Parameter estimation of triple diode photovoltaic model using an artificial ecosystem‐based optimizer

El‐Dabah, Mahmoud A.; El‐Sehiemy, Ragab A.; Becherif, Mohamed; Ebrahim, Mohamed A.

doi:10.1002/2050-7038.13043

Cited by 33 publications

(15 citation statements)

References 56 publications

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“…The definite five, seven, and nine parameters of SDM, DDM, and TDM, respectively, extracted by the proposed ESNSA and the original SNSA are characterized in Table 8. As shown in 10 between the proposed ESNSA, the original SNSA, and other recently reported techniques which are SMA, 27 RAO optimizer, 25 PSO, 9 BHCS, 16 ImCSA, 18 ISCE, 19 HFAPS, 29 and SA, 14 PSO, 9 LAPO, 28 FBI, 30 HSDA, 53 BHCS, SSA, RCGA, CSA, PSO, sunflower optimization, Gray Wolf-Cuckoo Search (GW-CS), AEO 31 for the three models. The results show that the proposed ESNSA manifests the minimum RMSE values for the three models compared to the original SNSA and other 13 illustrate that the proposed ESNSA expresses a strong performance compared to the original SNSA for the three models.…”

Section: Photo Watt-pwp201 Pv Modulementioning

confidence: 86%

“…Myriads of meta-heuristic algorithms have been successfully implemented for the PV models' parameter extraction involving simulated annealing (SA), 14 three point-based approaches (TPBA), 15 hybridized cuckoo search/biogeography based-optimizer (BHCS), 16 improved teachinglearning-based optimizers (ITLBO), 17 improved CS algorithm (ImCSA), 18 improved shuffled complex evolution (ISCE), 19 logistic chaotic Rao optimization algorithm (LCROA), 20 enhanced leader particle swarm optimization (EPSO), 21 fractional chaotic ensemble PSO (FC-EPSO) algorithm, 22 bat algorithm (BA), novel BA (NBA), and directional BA (DBA), 23 artificial electric field algorithm (AEFA), 24 grey wolf optimization (GWO), Rao algorithm (RAO), 25 PSO, 9 supply demand optimization, 26 slime mold algorithm (SMA), 27 lightning attachment procedure optimizer (LAPO), 28 hybridized firefly with pattern search techniques (HFAPS), 29 forensic-based investigations (FBI) algorithm, 30 and artificial ecosystem-based optimizer (AEO). 31 Additional improvements have been proposed to the meta-heuristics by emerging two or more optimizers that can enhance the performance of the individual algorithm, and consequently enhance the effectiveness and attractiveness when applying to complex optimization problems. In Chen and Yu, 16 a CSA has been merged with biogeography-based optimization and applied on the SDM and DDM.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Shaheen

Elsayed

Ginidi

et al. 2022

Energy Science & Engineering

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In this paper, an enhanced social network search algorithm (ESNSA) has been proposed to model the solar photovoltaic (PV) modules accurately and efficiently. The proposed algorithm is introduced to minimize the least root-mean-square error (RMSE) between the calculated and experimental data for the single, double, and triple diode models of Kyocera KC200GT, STM6(40/36), and Photowatt-PWP201 modules. The original SNSA was inspired by users on social networks and their many moods, including imitation, conversation, disputation, and innovation mood. Two strategies are presented for the ESNSA. The first strategy is the powerful exploitation strategy (PES), which is intended to increase the SNSA's performance by boosting searching around the best view of all users. The second strategy is to suggest an adaptable parameter to aid in the exploitation of iterations in the end. Diverse comparisons and statistical analyses for validation purposes are carried out for mono-crystalline STM6(40/36), multicrystalline KC200GT, and polycrystalline photowatt-PWP201 modules. The comparative studies and statistical measures show the consistency and accurateness of the proposed ESNSA. As a numerical application, for the mono-crystalline STM6(40/36) PV module, the proposed ESNSA achieves the least RMSE of 1.751631E−3, 1.769953E−3, and 1.696504E−3, respectively for the three models. Also, it shows high robustness compared to the original SNSA as it acquires the least standard errors for the three models of 2.56E−18, 1.76E−6, and 1.24E−5, respectively. Moreover, the proposed ESNSA provides a higher convergence speed where it is approximately reached to the least RMSE in less than 60%, 50%, and 60% of the iterations for the three models, respectively. Nevertheless, the proposed ESNSA provides better performance than miscellaneous published approaches in minimizing the RMSE, with high robust indices.

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Section: Photo Watt-pwp201 Pv Modulementioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Shaheen

Elsayed

Ginidi

et al. 2022

Energy Science & Engineering

View full text Add to dashboard Cite

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“…There several models that are presented in the literature to model the PV cell and modules as [30][31][32][33][34]. Among these models, the double diode model of the PV cell in Figure 1 presents an accepted accurate compared with single diode model in [35,36].…”

Section: Pv Modeling 221 Accurate Pv Modelingmentioning

confidence: 99%

A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems

et al. 2022

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Power system operation and planning studies face many challenges with increasing of renewable energy sources (RESs) penetration. These challenges revolve around the RESs uncertainty and its applications on probabilistic forecasting, power system operation optimization and power system planning. This paper proposes a novel and effective criterion for uncertainties modeling of the RESs as well as system loads. Four sorting stages are applied for the proposed uncertainty cases reduction. Added to that, it proposes three different uncertainty reduction strategies for obtaining different accuracy and speed options. The proposed reduction strategies are tested on medium and large scale distribution systems; IEEE 69-bus and 118-bus systems. The obtained results verify the effectiveness of the proposed criterion in uncertainties modeling in distribution systems with acceptable level of accuracy.

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“…The interval branch and bound global optimization algorithms were developed to find the optimal extracted parameter of different electrical photovoltaic models in [40]. The ecosystem optimizer was developed for finding the extracted parameters for the triple diode model [41]. Extracting the PV parameter at low radiation was implemented with the Marine Predators Optimizer [42].…”

Section: Introductionmentioning

confidence: 99%

Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems

et al. 2021

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The extraction of parameters of solar photovoltaic generating systems is a difficult problem because of the complex nonlinear variables of current-voltage and power-voltage. In this article, a new implementation of the Gorilla Troops Optimization (GTO) technique for parameter extraction of several PV models is created. GTO is inspired by gorilla group activities in which numerous strategies are imitated, including migration to an unknown area, moving to other gorillas, migration in the direction of a defined site, following the silverback, and competition for adult females. With numerical analyses of the Kyocera KC200GT PV and STM6-40/36 PV modules for the Single Diode (SD) and Double-Diode (DD), the validity of GTO is illustrated. Furthermore, the developed GTO is compared with the outcomes of recent algorithms in 2020, which are Forensic-Based Investigation Optimizer, Equilibrium Optimizer, Jellyfish Search Optimizer, HEAP Optimizer, Marine Predator Algorithm, and an upgraded MPA. GTO’s efficacy and superiority are expressed by calculating the standard deviations of the fitness values, which indicates that the SD and DD models are smaller than 1E−16, and 1E−6, respectively. In addition, validation of GTO for the KC200GT module is demonstrated with diverse irradiations and temperatures where great closeness between the emulated and experimental P-V and I-V curves is achieved under various operating conditions (temperatures and irradiations).

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Parameter estimation of triple diode photovoltaic model using an artificial ecosystem‐based optimizer

Cited by 33 publications

References 56 publications

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems

Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems

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