Identification of unknown parameters of a single diode photovoltaic model using particle swarm optimization with binary constraints

Bana, Sangram; Saini, R.P.

doi:10.1016/j.renene.2016.10.010

Cited by 106 publications

(27 citation statements)

References 25 publications

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“…Multiple studies in the literature present methods to extract these parameters for the single-diode model and Chin et al [15] presented a review on existing models. There are many types of methods and they are based on experimental [16,20,35,37], numerical, and optimization techniques [17][18][19][22][23][24][25][26][27][28][29][30][31][32][33][34]36,[38][39][40][41][42][43][44]47,49] or combinations of these [21,45,46,48].…”

Section: State Of the Art On Methods For Parameter Estimationmentioning

confidence: 99%

“…Other methods used time-varying acceleration coefficients particle swarm optimization (TVACPSO) [24], the hybrid adaptive Nelder-Mead simplex algorithm based on the eagle strategy [25], algorithms and adapting control parameters [27], genetic algorithms to estimate parameters [28], a chaotic whale optimization algorithm [29], particle swarm optimization [30], a multiple learning backtracking search algorithm [34], and combining translation method [35].…”

Section: State Of the Art On Methods For Parameter Estimationmentioning

confidence: 99%

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A New Simplified Five-Parameter Estimation Method for Single-Diode Model of Photovoltaic Panels

et al. 2019

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This work proposes a new simplified five-parameter estimation method for a single-diode model of photovoltaic panels. The method, based on an iterative algorithm, is able to estimate the parameter of the electrical single-diode model from the panel’s datasheet. Two iterative steps are used to estimate the five parameters starting from data provided by the manufacturer (nameplate values or I–V curves). The first step permits finding the optimal value of the diode ideality factor A, and the second step allows the calculation of the Rp value to improve the accuracy. A model that takes into account variations in temperature and solar irradiance has been used to validate the behavior of the output parameters. Compared to other estimation work, the proposed method shows the best result in the standard test condition (STC) and with a variable solar irradiance. Indeed, the optimization of the A, Rs, and Rp parameters allows guaranteeing the minimum error between I–V curves obtained from method and datasheet.

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Section: State Of the Art On Methods For Parameter Estimationmentioning

confidence: 99%

Section: State Of the Art On Methods For Parameter Estimationmentioning

confidence: 99%

A New Simplified Five-Parameter Estimation Method for Single-Diode Model of Photovoltaic Panels

et al. 2019

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“…Thus, dedicated efforts have been made to surmount the limitations of stochastic computational techniques through the establishment of alternative algorithms and internal parametric modifications. Examples of these approaches are PSO with binary constraints [38], guaranteed convergence PSO [39], improved chaotic whale optimization algorithm [40], improved shuffled complex evolution algorithm [37] and hybrid firefly algorithm with pattern search algorithm (HFAPS) [41]. Additionally, with the aim of combining the simplicity of analytical methods and efficiency of computational techniques various hybrid approaches have been introduced to estimate the PV cells and modules parameters [18, 21, 42, 43].…”

Section: Introductionmentioning

confidence: 99%

Simple and efficient estimation of photovoltaic cells and modules parameters using approximation and correction technique

Muhammad

Sangawi²,

Hashim

et al. 2019

PLoS ONE

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The behavior of solar cells and modules under various operational conditions can be determined effectively when their intrinsic parameters are accurately estimated and used to simulate the current-voltage ( I-V ) characteristics. This work proposed a new computational approach based on approximation and correction technique (ACT) for simple and efficient extraction of solar cells and modules parameters from the single-diode model. In this technique, an approximated value of series resistance ( R s ) was first derived and used to determine the initial value of parallel resistance ( R p ). Later, the final corrected values of R s and R p were obtained by resubstituting their approximated values in a five-loop iteration using the manipulated equations. For rapid evaluation and validation of the proposed technique, a software application was also created using MATLAB program. The correctness and robustness of the proposed technique was validated on five types of solar cells and modules operated at varied temperatures and irradiances. The lowest RMSE value was achieved for RTC France (7.78937E-4) and PVM 752 GaAs (2.10497E-4) solar cell. The legitimacy of ACT extracted parameters was established using a simple yet competitive implementation approach wherein the performance of the developed technique was compared with several state-of-the-art methods recently reported in the literature.

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“…Xiong et al solved the parameter extraction problem of different PV models by using several metaheuristics including symbiotic organisms search (SOS) algorithm [24], improved WOA based on two modified prey searching strategies [25], and hybrid DE with WOA [26]. In addition to the aforementioned metaheuristics, many more [27][28][29][30][31][32][33][34][35][36][37][38][39][40] have also been presented to solve the important problem. e abovementioned metaheuristics have, to some extent, proven themselves promising methods for the parameter extraction problem of PV models.…”

Section: Introductionmentioning

confidence: 99%

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.

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Identification of unknown parameters of a single diode photovoltaic model using particle swarm optimization with binary constraints

Cited by 106 publications

References 25 publications

A New Simplified Five-Parameter Estimation Method for Single-Diode Model of Photovoltaic Panels

A New Simplified Five-Parameter Estimation Method for Single-Diode Model of Photovoltaic Panels

Simple and efficient estimation of photovoltaic cells and modules parameters using approximation and correction technique

Application of Supply‐Demand‐Based Optimization for Parameter Extraction of Solar Photovoltaic Models

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