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Transient search optimization for electrical parameters estimation of photovoltaic module based on datasheet values
•A novel optimization method is presented for photovoltaic modeling.•An accurate three-diode photovoltaic model is used in this paper.•Transient search optimization is compared with other algorithms.•The simulation results of the Photovoltaic model are verified by the measured data. This paper prese...
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Published in: | Energy conversion and management 2020-06, Vol.214, p.112904, Article 112904 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •A novel optimization method is presented for photovoltaic modeling.•An accurate three-diode photovoltaic model is used in this paper.•Transient search optimization is compared with other algorithms.•The simulation results of the Photovoltaic model are verified by the measured data.
This paper presents a novel efficient metaheuristic algorithm called Transient Search Optimization (TSO), which is inspired by the transient process of the inductive and capacitive circuits. Also, this paper presents an objective function based on the datasheet of PV modules at standard test conditions (STC). Then, the TSO algorithm is applied to minimize the objective function to find the optimal nine parameters of the three-diode model (TDM) of the PV module. Also, the results of the proposed TSO algorithm are compared with that obtained by using other metaheuristic algorithms, where in this regard the TSO achieved the best results. The proposed technique is verified by applying it to find the optimal TDM of three commercially common PV modules with different cell types, rated power, and terminal voltage. Then, the simulated I-V and P-V characteristics of these PV modules matched with the measured data under many environmental conditions. Accordingly, the results have proved that the offered technique is useful to find the optimal TDM of all PV modules based on the dataset given by the manufacturers. |
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ISSN: | 0196-8904 1879-2227 |
DOI: | 10.1016/j.enconman.2020.112904 |