An analysis on the performance of metaheuristic algorithms for the estimation of parameters in solar cell models

Navarro, Mario A.; Oliva, Diego; Ramos-Michel, Alfonso; Haro, Eduardo H.

doi:10.1016/j.enconman.2022.116523

Cited by 24 publications

(6 citation statements)

References 58 publications

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“…Based on the results of a previous study conducted by Navarro et al [ 41 ], which compared the performance of eight recently proposed metaheuristic algorithms in optimizing solar cell parameters, the Artificial Hummingbird Algorithm (AHA) [ 39 ] was found to have the highest performance and resulted in minimal errors. Therefore, in the current paper, the AHA algorithm was selected as the most suitable choice among the eight algorithms studied for parameter extraction that will be included in the DDM.…”

Section: Methodsmentioning

confidence: 99%

A simulation and modeling approach of coupled thermal and electrical behavior of PV panels using the artificial hummingbird algorithm and two-dimensional finite difference-based model

Aalloul,

Elaissaoui,

Harkani

et al. 2024

Heliyon

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Section: Methodsmentioning

confidence: 99%

A simulation and modeling approach of coupled thermal and electrical behavior of PV panels using the artificial hummingbird algorithm and two-dimensional finite difference-based model

Aalloul,

Elaissaoui,

Harkani

et al. 2024

Heliyon

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“…It is possible to estimate these parameters using the solar cell mathematical model that describes the behavior of the solar cell. The single-diode solar cell model (SDSCM) is one of the most commonly used models in solar cell design [67].…”

Section: Experiments 4: Engineering Application Of Nsm-aha Algorithmmentioning

confidence: 99%

A novel artificial hummingbird algorithm improved by natural survivor method

Bakır

2024

Neural Comput & Applic

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The artificial hummingbird algorithm (AHA) has been applied in various fields of science and provided promising solutions. Although the algorithm has demonstrated merits in the optimization area, it suffers from local optimum stagnation and poor exploration of the search space. To overcome these drawbacks, this study redesigns the update mechanism of the original AHA algorithm with the natural survivor method (NSM) and proposes a novel metaheuristic called NSM-AHA. The strength of the developed algorithm is that it performs population management not only according to the fitness function value but also according to the NSM score value. The adopted strategy contributes to NSM-AHA exhibiting powerful local optimum avoidance and unique exploration ability. The optimization ability of the proposed NSM-AHA algorithm was compared with 21 state-of-the-art algorithms over CEC 2017 and CEC 2020 benchmark functions with dimensions of 30, 50, and 100, respectively. Based on the Friedman test results, it was observed that NSM-AHA ranked 1st out of 22 competitive algorithms, while the original AHA ranked 8th. This result highlights that the NSM update mechanism provides a remarkable evolution in the convergence performance of the original AHA algorithm. Furthermore, two constrained engineering problems including the optimization of single-diode solar cell model (SDSCM) parameters and the design of a power system stabilizer (PSS) are solved with the proposed algorithm. The NSM-AHA algorithm provided better results compared to other algorithms with a value of 9.86E − 04 root mean square error for SDSCM and 1.43E − 03 integral time square error for PSS. The experimental results showed that the proposed NSM-AHA is a competitive optimizer for solving global and engineering problems.

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“…Concerning the RTC France solar cell, the best result for the SDM has been provided by the Atomic Orbital Search (AOS) [32] with a fitness value of 7.752 × 10 −4 . The DDM's best result is 6.835 × 10 −4 , provided by the Artificial Hummingbird Algorithm (AHA) [31]. The TDM best result is 7.950 × 10 −4 provided by the Atomic Orbital Search (AOS) [32].…”

Section: Photovoltaic Parameters Extractionmentioning

confidence: 99%

“…Its model has been widely used to approve the performance of the optimizers. The parameters of three models (SDM, DDM, and TDM) have been identified for this type using the Artificial Hummingbird Algorithm (AHA) [31]. A similar study has been proposed in [32] using Atomic Orbital Search (AOS), and another one in [33] by using the Marine Predators Optimizer (MPA).…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems

et al. 2023

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For many electrical systems, such as renewable energy sources, their internal parameters are exposed to degradation due to the operating conditions. Since the model’s accuracy is required for establishing proper control and management plans, identifying their parameters is a critical and prominent task. Various techniques have been developed to identify these parameters. However, metaheuristic algorithms have received much attention for their use in tackling a wide range of optimization issues relating to parameter extraction. This work provides an exhaustive literature review on solving parameter extraction utilizing recently developed metaheuristic algorithms. This paper includes newly published articles in each studied context and its discussion. It aims to approve the applicability of these algorithms and make understanding their deployment easier. However, there are not any exact optimization algorithms that can offer a satisfactory performance to all optimization issues, especially for problems that have large search space dimensions. As a result, metaheuristic algorithms capable of searching very large spaces of possible solutions have been thoroughly investigated in the literature review. Furthermore, depending on their behavior, metaheuristic algorithms have been divided into four types. These types and their details are included in this paper. Then, the basics of the identification process are presented and discussed. Fuel cells, electrochemical batteries, and photovoltaic panel parameters identification are investigated and analyzed.

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An analysis on the performance of metaheuristic algorithms for the estimation of parameters in solar cell models

Cited by 24 publications

References 58 publications

A simulation and modeling approach of coupled thermal and electrical behavior of PV panels using the artificial hummingbird algorithm and two-dimensional finite difference-based model

A simulation and modeling approach of coupled thermal and electrical behavior of PV panels using the artificial hummingbird algorithm and two-dimensional finite difference-based model

A novel artificial hummingbird algorithm improved by natural survivor method

A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems

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