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

Younis, Abubaker; Bakhit, Abdelhamid; Onsa, Mahmoud Hassan; Hashim, Mohamed

doi:10.1016/j.egyr.2022.05.160

Cited by 18 publications

(12 citation statements)

References 115 publications

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“…The Firefly optimization algorithm (FA) introduced by [53,54] is a swarm-intelligence methodology that takes the Firefly position as the candidate solution. The Firefly brightness, the fitness value, is utilized by the algorithm to define the relationship between the fireflies, as the brighter ones are attractive, and the distance between them and the less-glowing fireflies is shortened.…”

Section: Stochastic Methods Firefly Algorithmmentioning

confidence: 99%

Wind Speed Forecast for Sudan Using the Two-Parameter Weibull Distribution: The Case of Khartoum City

Younis

Elshiekh

Osama

et al. 2023

Wind

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In this quick study, we estimated the Weibull distribution’s parameters using wind data collected between March 2017 and January 2018 using a twelve-meter mast meteorological station on the grounds of the National Energy Research Center in Khartoum. In order to quantify these descriptors, we relied on analytical and stochastic methods, subsequently enabling specialists from researchers, engineers, decision-makers, and policymakers to apprehend the wind characteristics in the vicinity. Hence, the computed scale and shape parameters were provided, in which the Firefly algorithm (FA) resulted in the most accuracy in terms of the coefficient of determination, which equaled 0.999, which we considered logical due to the observed nonlinearity in the wind speed numbers. On the contrary, the energy pattern factor method had the worst prediction capability depending on several goodness-of-fit metrics. This concise work is unique because it is the first to use data from Sudan to forecast local wind speeds using artificial intelligence algorithms, particularly the FA technique, which is widely used in solar photovoltaic modeling. Additionally, since classic estimating approaches act differently spatially, evaluating their efficacy becomes innovative, which was accomplished here. On a similar note, a weighted-average wind speed was found to equal 4.98 m/s and the FA average wind speed was 3.73 m/s, while the rose diagram indicated that most winds with potential energy equivalent to 3 m/s or more blow from the north.

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Section: Stochastic Methods Firefly Algorithmmentioning

confidence: 99%

Wind Speed Forecast for Sudan Using the Two-Parameter Weibull Distribution: The Case of Khartoum City

Younis

Elshiekh

Osama

et al. 2023

Wind

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“…Solving of problem for DDM means seven parameters to be estimated, namely I ph , I o1 , I o2 , 𝛼 1 , 𝛼 2 , R s , and R sh . [32,48,49]…”

Section: Equivalent Circuit Structure Of Ddmmentioning

confidence: 99%

“…Solving of problem for DDM means seven parameters to be estimated, namely I ph , I o1 , I o2 , α 1 , α 2 , R s , and R sh . [ 32,48,49 ]

\begin{equation}{{I}_{{\mathrm{DDM}} - C}} = {{I}_{{\mathrm{ph}}}} - {{I}_{{\mathrm{d}}1}} - {{I}_{{\mathrm{d}}2}} - {{I}_{{\mathrm{sh}}}}\end{equation}

\begin{equation}{{I}_{d1}} = {{I}_{o1}}\left( {{{e}^{\left( {\frac{{{{V}_{{\mathrm{DDM}} - C}} + {{R}_{\mathrm{s}}}\ {{I}_{{\mathrm{DDM}} - C}}}}{{{{\alpha }_1}\ {{V}_{\mathrm{t}}}}}} \right)}} - 1} \right)\end{equation}

\begin{equation}{{I}_{{\mathrm{d}}2}} = {{I}_{{\mathrm{o}}2}}\left( {{{e}^{\left( {\frac{{{{V}_{{\mathrm{DDM}} - C}} + {{R}_{\mathrm{s}}}\ {{I}_{{\mathrm{DDM}} - C}}}}{{{{\alpha }_2}\ {{V}_{\mathrm{t}}}}}} \right)}} - 1} \right)\end{equation}

\begin{equation} {{I}_{{\mathrm{sh}}}} = \frac{{{{V}_{{\mathrm{DDM}} - C}} + {{R}_{\mathrm{s}}}\ {{I}_{{\mathrm{DDM}} - C}}}}{{{{R}_{{\mathrm{sh}}}}}} \end{equation}

…”

Section: Definition Of the Pv Parameter Extraction Problemmentioning

confidence: 99%

“…Solving of problem for DDM means seven parameters to be estimated, namely I ph , I o1 , I o2 , α 1 , α 2 , R s , and R sh . [ 32 , 48 , 49 ]

where, I DDM − C and V DDM − C are the output current and voltage of PV cell, respectively, I d1 and I d2 are the 1 st and 2 nd diode currents, I o1 and I o2 are the diode saturation currents, α 1 and α 2 are the 1 st and 2 nd diode ideality factors, and I DDM − M and V DDM − M are the output current and voltage of PV module, respectively.…”

Section: Definition Of the Pv Parameter Extraction Problemmentioning

confidence: 99%

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Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm

Çetinbaş

2024

Global Challenges

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This study presents the parameter extraction of single, double, and triple‐diode photovoltaic (PV) models using the weighted leader search algorithm (WLS). The primary objective is to develop models that accurately reflect the characteristics of PV devices so that technical and economic benefits are maximized under all constraints. For this purpose, 24 models, 6 for two different PV cells, and 18 for six PV modules, whose experimental data are publicly available, are developed successfully. The second objective of this research is the selection of the most suitable algorithm for this problem. It is a significant challenge since the evaluation process requires using advanced statistical tools and techniques to determine the reliable selection. Therefore, seven brand‐new algorithms, including WLS, the spider wasp optimizer, the shrimp and goby association search, the reversible elementary cellular automata, the fennec fox optimization, the Kepler optimization, and the rime optimization algorithms, are tested. The WLS has yielded the smallest minimum, average, RMSE, and standard deviation among those. Its superiority is also verified by Friedman and Wilcoxon signed‐rank test based on 144 pairwise comparisons. In conclusion, it is demonstrated that the WLS is a superior algorithm in PV parameter extraction for developing accurate models.

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“…In recent years, more and more metaheuristic algorithms have been developed and applied in PV-model parameter estimation due to their advantages, such as ease of use, global optimization ability, strong robustness, etc. [2,3]. Metaheuristic algorithms by their nature mimic phenomena, laws, or mechanisms in nature or human society, and use intelligent iteration methods to conduct parallel, random, and directional exploration to find the optimal solution to the problem.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Parameter Extraction from a Solar Cell or a Photovoltaic Module by Combining Metaheuristic Algorithms with Different Simulation Current Calculation Methods

Qin,

Li,

Yang

et al. 2024

Energies

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In this paper, single-diode model (SDM) and double-diode model (DDM) parameters of the French RTC solar cell and the Photowatt PWP 201 photovoltaic (PV) module were extracted by combining five metaheuristic algorithms with three simulation current calculation methods (i.e., approximation method, Lambert W method and Newton–Raphson method), respectively. It was found that the parameter-extraction accuracies of the Lambert W (LW) method and the Newton–Raphson (NR) method are always approximately equal and higher than that of the approximation method. The best RMSEs (root mean square error) obtained by using the LW or the NR method on the solar cell and the PV module are 7.72986 × 10−4 and 2.05296 × 10−3 for SDM parameter extraction and 6.93709 × 10−4 and 1.99051 × 10−3 for DDM parameter extraction, respectively. The latter may be the highest parameter-extraction accuracy reported on the solar cell and the PV module so far, which is due to the adoption of more reasonable DDM parameter boundaries. Furthermore, the convergence curves of the LW and the NR method basically coincide, with a convergence speed faster than that of the approximation method. The robustness of a parameter-extraction method is mainly determined by the metaheuristic algorithm, but it is also affected by the simulation current calculation method and the parameter-extraction object. In a word, the approximation method is not suitable for application in PV-model parameter extraction because of incorrect estimation of the simulation current and the RMSE, while the LW and NR methods are suitable for the application for accurately calculating the simulation current and RMSE. In terms of saving computation resources and time, the NR method is superior to the LW method.

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

Cited by 18 publications

References 115 publications

Wind Speed Forecast for Sudan Using the Two-Parameter Weibull Distribution: The Case of Khartoum City

Wind Speed Forecast for Sudan Using the Two-Parameter Weibull Distribution: The Case of Khartoum City

Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm

Comparative Study of Parameter Extraction from a Solar Cell or a Photovoltaic Module by Combining Metaheuristic Algorithms with Different Simulation Current Calculation Methods

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