Neural network based integration of MPPT and diagnosis of degradation for photovoltaic module

The quaternaries In 1−x Ga x As y P 1−y are the main promising elements for the fabrication of optoelectronic devices. The adjustment of their physical parameters is assumed by the change of the molar fraction x and y. These parameters can be affected by the variation of temperature and pressure. To make the theoretical diagnosis of these materials, it is fundamental to know the energy gap ' E g ' and the lattice parameter ' a ', over a wide range of chemical compositions 0 ≤ x ≤ 0.47 and 0 ≤ y ≤ 1 , at different temperatures and pressures. We show that by using the Artificial Neural Network method optimized by the Levenberg Maquardt algorithm ANN-LM, it is possible to obtain results very close to the experiment. The scatter plot and error calculation show that the ANN-LM model provides more accurate values of the lattice parameter than those calculated by Vegard's law. On the other hand, the energy gap values Eg(x, y, T) estimated, using the ANN-LM model, proved to be close to the experimental values that those calculated by the empirical equations. In addition, the ANN-LM method allowed us to estimate with great accuracy the values of the energy gap at different temperatures and pressures Eg(P, T). Our work provides crucial information on the physical properties of the quaternary without the use of approximations, and without taking into account the hypothesis of a perfect agreement between InGaAsP and InP substrate.

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“…(21). The output of this node serves as input of another node of the following layer (Kumari et al 2016;Dkhichi et al 2016):…”

Section: Artificial Neural Network Optimized By the Levenberg-marquarmentioning

confidence: 99%

Theoretical diagnostic and prediction of physical properties of quaternary InGaAsP compound using artificial neural networks optimized by the Levenberg Maquardt algorithm

Tarbi¹,

Atmani²,

Sellam

et al. 2018

Opt Quant Electron

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“…In order to improve the conversion efficiency of solar energy, the characteristics of photovoltaic cells were in-depth study by people [1][2][3][4][5]. When people studied the characteristics of PV cell, they mainly focused on the volt-ampere characteristics of the solar cells output, which can be applied to the control of the maximum power output of battery, these parameters of the battery model including current Iph, reverse saturation current I0, diode quality sub, a series resistance (RS) and the shunt resistance Rsh, through the analysis on battery power input parameters we can control of photovoltaic energy conversion efficiency and battery fault situation in real-time, and then adjust the power to ensure the production smoothly has the very important significance [6][7].…”

Section: Introductionmentioning

confidence: 99%

Parameters Identification of Photovoltaic Cells Based on Differential Evolution Algorithm

Liu¹,

Qiao²,

Chong-lin³

et al. 2016

ITM Web Conf.

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Abstract.For the complex nonlinear model of photovoltaic cells, traditional evolution strategy is easy to fall into the local optimal and its identification time is too long when taking parameters identification, then the difference algorithm is proposed in this study, which is to solve the problems of parameter identification in photovoltaic cell model, where it is very difficult to achieve with other identification algorithms. In this method, the random data is selected as the initial generation; the successful evolution to the next generation is done through a certain strategy of difference algorithm, which can achieve the effective identification of control parameters. It is proved that the method has a good global optimization and the fast convergence ability, and the simulation results are shown that the differential evolution has high identification ability and it is an effective method to identify the parameters of photovoltaic cells, where the photovoltaic cells can be widely used in other places with these parameters.

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IC-based Variable Step Size Neuro-Fuzzy MPPT Improving PV System Performances

Harrag

Messalti

2019

Energy Procedia

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Neural network based integration of MPPT and diagnosis of degradation for photovoltaic module

Cited by 11 publications

References 12 publications

Theoretical diagnostic and prediction of physical properties of quaternary InGaAsP compound using artificial neural networks optimized by the Levenberg Maquardt algorithm

Theoretical diagnostic and prediction of physical properties of quaternary InGaAsP compound using artificial neural networks optimized by the Levenberg Maquardt algorithm

Parameters Identification of Photovoltaic Cells Based on Differential Evolution Algorithm

IC-based Variable Step Size Neuro-Fuzzy MPPT Improving PV System Performances

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