A parameter extraction technique exploiting intrinsic properties of solar cells

Tong, Nhan Thanh; Pora, Wanchalerm

doi:10.1016/j.apenergy.2016.05.064

Cited by 192 publications

(83 citation statements)

References 24 publications

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“…On the other hand, Tables 8 and 9 illustrates the calculated current and the identified parameters of the PV panel. From these tables, it can be observed that the results of [35] are better than the ABC method. However, the proposed CIABC gives the best results.…”

Section: The Polycrystalline Pv Panel Descriptionmentioning

confidence: 96%

“…In this experimental section, another two real datasets are used to further investigate the performance of the proposed CIABC approach, where the CIABC is used to identify the parameters of PV panels that namely, a polycrystalline and monocrystalline one [35]. In this experiment, the parameters of SC are modified as R sh = R p and I sh = I p in order to differentiate them from the single cell experiments.…”

Section: Ciabc On Pv Panelsmentioning

confidence: 99%

“…In general, the PV panel contains M solar cells which are interconnected in series, as well as the current of this panel (I) equals to I M . Assuming that the same level of photon flux is received by the M cells, then the same current and voltage are generated from these cells [35]. The most of the related literature the SD model is used for each element of the PV panel.…”

Section: Ciabc On Pv Panelsmentioning

confidence: 99%

“…Based on the values of the current (I SC ) (when the cells are short-circuited) and the voltage (V oc ) (when the cells are open-circuited), the I-V data is computed from each panel. The results of the proposed CIABC method are compared against two other methods called ABC and the results given in [35].…”

Section: Ciabc On Pv Panelsmentioning

confidence: 99%

“…The first one is for single SC, and it was extracted for the datasheet provided by the manufacturer. The second dataset corresponds to experimental information extracted from real PV modules [35]. The aim of using two information sources is to provide evidence of the capabilities of the CIABC for benchmark and real data.…”

mentioning

confidence: 99%

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A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells

et al. 2017

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The search for new energy resources is a crucial task nowadays. Research on the use of solar energy is growing every year. The aim is the design of devices that can produce a considerable amount of energy using the Sun's radiation. The modeling of solar cells (SCs) is based on the estimation of the intrinsic parameters of electrical circuits that simulate their behavior based on the current vs. voltage characteristics. The problem of SC design is defined by highly nonlinear and multimodal objective functions. Most of the algorithms proposed to find the best solutions become trapped into local solutions. This paper introduces the Chaotic Improved Artificial Bee Colony (CIABC) algorithm for the estimation of SC parameters. It combines the use of chaotic maps instead random variables with the search capabilities of the Artificial Bee Colony approach. CIABC has also been modified to avoid the generation of new random solutions, preserving the information of previous iterations. In comparison with similar optimization methods, CIABC is able to find the global solution of complex and multimodal objective functions. Experimental results and comparisons prove that the proposed technique can design SCs, even with the presence of noise.

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Section: The Polycrystalline Pv Panel Descriptionmentioning

confidence: 96%

Section: Ciabc On Pv Panelsmentioning

confidence: 99%

Section: Ciabc On Pv Panelsmentioning

confidence: 99%

Section: Ciabc On Pv Panelsmentioning

confidence: 99%

mentioning

confidence: 99%

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A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells

et al. 2017

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A new emperor penguin optimisation‐based approach for solar photovoltaic parameter estimation

Chauhan

Prakash

2021

Int Trans Electr Energ Syst

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Summary Mathematical model parameter estimation of solar photovoltaic (PV) cell, module and array represent a prodigious challenge in recent researches, where analytical, metaheuristic and hybrid techniques play a vital role for parameter extraction by using manufacturer's datasheet and experimental data, although the precise and highly reliable solution identification is still a complex problem to be solved. In this paper, a new Emperor Penguin Optimisation‐ (EPO) based parameter estimation approach for a single‐diode Rp solar PV model is presented that is cohesively analysed under different sets of temperature and irradiance (G). Validation of the proposed technique is perceived on the analysis of performance characteristics, that is, current‐voltage (I‐V) and power‐voltage (P‐V) of KC200GT, PWP201, and STP6 120‐36 PV modules under various simulation conditions. Moreover, estimated results are compared with the experimental data and several established estimation techniques in the literature for validation and demonstrate the proposed technique with highly precise outcomes having reduced computational cost for the PV model parameter extraction problem.

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Parameter Estimation of Solar Cells: A Multi‐Objective Approach

Hazra

Ganguli

2021

Electrical and Electronic Devices, Circuits, and Materials

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A parameter extraction technique exploiting intrinsic properties of solar cells

Cited by 192 publications

References 24 publications

A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells

A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells

A new emperor penguin optimisation‐based approach for solar photovoltaic parameter estimation

Parameter Estimation of Solar Cells: A Multi‐Objective Approach

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