Optimization of Photovoltaic System Performance Using �Advanced MPPT Techniques (P&O, INC, ANN)

Optimization of Photovoltaic System Performance Using �Advanced MPPT Techniques (P&O, INC, ANN)

1. UNIVERSITY OF ECHAHID HAMMA LAKHDAR EL OUED FACULTY OF TECHNOLOGY SECTOR : ELECTROMECHANICAL ENGINEERING SPECIALTY : ELECTROMECHANICAL Theme: Optimization of Photovoltaic System Performance Using Advanced MPPT Techniques (P&O, INC, ANN) SUPERVISOR: By the student: * Miloudi Khaled * Belkacmi Oualid * Ghouli Yahia * Ghemam Djeridi Mohammed 2024/2025 Optimization of Photovoltaic System Performance Using Advanced MPPT Techniques (P&O, INC, ANN)

2. WORK PLAN Introduction 01 PROBLEMATIC 02 OBJECTIF 03 OVER VIEW 04 TYPES ALGORITHMS OF MPPT TECHNIQUES 05 SIMULATION RESULTS (constant irradiance) 06 SIMULATION RESULTS (variable irradiance ) 07 COMPARISON OF EFFICIENCY BETWEEN ALGORITHMS(P&O, INC,ANN) 08 Conclusion 09 1 1

3. 1- Introduction 2 Photovoltaic system efficiency is affected by solar irradiance and temperature , causing power fluctuations MPPT techniques are applied to improve performance and reduce losses This study analyzes and compares these techniques under varying conditions

4. Problem 01 Problem 02 Problem 03 2- PROBLEMATIC The system has problems that hurt how well it works including: MPP: to compare Comparison difficulty ANN complexity Oscillation at MPP 3

5. 3- OBJECTIF Improved energy efficiency 01 Reduced power losses 02 Fast environmental response 03 Smart algorithm integration‏ 04 4 4

6. 4- OVER VIEW   This work starts by explaining how a photovoltaic system works and how it can be modeled with equations.Then , it looks at different MPPT methods and control techniques, like fuzzy logic controllers. At the end, the results from simulations are studied, and the main conclusions are presented 5 Block Diagram of the PV System

7. 5- TYPES ALGORITHMS OF MPPT TECHNIQUES Algorithm Incremental conductance (INC) • Algorithm Perturb and Observe (P&O) Artificial Neural Networks (ANN): 6

8. 6- SIMULATION RESULTS CONSTANT IRRADIANCE ( P&O Algorithm ) In this simulation , the Perturb and Observe (P&O) algorithm was used o obtain voltage, current, and power results. Figure (1) illustrates the overall system configuration, showing the integration of PV panels with the P&O-based MPPT controller 1)P&O Algorithm : 7 Figure (1):Schema of System Simulation With P&O

9. 6- SIMULATION RESULTS CONSTANT IRRADIANCE ( P&O Algorithm ) Figure (2): Duty Cycle Simulation Result Figure (5): Current Simulation Result Figure (4): Voltage Simulation Result Figure (3): Power Simulation Result 8

10. In this simulation, the Incremental Conductance (INC) algorithm was used to obtain voltage, current, and power results. Figure (6) illustrates the overall system configuration and the integration of PV panels with the INC-based MPPT controller 2) Incremental Conductance: 6- SIMULATION RESULTS CONSTANT IRRADIANCE ( INC Algorithm ) 9 Figure ( 6 ):Schema of System Simulation With INC

11. 6- SIMULATION RESULTS CONSTANT IRRADIANCE ( INC Algorithm ) Figure (7): Voltage Simulation Result Figure (8): Power Simulation Result Figure (9): Current Simulation Result 10

12. In this simulation, the Artificial Neural Network (ANN) algorithm was used to obtain the results of voltage, current, and power. Figure (10) illustrates the integration of solar panels with the ANN-based MPPT unit 3)Artificial Neural Network (ANN): 6- SIMULATION RESULTS CONSTANT IRRADIANCE ( ANN Algorithm ) 11 Figure ( 10 ):Schema of System Simulation With ANN

13. 6- SIMULATION RESULTS CONSTANT IRRADIANCE ( ANN Algorithm ) Figure (11): Duty Cycle Simulation Result Figure (12): Power Simulation Result Figure (13): Voltage Simulation Result Figure (14): Current Simulation Result 12

14. 7- SIMULATION RESULTS VARIABLE IRRADIANCE ( P&O Algorithm ) a) P&O Algorithm : Figure (15): Power Simulation Result Figure (16): Current Simulation Result Figure (17): Voltage Simulation Result 13

15. 7- SIMULATION RESULTS VARIABLE IRRADIANCE ( INC Algorithm ) b) Incremental Conductance: Figure (18): Power Simulation Result Figure (19): Voltage Simulation Result Figure (20): Current Simulation Result 14

16. 7- SIMULATION RESULTS VARIABLE IRRADIANCE ( ANN Algorithm ) c) Artificial Neural Network (ANN): Figure (21): Power Simulation Result Figure (22): Voltage Simulation Result Figure (23): Current Simulation Result 15

17. 8- COMPARISON OF EFFICIENCY BETWEEN ALGORITHMS(P&O, INC,ANN ) Constant Irradiance : Algorithm Efficiency Perturb and Observe (P&O) 94.72 % Incremental Conductance (INC) 95.58 % Artificial Neural Network (ANN) 96.63 % Algorithm Efficiency Perturb and Observe (P&O) 84.84 % Incremental Conductance (INC) 88.04% Artificial Neural Network (ANN) 95.06 % Table(2): Table of Efficiency Table(1): Table of Efficiency 16 V ariable Irradiance :

18. The ANN algorithm demonstrated superior performance as the most effective MPPT method in terms of efficiency, stability, and adaptability to varying irradiance. While the INC algorithm outperformed P&O, it remained less effective than ANN. P&O showed the lowest efficiency and stability, making ANN the most suitable choice for reliable and efficient energy extraction 9- Conclusion 17

19. Thank you for your attention