Measurement-based modeling of a semitransparent CdTe thin-film PV module based on a custom neural network
Semitransparent photovoltaic (STPV) can be employed in a wide application range to provide sunlight permeability for supplying solar electrical energy with some shading, which is preferable in hot areas. To predict the output power and formulate the performance of this type of photovoltaic (PV) syst...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/65351/1/65351.pdf |
| _version_ | 1796978080618643456 |
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| author | Sabry, Yasmeen Hussein Wan Hasan, Wan Zuha Sabry, Ahmad H. Ab Kadir, Mohd Zainal Abidin Mohd Radzi, Mohd Amran Shafie, Suhaidi |
| author_facet | Sabry, Yasmeen Hussein Wan Hasan, Wan Zuha Sabry, Ahmad H. Ab Kadir, Mohd Zainal Abidin Mohd Radzi, Mohd Amran Shafie, Suhaidi |
| author_sort | Sabry, Yasmeen Hussein |
| collection | UPM |
| description | Semitransparent photovoltaic (STPV) can be employed in a wide application range to provide sunlight permeability for supplying solar electrical energy with some shading, which is preferable in hot areas. To predict the output power and formulate the performance of this type of photovoltaic (PV) system, the proposed approach analyzes a Thin-Film solar cadmium telluride-type module and develops a custom neural network (CNN) for modeling its generated power expressed by its mathematical formula. Experiments for single and multilayer installation topologies are conducted for performance analysis. The coefficients of the model equation are investigated based on a set of power-current curves. The developed model adopts three factors: a minimum number of hidden neurons, the use of all measured data to train the network weights, and a linear output activation function to reduce the complexity of solving the network equations. The results specify the limit at which this type of PV starts generating power from the experimental measurements and the comparison with its equivalent normal PV module. The CNN-based STPV module is verified by comparing with the experimental measurements results, which shows a reasonable R-square, while its performance is evaluated on the silicon-based PV by comparing its behavior with the two-diode model PV in the MATLAB-based simulation. |
| first_indexed | 2024-03-06T09:49:27Z |
| format | Article |
| id | upm.eprints-65351 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:49:27Z |
| publishDate | 2018 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-653512018-10-08T02:25:26Z http://psasir.upm.edu.my/id/eprint/65351/ Measurement-based modeling of a semitransparent CdTe thin-film PV module based on a custom neural network Sabry, Yasmeen Hussein Wan Hasan, Wan Zuha Sabry, Ahmad H. Ab Kadir, Mohd Zainal Abidin Mohd Radzi, Mohd Amran Shafie, Suhaidi Semitransparent photovoltaic (STPV) can be employed in a wide application range to provide sunlight permeability for supplying solar electrical energy with some shading, which is preferable in hot areas. To predict the output power and formulate the performance of this type of photovoltaic (PV) system, the proposed approach analyzes a Thin-Film solar cadmium telluride-type module and develops a custom neural network (CNN) for modeling its generated power expressed by its mathematical formula. Experiments for single and multilayer installation topologies are conducted for performance analysis. The coefficients of the model equation are investigated based on a set of power-current curves. The developed model adopts three factors: a minimum number of hidden neurons, the use of all measured data to train the network weights, and a linear output activation function to reduce the complexity of solving the network equations. The results specify the limit at which this type of PV starts generating power from the experimental measurements and the comparison with its equivalent normal PV module. The CNN-based STPV module is verified by comparing with the experimental measurements results, which shows a reasonable R-square, while its performance is evaluated on the silicon-based PV by comparing its behavior with the two-diode model PV in the MATLAB-based simulation. IEEE 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/65351/1/65351.pdf Sabry, Yasmeen Hussein and Wan Hasan, Wan Zuha and Sabry, Ahmad H. and Ab Kadir, Mohd Zainal Abidin and Mohd Radzi, Mohd Amran and Shafie, Suhaidi (2018) Measurement-based modeling of a semitransparent CdTe thin-film PV module based on a custom neural network. IEEE Access, 6. pp. 34934-34947. ISSN 2169-3536 https://ieeexplore.ieee.org/abstract/document/8388207/ 10.1109/ACCESS.2018.2848903 |
| spellingShingle | Sabry, Yasmeen Hussein Wan Hasan, Wan Zuha Sabry, Ahmad H. Ab Kadir, Mohd Zainal Abidin Mohd Radzi, Mohd Amran Shafie, Suhaidi Measurement-based modeling of a semitransparent CdTe thin-film PV module based on a custom neural network |
| title | Measurement-based modeling of a semitransparent CdTe thin-film PV module based on a custom neural network |
| title_full | Measurement-based modeling of a semitransparent CdTe thin-film PV module based on a custom neural network |
| title_fullStr | Measurement-based modeling of a semitransparent CdTe thin-film PV module based on a custom neural network |
| title_full_unstemmed | Measurement-based modeling of a semitransparent CdTe thin-film PV module based on a custom neural network |
| title_short | Measurement-based modeling of a semitransparent CdTe thin-film PV module based on a custom neural network |
| title_sort | measurement based modeling of a semitransparent cdte thin film pv module based on a custom neural network |
| url | http://psasir.upm.edu.my/id/eprint/65351/1/65351.pdf |
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