Parameter extraction of solar photovoltaic models using queuing search optimization and differential evolution

Amr A. Abd El-Mageed, Amr A. Abohany, Hatem M.H. Saad, Karam M. Sallam

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


Given the photovoltaic (PV) model's multi-model and nonlinear properties, extracting its parameters is a difficult problem to solve. Furthermore, because of the features of the problem, the algorithms that are used to solve it are subject to becoming stuck in local optima. Nonetheless, proper estimation of the parameters is essential due to the large impact they have on the performance of the PV system in terms of current and energy production. Moreover, the majority of the previously proposed algorithms have satisfactory results for determining PV model parameters. However, for precision and robustness, they generally use a lot of computational resources, such as the quantity of fitness assessments. For alleviating the previous problems, in this paper, an improved queuing search optimization (QSO) algorithm dependent on the differential evolution (DE) technique and bound-constraint amendment procedure, which is called IQSODE, has been presented to efficiently extract the PV parameter values for various PV models. The DE algorithm is applied to each solution generated by the QSO algorithm in order to increase population diversity. IQSODE is tested against other state-of-the-art algorithms. The practical and statistical findings show that IQSODE outperforms other methods in extracting parameters from PV models such as single diode, double diode, and photovoltaic module models. Also, the performance of the proposed algorithm is assessed utilizing two practical manufacturer's datasheets (TFST40 and MCSM55). Statistically, the IQSODE outperforms other state-of-the-art algorithms in terms of convergence speed, reliability, and accuracy. Thus, the presented method is deemed to be a viable solution for PV model parameter extraction.

Original languageEnglish
Article number110032
Pages (from-to)1-31
Number of pages31
JournalApplied Soft Computing
Publication statusPublished - Feb 2023


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