Model Based Optimisation Algorithm for Maximum Power Point Tracking in Photovoltaic Panels
Faiçal Hamidi,
Severus Constantin Olteanu,
Dumitru Popescu,
Houssem Jerbi,
Ingrid Dincă,
Sondess Ben Aoun,
Rabeh Abbassi
Affiliations
Faiçal Hamidi
Laboratory “Modélisation, Analyse et Commande des Systèmes”, University of Gabes, LR16ES22 Gabes, Tunisia
Severus Constantin Olteanu
Automatic Control and Systems Engineering Department, Automatic Control and Computer Science Faculty, University “Politehnica” of Bucharest, 060042 Bucharest, Romania
Dumitru Popescu
Automatic Control and Systems Engineering Department, Automatic Control and Computer Science Faculty, University “Politehnica” of Bucharest, 060042 Bucharest, Romania
Houssem Jerbi
Department of Industrial Engineering, College of Engineering, University of Ha’il, Hail 1234, Saudi Arabia
Ingrid Dincă
Automatic Control and Systems Engineering Department, Automatic Control and Computer Science Faculty, University “Politehnica” of Bucharest, 060042 Bucharest, Romania
Sondess Ben Aoun
Department of Computer Engineering, College of Computer Science and Engineering, University of Ha’il, Hail 1234, Saudi Arabia
Rabeh Abbassi
Department of Electrical Engineering, College of Engineering, University of Ha’il, Hail 1234, Saudi Arabia
Extracting maximum energy from photovoltaic (PV) systems at varying conditions is crucial. It represents a problem that is being addressed by researchers who are using several techniques to obtain optimal outcomes in real-life scenarios. Among the many techniques, Maximum Power Point Tracking (MPPT) is one category that is not extensively researched upon. MPPT uses mathematical models to achieve gradient optimisation in the context of PV panels. This study proposes an enhanced maximisation problem based on gradient optimisation techniques to achieve better performance. In the context of MPPT in photovoltaic panels, an equality restriction applies, which is solved by employing the Dual Lagrangian expression. Considering this dual problem and its mathematical form, the Nesterov Accelerated Gradient (NAG) framework is used. Additionally, since it is challenging to ascertain the step size, its approximate value is taken using the Adadelta approach. A basic MPPT framework, along with a DC-to-DC convertor, was simulated to validate the results.
