Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules
Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization...
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doaj-a6f0b9208f144af6a6b5b3c4d666c68a2021-03-30T01:51:56ZengIEEEIEEE Access2169-35362020-01-01811110211114010.1109/ACCESS.2020.30007709110875Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV ModulesAhmed A. Zaki Diab0https://orcid.org/0000-0002-8598-9983Hamdy M. Sultan1https://orcid.org/0000-0002-7650-4497Ton Duc Do2https://orcid.org/0000-0002-8605-2666Omar Makram Kamel3Mahmoud A. Mossa4https://orcid.org/0000-0002-0308-3038Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, EgyptElectrical Engineering Department, Faculty of Engineering, Minia University, Minia, EgyptDepartment of Robotics and Mechatronics, School of Engineering and Digital Sciences (SEDS), Nazarbayev University, Nur-Sultan, KazakhstanElectrical and Computer Department, El Minia High Institute of Engineering and Technology, Minia, EgyptElectrical Engineering Department, Faculty of Engineering, Minia University, Minia, EgyptRecently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization Algorithm (COA) has been applied for extracting the unknown parameters involved in various models for the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The choice of COA algorithm for such an application is made because of its good tracking characteristics and the balance creation between the exploration and exploitation phases. Additionally, it has only two control parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE) value between the data based on the optimized parameters for each model and those based on the measured data of the solar cell and PV modules is adopted as the objective function. Parameters' estimation for various types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive statistical study has been performed to validate the accurateness and stability of the applied COA as a competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation results, as well as the statistical measurement, validate the superiority and the reliability of the COA algorithm not only for parameter extraction of different PV modules but also under different operating scenarios. With the COA, precise PV models have been established with acceptable RMSE of 7.7547×10<sup>-4</sup>, 7.64801×10<sup>-4</sup>, and 7.59756×10<sup>-4</sup> for SDM, DDM, and TDM respectively considering R.T.C. France solar cell.https://ieeexplore.ieee.org/document/9110875/Solar cellsPV modulesparameter extractionoptimizationcoyote optimization algorithmsingle diode model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ahmed A. Zaki Diab Hamdy M. Sultan Ton Duc Do Omar Makram Kamel Mahmoud A. Mossa |
spellingShingle |
Ahmed A. Zaki Diab Hamdy M. Sultan Ton Duc Do Omar Makram Kamel Mahmoud A. Mossa Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules IEEE Access Solar cells PV modules parameter extraction optimization coyote optimization algorithm single diode model |
author_facet |
Ahmed A. Zaki Diab Hamdy M. Sultan Ton Duc Do Omar Makram Kamel Mahmoud A. Mossa |
author_sort |
Ahmed A. Zaki Diab |
title |
Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules |
title_short |
Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules |
title_full |
Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules |
title_fullStr |
Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules |
title_full_unstemmed |
Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules |
title_sort |
coyote optimization algorithm for parameters estimation of various models of solar cells and pv modules |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization Algorithm (COA) has been applied for extracting the unknown parameters involved in various models for the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The choice of COA algorithm for such an application is made because of its good tracking characteristics and the balance creation between the exploration and exploitation phases. Additionally, it has only two control parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE) value between the data based on the optimized parameters for each model and those based on the measured data of the solar cell and PV modules is adopted as the objective function. Parameters' estimation for various types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive statistical study has been performed to validate the accurateness and stability of the applied COA as a competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation results, as well as the statistical measurement, validate the superiority and the reliability of the COA algorithm not only for parameter extraction of different PV modules but also under different operating scenarios. With the COA, precise PV models have been established with acceptable RMSE of 7.7547×10<sup>-4</sup>, 7.64801×10<sup>-4</sup>, and 7.59756×10<sup>-4</sup> for SDM, DDM, and TDM respectively considering R.T.C. France solar cell. |
topic |
Solar cells PV modules parameter extraction optimization coyote optimization algorithm single diode model |
url |
https://ieeexplore.ieee.org/document/9110875/ |
work_keys_str_mv |
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1724186243491692544 |