Parameter extraction of photovoltaic generating units using multi-verse optimizer

Ali, EsraaA; El‐Hameed, Mohamed A.; El‐Fergany, Attia A.; El-Arini, Mahdi

doi:10.1016/j.seta.2016.08.004

Cited by 75 publications

(32 citation statements)

References 31 publications

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“…The estimated parameters obtained with the fiveparameter procedure are also compared with the ones previously reported by other studies [8][9][10][11][12]. Ref.…”

Section: Resultsmentioning

confidence: 92%

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Simple vs complex models for solar cells

Sabadus

Paulescu

2019

E3S Web Conf.

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In this paper three different procedures for extracting the current-voltage characteristics of a crystalline photovoltaic module are studied. Each procedure is associated to a solar cell model characterized by a well-defined degree of complexity. The results emphasize that the simple models approximate the current-voltage characteristics of a solar cell as good as the more complex models. Even if all the procedures analysed in this paper approximate well the measured characteristics, the specific model parameters experience a large dispersion. From a broader perspective, the results raise a question mark on the ability of the current procedures to accurately extract the solar cell parameters.

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“…The estimated parameters obtained with the fiveparameter procedure are also compared with the ones previously reported by other studies [8][9][10][11][12]. Ref.…”

Section: Resultsmentioning

confidence: 92%

“…To obtain the five parameters, Ref. [9] applied a multi-verse optimization metaheuristic approach. Ref.…”

Section: Resultsmentioning

confidence: 99%

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Simple vs complex models for solar cells

Sabadus

Paulescu

2019

E3S Web Conf.

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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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