Important notes on parameter estimation of solar photovoltaic cell

Gnetchejo, Patrick Juvet; Essiane, Salomé Ndjakomo; Ele, Pierre; Wamkeue, R.; Wapet, Daniel Eutyche Mbadjoun; Ngoffé, S. Pérabi

doi:10.1016/j.enconman.2019.111870

Cited by 84 publications

(36 citation statements)

References 47 publications

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“…This section presents various experiments results including the approximation formulation, the accurate formulation results that are presented in section 5.1 and Section 5.2 respectively, and the 10 recent algorithms published: EVPS [ 6 ], TVACPSO [ 50 ], IMFO [ 35 ], IJAYA [ 19 ], ITLBO [ 25 ], CAO [ 37 ], SOS [ 39 ], MLBSA [ 38 ], MADE [ 36 ], GAMS [ 3 ]. The experiment of these algorithms was made based on the well known set of RTC France solar cell, with various parameters presented at Table 1 .…”

Section: Experiments Carried Out and Resultsmentioning

confidence: 99%

“…The increase in solar energy production is attributed to advanced field research and to the free availability of solar energy in the day time in many countries in Africa and elsewhere [ 2 ]. Therefore, field development is important for an accurate design of cells and PV modules that can predict the power delivery from the PV system [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Studies carried out by researchers in 2019 have proposed method of checking best accuracy results from PV parameters extraction. The obtained results pointed out that some proposed methods found in literatures were not accurate [ 3 ]. Nevertheless, the given answer of these authors reveal some inaccuracies in two distinct formulations that had always been presented in literature as similar [ 57 ].…”

Section: Introductionmentioning

confidence: 99%

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A combination of Newton-Raphson method and heuristics algorithms for parameter estimation in photovoltaic modules

Gnetchejo

Essiane

Dadjé

et al. 2021

Heliyon

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Parameters extraction is instrumental to standard PV cells design. Reports indicates that heuristic algorithms are the most effective methods for accurately determinining the values of parameters. However, local concentration is against recent heuristic methods, and they are inhibited producing optimal results. This paper seeks to show that combining the heuristics algorithms with the Newton Raphson method can considerably increased the accuracy of results. An inspired artifact technique from the drone squadron simulation from control center is proposed for the extraction of the best constitutive parameters. This study equally provides clarifications on the approaches recently reported and proposed to build objective function. Furthermore, comparative evaluation of the current ten best heuristics algorithms that are published in the PV estimation domain is also undertaken. Moreover, this study investigates the convergence of algorithms when points of the number of current-voltage characteristics are varied. The results from this study highlight the differences between the two formulation, and it shows the best formulation accuracy. The results obtained from seven study cases that are considered in this present study, with the combined Newton Raphson performance method and Drone Squadron optimisation, were employed to extract precise PV module parameters.The study of the numbers of points reveals that the algorithm converges and is more precise when the numbers of points of the I-V characteristic are reduced. However, if these points are minimal, the algorithm will be hindered from returning optimal results.

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Section: Experiments Carried Out and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A combination of Newton-Raphson method and heuristics algorithms for parameter estimation in photovoltaic modules

Gnetchejo

Essiane

Dadjé

et al. 2021

Heliyon

Self Cite

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“…Table 3 shows the comparison results of the single diode model including the parameters and the RMSE. Note that the parameters identification results of General Algebraic Modeling System (GAMS) [39] and LBSA [51] from literatures are also used to compare and the overall best RMSE values are highlighted in boldface.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Parameter Identification for Photovoltaic Models Using an Improved Learning Search Algorithm

et al. 2020

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As a renewable energy resource, solar photovoltaics (PV's) possess a promising future. Thus, it is important to simulate, evaluate and control the PV systems. To identify the parameters for the photovoltaic (PV) models, an improved learning search optimization algorithm (ILSA) is proposed in this paper. The proposed ILSA has the following three key features: (i) the constant self-adjustment rate changes with the iteration. (ii) a new part with a self-adaptive weighting of the current best and worst solution is introduced to learning patterns to guide the iterative direction. (iii) a perturbation method is added to avoid the algorithm falling into local optimum. In order to assess the effectiveness of ILSA relative to other state-of-the-art algorithms, single diode, double diode, and PV model are used for test. Our experimental results reveal that the ILSA performs well in terms of the accuracy of optimization solutions and effectiveness.

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“…However, in some papers in the Energy Conversion and Management journal (in Table 1 marked by *), inaccuracies occurred in parameter estimation of the PV cell using metaheuristic techniques. Namely, the results proposed in [15][16][17][18]21] do not correspond to the objective function [56].…”

Section: Simulation Resultsmentioning

confidence: 99%

Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach

et al. 2019

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Estimation of single-diode and two-diode solar cell parameters by using chaotic optimization approach (COA) is addressed. The proposed approach is based on the use of experimentally determined current-voltage (I-V) characteristics. It outperforms a large number of other techniques in terms of average error between the measured and the estimated I-V values, as well as of time complexity. Implementation of the proposed approach on the I-V curves measured in laboratory environment for different values of solar irradiation and temperature prove its applicability in terms of accuracy, effectiveness and the ease of implementation for a wide range of practical environment conditions. The COA-based parameter estimation is, therefore, useful for PV power converter designers who require fast and accurate model for PV cell/module.

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Important notes on parameter estimation of solar photovoltaic cell

Cited by 84 publications

References 47 publications

A combination of Newton-Raphson method and heuristics algorithms for parameter estimation in photovoltaic modules

A combination of Newton-Raphson method and heuristics algorithms for parameter estimation in photovoltaic modules

Parameter Identification for Photovoltaic Models Using an Improved Learning Search Algorithm

Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach

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