Electrical Characterization of a Photovoltaic Module Through Artificial Neural Network: A Review

Galphade, Rashmi

doi:10.11648/j.ijecec.20170301.12

Cited by 3 publications

(1 citation statement)

References 21 publications

(22 reference statements)

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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%

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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Section: Introductionmentioning

confidence: 99%

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

Gnetchejo¹,

Essiane²,

Ele³

et al. 2019

JPEE

View full text Add to dashboard Cite

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An Investigation on Multi-junction Solar Cell for Maximum Power Point Tracking Using P&O and ANN Techniques

Rani

Singh

2019

Lecture Notes in Electrical Engineering

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Characteristics Performance Prediction of PV Panel Using Cuckoo Search Algorithm

Hasan

Jawad

Abdullah

2019

IOP Conf. Ser.: Mater. Sci. Eng.

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This paper focuses on prediction and evaluation the correlation factors of PV panel characteristics in order to determine the implicit correlation forms for the efficiency and temperature of the system under Iraq climate conditions. Implicit correlation forms consisted on the ambient temperature, solar radiation, wind speed and humidity. Cuckoo search algorithm was employed as an intelligent optimization method to predict the correlation factors of each variable under different evaluation method which are, mean square error (MSE), integrate absolute error (IAE) and integrate square error (ISE). The results appeared that the proposed method was succeeded to predict both of the PV panel temperature and efficiency with MSE evaluation method compared with other methods. Where, the lowest MSE was recorded lower the 1 % for each characteristic.

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Electrical Characterization of a Photovoltaic Module Through Artificial Neural Network: A Review

Cited by 3 publications

References 21 publications

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

An Investigation on Multi-junction Solar Cell for Maximum Power Point Tracking Using P&O and ANN Techniques

Characteristics Performance Prediction of PV Panel Using Cuckoo Search Algorithm

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