Analysis of Current-Voltage Curves of Solar Cells by means of Artificial Neural Networks

2020 ◽  
Author(s):  
Axel Herguth
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Cells ◽  
Current Voltage ◽  
Artificial Neural

Classification of disc damage status by discovering knowledge from experimental data in marble cutting process

2016 ◽  
Vol 231 (13) ◽  
pp. 2407-2416 ◽  
Author(s):  
Emre Akarslan ◽  
Fatih O Hocaoğlu ◽  
Ismail Ucun
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Process ◽  
Mean Values ◽  
Current Voltage ◽  
The Status ◽  
Artificial Neural ◽  
Natural Stones ◽  
Industrial Injuries

In marble industry, it is of vital importance to determine the damaged discs on time to prevent possible industrial injuries. Therefore, in this study, it is proposed to classify the status of the cutting discs that are used while cutting the natural stones. To classify the deflections of the discs, 673 different experiments are performed. Cutting discs corresponding to four different damage classes (undamaged disc, less damaged disc, much damaged disc, and broken disc) are employed in the tests. Eight different parameters (cutting forces (Fx, Fy, Fz), noise, peripheral speed of the disc, current, voltage, power consumption) are measured and recorded in the experiments. For each experiment, mean values of different measured data are studied. Artificial neural networks are employed as classifiers. In the first stage, all of these mean values corresponding to eight parameters are selected as the input vectors of the artificial neural networks, whereas in the second stage, the dimension of input vector is decreased by leaving out the parameters one by one. In this stage, it is aimed to determine the most important parameter that caries much more information about the cutting process.

scholarly journals Application Of Artificial Neural Networks In Modeling Of Manufactured Front Metallization Contact Resistance For Silicon Solar Cells

2015 ◽  
Vol 60 (3) ◽  
pp. 1673-1678 ◽  
Author(s):  
M. Musztyfaga-Staszuk ◽  
R. Honysz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Cells ◽  
Contact Resistance ◽  
Screen Printing ◽  
Selective Laser Sintering ◽  
Laser Sintering ◽  
Silicon Solar Cells ◽  
Front Electrode ◽  
Artificial Neural

Abstract This paper presents the application of artificial neural networks for prediction contact resistance of front metallization for silicon solar cells. The influence of the obtained front electrode features on electrical properties of solar cells was estimated. The front electrode of photovoltaic cells was deposited using screen printing (SP) method and next to manufactured by two methods: convectional (1. co-fired in an infrared belt furnace) and unconventional (2. Selective Laser Sintering). Resistance of front electrodes solar cells was investigated using Transmission Line Model (TLM). Artificial neural networks were obtained with the use of Statistica Neural Network by Statsoft. Created artificial neural networks makes possible the easy modelling of contact resistance of manufactured front metallization and allows the better selection of production parameters. The following technological recommendations for the screen printing connected with co-firing and selective laser sintering technology such as optimal paste composition, morphology of the silicon substrate, co-firing temperature and the power and scanning speed of the laser beam to manufacture the front electrode of silicon solar cells were experimentally selected in order to obtain uniformly melted structure well adhered to substrate, of a small front electrode substrate joint resistance value. The prediction possibility of contact resistance of manufactured front metallization is valuable for manufacturers and constructors. It allows preserving the customers’ quality requirements and bringing also measurable financial advantages.

scholarly journals Optimization of Silicon Tandem Solar Cells Using Artificial Neural Networks

2019 ◽  
pp. 392-403
Author(s):  
Jatin Kumar Chaudhary ◽  
Jiaqing Liu ◽  
Jukka-Pekka Skön ◽  
Yen Wie Chen ◽  
Rajeev Kumar Kanth ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Cells ◽  
Tandem Solar Cells ◽  
Artificial Neural

scholarly journals Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films

Electronics ◽  
2018 ◽  
Vol 7 (8) ◽  
pp. 138 ◽  
Author(s):  
Syed Naqvi ◽  
Tallha Akram ◽  
Sajjad Haider ◽  
Muhammad Kamran ◽  
Aamir Shahzad ◽  
...  
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Accuracy ◽  
Mean Squared Error ◽  
Critical Parameters ◽  
Superconducting Thin Film ◽  
Current Voltage ◽  
Wide Range ◽  
Artificial Neural

Contemplating the importance of studying current–voltage curves in superconductivity, it has been recently and rightly argued that their approximation, rather than incessant measurements, seems to be a more viable option. This especially becomes bona fide when the latter needs to be recorded for a wide range of critical parameters including temperature and magnetic field, thereby becoming a tedious monotonous procedure. Artificial neural networks have been recently put forth as one methodology for approximating these so-called electrical measurements for various geometries of antidots on a superconducting thin film. In this work, we demonstrate that the prediction accuracy, in terms of mean-squared error, achieved by artificial neural networks is rather constrained, and, due to their immense credence on randomly generated networks’ coefficients, they may result in vastly varying prediction accuracies for different geometries, experimental conditions, and their own tunable parameters. This inconsistency in prediction accuracies is resolved by controlling the uncertainty in networks’ initialization and coefficients’ generation by means of a novel entropy based genetic algorithm. The proposed method helps in achieving a substantial improvement and consistency in the prediction accuracy of current–voltage curves in comparison to existing works, and is amenable to various geometries of antidots, including rectangular, square, honeycomb, and kagome, on a superconducting thin film.

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