Comparative Assessment between Five Control Techniques to Optimize the Maximum Power Point Tracking Procedure for PV Systems

Fathi Troudi, Houda Jouini, Abdelkader Mami, Nidhal Ben Khedher, Walid Aich, Attia Boudjemline and Mohamed Boujelbene
Additional contact information
Fathi Troudi: Laboratory LAPER, University Tunis El Manar, Tunis 2092, Tunisia
Houda Jouini: Laboratory LAPER, University Tunis El Manar, Tunis 2092, Tunisia
Abdelkader Mami: Laboratory LAPER, University Tunis El Manar, Tunis 2092, Tunisia
Nidhal Ben Khedher: Department of Mechanical Engineering, College of Engineering, Hail University, Hail 55476, Saudi Arabia
Walid Aich: Department of Mechanical Engineering, College of Engineering, Hail University, Hail 55476, Saudi Arabia
Attia Boudjemline: Department of Industrial Engineering, College of Engineering, Hail University, Hail 55476, Saudi Arabia
Mohamed Boujelbene: Department of Industrial Engineering, College of Engineering, Hail University, Hail 55476, Saudi Arabia

Mathematics, 2022, vol. 10, issue 7, 1-15

Abstract: Solar photovoltaic (PV) energy production is important in reducing global energy crises since it is transportable, scalable, and highly customizable dependent on the needs of the industry or end-user. In addition, compared to other renewable resources, photovoltaic systems can produce electricity without moving parts and have a long lifespan. Nevertheless, solar photovoltaic (PV) systems provide intermittent output electricity with a nonlinear output voltage. Due to this intermittent availability, PV installations are facing significant challenges. As a result, in PV power systems, a Maximum Power Point Tracker (MPPT), a power extraction mechanism, is required to assure maximum power delivery at any given moment. The main objective of this work is to study the MPPT method of extracting the maximum power from photovoltaic modules under different solar irradiation and temperatures. Several MPPT methods have been developed for photovoltaic systems to achieve MPP, depending on weather conditions and applications, ranging from simple to more complex methods. Among these methods, five techniques have been presented and compared that are P&O perturbation and observation method, INC incremental conductance method, the ANN neural network method, the open circuit voltage based neural network method FVCO, and the neural network method at the base of FCC (short circuit current).

Keywords: MPPT; P&O; incremental conductance INC; ANN neural network method; FVCO method; FCC neural network method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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