Hybrid African vultures-grey wolf optimizer approach for electrical parameters extraction of solar panel models

被引:15
|
作者
Soliman, Mahmoud A. [1 ]
Hasanien, Hany M. [2 ]
Turky, Rania A. [3 ]
Muyeen, S. M. [4 ]
机构
[1] Menoufia Univ, Dept Elect Engn, Fac Engn, Shibin Al Kawm 32511, Egypt
[2] Ain Shams Univ, Elect Power & Machines Dept, Fac Engn, Cairo 11517, Egypt
[3] Future Univ Egypt, Fac Engn & Technol, Dept Elect Engn, Cairo 11835, Egypt
[4] Qatar Univ, Dept Elect Engn, Fac Engn, Doha, Qatar
关键词
Hybrid African vultures-grey wolf; optimizer approach; Photovoltaic modeling; PV parameters extraction; Solar energy; Three-diode model; PHOTOVOLTAIC MODULES; DIODE MODEL; ALGORITHM; IDENTIFICATION; SINGLE; CELLS;
D O I
10.1016/j.egyr.2022.10.401
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Three-diode model (TDM) of photovoltaic (PV) cells is a significantly precise model that addresses the electrical and optical losses in such PVs. Due to its nonlinearity and multivariable characteristics, the TDM is a complex and debatable PV model. This article proposes a novel hybrid African vultures-grey wolf optimizer (AV-GWO) approach to precisely estimate the electrical parameters of such TDM. The AVO is a novel meta-heuristic approach inspired by African vultures' behavior in nature. In the hybrid approach offered, the vultures' updating position formula of the AVO is applied to update the key-group parameters in GWO, resulting in an enhanced GWO approach. A new objective function that depends on the current error is proposed in this study, which the AV-GWO minimizes to precisely estimate the optimal nine parameters of such TDM. The nine electrical parameters attained through the hybrid AV-GWO approach are compared with that obtained via other meta-heuristic methods. In that regard, the AV-GWO approach has achieved superior and outstanding outcomes. For more realistic studies, the offered AV-GWO is efficiently utilized to design the optimal parameters of TDM for two industrial KC200GT and MSX-60 PV cells. In the optimization process, the hybrid AV-GWO has recorded the lowest optimal fitness values of 8.475e-13 and 7.412e-12 for KC200GT and MSX-60, respectively. Additionally, the AV-GWO has recorded the shortest computing time in 0.43412 (s) and 0.3142 (s) for KC200GT and MSX-60, respectively, which reflects its rapid convergence, supremacy, and stability, among other approaches. Those PV cells' modeled I-V and P-V curves closely coincide with the real data measured under various climatic conditions. The error between these results is less than 0.4%. The high performance of the hybrid AV-GWO approach-based TDM is verified by examining its absolute current error with that realized from other PV models. Consequently, the outcomes have depicted that the offered AV-GWO approach is superior and can be used to generate a precise PV model of any industrial PV cell, which is a unique addition to the PVs market. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
引用
收藏
页码:14888 / 14900
页数:13
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