PV Modeling with MPPT Using Different Optimization Techniques /Ahmed Tarek Mohamed Ibrahim

Supervisor:
Ahmed G. Radwan

Thesis (M.A.)—Nile University, Egypt, 2024 .

"Includes bibliographical references"

Contents:
Contents
Page
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Chapters:
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.1 Thesis scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.2 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2. Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2 Photovoltaic (PV) modelling . . . . . . . . . . . . . . . . . . . . . 18
2.2.1 Empirical models . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.2 Numerical models . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.3 Analytical models . . . . . . . . . . . . . . . . . . . . . . . 21
2.3 Meta-Heuristic Techniques . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.1 Single solution-based techniques . . . . . . . . . . . . . . . . 27
2.3.2 Population based techniques . . . . . . . . . . . . . . . . . . 28
2.3.3 Recent advancements and hybrid approaches . . . . . . . . 32
2.4 Maximum Power Point Tracking (MPPT) . . . . . . . . . . . . . . 34
2.4.1 Conventional MPPT Techniques . . . . . . . . . . . . . . . 35
5
2.4.2 Global MPPT Techniques . . . . . . . . . . . . . . . . . . . 40
2.4.3 Hybrid MPPT Techniques . . . . . . . . . . . . . . . . . . . 44
3. PV Modeling using different optimization techniques . . . . . . . . . . . 46
3.1 Meta-Heuristic Optimization Techniques for PV Parameters Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.1.1 Water Cycle Algorithm (WCA) . . . . . . . . . . . . . . . . 47
3.1.2 The Arithmetic Optimization Algorithm (AOA) . . . . . . . 49
3.2 The PV mathematical model . . . . . . . . . . . . . . . . . . . . . 55
3.3 Problem Formulation, Results and Discussion . . . . . . . . . . . . 57
3.4 PI and FOPI Controller . . . . . . . . . . . . . . . . . . . . . . . . 61
3.5 DC-DC converter control method . . . . . . . . . . . . . . . . . . . 65
3.6 DC-DC converter results with different controller types . . . . . . . 68
4. Improved MPPT for Partially Shaded PV Systems through Accelerated
Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . 74
4.1 The Mathematical Model of the used PV Model . . . . . . . . . . . 76
4.2 DC–DC Converter Design . . . . . . . . . . . . . . . . . . . . . . . 77
4.3 Proposed MPPT algorithm . . . . . . . . . . . . . . . . . . . . . . 79
4.3.1 Particle Swarm Optimization Algorithm(PSO) . . . . . . . 79
4.3.2 Accelerated PSO Algorithm(APSO) . . . . . . . . . . . . . 81
4.4 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5. Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . 86
6. List of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Abstract:
The usage of PV systems as an effective source of renewable energy has increased
significantly in recent years. However, PV systems are inherently nonlinear and affected by varying weather conditions, which makes it challenging to get the maximum power from the system. This thesis proposes the use of optimization techniques
to model PV systems and achieve MPPT under varying conditions of weather. A
comprehensive literature review is conducted to identify the relevant optimization
techniques and MPPT algorithms. A mathematical model of a PV system is developed that captures its behavior under varying weather conditions, and the selected
optimization algorithms are integrated into the model to achieve MPPT. The performance of the optimization techniques and MPPT algorithms is validated using real
data from a real PV system, and the results are compared with conventional MPPT
techniques. The proposed approach demonstrates the effectiveness of optimization
techniques in modeling PV systems and achieving MPPT, and provides insights into
the design and optimization of PV systems for practical applications.
Keywords: PV Modules , Optmization Techniques, Maximum Power Point Tracking (MPPT), Metaheuristic Algorithms.

Text in English, abstracts in English and Arabic