Open AccessEvolutionary algorithm and parameters extraction for dye‐sensitised solar cells one‐diode equivalent circuit model
Author(s) -
Peng Wei,
Zeng Yun,
Gong Hao,
Leng Yongqing,
Yan Yonghong,
Hu Wei
Publication year - 2013
Publication title -
micro and nano letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.25
H-Index - 31
ISSN - 1750-0443
DOI - 10.1049/mnl.2012.0806
Subject(s) - photovoltaic system , particle swarm optimization , equivalent series resistance , internal resistance , algorithm , equivalent circuit , noise (video) , voltage , dye sensitized solar cell , evolutionary algorithm , diode , computer science , genetic algorithm , energy (signal processing) , electronic engineering , mathematics , mathematical optimization , engineering , power (physics) , electrical engineering , physics , artificial intelligence , electrode , battery (electricity) , quantum mechanics , statistics , electrolyte , image (mathematics)
With the aim of improving energy conversion efficiency of dye‐sensitised solar cells (DSCs), three evolutionary algorithms (EAs), namely genetic algorithm, particle swarm optimisation (PSO) and differential evolution, are investigated the first time to extract the DSCs parameters based on the single‐diode photovoltaic (PV) equivalent circuit model. By comparing the accuracy, calculation speed and anti‐noise ability of the three EA techniques, PSO shows the highest accuracy and the best anti‐noise property. To evaluate the parameters, especially the series‐internal resistance ( R s ) that is important for DSCs energy conversion efficiency, a batch of DSCs devices were made and the R s obtained by changing the series resistance value connected with the DSCs. The two methods give the R s approximately equal value, and almost same current–voltage figures based on PSO simulation with measured characteristics, which prove PSO is an efficient computational method and can be used to extract the parameters for the DSCs PV model.