Solar System Characteristics, Advantages, and Disadvantages

Aktaş, Ahmet; Kırçiçek, Yağmur

doi:10.1016/b978-0-323-88499-0.00001-x

Cited by 6 publications

(1 citation statement)

References 58 publications

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“…Every PV system is simple in its form and has been described as a system for generating electricity by absorbing photons from solar radiation. PV systems consist of PV modules, inverters, batteries, cables and safety devices [2]. PV modules are made of various layers, starting with the layer of glass with an anti-reflective coating, an encapsulation material, solar cell array, another encapsulation layer, and back sheet.…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

Application of Machine Learning to Predict I-V Characteristics of PV Modules Based on Steady-State Solar Simulator

Porowski,

Kowalik,

Gorzelnik

et al. 2024

Preprint

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Photovoltaic modules should pass series of tests and examinations, verifying the electrical and thermal characteristics of the module, before being released to the market, examining the properties of the modules should undergo appropriate durability and efficiency tests under real outdoor conditions with exposure to climatic conditions and under laboratory conditions. The performance of photovoltaic cells depends on many factors, such as solar irradiance, module operating temperature, installation location, weather conditions and module shading. In this paper, three selected photovoltaic modules were examined using a large-scale steady-state solar simulator. The current-voltage (I-V) characteristics of the photovoltaic modules were experimentally tested and analyzed. The next step was to implement a three-layer artificial neural network model (MLP). The experimental data obtained in the previous step coupled with output data from MLP were then used in global sensitivity analysis (GSA). Experiments carried out on a large-scale stationary solar simulator showed differences between the values declared by the manufacturer and the values obtained from measurements of PV modules. The first module tested achieved the maximum power point greater than that specified by the manufacturer, while the other two showed power drops; it was 85-87% for the second module, and 95-98% for the third, respectively. The performed global sensitivity analysis (GSA) for the MLP model showed that the parameters: eff (22.9) and Voc/V (14.19) have the largest effect on the power-voltage relationship, while U (7.29) has the smallest effect. The usefulness of machine learning (ML) methods in the comparative analysis of PV modules has been proved.

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Section: Introduction and State Of The Artmentioning

confidence: 99%

Application of Machine Learning to Predict I-V Characteristics of PV Modules Based on Steady-State Solar Simulator

Porowski,

Kowalik,

Gorzelnik

et al. 2024

Preprint

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Appropriate Technology for Solar Thermal Desalination by Concentration Applied the Humidification-Dehumidification Method

Romero

Campos-Celador

Lengerke

et al. 2023

Communications in Computer and Information Science

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Modeling of a solar thermal system as a power supply alternative for a resistor water distillation system using TRNSYS

Tarazona-Romero,

Ascanio-Villabona,

Quintero-Ruiz

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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The sizing of a solar thermal system to feed the water distillers in the laboratory of the Santander Technological Units is presented, proposing a comparative study between three calculation methods (f-chart, instantaneous and ACSOL) for the estimation of the surface of solar capture, finally supported by modeling in the TRNSYS software of the final system, to evaluate its behavior dynamically during one year. Initially, a search for information is carried out to establish the models to develop each of the calculation methods, additionally technical data is collected from the laboratory equipment to determine the consumption of hot water. Subsequently, each of the calculation methods is applied in order to size the catchment surface, to finally carry out a comparative study between the results obtained, determining which is the most appropriate method for the calculation and defining the dimensions of the same, to develop a modeling of the dynamic behavior of the system through the TRNSYS Software. The final result presents a storage system with an average temperature of 62.13 ° C and solar collectors with an average temperature of 58.7 ° C for one year of operation. Finally, the operating time of the resistive stills is reduced from 11 hours a day to 6 hours with the integration of the Thermosolar system.

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Solar System Characteristics, Advantages, and Disadvantages

Cited by 6 publications

References 58 publications

Application of Machine Learning to Predict I-V Characteristics of PV Modules Based on Steady-State Solar Simulator

Application of Machine Learning to Predict I-V Characteristics of PV Modules Based on Steady-State Solar Simulator

Appropriate Technology for Solar Thermal Desalination by Concentration Applied the Humidification-Dehumidification Method

Modeling of a solar thermal system as a power supply alternative for a resistor water distillation system using TRNSYS

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