A linear method to extract diode model parameters of solar panels from a single I–V curve

Lim, Li Hong Idris; Ye, Zhen; Ye, Jiaying; Yang, Di; Du, Hui

doi:10.1016/j.renene.2014.11.018

Cited by 65 publications

(14 citation statements)

References 14 publications

(15 reference statements)

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“…Least squares and Newton-Raphson method [29] Iterative curve fitting [30] Lambert W-functions [20,[31][32][33][34][35] Integral-based linear least square identification method [36,37] Linear interpolation/extrapolation [38] Chebyshev polynomials [39] Taylor's series expansion [40] Padé approximants [41] Symbolic function [42] Analytical mathematical method [43][44][45] Simple methods based on measured points [46] Metaheuristic methods are powerful in local searches, but they tend to get trapped in locally optimal values and depend on the photovoltaic module's manufacturer's data such as open circuit, short circuit, and maximum power points. Since the photovoltaic cell has triple non-linearity in current-voltage, power-voltage and in intrinsic parameters, deterministic methods cannot extract parameters accurately based on current, voltage and current derivatives with respect to the voltage at short circuit current, maximum power and open circuit voltage.…”

Section: Optimization Methods Referencementioning

confidence: 99%

Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

et al. 2018

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In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 • C and 1000 W/m 2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 • C and 1000 W/m 2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell.

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Section: Optimization Methods Referencementioning

confidence: 99%

Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

et al. 2018

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“…The photovoltaic module represented by its equivalent electrical diagram given in Figure 2, consists of a current source modeling the luminous flux, losses modeled by two resistors, the shunt resistance R sh , the series resistance R S . In the end, appears in this diagram, the diode for the polarization of the cell and the phenomenon of recombination of minority carriers [13] [14].…”

Section: Photovoltaic System Modelingmentioning

confidence: 99%

Reliability of the MPPT Control on the Energy Parameters of a Photovoltaic Generator

Jeannot¹,

Jacques²,

Jeannot³

2020

WJET

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This article describes a technique that allows a photovoltaic (PV) production unit to obtain the maximum power at all times. Here, we use the MPPT control via fuzzy logic on a DC/DC boost-type converter. In order to achieve our goals, we first proceeded to model a PV panel. The resulting model offers the possibility to better account for the influence of different physical quantities such as temperature, irradiation, series resistance, shunt resistance and diode saturation current. Thus, the maximum power to be provided by the PV system is acquired by fuzzification and defuzzification of the input and output variables of the converter. Subsequently, a virtual model of an 800 Watt PV prototype is implemented in the Matlab environment. The simulation results obtained and presented, show the feasibility and efficiency of the proposed technology. Indeed, for a disturbance caused by a variation in brightness, our system guarantees the maximum stable power after 1.4 s. While for a load variation, the maximum power is continuous.

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“…Further improvements to the OAM in Section II is on the lowering the number of data points on the I-V curve for solving B, C and D. In [16], Rs is determined independently through a binary search. If Rs is known, from (11),…”

Section: Improvementsmentioning

confidence: 99%

“…This was further extended in [14] which presents a two-step procedure for parameter identification. The linear least squares method was used, which was previously proposed in [15]- [16].…”

Section: Introductionmentioning

confidence: 99%

An Improved Oblique Asymptote Method for Parameter Identification of PV Panels

Chen

Idris

2018

2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC &Amp

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A single-diode model is the most important and broadly used tool for PV module design and analysis. The model has 5 parameters to be identified from the I-V characteristics curves. However, due to the lack of explicit form of I or V with the unknown 5 parameters, parameter identification is very difficult. Recent progress in PV model identification are discussed in this paper with the simulation of MATLAB against the measured data from a real PV module. An improved Oblique Asymptote Method is then proposed and compared with existing identification methods. Test results show that the proposed method achieves lower RMSE with less knowledge of I-V data points. Index Terms-single-diode model, parameter identification, oblique asymptote method.

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A linear method to extract diode model parameters of solar panels from a single I–V curve

Cited by 65 publications

References 14 publications

Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

Reliability of the MPPT Control on the Energy Parameters of a Photovoltaic Generator

An Improved Oblique Asymptote Method for Parameter Identification of PV Panels

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