Development of a suitable model for characterizing photovoltaic arrays with shaded solar cells

Karatepe, Engin; Boztepe, Mutlu; Çolak, Metin

doi:10.1016/j.solener.2006.12.001

Cited by 354 publications

(191 citation statements)

References 22 publications

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“…The main studies presented in the literature have used simulation results without testing experimental data from actual monitoring of photovoltaic systems (Chao et al, 2015). Most of the diagnostic methods are based on the comparison of the controlled data with the simulation results of the photovoltaic systems (Gokmen et al, 2012;Karatepe et al, 2007). In addition, some fault analyzes have compared data with actual measurements; meanwhile, other work is based on statistically analyzed data using learning methods (Díaz-Dor et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the impact of partial shading and its transmittance on the performance of crystalline silicon photovoltaic modules

Faye¹,

Ndiaye²,

Kobor³

et al. 2017

Int. J. Phys. Sci.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the impact of partial shading and its transmittance on the performance of crystalline silicon photovoltaic modules

Faye¹,

Ndiaye²,

Kobor³

et al. 2017

Int. J. Phys. Sci.

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“…The configurations of PV array in this paper are developed based on the previous research, explained in [9]. In Fig.…”

Section: Investigation Of Partial Shading Patternsmentioning

confidence: 99%

“…The necessary points in this method are only in the development of rule base for the amount and direction of current injection and the ANN training process. Another advantage of this method is always end up with single peak point that makes the conventional controller algorithms such constant voltage control, perturbation and observation, incremental conductance, and current feedback algorithms are working perfectly in identifying the optimum point [7,8].…”

Section: Introductionmentioning

confidence: 99%

Problem-solving mismatching losses of photovoltaic (PV) system under partially shaded conditions

Syafaruddin

2014

2014 Makassar International Conference on Electrical Engineering and Informatics (MICEEI)

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Abstract-One of the recent concerns in the photovoltaic (PV) system practice is solving the mismatching losses due to the partially shaded conditions. Mismatching problems mean that the I-V and P-V curves between the shaded and non-shaded parts of PV module are totally different. Under mismatching condition, multiple local peaks can be observed in the PV module characteristics which pose difficulties for controllers to track the maximum point. To overcome such problems, current injection method is used to compensate the output current of the shaded part. The amount of injected current and to which part of this current being injected are determined by utilizing the intelligent network by means of Artificial Neural Network (ANN). The proposed method is always end up with single local maximum point that makes the optimum point can be easily identified using conventional controller.

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“…Although there are prior researches focused on non-electrical models based on either artificial neural network (ANN) [9] or adaptive neuro-fuzzy inference scheme (ANFIS) [10], the equivalent electrical circuit is the most popular PV model. The core advantage of the electrical models is that unlike the other models it does not necessarily require measured data samples to be trained.…”

Section: Introductionmentioning

confidence: 99%

An accurate method for the PV model identification based on a genetic algorithm and the interior-point method

2014

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Citation: Dizqah, Arash, Maheri, Alireza and Busawon, Krishna (2014) Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) An Accurate Method for the PV Model Identification Based on a GeneticAlgorithm and the Interior-Point Method AbstractDue to the PV module simulation requirements as well as recent applications of model-based controllers, the accurate photovoltaic (PV) model identification method is becoming essential to reduce the PV power losses effectively. The classical PV model identification methods use the manufacturers provided maximum power point (MPP) at the standard test condition (STC). However, the nominal operating cell temperature (NOCT) is the more practical condition and it is shown that the extracted model is not well suited to it. The proposed method in this paper estimates an accurate equivalent electrical circuit for the PV modules using both the STC and NOCT information provided by the manufacturers. A multi-objective global optimization problem is formulated using only the main equation of the PV module at these two conditions that restrains the errors due to employing the experimental temperature coefficients. A novel combination of a genetic algorithm (GA) and the interior-point method (IPM) allows the proposed method to be fast and accurate regardless the PV technology. It is shown that the overall error, which is defined by the sum of the MPP errors of both the STC and the NOCT conditions, is improved by a factor between 5.1% to 31% depending on the PV technology.

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Development of a suitable model for characterizing photovoltaic arrays with shaded solar cells

Cited by 354 publications

References 22 publications

Evaluation of the impact of partial shading and its transmittance on the performance of crystalline silicon photovoltaic modules

Evaluation of the impact of partial shading and its transmittance on the performance of crystalline silicon photovoltaic modules

Problem-solving mismatching losses of photovoltaic (PV) system under partially shaded conditions

An accurate method for the PV model identification based on a genetic algorithm and the interior-point method

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