Open AccessSimplified swarm optimisation for the solar cell models parameter estimation problem
Author(s) -
Yeh WeiChang,
Lin Peijie,
Huang ChiaLing
Publication year - 2017
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2016.0473
Subject(s) - benchmark (surveying) , swarm behaviour , solar cell , computer science , photovoltaics , mathematical optimization , estimation theory , photovoltaic system , solar energy , soft computing , algorithm , mathematics , engineering , artificial neural network , artificial intelligence , geodesy , electrical engineering , geography
Solar energy applications and research are becoming increasingly popular, and photovoltaics (PVs) are among the most significant solar energy applications. To simulate and optimise PV system performance, the optimal parameters of the solar cell models should be estimated exactly. In this study, improved simplified swarm optimisation (iSSO), a recently introduced soft computing method based on simplified swarm optimisation, is proposed to minimise the least square error between the extracted and the measured data for the solar cell models parameter estimation of the single‐ and double‐diode model problems. Based on the new all‐variable difference update mechanism and survival of the fittest policy, the proposed algorithm is able to find an improved approximation for estimating the parameters of single‐ and double‐diode solar cell models. As evidence of the utility of the proposed iSSO, the authors present extensive computational results for two benchmark problems. The comparison of the computational results supports the proposed iSSO algorithm outperforms the previously developed algorithms for all of the experiments in the literature.