DocumentCode
2670199
Title
Intelligent control in photovoltaic systems by neural network
Author
Dkhichi, Fayrouz ; Oukarfi, Benyounes
Author_Institution
Electr. Eng. Dept., Hassan II Casablanca Univ., Mohammedia, Morocco
fYear
2015
fDate
25-26 March 2015
Firstpage
1
Lastpage
5
Abstract
The Artificial Neural Network (ANN) method studied in this paper is assigned as an intelligent control of photovoltaic (PV) system. The objective of this control is to make the load operate at the maximum electrical power generated by the PV module. In this aim, the ANN consists to track the optimal duty cycle of the electronic converter, in order to lead to the Maximum Power Point (MPP) of the PV system. Moreover, the two classical methods: Perturb and Observe (P&O) and Incremental Conductance (IncCon) are studied in the sake of comparison with the ANN method, by taking into consideration the efficiency, the speed and the robustness performance when the meteorological conditions change.
Keywords
maximum power point trackers; neurocontrollers; photovoltaic power systems; electronic converter; intelligent control; maximum electrical power generation; maximum power point; neural network; photovoltaic systems; Artificial neural networks; Intelligent control; Mathematical model; Maximum power point trackers; Oscillators; Software packages; Steady-state; Artificial Neural Network; Intelligent Control; Maximum Power Point Tracker; PV system;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems and Computer Vision (ISCV), 2015
Conference_Location
Fez
Print_ISBN
978-1-4799-7510-5
Type
conf
DOI
10.1109/ISACV.2015.7106181
Filename
7106181
Link To Document