DocumentCode
2675107
Title
Maximum power point tracking using GA-optimized artificial neural network for Solar PV system
Author
Ramaprabha, R. ; Gothandaraman, V. ; Kanimozhi, K. ; Divya, R. ; Mathur, B.L.
Author_Institution
Dept. of Electr. & Electron. Eng., SSN Coll. of Eng., Chennai, India
fYear
2011
fDate
3-5 Jan. 2011
Firstpage
264
Lastpage
268
Abstract
Solar energy is a green energy which is not only perennial but also accessible to every strata of the world. An easy way to convert solar energy into electric energy is to use Solar Photovoltaic (SPV) system. Solar panel is a power source having nonlinear internal resistance. As the intensity of light falling on the panel varies, its voltage as well as its internal resistance varies. To extract maximum power from the panel, the load resistance should be equal to the internal resistance of the panel. For this purpose maximum power point trackers (MPPT) are used. This paper proposes a new MPPT controller. The proposed MPPT controller is based on genetic algorithm (GA) optimized artificial neural network (ANN). For the simulation purpose an improved model of SPV is used. The MPPT is simulated and studied using MATLAB software.
Keywords
genetic algorithms; maximum power point trackers; neural nets; photovoltaic power systems; power engineering computing; solar cells; MATLAB software; MPPT controller; artificial neural network; electric energy; genetic algorithm; green energy; maximum power point tracking; nonlinear internal resistance; solar energy; solar panel; solar photovoltaic system; Artificial neural networks; Biological system modeling; Gallium; Integrated circuit modeling; Mathematical model; Photovoltaic systems; Resistance; ANN; GA; MATLAB; MPPT; Solar PV system;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrical Energy Systems (ICEES), 2011 1st International Conference on
Conference_Location
Newport Beach, CA
Print_ISBN
978-1-4244-9732-4
Type
conf
DOI
10.1109/ICEES.2011.5725340
Filename
5725340
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