Description
ABSTRACT
This paper exhibits performance of power of photovoltaic (PV) module in the case of shading effect. A comparison is made with performance of power of PV module void of MPPT solution. From the MATLAB simulation it is found that around 9.92% more average power generation is possible if MPPT (maximum power power point) solution is taken. To take the effect of partial shading a variation of irradiance profile has been proposed since change of irradiance causes the variation of output power to a great extent. Again to observe the performance of output power with MPPT Fuzzy logic control has been introduced for making the tracking fast and accurate. Mamdanicontrolhasbeenchosenasatechniqueforfuzzycontroller.Ontopofthis,mathematical structure of PV module has been prepared in MATLAB simulink to see output preview of PV module and this module has been linked to the fuzzy logic system to trace the peak power. In the simulation process the instantaneous power, average power and percentage power development are being analyzed with figures.
TABLE OF CONTENT
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
TABLE OF CONTENT
CHAPTER ONE
- INTRODUCTION
1.1 BACKGROUND OF THE STUDY
1.2 PROBLEM STATEMENT
1.3 AIM / OBJECTIVE OF THE STUDY
1.4 SIGNIFICANCE OF THE PROJECT
1.5 SCOPE OF THE PROJECT
CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 OVERVIEW OF SOLAR ENERGY
2.2 OVERVIEW OF PHOTOVOLTAIC SYSTEMS
2.3 PRINCIPLES OF PHOTOVOLTAIC TECHNOLOGY
2.4 COMPONENTS OF PHOTOVOLTAIC SYSTEMS
2.5 TYPES OF PHOTOVOLTAIC SYSTEMS
2.6 APPLICATIONS OF PHOTOVOLTAIC SYSTEMS
2.7 BENEFITS OF PHOTOVOLTAIC SYSTEMS
2.8 CHALLENGES OF PHOTOVOLTAIC SYSTEMS
2.9 HISTORICAL BACKGROUND OF SOLAR CELLS
2.10 THEORY OF SOLAR CELLS
2.11 EFFICIENCIES OF SOLAR PANEL
2.12 REVIEW OF PERFORMANCE OF A PHOTOVOLTAIC SYSTEM
2.13 METHODS OF PERFORMANCE EVALUATION
2.14 FACTORS AFFECTING PV SYSTEM PERFORMANCE
2.15 PERFORMANCE EVALUATION METRICS
2.16 COMMON CHALLENGES IN PERFORMANCE EVALUATION
2.17 STRATEGIES FOR PERFORMANCE IMPROVEMENT
2.18 CASE STUDIES OF PERFORMANCE EVALUATION
2.19 UNDERSTANDING FUZZY LOGIC CONTROL
2.20 CONCLUSION
CHAPTER THREE
3.0 METHODOLOGY
3.1 PV CELL MODELING
3.2 PV ARRAY MODELING
3.3 MPPT USING FUZZY
CHAPTER FOUR
4.0 RESULT ANALYSIS
CHAPTER FIVE
5.1 CONCLUSION
5.2 RECOMMENDATION
REFERENCES
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
In the modern age renewable energy sources are playing vital role. And sun energy is treated as a best resource among all energy sources with less carbon
emission. That’s why sun power is considered as one of big potential energy sources in earth making future aspect for consideration as in exhaustible main source of power (Farhat et al., 2011). The PV module efficiency mainly depends upon the materials used in solar cells and technical arrangement of the cells in module. At this moment, the efficiency of PV module is in range of 12 to 29% for conversion of sunlight to electricity (Farhat et al., 2011). For getting optimum energy from photovoltaic module, it is needed to run the module at maximum power using MPPT (Maximum power point tracking) followed by several common techniques like 1) perturb and observe process (Farhat et al., 2011)) incremental conductance process) fuzzy logic and neural network process.ImplementationoffuzzyandneuralnetworksforthecontrolofMPPT is an outstanding field for research. These processes related to artificial intelligence are suitable for promoting the tracing capability as regard to present conventional processes (Tafticht et al., 2018).
Fuzzy as a part of Artificial Intelligence takes its origin by professor LoftiZadeh who produced fuzzy set principle in1965. Among artificial intelligence based processes fuzzy has some advantages so that the algorithm for MPPT can be obtained easily (Tafticht et al., 2018).For maximization power of pv module with control of the duty cycleratio in the profile of the PV & IV curve. Perturb and observe method (Tafticht et al., 2018)as well as the incremental and conductance method utilized as the common MPPT techniques in hibits step length for selection of the duty cycle . So step size controls the MPPT operation to a great extent because for small step size the tracking process goes down the speed while for the large step size the fluctuation on maximum power point occurs.
For this reason to control the step size it is important to apply the intelligence technique like fuzzy logic and adaptive neuro fuzzy technique so that step size can be adapted according to the requirement (Tafticht et al., 2018). Fuzzy is normally utilized to activate system as human control in an automation fact. Fuzzy is capable for controlling step size by empirical methods and professional knowledge without the necessary understanding the detailed mathematical model of the existing plant. The input-output parameters of the required system are largely responsible to enhance effectiveness of fuzzy in determination of MPPT with control of duty cycle command.
Thought here are a good number of various input variables for MPPT algorithm input, slope of PV curve for photovoltaic cell is taken as the most utilized variable of input (Tafticht et al., 2018). Fuzzy is considered as most preferable method for seeking the maximum power of the pv system for ensuring stability and good response rate. For better output of Fuzzy control method, researchers are more intended to find MPPT solution with the Fuzzy logic in their various publications (Philibert et al., 2014)The fuzzy inherited MPPT algorithm is provided by research effort effectiveness & robustness of PV system. Actually the main problem is about to selection of the step size for MPPT tracking process in various methods of MPPT tracking system. In Fuzzy system this problem can be solved to a great extent since Fuzzy has a good platform to analyze the step size as the requirement base in decision making process.
In this paper the MPPT solution is made using Fuzzy logic to seek the power performance as well as effectiveness of the PV module having consideration of various factors. Also this properties to show the partial shading effect of photovoltaic module with MPPT solution followed by fuzzy logic control.
Fig.1 Electrical equivalent circuit diagram for photovoltaic cell
1.2 Statement of the problem
Photovoltaic (PV) systems are increasingly utilized as a sustainable energy source. However, optimizing their performance is essential for maximizing energy output, ensuring reliability, and reducing costs. Traditional methods of performance evaluation have limitations in dealing with the inherent uncertainties and variability in PV system operation. Fuzzy Logic Control (FLC) techniques offer a sophisticated alternative by effectively handling imprecise data and complex system behaviors. This article explores the significance of using FLC techniques for evaluating and optimizing the performance of PV systems (Philibert et al., 2014).
1.3 Objectives of the study
The key objectives include:
- To ensure the PV system operates at its maximum power point under varying environmental conditions
- To enhance the efficiency of the inverter, which converts DC electricity from the PV panels into AC electricity for use
- To effectively manage the distribution and utilization of energy in systems combining PV with other energy sources and storage solutions.
- To maintain optimal system performance despite changes in environmental conditions such as solar irradiance, temperature, and partial shading
- To reduce the complexity involved in designing and implementing control systems for PV systems
1.4 Significance Of The Study
Significance of Fuzzy Logic Control in PV Performance Evaluation
1. Handling Uncertainty and Variability
PV systems are subject to significant variability in solar radiance, temperature, and other environmental conditions. Traditional control methods may struggle to adapt to these changes efficiently. FLC techniques excel in managing uncertainty and variability, providing more accurate and reliable performance evaluations.
2. Improved Maximum Power Point Tracking (MPPT)
MPPT is crucial for extracting the maximum possible power from PV modules. Fuzzy logic-based MPPT algorithms can adapt to changing conditions more effectively than conventional methods. By continuously adjusting the operating point of the PV system, FLC ensures optimal performance and higher energy yields.
3. Enhanced System Efficiency
Fuzzy logic controllers can optimize various parameters within the PV system, such as inverter efficiency and battery charging cycles. By fine-tuning these parameters in real-time, FLC techniques improve overall system efficiency and reduce energy losses.
4. Robustness and Flexibility
FLC techniques are robust and can handle a wide range of operating conditions without requiring precise mathematical models of the system. This flexibility makes them suitable for different types of PV systems, including grid-tied, off-grid, and hybrid configurations.
5. Simplified Design and Implementation
Designing and implementing FLC systems is often more straightforward than developing traditional control algorithms. Fuzzy logic does not require complex mathematical modeling, making it easier to design controllers that can manage non-linearities and interactions within the PV system.
6. Improved Fault Detection and Diagnosis
FLC techniques can enhance fault detection and diagnosis in PV systems by identifying abnormal patterns and deviations from expected performance. Early detection of faults, such as shading, module degradation, or inverter issues, enables timely maintenance and reduces downtime.
Evaluating the performance of photovoltaic systems using Fuzzy Logic Control techniques offers significant advantages in handling the inherent uncertainties and variability in PV operations. FLC enhances MPPT, improves system efficiency, provides robust and flexible control, and simplifies design and implementation.
1.6 Scope of the study
The scope of this work covers evaluating the performance of photovoltaic system using fuzzy logic control techniques. Fuzzy logic control enhances MPPT, inverter efficiency, energy management in hybrid systems, fault detection, and environmental adaptability while simplifying system design. By leveraging the strengths of fuzzy logic, PV systems can achieve higher efficiency, reliability, and performance, contributing to the broader adoption of solar energy technologies.
CHAPTER FIVE
5.1 CONCLUSION
To investigate the power performance under partial shading effect on PV module, fuzzy logic control is used as the MPPT solution. Since fuzzy has a good scope for selection of the step size as the requirement after processing the input variables in the decision making process, it is a good scope to track the maximum power from the PV module. From the MPPT solution it is found that around 84% as maximum and around 10% as minimum output power can be developed.
5.2 RECOMMENDATION
Furthermore more research works are needed to seek power output under partial shading. A good number of factors such as temperature, series resistance of PV cell, ideality factor, clearness indexet care needed to observe the over all performance of PV module.
