IMPROVEMENT OF PHOTOVOLTAIC SYSTEM USING FUZZY LOGIC CONTROL TECHNIQUE
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Photovoltaic (PV) systems are grid-connected via an interfacing converter which operates with Maximum Power Point Tracking (MPPT) controller in order to feed the grid by the maximum allowable solar power. Nonlinear loads affect the system power quality. Conventionally single-phase shunt active power filter (APF) can be used to improve the power quality in terms of current harmonics mitigation and reactive power compensation. In this paper, the PV interfacing inverter is controlled using a predictive control technique to perform both functions of power quality improvement in addition to transferring the PV maximum power to the grid. A Fuzzy logic control algorithm is applied for MPPT. The proposed technique does not require an accurate system model and can easily handle system nonlinearity. The system performance is investigated using a MATLAB simulation model.
TABLE OF CONTENTS
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE PROJECT
- STATEMENT OF THE PROBLEM
- AIM AND OBJECTIVES OF THE PROJECT
- SCOPE OF THE STUDY
- SIGNIFICANCE OF THE STUDY
CHAPTER TWO
LITERATURE REVIEW
- FUZZY LOGIC
- FUZZIFICATION
- MEMBERSHIP FUNCTIONS
- FUZZY RULES
- INFERENCE ENGINE
- DEFUZZIFICATION
- PHOTOVOLTAIC SYSTEMS (PVS)
- PV CELL CHARACTERISTICS
- DC-DC CONVERTERS
- BATTERIES
- MAXIMUM POWER POINT TRACKING
- REVIEW OF RELATED STUDIES
- REVIEW OF RELATED STUDIES
CHAPTER THREE
METHODOLOGY
- MATLAB SIMULATION MODEL
- PV SYSTEM MODELLING
- MODEL OF CUK CONVERTER
CHAPTER FOUR
- PROPOSED MPPT USING FUZZY LOGIC CONTROL
- PROPOSED INVERTER CONTROL TECHNIQUES
- PERFORMANCE INVESTIGATION OF THE PROPOSED SYSTEM
CHAPTER FOUR
4.1 RESULTS AND DISCUSSION
CHAPTER FIVE
- CONCLUSIONS AND RECOMMENDATION
REFERENCES
CHAPTER ONE
1.0 INTRODUCTION
1.1 BACKGROUND OF THE STUDY
Harmonics is one of the power quality issues that influence to a great extent transformer overheating, rotary machine vibration, voltage quality degradation, destruction of electric power components, and malfunctioning of medical facilities (Henderson et al., 2014). Power quality improvement has been given considerable attention due to the intensive use of nonlinear loads and the limitations required by international standards such as IEEE519-2012(IEEE, 2012).Those limitations were set to limit the disturbances and avoid major problems in power system. Since linear and/or non-linear single-phase loads are rapidly increasing, zero sequence component and current harmonics are generated. This causes overheating of the associate distribution transformers that may lead to a system failure, especially in weak networks (Gruzs et al., 2010). Photovoltaic (PV) power supplied to the utility grid is gaining more and more visibility, while the world’s power demand is increasing. Global demand of electrical energy is growing by high rate due to the requirement of modern civilization. Recently, energy generated from clean, efficient and environmentally friendly sources has become one of the major challenges for engineers and scientists. Among them, PV application has received a great attention in research because it appears to be one of the most efficient and effective solutions to this environmental problem (Shimizu et al., 2013). There are two topologies used to connect the PV with the grid; two stages and single stage PV system. A two stage is the traditional type and consists of a CUK DC/DC converter direct coupled with PV array and agrid connected universal bridge inverter. In single stage PV system, the DC/AC inverter has more complex control goals; Maximum Power Point Tracking (MPPT) and output current control. Regardless its control complicity, single stage PV system is more efficient and cheaper than two stages system. For connecting the PV system to the grid, there are three widely used grid interactive PV systems; the centralized inverter system, the string inverter system and the AC modulator the Module Integrated Converter (MIC) system. Among these, the MIC system offers “plug and play” concept and greatly optimizes the energy yield (Shimizu et al., 2013).With these advantages, the MIC concept has become the trend for the future PV system development but challenges remain in terms of cost, reliability and stability for the grid connection (Carrasco et.al., 2016).Conventionally single phase shunt active power filter(APF) uses an inverter for harmonics elimination and reactive power compensation (Carrasco et.al., 2016).A grid connected PV system with active power filtering feature has been presented in (Kjær et al., 2015). However, measuring the load current is mandatory. In this paper, an inverter is used as a single- phase shunt active power in addition to interfacing a power of a photovoltaic (PV). Fuzzy Logic Control (FLC) is used as a robust controller for MPPT; this control technique can handle the model uncertainties in addition to easily handle the nonlinearity. The single-phase shunt active power filter (APF) uses a predictive control technique to mitigate of the grid current harmonics and improve the power factor. The proposed control strategy provides a multifunction with a simple controller incorporating Phase Locked Loop (PLL) independency, less sensors, ease of practical implementation, and reduced system size and cost.
1.2 STATEMENT OF THE PROBLEM
The increasing global need for renewable energy has become the main impetus of the energy sector, primarily because of the negative impact of fossil fuels on the environment (Akorede et al., 2012). Photovoltaic (PV) power generation is one of the most promising renewable energy technologies that can be utilized in industrial power systems and rural electrification (Chel et al., 2019).
Photovoltaic (PV) plant as a part of a smart grid system is investigated in this work. Tracking the maximum available power of the PV panel is essential and it is possible by various maximum power point (MPP) methods. Most of these methods are iterative, time consuming and do not work well in quickly changing environmental conditions.
1.3 AIM AND OBJECTIVES OF THE STUDY
The presented work aims to improve the PV system performance by using fuzzy logic control (FLC). The objectives of the system are:
- To improve solar system performance.
- To control the voltage of the solar panel to get the maximum power that can be produced by observing the PV system under any change in temperature and insolation
- To provide only as much power as is needed
1.4 SCOPE OF THE SYSTEM
Photovoltaic (PV) plant as a part of a smart grid system is investigated in this work. Tracking the maximum available power of the PV panel is essential and it is possible by various maximum power point (MPP) methods. Most of these methods are iterative, time consuming and do not work well in quickly changing environmental conditions.
In this work, pv interfacing inverter is controlled using a predictive control technique to perform both functions of power quality improvement in addition to transferring the PV maximum power to the grid. A Fuzzy logic control algorithm is applied for MPPT
1.5 SIGNIFICANCE OF THE STUDY
This study will serve as a means of improving solar system performance, and a means of controlling the voltage of the solar panel to get the maximum power that can be produced by observing the PV system under any change in temperature and insolation.
CHAPTER FIVE
5.1 CONCLUSION
In this paper, a PV system is interfaced to the grid via a multifunctional interfacing inverter. A MPPT fuzzy logic controller is employed to feed the grid by the maximum allowable PV power. A simple predictive current control algorithm is used. The system performance is investigated using a MATLAB/ Simulink model at different cases of load variation, atmospheric temperature variation and solar irradiation variation. The inverter achieves functions of supplying the available power from the PV unit into the loads in addition to improving the power quality in terms of grid current THD and power factor.
5.2 RECOMMNENDATION
The aim of a photovoltaic power system is to provide only as much power as is needed, an approach known as power matching. In this approach, any power in a solar panel that remains non- extracted (i.e., because it is not functioning at the current maximum power point) is then released as surface heat. To implement this approach in a real-life situation, such as, for instance, a water pump in a remote village, the MPPT circuit needs to have some way to know the amount of power required to provide appropriate maximum power tracking to meet the power needs. Changes such as these will significantly enhance the system’s functionality.
Future work could include methods for applying a fuzzy logic algorithm in a dedicated single-chip microcontroller. As well, a Galileo board could be used rather than two Arduino boards to satisfy memory space restrictions and boost microcontroller speed when testing and comparing two types of MPPTs. This would also shorten the time required for hardware setup. Moreover, the circuit in general could be changed so that it can provide power to the control circuit by utilizing MPPT- charged batteries. Overall, although we conclude that we satisfactorily developed and implemented a fuzzy logic-based maximum power point tracker for a photovoltaic power system, more work still needs to be done to convert lab prototype to a commercial product. This thesis provides a strong foundation for such work.
CHAPTER TWO: The chapter one of this work has been displayed above. The complete chapter two of"improvement of photovoltaic system using fuzzy logic control technique"is also available. Order full work to download. Chapter two of"improvement of photovoltaic system using fuzzy logic control technique"consists of the literature review. In this chapter all the related work on"design and construction of an arduino based public address system"was reviewed.
CHAPTER THREE: The complete chapter three of"improvement of photovoltaic system using fuzzy logic control technique"is available. Order full work to download. Chapter three of"improvement of photovoltaic system using fuzzy logic control technique"consists of the methodology. In this chapter all the method used in carrying out this work was discussed.
CHAPTER FOUR: The complete chapter four of"improvement of photovoltaic system using fuzzy logic control technique"is available. Order full work to download. Chapter four of"improvement of photovoltaic system using fuzzy logic control technique"consists of all the test conducted during the work and the result gotten after the whole work
CHAPTER FIVE: The complete chapter five of"improvement of photovoltaic system using fuzzy logic control technique"is available. Order full work to download. Chapter five of"improvement of photovoltaic system using fuzzy logic control technique"consist of conclusion, recommendation and references.
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