A comprehensive and critical review of bio-inspired metaheuristic frameworks for extracting parameters of solar cell single and double diode models

In today’s world of sustainability and environmentally friendly trends, renewable energy technologies like solar cells play a vital role, making producing the optimal capacity a legitimate aspiration for such technologies’ operators. This article presents an exhaustive and critical review of twenty...

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Published in:Energy reports Vol. 8; pp. 7085 - 7106
Main Authors: Younis, Abubaker, Bakhit, Abdalaziz, Onsa, Mahmoud, Hashim, Mohsin
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2022
Elsevier
Subjects:
Equivalent circuit model
I–V characteristics
Metaheuristics
Parameter identification
Photovoltaic performance
Stochastic optimization
I–V characteristics
Photovoltaic performance
Stochastic optimization
Equivalent circuit model
Parameter identification
Metaheuristics
ISSN:2352-4847, 2352-4847
Online Access:Get full text
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Abstract In today’s world of sustainability and environmentally friendly trends, renewable energy technologies like solar cells play a vital role, making producing the optimal capacity a legitimate aspiration for such technologies’ operators. This article presents an exhaustive and critical review of twenty bio-inspired metaheuristic techniques and their mathematical contexts brought to action by stochastic optimization to identify the parameters of solar cell single diode (SDM) and double diode (DDM) models. The novelty embedded within this appraisal lies in rating these algorithms and their associated variants according to the accuracy of the parameter extraction measured by the root mean square error, which would save effort and time for future researchers and users alike. Additionally, this work outlines a general methodology or pattern followed by every algorithm within the bio class to find the global best solution. Hence, we have concluded: (1) SDM is preferred for solar cell parameter identification. (2) The Firefly algorithm outpaces the other algorithms within the entire class, while the genetic algorithms have mediocre performance, especially with DDM. (3) The Bat algorithm variants are the most developed in terms of performance among other algorithms’ variants. 4) The differential evolution and the Cuckoo search variants have stable performance compared with their standard algorithms. (5) The swarm-intelligence algorithms have the best performance with SDM and DDM compared to the other sub-categories. Lastly, this paper delivers the advantages and disadvantages of the different optimization philosophies reviewed. [Display omitted] •Bio-inspired algorithms engaged with solar cell SDM and DDM are reviewed.•The bio-metaheuristic algorithms perform better with SDM.•The Firefly algorithm is the most effective parameter extraction method.•The Bat algorithm has the most matured variants compared to the other algorithms.•Gains and shortcomings of every algorithm are quickly provided.
AbstractList In today’s world of sustainability and environmentally friendly trends, renewable energy technologies like solar cells play a vital role, making producing the optimal capacity a legitimate aspiration for such technologies’ operators. This article presents an exhaustive and critical review of twenty bio-inspired metaheuristic techniques and their mathematical contexts brought to action by stochastic optimization to identify the parameters of solar cell single diode (SDM) and double diode (DDM) models. The novelty embedded within this appraisal lies in rating these algorithms and their associated variants according to the accuracy of the parameter extraction measured by the root mean square error, which would save effort and time for future researchers and users alike. Additionally, this work outlines a general methodology or pattern followed by every algorithm within the bio class to find the global best solution. Hence, we have concluded: (1) SDM is preferred for solar cell parameter identification. (2) The Firefly algorithm outpaces the other algorithms within the entire class, while the genetic algorithms have mediocre performance, especially with DDM. (3) The Bat algorithm variants are the most developed in terms of performance among other algorithms’ variants. 4) The differential evolution and the Cuckoo search variants have stable performance compared with their standard algorithms. (5) The swarm-intelligence algorithms have the best performance with SDM and DDM compared to the other sub-categories. Lastly, this paper delivers the advantages and disadvantages of the different optimization philosophies reviewed.
In today’s world of sustainability and environmentally friendly trends, renewable energy technologies like solar cells play a vital role, making producing the optimal capacity a legitimate aspiration for such technologies’ operators. This article presents an exhaustive and critical review of twenty bio-inspired metaheuristic techniques and their mathematical contexts brought to action by stochastic optimization to identify the parameters of solar cell single diode (SDM) and double diode (DDM) models. The novelty embedded within this appraisal lies in rating these algorithms and their associated variants according to the accuracy of the parameter extraction measured by the root mean square error, which would save effort and time for future researchers and users alike. Additionally, this work outlines a general methodology or pattern followed by every algorithm within the bio class to find the global best solution. Hence, we have concluded: (1) SDM is preferred for solar cell parameter identification. (2) The Firefly algorithm outpaces the other algorithms within the entire class, while the genetic algorithms have mediocre performance, especially with DDM. (3) The Bat algorithm variants are the most developed in terms of performance among other algorithms’ variants. 4) The differential evolution and the Cuckoo search variants have stable performance compared with their standard algorithms. (5) The swarm-intelligence algorithms have the best performance with SDM and DDM compared to the other sub-categories. Lastly, this paper delivers the advantages and disadvantages of the different optimization philosophies reviewed. [Display omitted] •Bio-inspired algorithms engaged with solar cell SDM and DDM are reviewed.•The bio-metaheuristic algorithms perform better with SDM.•The Firefly algorithm is the most effective parameter extraction method.•The Bat algorithm has the most matured variants compared to the other algorithms.•Gains and shortcomings of every algorithm are quickly provided.
Author Younis, Abubaker
Onsa, Mahmoud
Hashim, Mohsin
Bakhit, Abdalaziz
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  givenname: Mahmoud
  surname: Onsa
  fullname: Onsa, Mahmoud
  organization: Department of Mechanical Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan
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  givenname: Mohsin
  surname: Hashim
  fullname: Hashim, Mohsin
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Keywords I–V characteristics
Photovoltaic performance
Stochastic optimization
Equivalent circuit model
Parameter identification
Metaheuristics
Language English
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Snippet In today’s world of sustainability and environmentally friendly trends, renewable energy technologies like solar cells play a vital role, making producing the...
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SubjectTerms Equivalent circuit model
I–V characteristics
Metaheuristics
Parameter identification
Photovoltaic performance
Stochastic optimization
Title A comprehensive and critical review of bio-inspired metaheuristic frameworks for extracting parameters of solar cell single and double diode models
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