Maximum Power Point Tracking Of Polycrystalline Photovoltaic Cells Based On Optimized Adaptive Differential Conductance Technique

ABSTRACT

Maximum power point tracking of a polycrystalline Photovoltaic (PV) cell using an Optimized Adaptive Differential Conductance (OADC) technique was proposed in this work. This technique enhances the optimum power transfer from PV panel to the load by reducing the tracking latency and increasing convergence efficiency and speed unlike the conventional Incremental conductance. In this research work, current at maximum power point and voltage at maximum power point can be calculated without data sheet by using an algorithm developed for it. The performance of the algorithm developed in this work was evaluated by simulating the maximum power point tracking of PV cell at irradiance of 1000W/M2, 800W/M2, and 600W/M2 at 298k and some fixed temperatures. Based on the simulation results, it was observed that the impedance of the panel varied inversely as the irradiance while impedance of the load is not affected by irradiance. This technique was also validated by comparison with conventional incremental conductance technique. The resultant conductance of this technique is 0.0030 mho at maximum power where as conventional has -0.0418 mho which is less than zero. This technique has a relative improvement of 6.0558% compared to conventional incremental conductance technique. The simulation was done using Matrix Laboratory (MATLAB).

 

 

TABLE OF CONTENTS

PRELIMINARY PAGES
COVER PAGE – – – – – – – – – I
APPROVAL PAGE – – – – – – – – – II
CERTIFICATION – – – – – – – – III
DEDICATION – – – – – – – – – IV
ACKNOWLEDGMENT – – – – – – – – V
ABSTRACT – – – – – – – – – – VI
TABLES OF CONTENT – – – – – – – – VII
LIST OF FIGURES – – – – – – – – XI
LIST OF TABLES – – – – – – – – – XIII
LIST OF ABBREVIATIONS – – – – – – – XVI
DEFINITION OF KEY TERMS – – – – – – – XVIII
LETTERS USED AND THEIR MEANINGS – – – – – XIX
CHAPTER ONE: INTRODUCTION
1.1. Background of the Study – – – – – – – 1
1.2. Problem Statement – – – – – – – – 5
1.3. Research Objectives – – – – – – – – 6
1.4. Scope of the Study – – – – – – – – 7
1.5. Significance of the Study – – – – – – – – 7
1.6. Research Methodology – – – – – – – – 7
1.7. Plan of Thesis – – – – – – – – – 8
CHAPTER TWO: LITERATURE REVIEW
2.1. Overview of Maximum Power Point Tracking – – – – – 9
2.1.1. Structure of the Solar Cell – – – – – – – 11
2.1.2. Operating Principle of PV Cell – – – – – 13
2.2. Maximum Power Point Tracking – – – – – – – 14
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2.2.1 Theory of Maximum Power Point Tracking – – – – 15
2.2.2 Principles of MPPT Technique – – – – – – 16
2.2.3 Incremental Conductance Technique – – – – – 17
2.3. Review of Related Literature – – – – – – – 20
2.3.1 Non-Intelligence Maximum Power Point Tracking Techniques – – 20
2.3.1.1 Incremental Conductance (INC) Technique – – – – 21
2.3.1.1 Incremental Conductance Algorithm – – – – 22
2.3.1.1.2 An Improved Incremental Conductance (IINC) Technique – 24
2.3.1.2. Perturb and Observe (P&O) Technique – – – – – 27
2.3.1.2.1. Adaptive Perturb and Observe (AP&O) Technique – – 31
2.3.1.3. Fractional Short Circuit Current (FSCC) Technique – – 37
2.3.1.4 Fractional Open Circuit Voltage (FOCV) Technique – – – 38
2.3.1.5 A Variable Indicator and Scaling Factors Technique – – – 39
2.3.1.6 Centralized, Distributed and Reconfiguration Technique
for Mismatching Conditions – – – – – – 39
2.3.1.7 Random Search Method (RSM) – – – – – – 40
2.3.1.7.1 Application of RSM for GMPP for PV Systems – – – 41
2.3.1.8 Dividing Rectangular Search (DIRECT SA) Technique – – – 42
2.3.2 Intelligent MPPT Techniques – – – – – – 43
2.3.2.1 Fuzzy Logic Control (FLC) Technique – – – – 43
2.3.2.1.1 A Takagi–Sugeno (T-S) Fuzzy-Model Technique – – 47
2.3.2.2 Artificial Neural Network (ANN) – – – – – 48
2.3.2.3 Particle Swamp Optimization (PSO) Technique – – – 49
2.3.2.3.1 Improved Particle Swarm Optimization (IPSO) – – 51
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2.4 Comparison of the Existing MPPT Techniques – – – – – 54
2.5. Summary of the Reviewed Existing Technique Discussed in this Thesis – 57
CHAPTER THREE: RESEARCH METHODOLOGY AND PROPOSED MODEL
3.1. Introduction – – – – – – – – – 59
3.2. Initial Model – – – – – – – – – 59
3.3. Proposed Optimized Adaptive Differential Conductance Technique – – 60
3.4: Proposed Model for the Optimized Adaptive Differential
Conductance Technique – – – – – – – – 67
CHAPTER FOUR EVALUATION, RESULTS AND DISCUSSION
4.1. Ideal Conditions for Incremental Conductance Technique – – – – 70
4.2. Simulation of the Developed Technique – – – – – – 71
4.3. Simulation Parameters – – – – – – – – 72
4.4. Simulated Data – – – – – – – – – 73
4.5. Results and Discussions – – – – – – – – 74
4.6. Model Validation – – – – – – – – 78
4.6.1: Maximum Power Performance Validation – – – – 79
4.7. Performance Metrics – – – – – – – – 80
CHAPTER FIVE: SUMMARY, RECOMMENDATION AND CONCLUSION
5.1. Summary – – – – – – – – – 81
5.2. Achievements – – – – – – – – – 81
5.3. Recommendation – – – – – – – – – 82
5.4. Conclusion – – – – – – – – – 82
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Reference – – – – – – – – – – 83
Appendixes – – – – – – – – – – 86

 

 

CHAPTER ONE

INTRODUCTION
1.1. Background of the Study
Energy plays an important role in our daily life activities. Increase in population, urbanization and industrialization lead to the increase in energy demand. The increase in energy demand causes the depletion of conventional sources of energy like fossil fuels and this will lead to high cost and limited energy supply. In order to meet future energy requirement, renewable sources of energy have attracted the interest of researchers. Renewable energy sources are expected to play an important role in meeting the world’s power demand, due to their abundant availability and less impact on the environment. The sources of energy that can be replenished in nature are known as renewable sources of energy. They are energy sources which are not destroyed when their energy is utilized. Solar energy, wind energy, tidal energy and geothermal energy are the examples of renewable energy.
Solar energy is currently considered as one of the most useful renewable energy sources, as it is pollution free, inexhaustible and relatively abundant all over the globe. There are two techniques by which solar energy can be converted into electrical energy, thus:
 Solar thermal techniques: The solar collector concentrates sunlight to heat a heat transfer fluid to a high temperature. The hot heat transfer fluid is then used to generate steam that drives the power conversion subsystem, producing electricity. Thermal energy storage provides heat for operation during periods without adequate sunshine. The major disadvantage of this method is that huge amount of energy is lost during transfer.
 Solar photovoltaic (PV) techniques: This is the most convenient way of generating electricity from solar energy using PV cells. Photovoltaic cells generate electricity based
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on photovoltaic effect which is widely used to produce electricity from solar energy [1][2].
A comparative study of the world energy consumption released by International Energy Agency (IEA) shows that in 2050, more than 45% of necessary energy in the world will be exclusively produced by solar systems [3]. The basic structure unit of solar systems is the PV module, which itself is composed of solar cells. A solar cell converts energy in the photons of sunlight into electricity by the means of the photovoltaic phenomenon found in certain types of semiconductor materials such as silicon and selenium. In isolated operation, PV cell produces a negligible amount of power. To produce substantial electrical output power, solar cells are connected in series and parallel to form a PV module. PV cells are connected in series to increase voltage output and connected in parallel to increase the current output.
The power output efficiency of solar module depends on many factors such as temperature, irradiance and spectral characteristics of sunlight [4]. Temperature: solar cells are sensitive to temperature. Increase in temperature reduces the band gap of a semiconductor, thereby affecting most of the semiconductor material parameters. In a solar cell, the parameter most affected by an increase in temperature is the open-circuit voltage. As the temperature increases, the open circuit voltage decreases, thereby decreasing the fill factor and also decreasing the efficiency of a solar cell. A solar cell is recommended to operate normally at 25 degree Celsius [5]. Temperature also plays an important role in determining the solar cell efficiency. As the temperature increases the rate of the photon generation increases thus reverse saturation current increases rapidly and this reduces the band gap. This effect leads to marginal changes in current but the major changes in voltage. The cell voltage reduces by 2.2mV per degree rise of temperature. Temperature acts like a negative factor affecting solar cell performance. Solar cells give their full performance on cold
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and sunny days rather than on hot and sunny weather [6]. Irradiance is defined as the measure of power density of sunlight received at a location on the earth and is measured in W/ , where irradiation is the measure of energy density of sunlight. The term irradiance and irradiation is related to solar components. As the solar isolation keeps on changing throughout the day similarly I-V and P-V characteristics varies. With the increasing solar irradiance both the open circuit voltage and the short circuit current increases and hence the maximum power point varies [6]. Changing the light intensity incident on a solar cell changes all solar cell parameters, including short-circuit current, open-circuit voltage, fill factor, efficiency and the impact of series and shunt resistances. The light intensity on a solar cell is called the number of suns, where a sun corresponds to standard illumination at 1kw . Solar cells experience daily variations in conditions, a solar cell with a high shunt resistance retains a greater fraction of its original power than a solar cell with a low shunt resistance [5]. The spectral characteristic of sunlight leads to increase in wavelength of a light and decreases in the energy. Energy of photon ranges from 1.65eV to 3.1eV for visible light spectrum and its corresponding wavelengths are 750 and 400 nanometers respectively. A typical silicon solar cell requires 1.1eV in order for electrons to flow out of the cells and through the circuitry of the solar panel system. At energies lower than 1.1eV, photons do not have enough energy to extricate electrons but at shorter wavelengths and higher energies, silicon electron will get energized and current will flow. Finally, shorter wavelengths and higher photon energies do not correspond with an increase in electrical current. When a photon of higher energy impacts a solar cell, energy above 1.1eV is given off as heat. For higher efficiency of PV cells to be achieved a technique of multi-layer design has been adopted. The top layer absorbs shorter wavelengths and bottom converts the
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longer ones. The result is significantly better conversion efficiency and better energy output [7] [8].
For large electric power generation, solar modules are interconnected to form solar arrays. Solar arrays (system) may be installed as a stand-alone system or as a building integrated photovoltaic (BIPV) system. Both stand-alone system and building integrated photovoltaic (BIPV) system can be connected to the utility grid to ensure effective utilization of generated power and to eliminate the cost of power storage system.
PV systems are rated in terms of maximum power which is the highest power that can be generated by PV system under Standard Test Condition (STC). At STC the temperature is 25°C, solar irradiance is 1000W , air mass is 1.5, wind speed is 0m/s and solar panels tilt angle when it face south is 30° [9] . Under normal operating conditions, maximum power generation from PV is not possible because the PV panel cannot always be operating at optimum power. PV systems generate highest power when the incident sun beam is perpendicular to the panel. The power from PV systems is increased by adding PV Efficiency Enhancement (EE) systems. Examples of EE systems are the solar tracking system and maximum power point tracking systems.
Solar tracking system was the conventional method used to align PV panel to the direction where the solar irradiation is highest. The main drawback of this technique was that the solar tracking system is so expensive, difficult to maintain and the power generated is not well utilized due to power losses during transfer. Due to the drawbacks of solar tracking techniques, maximum power point tracking was introduced. For the power generated by the PV system to be utilized well, maximum power point tracking technique is utilized to enhance the utilization of power generated by the PV. Not all the power generated by PV panel is transferred to the load.
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PV modules transfer the highest percentage of power generated to the load at Maximum Power Point (MPP). Maximum power point (MPP) is a point along the P-V characteristic of a PV panel where the photovoltaic impedance is equal to the load impedance. It is also a point in a photovoltaic panel where there is negligible energy loss in the transmission of the generated power to the load. MPP is also defined as a range in a PV panel where peak power transfer is obtained. MPP along the P-V curve is detected using maximum power point tracking (MPPT) techniques. Figure 1 is a typical photovoltaic cell showing P-V curve arrangement with its MPPs. Figure 1: A Typical P-V curve for a photovoltaic Cell [10]
MPPT is the method of operating the photovoltaic system in a manner that allows the modules to transfer all the power generated to the load. It is implemented in charge controllers alongside battery charge level monitoring system. MPPT varies the electrical operating point of the PV system so that the module will deliver nearly all the generated power to the load. It ensures that maximum power is transferred from the photovoltaic (PV) panel to the load.
1.2. Problem Statement
Problems encountered in maximum power point tracking are as follows:
 The Inability of the maximum power tracker to converge at maximum power point without so much delay when there is atmospheric change: This implies that during
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tracking to obtain the optimum power point, there is skipping in the power-voltage tracking curve. Skipping may truly occur at that point where maximum power point is located in the PV panel. For that reason it has to re-track in order to locate the maximum point where the source impedance will match that of the load and that leads to low power transfer. There will be low Power transfer because maximum power is being transferred at optimum power point but in this case it takes so long to locate the maximum point. This is one of the major problems of conventional incremental conductance of which this proposed technique will solve.
 Lack of Efficiency and speed during tracking: Because of the problem explained above, there would be re-tracking along the power-voltage curve and efficiency will not be maximized due to delay that occurs when there is tracking delay in optimum power- voltage convergence. This will lead to latency in tracking for optimum power point.
 Low energy conversion: Because under a rapid irradiation change, the operating point moves away from the optimal point. The converted energy will also be very small because of tracking latency which makes the grid/load to take longer period of time to be charged/accept power from the PV.
 At steady state, photovoltaic output power oscillates around the maximum power point.
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1.3. Research Objectives
The goals of the research are as follows:
 To develop an optimized adaptive differential conductance model for efficient convergence and robustness in maximum power tracking which solves the major problem of conventional incremental conductance.
 To ensure that speed and accuracy are obtained during tracking which enhances/maximizes power to the load and also solve one of the problems of conventional incremental conductance.
 To develop an algorithm for data generation.
 To compare the effectiveness of the proposed algorithm with the conventional incremental conductance algorithm.
 To develop Current Maximum Power Point and Voltage Maximum Power Point algorithms to ensure efficiency.
1.4. Scope of the Study
The scope of this research work is limited to: Development of optimized adaptive differential conductance model for photovoltaic systems, Voltage at maximum power point and current at maximum power point algorithms. An algorithm was developed for data generation, simulation and validation. Ten existing maximum power point trackers were compared. Performance validation of Proposed optimized adaptive differential conductance model with conventional incremental conductance model. MATLAB was used as a testing tool and for software analysis. Results of the proposed model was Compared with results of existing models.
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1.5. Significance of the Study
The actualization of this project will significantly improve the maximum power that will be transferred from the PV panel to the load and that will lead to reliable power supply/consumption and efficiency.
1.6. Research Methodology
 Model development: the model was developed based on change in source impedance which was used to vary the pulse width of the control signals to match the impedance of the source and the load to ensure maximum power transfer. The change in the resultant conductance was achieved by obtaining the difference between the panel conductance and the load conductance. The new operating point is set based on differences between the source and the load conductance as the irradiance in the PV panel varies. The speed rate was achieved by the algorithm moving the operating point to as close as possible to the maximum Power Point along the power-voltage curve to ensure efficiency.
 Model simulation and data collection: the model developed was run and data collected.
 Data analysis: the data collected was analyzed.
 Validation: the data analyzed was compared to the conventional incremental conductance technique for performance.
 Software used: MATLAB software was used for all testing and analysis in this work.
1.7. Plan of Thesis
This work was further organized as follows: Chapter two reviewed all the maximum power point tracking techniques and related works. Chapter three detailed the research methodology and the proposed model developed. In Chapter four, the result of the research was discussed and
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validated while conclusions were drawn and recommendations made in Chapter five of the research. Finally, the work ended with References and Appendixes.
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