Dynamic modeling of grid-connected photovoltaic system using artificial neural network and genetic algorithm
Joint Authors
Rezvani, Alireza
Izadbakhsh, Maziar
Gandomkar, Majid
Source
Issue
Vol. 11, Issue 2 (30 Jun. 2015), pp.131-144, 14 p.
Publisher
Publication Date
2015-06-30
Country of Publication
Algeria
No. of Pages
14
Main Subjects
Topics
Abstract EN
Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy.
The aim of this study is to simulate and control of a grid-connected PV source using artificial neural network (ANN) and genetic algorithm (GA) controller.
Also, for tracking the maximum power point (MPP), ANN and GA are used.
Data are optimized by GA and then these optimized data are applied in the neural network training.
The simulation results are presented by using Matlab/Simulink and show that the ANN—GA controller can meet the need of the load easily and have less fluctuations around the maximum power point (MPP), also it can increase convergence speed to achieve the MPP.
Moreover, to control both line voltage and current, a grid side P-Q controller has been applied
American Psychological Association (APA)
Rezvani, Alireza& Izadbakhsh, Maziar& Gandomkar, Majid. 2015. Dynamic modeling of grid-connected photovoltaic system using artificial neural network and genetic algorithm. Journal of Electrical Systems،Vol. 11, no. 2, pp.131-144.
https://search.emarefa.net/detail/BIM-577746
Modern Language Association (MLA)
Rezvani, Alireza…[et al.]. Dynamic modeling of grid-connected photovoltaic system using artificial neural network and genetic algorithm. Journal of Electrical Systems Vol. 11, no. 2 (Jun. 2015), pp.131-144.
https://search.emarefa.net/detail/BIM-577746
American Medical Association (AMA)
Rezvani, Alireza& Izadbakhsh, Maziar& Gandomkar, Majid. Dynamic modeling of grid-connected photovoltaic system using artificial neural network and genetic algorithm. Journal of Electrical Systems. 2015. Vol. 11, no. 2, pp.131-144.
https://search.emarefa.net/detail/BIM-577746
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 144
Record ID
BIM-577746