COMPARATIVE STUDY OF MPPT TECHNIQUES FOR PHOTOVOLTAIC SYSTEM

COMPARATIVE STUDY OF MPPT TECHNIQUES FOR PHOTOVOLTAIC SYSTEM

1. UNIVERSITY OF ECHAHID HAMMA LAKHDAR EL OUED FACULTY OF TECHNOLOGY SECTOR : ELECTROMECHANICAL ENGINEERING SPECIALTY : ELECTROMECHANICAL Theme: COMPARATIVE STUDY OF MPPT TECHNIQUES FOR PHOTOVOLTAIC SYSTEM SUPERVISOR: By the student: 2024/2025

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

3. INTRODUCTION 01. The growing demand for electricity and the limitations of fossil fuels highlight the need for renewable energy. Solar power, through photovoltaic (PV) systems, offers a sustainable solution. This study compares three MPPT techniques—P&O, INC, and ANN—to improve PV system efficiency. 01

4. 02. PROBLEMATIC 02 how can a comparative evaluation of MPPT techniques can help determine the most effective method to enhance photovoltaic system performance, considering varying environmental conditions, cost-efficiency, technological complexity, and each method’s adaptability to dynamic solar energy fluctuations ?

5. 03. OBJECTIF 03 evaluate and compare the performance of different MPPT algorithms used in PV systems . determine the efficiency and responsiveness of each algorithm under variable environmental conditions. identify the strengths and weaknesses of P&O, INC, and ANN techniques . suggest the most suitable algorithm for maximizing power output in real-world solar energy applications.

6. 04. OVERVIEW 04 we present and explain the principle of operation of a photovoltaic system and a photovoltaic cell as well as its mathematical model . will be devoted to the MPPT command, where we present the origins as well as the different types of MPPT command that exist . will be entirely devoted to studying the types of algorithms and the MPPT controller, where we will present and explain its principle. we will expose and discuss the results of simulation obtained then we end our work with a general conclusion .

7. 05. TYPES ALGORITHMS OF MPPT TECHNIQUES The Perturb and Observe (P&O) MPPT method works by slightly changing the PV voltage and observing the change in power. If power increases, the system is below the Maximum Power Point (MPP); if it decreases, it has passed the MPP. I. Algorithm Perturb and Observe (P&O): 05 Figure (1): Ppv VS Vpv Characteristic of a Solar Panel

8. 05. TYPES ALGORITHMS OF MPPT TECHNIQUES The Incremental Conductance (INC) MPPT algorithm uses the conductance (G = I/V) and its change ( dG ) to locate the Maximum Power Point (MPP). If dG > -G, the duty cycle is reduced; if dG < -G, it is increased. This adjustment continues until the MPP is reached. II . Algorithm Incremental conductance (INC): 06 Figure (2): Flowchart for The Incremental Conductance Algorithm.

9. 05. TYPES ALGORITHMS OF MPPT TECHNIQUES An artificial neural network (ANN) consists of multiple layers of processors. The first layer receives raw input data, and each subsequent layer processes and passes the data forward. The final layer produces the output, mimicking how the human brain processes visual information. III . Artificial Neural Networks (ANN): 07 Figure (3): Functioning of Artificial Neural Networks

10. 06. SIMULATION RESULTS (constant irradiance) a) P&O Algorithm : 08 In this simulation, we used the Perturb and Observe (P&O) algorithm to obtain results for power, voltage, and current. Figure (4) represents the general system simulation, showcasing the overall setup and performance of the P&O -based MPPT system. Figure ( 4 ):Schema of System Simulation With INC

11. Figure ( 5 ): Duty Cycle Simulation Result 09 Figure ( 6 ): Power Simulation Result Figure ( 7 ): Voltage Simulation Result Figure ( 8 ): Current Simulation Result

12. 06. SIMULATION RESULTS (constant irradiance) b) Incremental Conductance: 10 In this simulation, we used the Incremental Conductance (INC) algorithm to obtain results for power, voltage, and current. Figure (9) represents the general system simulation, showcasing the overall setup and performance of the INC-based MPPT system. Figure ( 9 ):Schema of System Simulation With INC

13. Figure ( 10 ): Power Simulation Result 11 Figure ( 11 ): Voltage Simulation Result Figure ( 12 ): Current Simulation Result

14. 06. SIMULATION RESULTS (constant irradiance) c) Artificial Neural Network (ANN) 12 In this simulation, we used the Artificial Neural Network (ANN)algorithm to obtain results for power, voltage, and current. Figure ( 13 ) represents the general system simulation, showcasing the overall setup and performance of the ANN-based MPPT system. Figure ( 13 ):Schema of System Simulation With ANN

15. Figure ( 14 ): Duty Cycle Simulation Result Figure ( 15 ): Power Simulation Result Figure ( 17 ): Voltage Simulation Result Figure ( 16 ): Current Simulation Result 13

16. 07. RESULTS (variable irradiance) SIMULATION a) P&O Algorithm : 14 Figure( 19 ): Voltage Simulation Result Figure ( 18 ): Current Simulation Result Figure ( 20 ): Power Simulation Result

17. 07. RESULTS (variable irradiance) SIMULATION b) Incremental Conductance: 15 Figure ( 21 ): Power Simulation Result Figure ( 22 ): Voltage Simulation Result Figure ( 23 ): Current Simulation Result

18. 07. RESULTS (variable irradiance) SIMULATION c) Artificial Neural Network (ANN) 16 Figure ( 24 ): Power Simulation Result Figure ( 25 ): Voltage Simulation Result Figure ( 26 ): Current Simulation Result

19. 08. COMPARISON OF EFFICIENCY BETWEEN ALGORITHMS (P&O, INC, ANN) 17 Constant Irradiance(1000W/m 2 ) Variable Irradiances (1000, 800, 600,1000W/m 2 )

20. 09. CONCLUSION The study reviews solar radiation basics, PV energy conversion, and factors influencing solar cell efficiency. It explains the role of DC-DC converters and introduces MPPT for optimizing energy output. Among the MPPT methods, Artificial Neural Networks (ANN) are found to be the most efficient, stable, and adaptable, outperforming P&O and INC techniques. 18

21. THANKS FOR YOUR ATTENTION Please keep this slide for attribution [email protected] [email protected] bachasouals @gmail.com