Modeling of Electrical Characteristics of Photovoltaic Cell Considering Single-Diode Model

Azzouzi, Mohammed; Popescu, Dan; Bouchahdane, M.

doi:10.18178/jocet.2016.4.6.323

Cited by 47 publications

(33 citation statements)

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“…figure 8 it appears that the greater the intensity the greater the fill factor and efficiency. This is consistent with the phrase that has conducted research on the fill factor and efficiency of solar cells for variations in intensity from 250 W/m 2 to 1000 W/m 2 and the greater the intensity of the higher current while the voltage is almost unchanged and the conclusion is the greater the intensity the greater the power the maximum achieved [18], [19].…”

Section: Performance Of Solar Cells At Various Intensitysupporting

confidence: 89%

Determination of Fill Factor and Efficiency in Solar Cell Type (99 × 69) mm2 with Arduino Uno R3 Based Drive assisted by Logger Pro 3.14.1

Hamzah

Toifur

Ishafit

2019

Indonesia. Rev. Phys.

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The purpose of this research is to determine the fill factor and efficiency of solar cell type (99×69) mm 2 with arduino uno R3 based automatic drive assisted logger pro 3.14.1. This study aims to determine the value of the fill factor and the efficiency of solar cell type (99×69) mm 2 on manual and automatic drives with a microcontroller collaboration using Arduino Uno R3 as a data acquisition tool. The use of samples in research on fill factors and the efficiency of solar cells with automatic drive rotating the surface of the solar cell follows the motion of the light source from 0 to 90 compared to without the automatic drive from 10 to 70. The test results are then implemented to determine the fill factor and efficiency in variations in light intensity. In this experiment the type of solar cell used polycrystalline type (99×69) mm 2 was presented in front of a Philips 100 W/220 V light bulb at a distance of 18 cm and the solar cell automatic drive was controlled via an arduino uno R3 microcontroller. Current and voltage data acquisition is carried out with the help of DCP-BTA currents and VP-BTA voltage probes that are connected to the mini labquest transducer and displayed to a computer through software logger pro 3.14.1. Furthermore, the solar cell voltage-current data is diffitting using the exponential equation in logger pro software. Based on the results of solar cell research type (99×69) mm 2 percentage of the fill factor value of 60,2  0,5 and efficiency of 26  0,4 in manual motion. Whereas in the automatic drive percentage fill factor 66,3  0,4 and efficiency 26  0,1. Percentages in automatic drive can increase the accuracy and precision of current and voltage readings so that the fill factor increases by 10% while efficiency does not change. Furthermore, the greatest variation in light intensity is 993.34 W/m 2 with a fill factor of 71% and an efficiency of 31%. This fill factor and efficiency have an exponential relationship with light intensity.

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Section: Performance Of Solar Cells At Various Intensitysupporting

confidence: 89%

Determination of Fill Factor and Efficiency in Solar Cell Type (99 × 69) mm2 with Arduino Uno R3 Based Drive assisted by Logger Pro 3.14.1

Hamzah

Toifur

Ishafit

2019

Indonesia. Rev. Phys.

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“…The equivalent circuit model consists of a current source and a diode connected in parallel. This model is called the single mechanism, three parameters model (1M3P) [8]. The three unknown parameters in the ideal model are the diode ideality constant, α, diode reverse bias saturation current, Io and current generated from the light, Iph.…”

Section: M5p Pv Panel Modelmentioning

confidence: 99%

Modelling of Indoor Light Energy Harvesting for IoT

Ragunathan

Drieberg

Aziz

et al. 2019

International Journal of Simulation: Systems, Science &Amp; Technology

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Network communications have been evolving with the rise of the Internet of Things (IoT). Low powered IoT devices, which consume less power, can be powered up using an energy harvesting system instead of batteries. Due to the wide availability of indoor lighting in offices, homes, factories, malls and hospitals, energy can be harvested through these sources. A photovoltaic panel that converts light energy into electrical energy is used to harvest the power from these indoor lighting. However, most manufacturers provide datasheet values only at limited values of illuminance. Thus, it is important to have an accurate model of photovoltaic panel that can predict and estimate its performance at all operating conditions. This paper presents the methodology for modelling an accurate Single Mechanism Five Parameter (1M5P) model for indoor light energy harvesting. The accuracy of the model is highly dependent on the parameter extraction technique used. A high accuracy technique for outdoor was adapted for the indoor application. An experiment was also undertaken to determine the equivalent irradiance for different values of illuminance. The proposed model was applied on a commercial PV panel and its I V and P-V curves were obtained. Then, a controlled experiment is carried out with the PV panel under the indoor light condition. The results for both the model and experiment were compared, and they were found to be in good agreement, thus validating the accuracy of the proposed model.

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“…CPV systems normally use multi-junction cells and optical lenses to concentrate sunbeams onto the PV cell in order to increase efficiency, although their model can also be analogous to the conventional solar cells. The electrical behaviour of a CPV module can, therefore, be approximated by the solar cell with series and shunt resistance [27], which is shown in Figure 3: a forward-biased diode with an ideality factor m models the recombination of electrons inside the cell and is connected in parallel with the current generator I ph , which is the photogenerated current. The voltage and the current generated by the CPV module are V pv and I pv , respectively, whereas the resistances R p and R s model the electrical losses.…”

Section: Model Of the Cpv Modulesmentioning

confidence: 99%

Control Scheme of a Concentration Photovoltaic Plant with a Hybrid Energy Storage System Connected to the Grid

Roncero‐Sánchez

Torres²,

Vázquez

2018

Energies

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Abstract:In the last few decades, renewable energy sources (RESs) have been integrated into the electrical grid in order to curb the deficiency of energy owing to, among other factors, the depletion of fossil fuels and the increasing awareness of climate change. However, the stochastic nature of these sources, along with changes in levels of energy consumption, signifies that attention now needs to be paid for energy storage systems (ESSs). One of the most promising RESs is concentration photovoltaic (CPV) energy, owing to the high efficiency obtained and its sustainability regarding environmental issues. However, as CPV systems work only with direct solar radiation, they require ESSs in order to smooth the variations in the energy generated. This paper deals with the integration into the grid of a CPV plant that employs a hybrid ESS (HESS) based on ultracapacitors and batteries. The HESS allows the complete system to inject a constant active power level into the grid and thus flatten the profile of the energy generated. This goal is achieved by using a power electronic topology based on various DC-DC converters and a DC-AC converter, both of which share the same DC link. The control system is tailored in order to decouple the active-power and the reactive-power injections. Simulation results obtained using PSCAD/EMTDC (Power System Computer Aided Design/Electromagnetic Transient Direct Current) show the resulting performance of a 200 kW CPV plant with a hybrid ESS.

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Modeling of Electrical Characteristics of Photovoltaic Cell Considering Single-Diode Model

Cited by 47 publications

References 0 publications

Determination of Fill Factor and Efficiency in Solar Cell Type (99 × 69) mm2 with Arduino Uno R3 Based Drive assisted by Logger Pro 3.14.1

Determination of Fill Factor and Efficiency in Solar Cell Type (99 × 69) mm2 with Arduino Uno R3 Based Drive assisted by Logger Pro 3.14.1

Modelling of Indoor Light Energy Harvesting for IoT

Control Scheme of a Concentration Photovoltaic Plant with a Hybrid Energy Storage System Connected to the Grid

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