Custom Neural networks modelling for semitransparent thin film photovoltaic

Thin-Film solar module of cadmium telluride (CdTe) is one of the Semi-transparent PV (STPV) that can be employed in a wide application range as a means to sunlight permeability while supplying solar electrical energy with some shading which also preferable in hot areas. The power generated by...

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Main Author:	Sabri, Sabri Yasameen Hussein
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Electrical engineering Photovoltaic power systems
Online Access:	http://psasir.upm.edu.my/id/eprint/71468/1/FK%202018%20113%20-%20IR.pdf
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spelling	my-upm-ir.714682019-11-13T07:13:43Z Custom Neural networks modelling for semitransparent thin film photovoltaic 2018-07 Sabri, Sabri Yasameen Hussein Thin-Film solar module of cadmium telluride (CdTe) is one of the Semi-transparent PV (STPV) that can be employed in a wide application range as a means to sunlight permeability while supplying solar electrical energy with some shading which also preferable in hot areas. The power generated by solar photovoltaic (PV) is highly affected by the weather environment. The prediction of a PV harvested energy and the system performance requires an accurate and reliable modelling as a formula and simulation design before installation. Silicon-based PV module with specifications equivalent to that for the STPV for comparison purposes. The proposed approach analyses the empirical data of a Thin-Film STPV module of CdTe type towards modelling. A developed Custom Neural Network (CNN) has been functioning for modelling the PV generated power based on laboratory and in-situ measurements. Experiments for different PV panel installation topologies have been conducted for performance analysis. Several standard single independent variable fitting modelling equations have been addressed as a basic modelling for I-V and P-V characteristic curves such as; Polynomial, Exponential, and Gaussian as parametric models. The developed CNN modelling has been implemented on both; I-V, P-V characteristic curves, and to simulate the power pattern of the PV module by adopting three factors; a minimum number of the hidden neurons, the use of all measured data for training the network weights, and linear output activation function, these factors were examined to reduce the complexity of solving the network equations. Silicon-based PV has been used in all modeling stages to validate the proposed methodology. The simulation has been performed by the MATLAB-Simulink environment. The result highlights the limit at which the STPV starts generating power via comparing with its equivalent silicon-based PV module. The proposed CNN modelling has the best goodness-of-fit than other relative models, and it is verified by the comparison between the measured and modelled outcomes which shows reasonable R-square value. The experiments have been conducted on different Thin-Film STPV modules; 48W and 40% transparency, 62W and 20% transparency, and 72W and 10% transparency. The results show that for a single module, the daily harvested energy is =190.01Wh, while that double module is= 218.48Wh, which satisfies the analysis of the single module measurements and that each individual thin film module can only generate power at a high certain level of irradiance. The results of the proposed CNN attain a correlation coefficient of 0.986 and show different fitting accuracy depends on several factors for each individual method. The proposed approach can facilitate the modelling strategy for other types of PV modules. Electrical engineering Photovoltaic power systems 2018-07 Thesis http://psasir.upm.edu.my/id/eprint/71468/ http://psasir.upm.edu.my/id/eprint/71468/1/FK%202018%20113%20-%20IR.pdf text en public masters Universiti Putra Malaysia Electrical engineering Photovoltaic power systems
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Electrical engineering Photovoltaic power systems
spellingShingle	Electrical engineering Photovoltaic power systems Sabri, Sabri Yasameen Hussein Custom Neural networks modelling for semitransparent thin film photovoltaic
description	Thin-Film solar module of cadmium telluride (CdTe) is one of the Semi-transparent PV (STPV) that can be employed in a wide application range as a means to sunlight permeability while supplying solar electrical energy with some shading which also preferable in hot areas. The power generated by solar photovoltaic (PV) is highly affected by the weather environment. The prediction of a PV harvested energy and the system performance requires an accurate and reliable modelling as a formula and simulation design before installation. Silicon-based PV module with specifications equivalent to that for the STPV for comparison purposes. The proposed approach analyses the empirical data of a Thin-Film STPV module of CdTe type towards modelling. A developed Custom Neural Network (CNN) has been functioning for modelling the PV generated power based on laboratory and in-situ measurements. Experiments for different PV panel installation topologies have been conducted for performance analysis. Several standard single independent variable fitting modelling equations have been addressed as a basic modelling for I-V and P-V characteristic curves such as; Polynomial, Exponential, and Gaussian as parametric models. The developed CNN modelling has been implemented on both; I-V, P-V characteristic curves, and to simulate the power pattern of the PV module by adopting three factors; a minimum number of the hidden neurons, the use of all measured data for training the network weights, and linear output activation function, these factors were examined to reduce the complexity of solving the network equations. Silicon-based PV has been used in all modeling stages to validate the proposed methodology. The simulation has been performed by the MATLAB-Simulink environment. The result highlights the limit at which the STPV starts generating power via comparing with its equivalent silicon-based PV module. The proposed CNN modelling has the best goodness-of-fit than other relative models, and it is verified by the comparison between the measured and modelled outcomes which shows reasonable R-square value. The experiments have been conducted on different Thin-Film STPV modules; 48W and 40% transparency, 62W and 20% transparency, and 72W and 10% transparency. The results show that for a single module, the daily harvested energy is =190.01Wh, while that double module is= 218.48Wh, which satisfies the analysis of the single module measurements and that each individual thin film module can only generate power at a high certain level of irradiance. The results of the proposed CNN attain a correlation coefficient of 0.986 and show different fitting accuracy depends on several factors for each individual method. The proposed approach can facilitate the modelling strategy for other types of PV modules.
format	Thesis
qualification_level	Master's degree
author	Sabri, Sabri Yasameen Hussein
author_facet	Sabri, Sabri Yasameen Hussein
author_sort	Sabri, Sabri Yasameen Hussein
title	Custom Neural networks modelling for semitransparent thin film photovoltaic
title_short	Custom Neural networks modelling for semitransparent thin film photovoltaic
title_full	Custom Neural networks modelling for semitransparent thin film photovoltaic
title_fullStr	Custom Neural networks modelling for semitransparent thin film photovoltaic
title_full_unstemmed	Custom Neural networks modelling for semitransparent thin film photovoltaic
title_sort	custom neural networks modelling for semitransparent thin film photovoltaic
granting_institution	Universiti Putra Malaysia
publishDate	2018
url	http://psasir.upm.edu.my/id/eprint/71468/1/FK%202018%20113%20-%20IR.pdf
_version_	1747813007956639744

Custom Neural networks modelling for semitransparent thin film photovoltaic

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