Solar PV parameter estimation using multi-objective optimisation

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Rawat, N
Thakur, P
Jadli, U
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2019
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Abstract

The estimation of the electrical model parameters of solar PV, such as light-induced current, diode dark saturation current, thermal voltage, series resistance, and shunt resistance, is indispensable to predict the actual electrical performance of solar photovoltaic (PV) under changing environmental conditions. Therefore, this paper first considers the various methods of parameter estimation of solar PV to highlight their shortfalls. Thereafter, a new parameter estimation method, based on multi-objective optimisation, namely, Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is proposed. Furthermore, to check the effectiveness and accuracy of the proposed method, conventional methods, such as, ‘Newton-Raphson’, ‘Particle Swarm Optimisation, Search Algorithm, was tested on four solar PV modules of polycrystalline and monocrystalline materials. Finally, a solar PV module photowatt PWP201 has been considered and compared with six different state of art methods. The estimated performance indices such as current absolute error matrics, absolute relative power error, mean absolute error, and P-V characteristics curve were compared. The results depict the close proximity of the characteristic curve obtained with the proposed NSGA-II method to the curve obtained by the manufacturer’s datasheet.

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Bulletin of Electrical Engineering and Informatics

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8

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4

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© The Author(s) 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.

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Photovoltaic power systems

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Rawat, N; Thakur, P; Jadli, U, Solar PV parameter estimation using multi-objective optimisation, Bulletin of Electrical Engineering and Informatics, 2019, 8 (4), pp. 1198-1205

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