Photovoltaic System Design and Performance

Sark, W.G.J.H.M. van

doi:10.3390/en12101826

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…(STC: temperature of 25 • C, irradiance of 1000 W/m 2 and air mass 1.5 (AM1.5) spectrum). The E out depends not only on the environmental and installation conditions of the PV system but also on failures and losses due to shading [23], dust and degradation [22,24,25] as shown by the coefficients F S and L F which consider the efficiency reduction when these phenomena occur. These phenomena affect the functioning of the module in differing invasive ways: failures can be catastrophic, locking the single-cell or module operation, or by deterioration, reducing the operation of the single cell or the entire module progressing in the file of the systems.…”

Section: Pv Systems Efficiencymentioning

confidence: 99%

Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

et al. 2020

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This paper proposes an innovative approach to classify the losses related to photovoltaic (PV) systems, through the use of thermographic non-destructive tests (TNDTs) supported by artificial intelligence techniques. Low electricity production in PV systems can be caused by an efficiency decrease in PV modules due to abnormal operating conditions such as failures or malfunctions. The most common performance decreases are due to the presence of dirt on the surface of the module, the impact of which depends on many parameters and conditions, and can be identified through the use of the TNDTs. The proposed approach allows one to automatically classify the thermographic images from the convolutional neural network (CNN) of the system, achieving an accuracy of 98% in tests that last a couple of minutes. This approach, compared to approaches in literature, offers numerous advantages, including speed of execution, speed of diagnosis, reduced costs, reduction in electricity production losses.

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Section: Pv Systems Efficiencymentioning

confidence: 99%

Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

et al. 2020

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“…The second and third nodes follow closely behind the main one, and both are Special Issues [11]. [54] The rest are smaller nodes:…”

Section: Prediction Algorithms Applied To the Power Communitymentioning

confidence: 91%

“…• "Power electronics in renewable energy systems" [11]. • "Photovoltaic system design and performance" with one citation [54].…”

Section: Prediction Algorithms Applied To the Power Communitymentioning

confidence: 99%

Energies and Its Worldwide Research

et al. 2020

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Energy efficiency and management is certainly one of the key drivers of human progress. Thus, the trends in the energy research are a topic of interest for the scientific community. The aim of this study is to highlight global research trends in this field through the analysis of a scientific journal indexed exclusively in the energy and fuels category. For this purpose, a journal has been selected that is in the center of the category considering its impact factor, which is only indexed in this category and of open access, Energies of the publisher MDPI. Therefore, a bibliometric analysis of all the contents of the journal between 2008 and 2020, 13,740 documents published, has been carried out. Analyzing the articles that are linked to each other by their citations, 14 clusters or research topics have been detected: smart grids; climate change–electric energy community; energy storage; bioenergy sources; prediction algorithms applied to power; optimization of the grid link for renewable energy; wind power; sustainability of power systems; hydrocarbon improvements; conversion of thermal/electrical energy; electric motor advancements; marine renewable energy; hydropower and energy storage; and preventive techniques in power transformers. The main keywords found were electric vehicle, renewable energy, microgrid, smart grid, and energy efficiency. In short, energy research remains necessary to meet the future challenge of sustainable energy with high efficiency and the exploration of new renewable resources, all for increasingly sustainable cities.

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“…The most important parameter of a single PV cell, and the entire module, is the current-voltage characteristic I-V (Figure 1a) [4]. The value of the current drawn from the PV source depends primarily on the intensity of the radiation that falls on the photovoltaic active surface.…”

Section: Monitoring Pv Installationmentioning

confidence: 99%

RFID Sensors for Monitoring Glazing Units Integrating Photovoltaic Modules

et al. 2022

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The paper focuses on the synthesis of semi-passive RFID transponders-sensors that are intended to integrate with active glazing units with built-in photovoltaic cells. The main purpose of the designed construction of the UHF RFID device is to provide diagnostic information in the monitoring system of a photovoltaic micro-power plant. Furthermore, the RFID sensor is aimed at being implemented at various stages of the product life cycle: production, distribution, storage, installation, common operation, service/maintenance and disposal. In the presented research work, particular attention is paid to several aspects of the RFID sensor synthesis: use of the energy, generated periodically in the PV cells, to power the monitoring device that has to act permanently; specification of the PV module parameters that have to be monitored in the diagnostic process; implementation of data acquisition and energy management models in an electrical circuit; wireless data transfer to the master unit (monitoring host), even in the absence of power supply (e.g., module damage, blackout), using a standardized communication protocol IEC 18000-63 used in the RFID technology; and the design of the antenna system taking into consideration limitations of electronic technology and the material properties of substrates and glasses used in PV modules and RFID sensors. Based on the results of the investigations, the modular structure of the RFID sensor demonstrator is proposed. Moreover, several diagnostic scenarios are analyzed in detail. On the basis of the provided considerations, it is shown that in order to find a malfunctioning component, it is enough to compare the voltages on the photovoltaic modules that are in the close vicinity.

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Photovoltaic System Design and Performance

Cited by 6 publications

References 21 publications

Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

Energies and Its Worldwide Research

RFID Sensors for Monitoring Glazing Units Integrating Photovoltaic Modules

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