Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer

Abd-ElHady Ramadan, Salah Kamel, Tahir Khurshaid, Seung-Ryle Oh and Sang-Bong Rhee
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Abd-ElHady Ramadan: Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
Salah Kamel: Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
Tahir Khurshaid: Department of Electrical Engineering, Yeungnam University, Gyeongsan 38541, Korea
Seung-Ryle Oh: Korea Electric Power Company (KEPCO), Deajon 24056, Korea
Sang-Bong Rhee: Department of Electrical Engineering, Yeungnam University, Gyeongsan 38541, Korea

Sustainability, 2021, vol. 13, issue 12, 1-16

Abstract: The enhancement of photovoltaic (PV) energy systems relies on an accurate PV model. Researchers have made significant efforts to extract PV parameters due to their nonlinear characteristics of the PV system, and the lake information from the manufactures’ PV system datasheet. PV parameters estimation using optimization algorithms is a challenging problem in which a wide range of research has been conducted. The idea behind this challenge is the selection of a proper PV model and algorithm to estimate the accurate parameters of this model. In this paper, a new application of the improved gray wolf optimizer (I-GWO) is proposed to estimate the parameters’ values that achieve an accurate PV three diode model (TDM) in a perfect and robust manner. The PV TDM is developed to represent the effect of grain boundaries and large leakage current in the PV system. I-GWO is developed with the aim of improving population, exploration and exploitation balance and convergence of the original GWO. The performance of I-GWO is compared with other well-known optimization algorithms. I-GWO is evaluated through two different applications. In the first application, the real data from RTC furnace is applied and in the second one, the real data of PTW polycrystalline PV panel is applied. The results are compared with different evaluation factors (root mean square error (RMSE), current absolute error and statistical analysis for multiple independent runs). I-GWO achieved the lowest RMSE values in comparison with other algorithms. The RMSE values for the two applications are 0.00098331 and 0.0024276, respectively. Based on quantitative and qualitative performance evaluation, it can be concluded that the estimated parameters of TDM by I-GWO are more accurate than those obtained by other studied optimization algorithms.

Keywords: solar photovoltaic; three diode model; parameters; optimization; improved gray wolf optimizer; RMSE; polycrystalline; statistical analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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