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|Title:||Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm||Authors:||Biswas, Partha Pratim
Suganthan, Ponnuthurai Nagaratnam
Amaratunga, Gehan A. J.
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2018||Source:||Biswas, P. P., Suganthan, P. N., Wu, G., & Amaratunga, G. A. J. (2019). Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm. Renewable Energy, 132, 425-438. doi:10.1016/j.renene.2018.07.152||Journal:||Renewable Energy||Abstract:||A solar cell or photovoltaic (PV) module is electrically represented by an appropriate circuit model with certain defined parameters. The parameters are required to be correctly computed from solar cell characteristic and/or a set of experimental data for simulation and control of the PV system. However, experimental data or accurate characteristic data (i.e. current-voltage or I-V curve) of a PV module may not be readily available. The manufacturer of a PV system usually provides relevant information on open circuit, short circuit and maximum power points. Therefore, an alternate approach is to estimate the PV system parameters by utilizing the I-V characteristic data at these three major points. The process involves formulation and solution of complex non-linear equations from an adopted solar cell model. This paper proposes an application of an advanced adaptive differential evolution algorithm on the problem of PV module parameter estimation using minimum available information from the manufacturer datasheet by implementing single-diode and double-diode models. Linear population size reduction technique of success history based adaptive differential evolution (L-SHADE) algorithm is implemented to minimize the error of current-voltage relationships at the above-mentioned three important points defining the I-V characteristic. The algorithm facilitates evolution of solutions that result in almost zero error (<10−12) at these three major points. All relevant parameters of the PV cell are optimized by the algorithm without any assumption or predetermination of parameters. It is observed that a set of feasible solutions (parameters) is obtained for the PV module from multiple runs of the algorithm. The fact of attaining several probable solutions from datasheet information using few other metaheuristics is also discussed in this work.||URI:||https://hdl.handle.net/10356/139914||ISSN:||0960-1481||DOI:||10.1016/j.renene.2018.07.152||Rights:||© 2018 Elsevier Ltd. All rights reserved. This paper was published in Renewable Energy and is made available with permission of Elsevier Ltd.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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