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

• Published in: Applied soft computing Vol. 134; p. 110032
• Main Authors: Abd El-Mageed, Amr A., Abohany, Amr A., Saad, Hatem M.H., Sallam, Karam M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2023
• Subjects: Differential evolution; Parameter identification; Photovoltaic models; Queuing search algorithm; Solar cell; Solar cell; Differential evolution; Queuing search algorithm; Parameter identification; Photovoltaic models
• ISSN: 1568-4946
• DOI: 10.1016/j.asoc.2023.110032

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. •Improved QSO with DE and bound-constraint amendment (IQSODE) is proposed to discover optimal PV model parameters’ values.•The optimal parameters’ values for three PV models (SDM, DDM, and PVMMs) are specified to prove the adequacy of IQSODE.•The efficacy of the suggested IQSODE is also examined utilizing two commercial models (TFST40 and MCSM55 models).•The significance of IQSODE is validated against well-established algorithms employed for inferring the PV coefficients.•The outcomes reveal the robustness and accuracy of IQSODE in extracting the coefficients of PV models.