Boosting slime mould algorithm for parameter identification of photovoltaic models

Estimating the photovoltaic model's unknown parameters efficiently and accurately can determine the solar cell's efficacy in converting the solar energy into electricity. For this purpose, this work proposes an advanced slime mould algorithm (SMA) integrated Nelder-Mead simplex strategy an...

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Published inEnergy (Oxford) Vol. 234; p. 121164
Main Authors Liu, Yun, Heidari, Ali Asghar, Ye, Xiaojia, Liang, Guoxi, Chen, Huiling, He, Caitou
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.11.2021
Elsevier BV
Subjects
Algorithms
Chaos theory
diodes
electricity
energy
France
High temperature
Irradiance
light intensity
Mathematical models
Optimization
Parameter estimation
Parameter identification
Photovoltaic cells
Photovoltaics
Random numbers
Slime
Slime molds
Slime mould algorithm
Solar cells
solar collectors
Solar energy
Solar energy conversion
Stability
Swarm-intelligence
France
Swarm-intelligence
Parameter estimation
Slime mould algorithm
Optimization
Online AccessGet full text
ISSN0360-5442
1873-6785
DOI10.1016/j.energy.2021.121164

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Abstract Estimating the photovoltaic model's unknown parameters efficiently and accurately can determine the solar cell's efficacy in converting the solar energy into electricity. For this purpose, this work proposes an advanced slime mould algorithm (SMA) integrated Nelder-Mead simplex strategy and chaotic map, called CNMSMA. Chaotic maps replace the random number rand that affects the choice of location updating strategy to improve the exploratory patterns. Also, Nelder-Mead simplex is introduced to reinforce the intensification capacity of the algorithm. The effectiveness of CCNMSMA has been verified in a single diode model, double diode model, and three diode models for RTC France solar cell and PVM 752 GaAs cell. Three commercial PV module models, which are the ST40, SM55, and KC200GT, are also utilized to verify the stability of CNMSMA under various temperatures and irradiances. The simulation results demonstrate that a developed SMA-based method can accurately extract the unknown photovoltaic solar cells' unknown parameters and achieve excellent convergence rapidity and stability performance. Also, no matter under insufficient irradiance or high-temperature conditions, CNMSMA is still without losing its accuracy and shows satisfactory stability. Accordingly, the proposed algorithm could act as a reliable and developed tool for extracting significant unknown parameters of photovoltaic models. This research will be supported by https://aliasgharheidari.com. •An improved Slime Mound Algorithm (CNMSMA) is proposed to extract parameters of photovoltaic system.•Ten chaotic maps are combined with Nelder-Mead simplex to design a new optimizer CNMSMA.•CNMSMA were evaluated under different irradiance levels and temperature levels.•CNMSMA has better optimization performance and stabilikty than other competitive algorithms.
AbstractList Estimating the photovoltaic model's unknown parameters efficiently and accurately can determine the solar cell's efficacy in converting the solar energy into electricity. For this purpose, this work proposes an advanced slime mould algorithm (SMA) integrated Nelder-Mead simplex strategy and chaotic map, called CNMSMA. Chaotic maps replace the random number rand that affects the choice of location updating strategy to improve the exploratory patterns. Also, Nelder-Mead simplex is introduced to reinforce the intensification capacity of the algorithm. The effectiveness of CCNMSMA has been verified in a single diode model, double diode model, and three diode models for RTC France solar cell and PVM 752 GaAs cell. Three commercial PV module models, which are the ST40, SM55, and KC200GT, are also utilized to verify the stability of CNMSMA under various temperatures and irradiances. The simulation results demonstrate that a developed SMA-based method can accurately extract the unknown photovoltaic solar cells' unknown parameters and achieve excellent convergence rapidity and stability performance. Also, no matter under insufficient irradiance or high-temperature conditions, CNMSMA is still without losing its accuracy and shows satisfactory stability. Accordingly, the proposed algorithm could act as a reliable and developed tool for extracting significant unknown parameters of photovoltaic models. This research will be supported by https://aliasgharheidari.com. •An improved Slime Mound Algorithm (CNMSMA) is proposed to extract parameters of photovoltaic system.•Ten chaotic maps are combined with Nelder-Mead simplex to design a new optimizer CNMSMA.•CNMSMA were evaluated under different irradiance levels and temperature levels.•CNMSMA has better optimization performance and stabilikty than other competitive algorithms.
Estimating the photovoltaic model's unknown parameters efficiently and accurately can determine the solar cell's efficacy in converting the solar energy into electricity. For this purpose, this work proposes an advanced slime mould algorithm (SMA) integrated Nelder-Mead simplex strategy and chaotic map, called CNMSMA. Chaotic maps replace the random number rand that affects the choice of location updating strategy to improve the exploratory patterns. Also, Nelder-Mead simplex is introduced to reinforce the intensification capacity of the algorithm. The effectiveness of CCNMSMA has been verified in a single diode model, double diode model, and three diode models for RTC France solar cell and PVM 752 GaAs cell. Three commercial PV module models, which are the ST40, SM55, and KC200GT, are also utilized to verify the stability of CNMSMA under various temperatures and irradiances. The simulation results demonstrate that a developed SMA-based method can accurately extract the unknown photovoltaic solar cells' unknown parameters and achieve excellent convergence rapidity and stability performance. Also, no matter under insufficient irradiance or high-temperature conditions, CNMSMA is still without losing its accuracy and shows satisfactory stability. Accordingly, the proposed algorithm could act as a reliable and developed tool for extracting significant unknown parameters of photovoltaic models. This research will be supported by https://aliasgharheidari.com.
ArticleNumber 121164
Author Heidari, Ali Asghar
Chen, Huiling
Liang, Guoxi
Ye, Xiaojia
Liu, Yun
He, Caitou
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  givenname: Caitou
  surname: He
  fullname: He, Caitou
  email: hctou89@zju.edu.cn
  organization: Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
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Snippet Estimating the photovoltaic model's unknown parameters efficiently and accurately can determine the solar cell's efficacy in converting the solar energy into...
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SubjectTerms Algorithms
Chaos theory
diodes
electricity
energy
France
High temperature
Irradiance
light intensity
Mathematical models
Optimization
Parameter estimation
Parameter identification
Photovoltaic cells
Photovoltaics
Random numbers
Slime
Slime molds
Slime mould algorithm
Solar cells
solar collectors
Solar energy
Solar energy conversion
Stability
Swarm-intelligence
Title Boosting slime mould algorithm for parameter identification of photovoltaic models
URI https://dx.doi.org/10.1016/j.energy.2021.121164
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