Parameters Identification for Photovoltaic Module Based on an Improved Artificial Fish Swarm Algorithm

A precise mathematical model plays a pivotal role in the simulation, evaluation, and optimization of photovoltaic (PV) power systems. Different from the traditional linear model, the model of PV module has the features of nonlinearity and multiparameters. Since conventional methods are incapable of...

Full description

Saved in:
Bibliographic Details
Published in:TheScientificWorld 2014-01, Vol.2014 (2014), p.1-12
Main Authors: Han, Wei, Chen, Ling, Wang, Hong-hua
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Animals
Artificial Intelligence - trends
Environmental conditions
Fishes
Heuristic
Models, Biological
Photovoltaic cells
Photovoltaics
Silicon wafers
Solar energy
Swarm intelligence
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A precise mathematical model plays a pivotal role in the simulation, evaluation, and optimization of photovoltaic (PV) power systems. Different from the traditional linear model, the model of PV module has the features of nonlinearity and multiparameters. Since conventional methods are incapable of identifying the parameters of PV module, an excellent optimization algorithm is required. Artificial fish swarm algorithm (AFSA), originally inspired by the simulation of collective behavior of real fish swarms, is proposed to fast and accurately extract the parameters of PV module. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated by various parameters of PV module under different environmental conditions, and the testing results are compared with other studied methods in terms of final solutions and computational time. The simulation results show that the proposed method is capable of obtaining higher parameters identification precision.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2014/859239