A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm

• Published in: Renewable energy Vol. 78; pp. 105 - 113
• Main Authors: Khanna, Vandana, Das, B.K., Bisht, Dinesh, Vandana, Singh, P.K.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2015
• Subjects: algorithms; diodes; Parameter estimation; PSO algorithm; renewable energy sources; Solar cell; solar cells; Three-diode model; Two-diode model; Solar cell; Parameter estimation; Three-diode model; PSO algorithm; Two-diode model
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2014.12.072

A new lumped-parameter equivalent circuit model using three-diodes is presented in this work for large area (∼154.8 cm2) industrial silicon solar cells. The estimation of values of ideality factors n1 (>1) and n2 (>2) using a Particle Swarm Optimization (PSO) algorithm for the two-diode model of the industrial samples has been found not to be in conformity with the theoretical values (n1 = 1 and n2 = 2 in the literature). The two diodes of the two-diode model are not able to define the different current components of the solar cells clearly. A model with three-diodes has been proposed to better explain the experimental data. In the proposed model, we considered the series resistance, Rs, of the solar cell to vary with the current flowing through the solar cell device. All the parameters of the proposed model have been estimated using a PSO algorithm and they were compared with the parameters of the two-diode model. The new model has been found to be a better model to define clearly the different current components of the large size industrial silicon solar cells. •A three diode model using three diodes better explains the I–V characteristics of large size industrial silicon solar cells.•PSO algorithms can estimate the various parameters of the three diode model of such industrial silicon solar cells.•Increase of series resistance with current through such industrial silicon solar cells is within 10%.