Research on the MPPT algorithms of photovoltaic system based on PV neural network

Author

Jie, Long ; Ziran, Chen

Author_Institution

Chongqing Educ. Coll., Chongqing, China

fYear

2011

fDate

23-25 May 2011

Firstpage

1851

Lastpage

1854

Abstract

In order to solve Maximum Power Point Tracking (MPPT) technology difficulties in photovoltaic system, 2-level neural network-genetic optimal algorithm is employed to estimate the photovoltaic battery model, taking into account possible influencing factors for battery output power such as light intensity, circumstance temperature, battery junction temperature, battery position. This method overcomes both power loss caused by oscillation around maximum power point with the traditional hill-climbing method and difficulty of training data with traditional neural network algorithm.

Keywords

genetic algorithms; neural nets; power control; solar cells; 2-level neural network genetic optimal algorithm; MPPT algorithm; PV neural network algorithm; battery junction temperature; battery position; circumstance temperature; hill-climbing method; maximum power point tracking technology; oscillation; photovoltaic battery model; photovoltaic system; power loss; training data; Artificial neural networks; Batteries; Genetic algorithms; Photovoltaic systems; Temperature; Training; Function transform of semiconductor memory; Genetic algorithm; MPPT; Neural network; Photovoltaic system;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2011 Chinese

Conference_Location

Mianyang

Print_ISBN

978-1-4244-8737-0

Type

conf

DOI

10.1109/CCDC.2011.5968501

Filename

5968501