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A genetic algorithm approach to parameter estimation for PV modules

Citation

Zhang, Y and Lyden, S and Leon de la Barra, BA and Haque, ME, A genetic algorithm approach to parameter estimation for PV modules, Proceedings of the 2016 IEEE Power and Energy Society General Meeting, 17-21 July 2016, Boston, Massachusetts, pp. 1-5. ISBN 978-1-5090-4168-8 (2016) [Refereed Conference Paper]


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Copyright Statement

Copyright 2016 IEEE

Official URL: http://www.pes-gm.org/2016/ieee-pes-gm-proceedings

DOI: doi:10.1109/PESGM.2016.7741781

Abstract

Accurate modelling of photovoltaic (PV) modules is necessary to understand PV cell operation and to develop maximum power point tracking (MPPT) algorithm for efficient operation of the PV system. A variety of models are proposed in the literature that use a current source, diodes and resistors to represent a PV module. The parameter values involved in the model need to be accurately estimated to improve the model accuracy. The values of the series resistance and diode’s ideality factor could be improved in previous research. This paper proposes a genetic algorithm approach to parameter estimation for PV modules. A parameter estimation technique proposed in previous research is also explored and analyzed. Results show that the proposed parameter estimation technique reduces the errors at the remarkable points when compared to the existing method.

Item Details

Item Type:Refereed Conference Paper
Keywords:modelling, parameter estimation, photovoltaic module, genetic algorithm
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Renewable Power and Energy Systems Engineering (excl. Solar Cells)
Objective Division:Energy
Objective Group:Mining and Extraction of Energy Resources
Objective Field:Mining and Extraction of Energy Resources not elsewhere classified
Author:Zhang, Y (Mr Ben Zhang)
Author:Lyden, S (Dr Sarah Lyden)
Author:Leon de la Barra, BA (Dr Bernardo Leon de la Barra)
ID Code:111744
Year Published:2016
Deposited By:Engineering
Deposited On:2016-10-05
Last Modified:2017-11-06
Downloads:0

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