NDLI: Extracting solar cell model parameters based on chaos particle swarm algorithm

Content Provider	IEEE Xplore Digital Library
Author	Huang Wei Jiang Cong Xue Lingyun Song Deyun
Copyright Year	2011
Description	Author affiliation: School of Automation, Hangzhou Dianzi University, China (Huang Wei; Jiang Cong; Xue Lingyun) \|\| Department of Research and Development, Hangzhou Yuhang Electric Apparatus Ltd., China (Song Deyun)
Abstract	Utilizing the numerical analysis and optimization method for extracting solar cells model parameters, one recurrent issue refers to the difficulty in initializing the parameters. Moreover, those methods using solar cells exponential model are sensible to small changes in the data measured. A chaotic particle swarm optimization algorithm (CPSO) was presented for extracting solar cell model parameters, in which the global search performance and local convergence of particle swarm optimization (PSO) were improved by introducing a chaos search. The CPSO searched for optimal parameters without strict limitation on the search ranges. The procedure is illustrated by applying it to parameters extraction using the current-voltage data measured from a silicon cell and a solar module. The results demonstrate that the method can reduce the influence of experimental data measurement accuracy, and the statistical analysis data of fitting (I–V) characteristics curves are better than that of other published methods.
Starting Page	398
Ending Page	402
File Size	228887
Page Count	5
File Format	PDF
ISBN	9781424480364
e-ISBN	9781424480395
DOI	10.1109/ICEICE.2011.5777246
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaotic search Photovoltaic cells Current measurement Fitting Solar cells model Mathematical model Particle swarm optimization Equations Optimization Parameter extraction
Content Type	Text
Resource Type	Article

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