Control and Optimization of an Autonomous Photovoltaic System using Metaheuristic Methods

Houam, Yehya (2021) Control and Optimization of an Autonomous Photovoltaic System using Metaheuristic Methods. Doctoral thesis, Université Mohamed Khider – Biskra.

[img] Text
Thesis Doctorat final Houam Yehya 2021.pdf

Download (19MB)

Abstract

The sun is an almost inexhaustible source of energy that returns to the surface of the earth a radiation that represents each year about 15000 times the energy consumption of humanity. It appears as an important source such that the amount of solar energy that reaches the surface of the earth during a day is greater ten times than that consumed.Through the photovoltaic effect, the energy contained in the sunlight can be converted largely at electrical energy. The geographical location of Algeria favors the development of the use of solar energy but always its yield has been considered insufficient for produce an important energy. Among several parameters influencing this yield are the irradiation flux and the temperature. Several studies have been presented to improve the power yield and thus profit the maximum of the power conversion obtained. we propose in this study, application of the different techniques: classic, artificial intelligence and metaheuristic, in order to ameliorate the perfermances of the maximum power point tracking (MPPT) controller and maximize the output power of the photovoltaic generator intended to an autonomous photovoltaic system under varying environmental conditions.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Global maximum power point tracking (GMPPT), Meta-heuristic Methods, Partial Shading Case (PSC), Photovoltaic (PV), Standalone system.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculté des Sciences et de la technologie > Département de Génie Electrique
Depositing User: Mr. Mourad Kebiel
Date Deposited: 30 Sep 2021 09:21
Last Modified: 30 Sep 2021 09:21
URI: http://thesis.univ-biskra.dz/id/eprint/5537

Actions (login required)

View Item View Item