. the cost of installing solar had fallen from $55 per peak watt in 1976 to about $4 per peak watt in 2001 [1]. PV modules output efficiency has also increased in recent years. PV cells, having power conversion efficiencies as high as 31%, have been developed in a laboratory environment over the last decade [2]. With these growths in photovoltaic technology, there is no doubt that PV will have a good stand in the near future. However in this thesis, the emphasis is on the study of PV system control part. 1.2.
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AMERICAN UNIVERSITY OF BEIRUT
MAXIMUM POWER POINT TRACKING SYSTEM: AN ADAPTIVE ALGORITHM FOR SOLAR PANELS
by MOHAMMED ALI SERHAN
A thesis submitted in partial fulfillment of the requirements
for the degree of Master of Engineering to the Department of Electrical and Computer Engineering
of the Faculty of Engineering and Architecture at the American University of Beirut
Beirut, Lebanon January 2005
AMERICAN UNIVERSITY OF BEIRUT
MAXIMUM POWER POINT TRACKING SYSTEM: AN ADAPITIVE ALGORITHM FOR SOLAR PANELS
by MOHAMMED ALI SERHAN
Approved by: ______________________________________________________________________ Dr. Sami Karaki, Professor Advisor Electrical and Computer Engineering ______________________________________________________________________ Dr. Fouad Morad, Professor Member of Committee Electrical and Computer Engineering ______________________________________________________________________ Dr. Riad Chedid, Professor Member of Committee Electrical and Computer Engineering Date of thesis defense: January 7, 2005
AMERICAN UNIVERISTY OF BEIRUT
THESIS RELEASE FORM
I, Mohammed Ali Serhan authorize the American University of Beirut to supply copies of my thesis to
libraries or individuals upon request. do not authorize the American University of Beirut to supply copies of my thesis to
libraries or individuals for a period of two years starting with the date of the thesis defense.
____________________ Signature
____________________ Date
v
ACKNOWLEDGMENTS
I would like to thank my advisor Dr. Sami Karaki for the enlightening advice and guidance he provided during the elaboration of this work. Many thanks go to Dr. Fouad Mrad and Dr. Riad Chedid, who were in my committee, for providing a lot of insightful comments during the presentation of this thesis. I'm also very grateful to the dearest Dr. Lana Al Chaar, who proposed the topic in the first place, for her support. All respect and admiration go to Mr. Antoine Al Asal, a true brother, partner and friend. Warm love and support from my parents is the main factor of success throughout all years of study. Lastly, praise goes to Almighty Allah whose help and guidance has given me the strength needed to complete this work.
AN ABSTRACT OF THE THESIS OF
Mohammed Ali Serhan for Master of Engineering Major: Electrical Engineering
Title: Maximum Power Point Tracking System: An Adaptive Control Algorithm for Solar Panels.
Energy is fundamental to the wellbeing of our society—it powers our homes, businesses, and industries. However, energy, obtained from fossil fuels is presenting challenges to many nations; not only these energy resources are depletable but are also major contributors to atmospheric pollution and global warming. ‘Renewable Energy’ is a new trend in clean energy production. This includes power generated from water, wind, solar radiation, biomass and other resources. This development of renewable power sources will save fossil fuel resources, and help improve the quality of our environment. One of the renewable energy sources, Photovoltaic systems have a great potential because it makes use of the most abundant energy on earth that is sunlight.
As the maximum power operating point (MPP) of the PV module changes with atmospheric conditions, e.g. solar radiation and temperature, an important consideration in the design of efficient PV system is to track the MPP correctly. The objective of this thesis is to design and build a Maximum Power Point Tracker (MPPT) to charge a lead acid battery.
The design consists of a PV panel, a 12V battery, H-bridge converter and a control module that uses the PIC16F874 microcontroller. The controller obtains the current and voltage values from the PV array and performs pulse width modulation (PWM) on the converter to charge the battery with maximum available power. Battery’s state of charge is also controlled by the microcontroller to protect the battery from being overcharged. The Perturb and Observe (PAO) method is used as an algorithm to track the maximum power point of the PV array. The performance of the PAO algorithm in tracking maximum power point has been improved by implementing an adaptive perturbation scheme to track correctly the MPP in case of rapidly varying weather conditions and to get high conversion efficiency.
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CONTENTS
Page
ACKNOWLEDGEMENTS ...………………………………..………… v ABSTRACT…...……………………………………….……………….….… vi LIST OF ILLUSTRATIONS……………….…………………...……… x LIST OF TABLES......................................................................................... xv Chapter 1. INTRODUCTION…….….…………………………………………... 1 1.1. An Overview ……………………..…………..…….………..... 1 1.2. Brief Background ………….…………………...……………..... 1 1.3. Problem definition and motivation …...……………………..….. 2 1.4. Scope ……………………………………………………………… 3 2. LITERATURE REVIEW …………………………………………. 4 2.1 Maximum Power Point Tracker …..…………....................... 4 2.2. Switched-Mode Converters….……………......................... 5 2.3. Controller ………….………………………………………….... 5 2.3.1 Voltage feedback control …………………………….. 5 2.3.2 Power feedback control ………………………………. 6 2.4. MPPT control Algorithms …………………………………....... 6 2.4.1 Perturb and Observe technique ……………………….. 6 2.4.2 Incremental Conductance technique ………………….. 8 2.4.3 Constant Reference Voltage ………………………….. 10 2.4.4 Other techniques: CMPPT, VMPPT…………………… 11 2.5. Comparative Study………………………………………………. 12 vii
viii
2.6. Contribution of this Thesis………………………………………… 13 3. SYSTEM COMPONENTS MODELING ............…………... 14 3.1. Photovoltaic ………..……………………………………....…….. 14 3.1.1 Solar Cells ………………………………………………. 14 3.1.2 Photovoltaic effect …………… ………………………… 15 3.1.3 Electric Model of the PV cell …………………………… 15 3.3.4 Irradiation and cell Temperature effect ………………. 17 3.3.5 PV models and PV arrays ………………………………. 18 3.2. Battery ……………………………………………………………. 18 3.2.1 Lead Acid battery ………………………………………. 19 3.2.2 Battery Chemistry ……………………………………. 19 3.2.3 Amp-Hour Capacity and Charge Rate ……………….. 20 3.2.4 State of Charge 20 3.2.5 Deep cycles vs. starter batteries ……………………… 21 3.2.6 Lifespan of batteries ……………………………………. 22 3.2.7 Battery hazards …………………………………………. 22 3.3. DC-DC Converters …………………………….………………… 23 3.3.1 Switching converter Topologies ……………………….. 23 3.3.2 Non-Isolated Switching converters …………………….. 24 3.3.2.1 Buck Converter 24 3.3.2.2 Boost Converter 32 3.3.2.3 Buck-Boost Converter 34 3.3.3 Isolated DC-DC Converters ……………………………. 35 3.3.3.1 Flyback Conveter 36 3.3.3.2 Forward Converter 37 3.3.3.3 H-bridge Converter 38 3.4. Zero Voltage Switching ……………………..…………………… 39
4. MPPT SIMULINK MODEL ………………………………… 42 4.1. Introduction……………………………....…….………………….. 42 4.2. Simulink Blocks …………….……………………………………. 42 4.3. What is an S-Function …………………………………………… 43 4.4. Implementation of PV cell using S-Function ………………….… 44
ix
4.5 Converter and Controller Blocks ………………………………… 45 4.6 Battery Block ……………………………………………………. 49 5. MPPT SYSTEM IMPLEMENTATION …………………… 52
5.4. Software Design …………………………………...……………... 66 5.4.1. Main Program …………………………………………. 66 5.4.2. Charging-Tracking mode ……………………………… 67
6. SYSTEM RESULTS AND DISCUSSION……………….... 74 6.1. Introduction ……………………………..…..….………………... 74 6.2. PV model Validation ……………………..…………………….... 74 6.3. Simulink Simulation Results …………………………….……..… 78 6.4. Hardware Results ………………………………………………… 81 6.5. Comparison of Tracking algorithm ………….………………….. 85 6.5.1 Minor change in the weather conditions ………………. 85 6.5.2 Major change in the weather conditions ………………. 87 6.6. Power Budget ................................................................................. 88 6.6.1. Inductor conduction loss 88 6.6.2. Diode conduction loss 89 6.6.3. MOSFET conduction loss 89 6.6.4. Transformer power loss 90
6.6.5. Other power factor factors 92 7. CONCLUSIONS AND FUTURE WORK …......................... 94 7.1. Summary ………………………………………………………... 94 7.2. Testing Environment ……………………………………………… 94 7.3. Better Tracking Algorithm ……………………………………….. 95 7.4. Simulink Model ………………………………………………….. 96 7.5. Future Work ……………………………………………………… 96 APPENDIX A……..………………………………………………………….. 98 1. Matlab program code ……………………………………………... 98 APPENDIX B…………………………………………………………………. 102 1. PCB circuit design ……………………………………………..... 102 APPENDIX C…………………………………………………………………. 105 1. Assembly program code ………………..………………………..... 105 APPENDIX D……………………………………………………………….. 130 1. Datasheet ………………………………………………………... 130
REFERENCES……..……………………………………………………… 132
x
xi
ILLUSTRATIONS Figure Page 1.1. (a) The I-V characteristic curve, (b) The PV panel Maximum Power
3.13. Voltage and current waveforms (Boost Converter) ……………………. 33 3.14. Schematic for buck-boost converter ........................................................ 34
3.15. Waveforms for buck-boost converter …………………………………. 35
4.8. Different current levels with respect to variable duty cycle ................... 48
4.9. MPPT tracking scheme using a variable step size ……………………. 49
4.10. Battery Model Block ………………………......................................... 50 4.11. PV array 'MPPT' system ………………………………………………. 50 5.1. The PV maximum power point tracking system …………………........ 53 5.2. Converter and Controller ……………………………………………… 55 5.3. (a) 50% duty cycle switching (b) variable duty cycle switching……… 56 5.4. Bridge output due to small and large phase difference ………………. 56
xiii
5.5. Case of zero phase difference between V1 and V2 ……………………. 57 5.6. Case of V1 and V2 being out of phase ……………………………….. 57 5.7. PWM output controls the Bridge output ……………………………… 58 5.8. PWM, Bridge, Rectified and Filtered voltage waveforms …………… 58 5.9. Phase splitter output waveforms …………………………………….. 59 5.10. Phase splitter schematic diagram ……………………………………. 60 5.11. Driver A output waveforms ………………………………………….. 60 5.12. Driver ‘A’ schematic diagram ……………………………………….. 61 5.13. Charging and discharging stages …………………………………….. 62 5.14. Bridge Converter schematic diagram ………………………………… 63 5.15. Transformer, Rectifier and Filter waveforms ……………………….. 64 5.16. Main Program Flow Chart ……………………………………………. 67 5.17. The conventional PAO algorithm Flow Chart ………….………….… 68 5.18 Scanning with small step in case of varying weather conditions …….. 69 5.19 The Adaptive PAO algorithm Flow Chart ……………………………. 70 5.20. Hunting with large step size ∆ ………………………………………. 70 5.21. Hunting with smaller step size ∆/2 ………………………………….. 71 5.22. MPP scanning direction ……………………………………………… 72 5.23. Locking on the MPP with duty-cycle ratio 1/255 …………………… 72 5.24. Scanning in case of varying weather conditions …………………….. 73 6.1. I-V characteristic curves for different Insolation levels ……………… 75 6.2. P-V characteristic curves for different Insolation levels …………….. 75 6.3. I-V characteristic curves for different temperatures …………………. 76 6.4. P-V characteristic curves for different temperatures ………………… 76 6.5. Load circuit schematic diagram ……………………………………… 77
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6.6. PV characteristic curves under different Insolation levels …………. 77 6.7. Characteristic curves of the 'BP 380' PV module using the load circuit 78 6.8. Characteristic curves of the 'BP 380' module using the Simulink model 78 6.9. MPPT Simulink Model ………………………………………………... 79 6.10. P-V characteristic curve of the PV array at 800W/m2, 27oC …………. 79 6.11. Switching Power, Voltage, and Current supplied to the converter …… 80 6.12. Power, Voltage, and Current delivered to the battery ………………… 80 6.13. Characteristic curves: (a) I-V curve, (b) P-V curve ………………….. 81 6.14. VB performance monitor ……………………………………………. 82 6.15 Tracking the MPP in case of varying Insolation level ………………. 84 6.16 Tracking scheme for minor change in the weather conditions ………. 86 6.17 Tracking scheme for major change in the weather conditions ………. 88 6.18 Primary winding current waveform…………………………………… 90 6.19 Waveforms across the terminals of the transformer ………………… 90 6.20. Diodes’ current waveform during different intervals ………………… 91 6.21. Transformer windings contribution to power loss …………………… 92
xv
TABLES Table Page 6.1. MPP readings on a sunny day in October……........................................ 81
6.2. Snapshots of MPP Recorded Data............................................................ 83
6.3. MPP Daily Average Recorded Data 2004…............................................ 83
6.4. Tracking with adaptive incremental step ∆a ………………………..…….. 87
6.5. Scanning with adaptive incremental step ∆a ………………………..…….. 88
6.6. Power Budget……………………........................................................... 93
To My Family …..
AND
To All Palestinians Who Struggle For Freedom!
CHAPTER 1
INTRODUCTION
1.1. An Overview
As green house effects and environmental issues become more of a concern,
renewable energy is one of the options in reducing pollution. Furthermore, fossil fuel
resources used in the production of power are dwindling and becoming more expensive;
‘Renewable Energy’ is the new trend in energy production to reduce emission in the
long run. This includes power generated from water, wind, solar radiation, biomass and
other resources. These resources are considered to be clean and continuously found in
nature.
PV modules used to be expensive, but in recent years, their price has been
slowly dropping, and as they become increasingly economical, they will be used in
more applications. In the U.S. the cost of installing solar had fallen from $55 per peak
watt in 1976 to about $4 per peak watt in 2001 [1]. PV modules output efficiency has
also increased in recent years. PV cells, having power conversion efficiencies as high as
31%, have been developed in a laboratory environment over the last decade [2]. With
these growths in photovoltaic technology, there is no doubt that PV will have a good
stand in the near future. However in this thesis, the emphasis is on the study of PV
system control part.
1.2. Brief Background
The world trend nowadays is to find a non-depletable and clean source of
energy. The most effective and harmless energy source is probably solar energy, which
1
for many applications is so technically straightforward to use. Use of solar energy
instead of fuel combustion, particularly for simple application like low and medium
temperature water heating and for stand alone PV systems in rural areas, can reduce the
load on the environment.
Solar energy for electricity generation can be harvested by the use of
photovoltaic (PV) array, which has an optimum operating point called the maximum
power point (MPP) as shown in Fig. 1.1. This MPP varies depending on cell
temperature and the present insolation level [3]. To get the maximum power from the
PV, a maximum power point tracker (MPPT) must be used.
Fig. 1.1 (a) The I-V characteristic curve, (b) The PV panel Maximum Power Point
1.3. Problem Definition and Motivation
Several maximum power point tracker (MPPT) algorithms are implemented to
track this MPP, yet many research works to implement a low-cost highly efficient
MPPT algorithm are being conducted. This algorithm should respond in a short time to
the change in the atmospheric conditions to avoid energy loss. Moreover it should not
2
be stuck in local power peaks if any; this happens in case of partial shadowing or dust
on the PV panel.
Furthermore, the converter has to be very efficient, in order to transfer more
energy to the load. This is achieved by using a simple soft-switched topology. Much
higher conversion efficiency at lower cost will then result, making the MPPT an
affordable solution for small PV energy systems.
1.4. Scope
The objective of this thesis is to design and build an experimental model,
develop a Simulink model of a stand-alone photovoltaic system with an MPPT
controller, and to analyze its operation.
Chapter 2 reviews the various literature of maximum power point tracking
algorithms. Perturb and Observe, Incremental conductance, Constant reference voltage
and other algorithms are presented in this chapter.
Chapter 3 will highlight the equations that are needed to implement the MPPT
system. PV cells electrical representation and current-voltage relation, Lead acid battery
chemistry and hazards, and dc-dc converters are presented in this chapter.
Chapter 4 and Chapter 5 show the Simulink block and the experimental model
that were implemented. Chapter 6 will discuss the simulated and experimental results.
In this chapter the hardware model data for changing weather conditions are evaluated.
Chapter 7 will conclude the thesis and will look on the future development of
the thesis. References and appendices are attached at the end of this thesis report.
3
4
CHAPTER 2
LITERATURE REVIEW
2.1. Maximum Power Point Tracker
Solar energy can be harvested by the use of a photovoltaic (PV) array, which
has an optimum operating point called the maximum power point (MPP) as shown in
Fig. 1.1(b). The I-V curve will change as the temperature and insolation levels change
as shown in Fig. 2.1, thus the MPP will vary accordingly [4]. So we need to control
either the operating voltage or the current to get maximum power from the PV panel at
the prevailing temperature and insolation conditions using a maximum power point
tracker (MPPT) which should meet the following conditions [5]:
• Operate the PV system as close as possible to the MPP irrespective of the
atmospheric changes.
• Have low cost and high conversion efficiency.
• Provide an output interface compatible with the battery-charging
Immediately after recording the MPP data, the MPPT hardware system was set
into action. The visual basic VB program, developed to draw the voltage, current, and
power versus time during tracking action, recorded the following data: Psavg = 53.4W,
Isavg = 4.1A and Vsavg = 13V as shown in Fig. 6.14, where Psavg is the power extracted
form the solar panel. Then the tracking efficiency was calculated as: ξT = Psavg / Pmpp =
53.4 / 56.168 = 0.95. The power electronics efficiency was also measured: ξPE = Pb /
Psavg =43.3/ 53.4 = 0.81, where Pb is the average voltage delivered to the battery.
Fig. 6.14 VB performance monitor
Table 6.2 shows a few selected results taken on various dates and different
daytimes for few minutes of MPPT system operation. It is worth mentioning that as the
solar current Is increases, the power electronics efficiency decreases. This is due to the
fact that the power losses in the hardware design are calculated as the square of the solar
current Is multiplied by the equivalent resistance (Ploss = I2 R), while the input power is
directly proportional to the current and voltage (Pinput=I V). Table 6.3 presents the daily
average reading obtained between 10:00 am and 5:00 pm for the various mentioned
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dates. The average value for the tracking efficiency was recognized at 95% while the
power electronics efficiency at 80% only.
Table 6.2 Snapshots of MPP Recorded Data
Date-Hour Weather condition
Pmmp (W)
Is (A)
Vs (V)
Ps (W)
Pb
(W)
ξT %
ξPE %
29/4/2004 12:50 pm
Sunny day Few clouds
53.4 3.28 15.37 50.42 39.8 94.4 78.9
29/4/2004 3:40 pm
Sunny day Few clouds
50 3.0 15.77 47.32 38 94.6 80.3
30/4/2004 3:30 pm
Sunny day Few clouds
51 3.18 15.12 48.09 38.48 94.3 80
30/4/2004 5:13 pm
Sunny day Few clouds
46.4 2.85 15.51 44.21 36 95.2 81.4
20/10/2004 11:35 am
Hot Sunny day
56 3.4 15.51 52.75 41.8 94.2 79.2
20/10/2004 11:55 am
Hot Sunny day
58 3.3 16.82 55.68 45.34 96 81.4
23/11/2004 10:30 am
Cold Sunny day
49.79 3.06 15.68 48 38.7 96.4 80.6
23/11/2004 10:40 am
Cold Sunny day
50.5
3.06 15.68 48
38.7
95
80.6
Table 6.3 MPP Daily average Recorded Data 2004
Date Weather condition
Pmmp(W) Ps(W) Pb (W) ξT % ξPE %
29/4/2004
Sunny day Few clouds
49.9 47.47 37.97 95 80
30/4/2004
Sunny day Few clouds
49.8 47.2 38.2 94.7 81
20/10/2004
Hot Sunny day
54.7 52.32 42 95.6 80.3
23/10/2004
Hot Sunny day
54.4 51.8 41 95.2 79
23/11/2004
Cold Sunny day
62.7 59.7 48.9 95.2 82
24/11/2004
Cold Sunny day
60.4 57.5 46.2 95.2 80
Average value 95 80
84
On a cold sunny day, the average power extracted from the PV module is
greater than that of a hot sunny day. This is due to the fact that Voc increases as
temperature decreases. Consequently the MPP value is raised as shown in Fig. 6.4.
Fig. 6.15 shows how fast is the PAO algorithm, implemented inside the
controller, in tracking the MPP in case of changing weather conditions (varying
insolation level due to the presence of clouds).
Fig. 6.15 Tracking the MPP in case of varying Insolation level
The peaks, present inside the circled region, occur when the MPPT system
starts up and quickly searches for the duty cycle, which loads the PV module at the
MPP. These peaks are issued due to the VB software failure in interpreting the first
input byte by the micrcontroller at startup. These peaks are present for just few
microseconds. Curve (a) represents the PV module varying MPP values due to changing
85
weather conditions while curve (b) represents the power extracted from the PV module.
Still the tracking efficiency ξTr was 95% on average.
6.5. Comparison of Tracking Algorithms
The adaptive PAO algorithm showed better performance than the
conventional PAO described in the literature [14] under varying weather conditions
because it can lock on the new MPP with less time than the conventional PAO can. To
compare between the two algorithms, two cases are considered. The first case compares
between the speeds of the algorithms for a minor change in the weather conditions
while the second compares between the speeds for a major change in the weather
conditions.
6.5.1. Minor change in the weather conditions
The conventional PAO algorithm locks on the MPP with a small incremental
step size ∆c of value 1/255. So if a quick change in the weather conditions occurs, the
conventional PAO algorithms starts searching for the new MPP with this small
incremental step as shown in Fig. 6.16. In the shown case the old MPP readings were
recognized at 50.76W and 2.75A for a 600W/m2 radiation flux and 27oC temperature,
while the new MPP readings at 77.86W and 4.14A for a 900W/m2 radiation flux and
27oC temperature. The current values for the old and new MPP on the 255 scale are 117
and 176. For the conventional PAO method, 60 iterations are needed to lock on the new
MPP (176-117= 59, 59+1=60). In the adaptive PAO method, the incremental step ∆a is
doubled after four consecutive increasing power steps. This way the number of
iterations is reduced to 21 based on the tracking scheme shown in Table 6.4. This table
86
shows how the incremental step is changing as the power is increasing or decreasing.
The initial incremental step ∆a is taken to be 1 assuming that system was locking on a
MPP. So after four consecutive steps the current is 121 (=117 + 1× 4). Since the power
is still increasing the incremental step is doubled (2×1) and after four consecutive steps
the current is 129 (=121+4×2). The scanning method will continue until a power drop is
sensed, where the scanning direction will be reversed, the incremental step is halved and
the increments will be changed into decrements and vice versa. When the value of the
current reaches 177, a power drop is sensed so the direction of scanning is reversed, the
incremental step is halved (4), and the current is decremented. After one iteration the
value of the current reaches 173 (=177 – 1 × 4)) where another power drop is detected,
so again the direction of scanning is reversed, the incremental step is halved (2), but
now the current is incremented. This tracking scheme will continue until the
incremental step ∆a reached the smallest value i.e. one at the MPP. This means that the
adaptive PAO scheme is 2.875 (60/21) times faster than the conventional one. Thus the
tracking efficiency was increased by 10% using this adaptive technique where it was
85% on the best estimate for the conventional algorithm.
Fig. 6.16 Tracking scheme for minor change in the weather conditions
87
Table 6.4 Tracking with adaptive incremental step ∆a
∆a value Previous current value
Number of iterations
Number of increments
New current value
Power condition
1 117 4 4 121 increase
2 121 4 8 129 increase
4 129 4 16 145 increase
8 145 4 32 177 drop
4 177 1 - 4 173 drop
2 173 2 4 177 drop
1 177 2 - 2 175 drop
Total number of iterations 21
6.5.1. Major change in the weather conditions
In this case the old MPP readings were recognized at 22.4W and 1.36A for a
300W/m2 radiation flux and 30oC temperature, while the new MPP readings at 82.4W
and 4.56A for a 1000W/m2 radiation flux and 30oC temperature as shown in Fig. 6.17.
The current values for the old and new MPP on the 255 scale are 58 and 194. For the
conventional PAO method, 137 iterations are needed to lock on the new MPP (194-58=
136, 136+1=137). In the adaptive PAO method, the number of iterations is reduced to
33 based on the tracking scheme shown in Table 6.5 where exactly the same procedure
is followed as in the previous case. So the adaptive PAO scheme is 4.1 (=137/ 33) times
faster than the conventional one in this case. Therefore the adaptive PAO algorithm
reacts faster than the conventional one, especially under quickly varying weather
conditions where the conventional technique fails to track the MPP correctly.
88
Fig. 6.17 Tracking scheme for major change in the weather conditions
Table 6.5 Scanning with adaptive incremental step ∆a
∆a value Previous current value
Number of iterations
Number of increments
New current value
Power condition
1 58 4 4 62 increase
2 62 4 8 70 increase
4 70 4 16 86 increase
8 86 4 32 118 increase
16 118 4 64 182 increase
32 182 1 32 214 drop
16 214 2 -32 182 drop
8 182 2 16 198 drop
4 198 2 -8 190 drop
2 190 3 6 196 drop
1 196 3 -3 193 drop
Total number of iterations 33
89
6.6. Power Budget
The power losses in the whole design system is calculated and summarized in
Table 6.6. The MPPT system was assumed to operate at MPP (70W) and the switching
frequency f of 40 kHz (T = 1/f = 25µs) with the duty cycle D set to 65%. The load
current IL was 5.8A and a battery voltage of 12V.
6.6.1. Inductor conduction loss
The conduction loss in the inductor can be found by considering the load
current and the winding copper resistance. The measured inductor current is equal to
5.8A and the measured inductor resistance is 0.03Ω. Therefore, the power loss due to
the conduction loss in the inductor is: Pind = I2ind . Rind = 1.009W
6.6.2. Diode conduction loss
The diode conduction loss can be calculated using the following equation:
Pd = IL . Vf = 3.306W
where, from data sheet the forward bias of the diode, Vf = 0.57V.
6.6.3. MOSFET conduction loss
Fig. 6.18 shows the waveform for the current Ip that flows in the primary
winding of the transformer T5 shown before in Fig. 5.14. Since the transformer turns
ratio is 1:1.5, then: Ipon = 1.5 IL = 8.7A.
90
Fig. 6.18 Primary winding current waveform
The power loss due to the MOSFETs conduction loss is calculated by
considering the switching current waveforms for each pair of switches as shown in Fig.
6.19.
PTr = (Ipon)2. Ron. ton /(2T) = 0.59W
Where, D = ton / T and from data sheet the on resistance of the MOSFET, Ron =
0.024Ω.
Then the power loss PTR for the four MOSFETs is:
PTR = 4.PTr = 2.36W
Fig. 6.19 Waveforms across the terminals of the transformer
91
6.5.4. Transformer power loss
The measured resistance for the primary winding Rpri is 0.0053Ω while that of
the secondary winding Rsec is 0.012Ω.
The waveforms for the current that flows in the secondary winding of the
transformer and the rectifying diodes (D1 and D2) are shown in Fig. 6.20.
Fig. 6.20 Diodes’ current waveform during different intervals
Thus the average power loss in the transformer windings over one cycle can be
divided into two intervals t1 and t2.
During interval t1, only diode D1 of Fig. 5.14 is conducting so the primary
winding and half of the secondary winding contribute to power loss as shown in Fig.
6.21(a). During interval t2, both diodes are conducting so only the secondary winding
contribute to the power losses as shown in Fig. 6.21(b). These two intervals will repeat
but for the next MOSFETs switching period where diode D2 will be conducting as
shown in Fig. 6.21(c).
92
Fig. 6.21 Transformer windings contribution to power loss
Thus the energy loss in the transformer during interval t1 can be calculated as:
Et1 = [(Ipon)2 . Rpri + (IL)2 . Rsec ]. ton
During interval t2 the energy loss is calculated as:
Et2 = 2 . [(IL/2)2 . Rsec] . (T-ton)
The average power loss in the transformer Ptrans over one period T is given by:
Ptrans = (Et1 + Et2)/ T = 0.594W
6.5.5. Other power loss factors
Other factors contribute to power loss, among which are the voltage and
current sensing circuit, the microcontroller circuit, the printed copper track resistances
and copper wiring resistance.
A rough estimate calculation was made for the power loss due to these factors
and was found to be 6W.
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Table 6.5 Power Budget
Power Budget Component Input Power Power Loss
BP380 Solar Panel 70W
Inductor 1.009W
Diode 3.306W
MOSFETs 2.36W
Transformer 0.594W
Other Factors 6W
Balance 70W- 13.27W= 56.73W
Power electronics efficiency
ξPE = 56.73/ 70 = 81%
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CHAPTER 7
CONCLUSION AND FUTURE WORK
7.1. Summary
Photovoltaic power production is gaining more significance as a renewable
energy source due to its many advantages. These advantages include pollution free
energy production scheme, ease of maintenance, no noise and direct sunbeam to
electricity conversion [29]. However the high cost of PV installations still forms an
obstacle for this technology. Moreover the PV array output power fluctuates as the
weather conditions, such as the Insolation level, and cell temperature. In order to make
use of the high initial cost it is very important to extract maximum power from the solar
panels for all weather conditions [30]. Stand-alone PV systems cannot supply enough
energy all day long or when no or little solar irradiation exits. Battery storage
capabilities are required in these systems.
So when the PV array is used as a source of power supply to charge a 12V lead
acid battery, it is necessary to use the MPPT to get maximum power from the PV array.
In this work, the MPPT is implemented by using an H-bridge converter, which is
designed to operate under continuous conduction mode and a microcontroller to control
the PWM signals to the converter and also to monitor the state of charge of the battery.
The Perturb and Observe Algorithm is used as the control algorithm for the MPPT.
7.2. Testing Environment
The solar system consists of an 80W PV module, a converter, a controller and a
70Ah battery. The system was designed, built and tested for a period of ten months and
95
for different weather conditions. As the main system component, an H-bridge converter
was constructed for the design. By changing the transformer windings ratio, this bridge
can be easily made to work as either a buck or a boost converter. A turn ratio greater
than one means that the output signal will be amplified resulting in a boosted signal,
whereby a turn ratio smaller than one will result in reducing the output signal amplitude
which is the case of buck converter. Moreover unlike the buck converter, the H-bridge
converter operates with 50% duty cycle driving signals, which reduces the switching
failure associated with the buck converter during the turning on or off transitions for
small or large duty cycle driving signals. However the H-bridge converter has four
switching devices while the buck converter has only one. Having an excellent
combination of features, performance, and low power consumption, the PIC16F874
microcontroller is used in the control section. It has 4K x 14 bits of flash memory, 192 x
8 bytes of data memory (RAM), two D/A and five A/D channels. The tracking
algorithm is implemented in the microcontroller where it senses the present values of
the solar current and voltage and compares resultant power with the previous power and
accordingly controls the PWM scheme of the converter.
7.3. Better Tracking Algorithm
The Perturb and Observe, Incremental Conductance, and other maximum
power point tracking algorithms were reviewed and discussed. Although the ICT
technique offers higher tracking efficiency, the PAO algorithm was chosen to track the
MPP since it has lower cost, easier circuitry and less complicated algorithm. However
the conventional PAO was developed to respond faster for quickly varying weather
conditions. An adaptive incremental step was introduced where the performance of the
96
algorithm was 2.5 to 4 times faster than the conventional one depending on the location
of the new MPP with respect to the old one. Moreover experimental results have shown
that the MPPT using an adaptive PAO has a tracking efficiency of 95% with a converter
efficiency examined and measured to be 80%.
7.4. Simulink Model
The solar system was also designed and simulated using the matlab Simulink
environment. A model that represents the solar module was created using the Simulink
S-function capability. The controller was also implemented with the help of the S-
function block. The converter and battery were modeled using the Simulink power
electronics library. To match the hardware system and the Simulink software
simulation model, both were tested for a specific flux density and temperature. The
MPP reading of both systems showed that they match up as high as 99%.
7.5. Future Work
The MPPT system that was designed and tested can achieve 76% of total
conversion efficiency so it is still possible to improve its efficiency. The component
choice is very important in the design of the MPPT system. Higher power conversion
efficiency can be achieved by using rectifying diodes with less forward bias voltages,
inductors of lower resistive material, transformer with high magnetic flux density, and
MOSFETs with lower on-state resistance. Moreover the size of the MPPT could be
more compact if surface mount devices SMDs are used and if the system is to operate at
higher switching frequencies where the size of the inductor and transformer will be
reduced. This is part of what should be done in the future for a simple stand-alone PV
97
system. However to build a complete system utilizing solar energy for power
generation, a highly efficient dc-ac converter should be implemented. Now instead of
controlling four, six switches should be driven by the control section where it should
keep supplying the correct phase (120o) between the lines. Moreover the transformer-
winding ratio should be carefully chosen to provide 220V on the secondary windings.
APPENDIX A
MATLAB PROGRAM CODE
function k = pulse01(t,x,u) k(1) = u(1); I = u(2); V = u(3); Pr = u(4); delta = u(5); Pn = I * V; if(delta > 0.000000001) if (Pn >= Pr) k(1) = k(1) + delta; if (k(1) < 3) k(1) = 3; end if (k(1) > 24) k(1) = 23; end elseif (Pn < Pr) delta = delta/2; k(1) = k(1) - delta; if (k(1) < 3) k(1) =3; end if (k(1) > 24) k(1) = 23; end end end k(3) = (delta); Pr = Pn;
98
k(2) = Pr; function [sys,x0,str,ts]= pulse01_s(t,x,u,flag) switch flag, case 0 % Initialization s = simsizes; s.NumContStates = 0; s.NumDiscStates = 0; s.NumOutputs = 3; % dynamically sized s.NumInputs = 5; % dynamically sized s.DirFeedthrough = 1; % has direct feedthrough s.NumSampleTimes = 1; sys = simsizes(s); x0 = []; str = []; ts = [-1 0]; % inherited sample time case 3 sys= pulse01(t,x,u); case 1, 2, 4, 9 sys=[]; otherwise error(['Unhandled flag = ',num2str(flag)]); end
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% this mfile represents the PV module voltage and current relations. % it has three inputs which are the insolation G, the temperature T and the current I function V = PV_Cell(t,x,u) G= u(1); %here is the 1st input G T= u(2); %here is the 2nd input T I= u(3); %here is the 3rd input I Rs=0.1; Rsh=10000; Gnom=1000; k=1.38e-23; q=1.6e-19; A=1.5; Vg=1.12; Ns=36; Voc_T1=22.1/Ns; T1=273+25; T3=273+75; Isc_T1=4.8; Isc_T3=5.04; T0=273+T ; %T k0=(Isc_T3-Isc_T1)/(T3-T1); I0_T1=Isc_T1/(exp((q*Voc_T1)/(A*k*T1))-1); b=(Vg*q)/(A*k); b1=q/(A*k*T0); Iph_T1=Isc_T1*G/Gnom; %G Iph=Iph_T1*(1+k0*(T0-T1)); I0=I0_T1*[(T0/T1)^ (3/A)]*[exp(-b*[(1/T0)-(1/T1)])]; Isc=(k0*(T0-T1)+Isc_T1); % Output Voltage: if (I >= Iph) V=0; else V=(1/b1)*[log(((Iph-I)/I0)+1)]*36; end
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function [sys,x0,str,ts]= PV_Cell_s(t,x,u,flag) switch flag, case 0 % Initialization ; s = simsizes; s.NumContStates = 0; s.NumDiscStates = 0; s.NumOutputs = 1; % dynamically sized s.NumInputs = 3; % dynamically sized s.DirFeedthrough = 1; % has direct feedthrough s.NumSampleTimes = 1; sys = simsizes(s); x0 = []; str = []; ts = [-1 0]; % inherited sample time case 3 sys= PV_Cell(t,x,u); case 1, 2, 4, 9 sys=[]; otherwise error(['Unhandled flag = ',num2str(flag)]); end
;preparing variables ;loop register CLRF LOOP ;defaulttime register MOVLW H'04' MOVWF DEFAULTTIME ;same register (no. of passes before incrementing step) MOVLW H'07' MOVWF SAME ;initialization of step register by 32 decimal (H'20') MOVLW H'20' MOVWF STEP ;initialization of direction register CLRF DIRECTION ;initialization of reference register CLRF REFH CLRF REFL ;initialization of result register MOVLW H'30' MOVWF RESULT ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;port configuration BSF STATUS,RP0 ;A - analog input MOVLW H'FF' MOVWF TRISA
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;B - digital output - duty cycle variable MOVLW H'00' MOVWF TRISB ;C - pin no.1: capture input ;C - pin no.2: pwm output ;C - pin no.6: serial communication with pc (TX)(PROJECTED) ;C - pin no.7: serial communication with pc (RX)(PROJECTED) MOVLW H'B3' MOVWF TRISC ;E - analog input MOVLW H'07' MOVWF TRISE ;D - digital output MOVLW H'00' MOVWF TRISD BCF STATUS,RP0 ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;pwm preparation ;setting timer2 period BSF STATUS,RP0 MOVLW H'40' MOVWF PR2 BCF STATUS,RP0 ;setting a dummy pulse width for startup MOVLW H'20' MOVWF CCPR1L ;activate timer 2 with zero prescaling on input and output MOVLW H'04'
108
MOVWF T2CON ;setting pwm mode for module 1 MOVLW H'0F' MOVWF CCP1CON ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;capture preparation ;setting the mode of timer1 MOVLW H'05' MOVWF T1CON ;preparing module 2 to operate as a capture module on rising edge MOVLW H'05' MOVWF CCP2CON ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;timer0 preparation ;initializing the period register (used by TMR0) MOVLW H'C3' MOVWF SCRATPER COMF SCRATPER,F INCF SCRATPER,F ;scratper by now, contains the exact number needed by TMR0 at 50 Hz. MOVF SCRATPER,W MOVWF PERIOD ;initializing TIMER0 BSF STATUS,RP0 MOVLW H'C2' MOVWF OPTION_REG
109
BCF STATUS,RP0 ;presetting timer0 with the default value MOVF PERIOD,W MOVWF TMR0 ;initializing the same register CLRF SAME ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;preparing the a/d conversion module ;configuring analog pins, and output data format in ADRESH, and ADRESL BSF STATUS,RP0 MOVLW H'00' MOVWF ADCON1 BCF STATUS,RP0 ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;arranging for interrupts globally ;enabling TIMER0 interrupts BSF INTCON,T0IE ;enabling capture interrupts BSF STATUS,RP0 BSF PIE2,CCP2IE BCF STATUS,RP0 ;enabling peripheral interrupts
;capture interrupt branch CAPTINT CLRF TMR1L CLRF TMR1H BCF PIR2,CCP2IF MOVF CCPR2H,W MOVWF SCRAT1PER COMF SCRAT1PER,F INCF SCRAT1PER,F MOVF SCRAT1PER,W MOVWF PERIOD MOVLW H'04' MOVWF DEFAULTTIME RETFIE ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;timer 0 interrupt branch ;recharge timer with period value TMR0SR MOVF PERIOD,W MOVWF TMR0 ;clear timer0 overflow flag BCF INTCON,T0IF INCF LOOP,F ;check turn of sumup,communication,or normal MOVF LOOP,W SUBLW H'80' BTFSC STATUS,Z GOTO SUMUP BTFSC STATUS,C GOTO NORMAL
112
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;collecting data for i and v averages NORMAL MOVLW VPOINTER MOVWF ADCON0 CALL WAITACQ BSF ADCON0,2 WAIVOL BTFSC ADCON0,2 GOTO WAIVOL ;add the resulting voltage sample to the voltage accumulator (data available in adresh) CALL ADDVACCU MOVLW IPOINTER MOVWF ADCON0 CALL WAITACQ BSF ADCON0,2 WAICURR BTFSC ADCON0,2 GOTO WAICURR CALL ADDCURRACCU GOTO FUNNEL1 ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; SUMUP DECFSZ DEFAULTTIME,F GOTO BULK MOVF SCRATPER,W MOVWF PERIOD ;obtaining averages BULK MOVLW H'07'
113
MOVWF COUNTER SHIFTV BCF STATUS,C RRF VACCUH,F RRF VACCUL,F DECFSZ COUNTER,F GOTO SHIFTV MOVF VACCUL,W MOVWF VAVERAGE CLRF VACCUL MOVLW H'07' MOVWF COUNTER SHIFTI BCF STATUS,C RRF IACCUH,F RRF IACCUL,F DECFSZ COUNTER,F GOTO SHIFTI MOVF IACCUL,W MOVWF IAVERAGE CLRF IACCUL ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;performing multiplication CALL IVMULTIPL MOVF ACCUH,W MOVWF TESTH MOVF ACCUL,W MOVWF TESTL ;compare the new test value with the refference VALUE MOVF REFH,W SUBWF TESTH,W ;TESTH = REFH?
114
BTFSC STATUS,Z GOTO HEQUAL ;TESTH = REFH, continue comparing TESTL and REFL ;testh not equal to REFH. find which is greater. BTFSC STATUS,C GOTO TESTGTREF ;test is greater than refference GOTO REFGTTEST ;refference is greater than test ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; HEQUAL MOVF REFL,W SUBWF TESTL,W ;TESTL = REFL? BTFSC STATUS,Z GOTO TESTGTREF ;behave as if test is greater than refference BTFSC STATUS,C GOTO TESTGTREF ;test is greater than refference GOTO REFGTTEST ;refference is greater than test ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; REFGTTEST INCF DIRECTION,F ;reorient the direction of search ;divide step by 2 BCF STATUS,C RRF STEP,F ;reset the same register CLRF SAME ;goto next stage GOTO FUNNEL2 ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
115
TESTGTREF INCF SAME,F ;check whether the step remained in the same direction for more than 4 times MOVLW H'07' SUBWF SAME,W BTFSS STATUS,C ;not yet GOTO FUNNEL2 ;same greater than or equal to H'07' CLRF SAME ;multiply step by 2 BCF STATUS,C RLF STEP,F ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;check whether step is within boundaries (1<=step<=32) FUNNEL2 MOVLW H'01' SUBWF STEP,W BTFSS STATUS,C ;less than 1 GOTO EQUALIZE1 MOVF STEP,W SUBLW H'20' BTFSS STATUS,C ;greater than h'20'
MOVWF RESULT ;change the direction of scanning INCF DIRECTION,F GOTO FUNNEL3 ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; SUBSTEP MOVF STEP,W SUBWF RESULT,F BTFSC STATUS,C GOTO FUNNEL3 ;result is less than H'00' CLRF RESULT ;change the direction of scanning INCF DIRECTION,F ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;set recent test values as future refference values FUNNEL3 MOVF TESTH,W MOVWF REFH MOVF TESTL,W MOVWF REFL ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;result correction if output voltage exceeds maximum value FUNNEL1 MOVLW VOPOINTER MOVWF ADCON0
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