Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm

Allam, Dalia; Yousri, Dalia; Eteiba, Magdy B.

doi:10.1016/j.enconman.2016.06.052

Cited by 293 publications

(126 citation statements)

References 33 publications

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“…In the sense of optimization problem for accurate estimation, the error function for the SCPIP can be transformed into equations (5) and (6) for the SD and DD, respectively. The X in these equations represents the decision variables to be optimized for the SCPIP, where Table 1 shows the descriptions of the decision variables and their lower and upper bounds for the SD and DD models [22,27,36].…”

Section: Parameter Estimation Problemmentioning

confidence: 99%

“…Therefore, it is vital to identify the parameters of solar cells or modules based on nonlinear mathematical models. Among a variety of existing models in the literature, the main ones are the single-diode model (SD), the double-diode model (DD), and the PV module model [4][5][6]. The problem of extracting the parameters of solar cells from the experimental data is called the solar cell parameter identification problem (SCPIP) in literature.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the popular metaheuristic algorithms, simulated annealing algorithm [12], genetic algorithm [13,14], particle swarm optimization algorithm [15,16], differential evolution algorithm [17][18][19][20], pattern search [21], artificial bee colony algorithm [22] are widely used for the SCPIP. In addition to these well-known heuristic algorithms, there exist several papers in the literature which consider more recent approaches, such as bacterial foraging algorithm [23,24], teaching-learning-based optimization algorithm [25][26][27], biogeography-based optimization algorithm [28], chaos optimization algorithm [29], artificial fish swarm algorithm [30], bird mating optimizer approach [31], artificial immune system [32], evolutionary algorithm [1], cat swarm optimization algorithm [33], moth-flame optimization algorithm [5], JAYA optimization algorithm [34,35], chaotic whale optimization algorithm [36], imperialist competitive algorithm [37], bee pollinator flower pollination algorithm [38], shuffled complex evolution algorithm [39], memetic algorithm [40], interior search algorithm [41], collaborative swarm intelligence approach [42], and cuckoo search algorithm [43]. On the other hand, it has been proven by No-Free-Lunch theorem [44] that none of these algorithms is able to solve all type of optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Küçükoğlu

2019

International Journal of Photoenergy

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Solar cell parameter identification problem (SCPIP) is one of the most studied optimization problems in the field of renewable energy since accurate estimation of model parameters plays an important role to increase their efficiency. The SCPIP is aimed at optimizing the performance of solar cells by estimating the best parameter values of the solar cells that produce an accurate approximation between the current vs. voltage (I−V) measurements. To solve the SCPIP efficiently, this paper introduces an adaptive variant of the electromagnetic field optimization (EFO) algorithm, named adaptive EFO (AEFO). The EFO simulates the attraction-repulsion mechanism between particles of electromagnets having different polarities. The main idea behind the EFO is to guide electromagnetic particles towards global optimum by the attraction-repulsion forces and the golden ratio. Distinct from the EFO, the AEFO searches the solution space with an adaptive search procedure. In the adaptive search strategy, the selection probability of a better solution is increased adaptively whereas the selection probability of worse solutions is reduced throughout the search progress. By employing the adaptive strategy, the AEFO is able to maintain the balance between exploration and exploitation more efficiently. Further, new boundary control and randomization procedures for the candidate electromagnets are presented. To identify the performance of the proposed algorithm, two different benchmark problems are taken into account in the computational studies. First, the AEFO is performed on global optimization benchmark functions and compared to the EFO. The efficiency of the AEFO is identified by statistical significance tests. Then, the AEFO is implemented on a well-known SCPIP benchmark problem set formed as a result of real-life physical experiments based on single- and double-diode models. To validate the performance of the AEFO on the SCPIP, extensive experiments are carried out, where the AEFO is tested against the original EFO, AEFO variants, and novel metaheuristic algorithms. Results of the computational studies reveal that the AEFO exhibits superior performance and outperforms other competitor algorithms.

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Section: Parameter Estimation Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Küçükoğlu

2019

International Journal of Photoenergy

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“…Their main advantage is that they do not need continuity and differentiability of the objective function In the last decade, metaheuristics have been frequently applied for parameter estimation of circuit model parameters of solar PV cells. The main develops in recent research are: genetic algorithm (GA) [25], grey wolf optimization (GWO) [26], particles swarm optimization (PSO) [27], moth-flame optimization algorithm (MFOA) [28], harmony search (HS) [29], artificial neural network (ANN) [30], multi-verse optimizer (MVO) [31], bond-graph based modelling [32], cuckoo search (CS) [33], bacterial foraging optimization [34], multiple learning backtracking search algorithm (MLBSA) [35], whale optimization algorithm (WAO) [36], salp swarm-inspired algorithm (SSA) [37]… New metaheuristic algorithms have been also recently developed to solve mathematic and engineering problems. [38] used World Cup Optimization (WCO) algorithm to find the optimal parameters of PID controller; in [39] a new algorithm based on Variance Reduction of Guassian Distribution is proposed; a new algorithm based on the invasive weed by the quantum computing is proposed by [40]; [41] combined Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) to train wavelet neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm has been applied to extract the best parameters of a PV cell and module under uniform and partial shading conditions. Five recent algorithms (SSA [37], GWO, MFOA [28], WAO [36], MVO [31]) are also implemented on the same computer with the parameters gave by authors. The result obtained from the EVPS is compared with other recent methods in the literature and different results obtain to demonstrate the high quality of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

Gnetchejo¹,

Essiane²,

Ele³

et al. 2019

JPEE

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To evaluate the performance of a photovoltaic panel, several parameters must be extracted from the photovoltaic. These parameters are very important for the evaluation, monitoring and optimization of photovoltaic. Among the methods developed to extract photovoltaic parameters from current-voltage (I-V) characteristic curve, metaheuristic algorithms are the most used nowadays. A new metaheuristic algorithm namely enhanced vibrating particles system algorithm is presented here to extract the best values of parameters of a photovoltaic cell. Five recent algorithms (grey wolf optimization (GWO), moth-flame optimization algorithm (MFOA), multi-verse optimizer (MVO), whale optimization algorithm (WAO), salp swarm-inspired algorithm (SSA)) are also implemented on the same computer. Enhanced vibrating particles system is inspired by the free vibration of the single degree of freedom systems with viscous damping. To extract the photovoltaic parameters using enhanced vibrating particles system algorithm, the problem can be set as an optimization problem with the objective to minimize the difference between measured and estimated current. Four case studies have been implemented here. The results and comparison with other methods exhibit high accuracy and validity of the proposed enhanced vibrating particles system algorithm to extract parameters of a photovoltaic cell and module.weather conditions change, analytical methods become ineffective [18].Numerical extraction techniques based on some algorithm fit the points on the PV characteristic curve. Compared to the analytical method, an accurate result can be attained since the algorithm tries to consider all points on the characteristic curve [19]. In the literature, the Newton-Raphson method is the most used [19] [20] [21]; In [22], a numerical method is proposed for modeling and the simulation of PV. The method finds the five parameters from the current-voltage characteristic by using three points of the curve (maximum power, open circuit and short circuit). In [23], the Levenberg Marquardt algorithm is

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

El‐Dabah

El‐Sehiemy

Becherif

et al. 2021

Int Trans Electr Energ Syst

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This manuscript proposes a modern optimization framework for parameter extraction of a triple-diode model of the unknown solar cell and Photovoltaic (PV) module parameters. The suggested optimization framework is based on applying a new metaheuristic optimization algorithm called Artificial Ecosystem-based Optimizer (AEO) to determine the nine unknown parameters of the triple-diode model of PV equivalent circuit model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. In this context, the root means squared error (RMSE)between the measured and estimated data is considered as the primary objective function. This objective function achieves the closeness degree between the estimated and experimental data. On the way to accomplish this study, the proposed AEO is carried out on three different commercial PV cells/modules.To assess the proposed algorithm, a comprehensive comparison study is used compared with several well-matured optimization algorithms reported in the literature. The attained numerical results prove the high precision and fast response of the proposed AEO algorithm for identifying multiple PV models.

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Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm

Cited by 293 publications

References 33 publications

Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

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

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