New monitoring method to characterize individual modules in large photovoltaic systems

Ortega, E.; Aranguren, Gerardo; Jimeno, J.C.

doi:10.1016/j.solener.2019.09.099

Cited by 14 publications

(9 citation statements)

References 39 publications

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“…Using concentrators in solar PV plants increases the solar energy specific flow incident on the solar photovoltaic cell, thereby reducing the solar photovoltaic cell area, and consequently, the cost of solar photovoltaic arrays of the same power. Concentrated radiation increases the efficiency of converting solar radiation from a solar photovoltaic cell, which is also noted in the works of other authors [24][25][26][27][28].…”

Section: Practical Application and Resultssupporting

confidence: 68%

Improving the Efficiency of Stand-Alone Solar PV Power Plants

Akhmetshin¹,

Tuhvatullin²,

Atnagulov³

et al. 2021

IJSDP

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As a source of alternative energy, solar energy has apparent advantages, including a renewable, inexhaustible, and environmentally friendly resource. However, it has not become widely spread in the Russian Federation. Among the disadvantages of using solar energy are high equipment cost, low efficiency of photovoltaic solar cells, the generated electrical energy instability. The spatio-temporal variability of solar access causes electrical energy instability. It is possible to increase solar photovoltaic plant efficiency by using a tracking system to change the plant sun's spatial orientation. The paper offers mathematical and simulation models of a solar photovoltaic plant with a solar tracking system that allows the plant to be automatically oriented to the sun by matching the production mode and the solar access level. The use of the azimuth plant control system on the sun will increase the power production of the solar PV plant by an average of 28%. The same value will increase by 40% when using the full plant control system.

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Section: Practical Application and Resultssupporting

confidence: 68%

Improving the Efficiency of Stand-Alone Solar PV Power Plants

Akhmetshin¹,

Tuhvatullin²,

Atnagulov³

et al. 2021

IJSDP

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“…Based on the same concept, a fourindependent-channel dynamic load was designed for PV cells and panel characterization developed around the NI cRIO platform [31,47,48,83]. For PV array characterization, Chen et al [84] designed a capacitor-based I-V tracer based on an embedded system. The system designed consists of capacitors, two IGBT transistors, an insulated solid-state relay (SSR) and a TMS320F28335 digital signal processor (DSP) connected to an LCD.…”

Section: Capacitor-based Methods (Cbm)mentioning

confidence: 99%

“…The majority of the I-V tracer was supposed to disconnect the PV module from the PV string in order to measure the I-V characteristic. Ortega et al [84] propose a new I-V tracer design based on CBM. Two very small capacitors in the range of microfarads are used for measuring the I-V characteristics in three steps, without using power electronics components.…”

Section: Capacitor-based Methods (Cbm)mentioning

confidence: 99%

Comprehensive Review of Methods and Instruments for Photovoltaic–Thermoelectric Generator Hybrid System Characterization

Cotfas

2020

Energies

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Finding new sustainable energy sources or improving the efficiencies of the existing ones represents a very important research and development direction. The hybridization approach is one solution for increasing the efficiency of the existing energy sources. In the case of photovoltaic technology, the hybridization of the photovoltaic panels (PV) with thermoelectric generators (TEGs) has become a more interesting solution for the research community in the last decade. Thus, a comprehensive review of the characterization methods and instruments used in PV-TEG hybrid system study represents the objective of this work. PV and TEG equivalent circuits are presented. The instruments and software applications used for the measurements and simulations are presented and analyzed. The analysis of the literature reveals that there are many papers that offer partial or no information about the instruments used or about the measurement quality (accuracies, uncertainties, etc.). In hybrid system modeling, the preferred software applications are MATLAB (MathWorks, Natick, MA, USA) and COMSOL Multiphysics (Comsol, Burlington, MA, USA), while for experimental studies based on computers, LabVIEW (NI, Austin, TX, USA) is preferred. This review work could be interesting for researchers and engineers who are interested in finding solutions for characterizing or monitoring hybrid system components, but it is not limited to these.

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“…Recently, Ortega et al proposed a continuous monitoring of PV power plants using measurement devices attached to every module. 20 Kropp et al manage to predict cell level characteristics from two EL measurements using simulations and finally calculate the module power for a single test module. 21 In contrast to the IR-based approach, our method does not require a reference temperature, since the magnitude of EL measurements mainly depends on camera characteristics and the excitation current, which is known in advance.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, Ortega et al. proposed a continuous monitoring of PV power plants using measurement devices attached to every module 20 . Kropp et al.…”

Section: Related Workmentioning

confidence: 99%

Deep‐learning‐based pipeline for module power prediction from electroluminescense measurements

Hoffmann¹,

Buerhop‐Lutz

Reeb

et al. 2021

Progress in Photovoltaics

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Automated inspection plays an important role in monitoring large-scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of defects on solar modules, but have not been used to determine the power of a module. However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string. By now, this is commonly determined by measurements that require to discontact or even dismount the module, rendering a regular inspection of individual modules infeasible. In this work, we bridge the gap between electroluminescense measurements and the power determination of a module. We compile a large dataset of 719 electroluminescense measurements of modules at various stages of degradation, especially cell cracks and fractures, and the corresponding power at maximum power point. Here, we focus on inactive regions and cracks as the predominant type of defect. We set up a baseline regression model to predict the power from electroluminescense measurements with a mean absolute error (MAE) of 9.0 ± 8.4W P (4.0 ± 3.7%). Then, we show that deep learning can be used to train a model that performs significantly better (7.3 ± 6.5W P or 3.2 ± 2.7%) and propose a variant of class activation maps to obtain the per cell power loss, as predicted by the model. With this work, we aim to open a new research topic. Therefore, we publicly release the dataset, the code, and trained models to empower other researchers to compare against our results. Finally, we present a thorough evaluation of certain boundary conditions like the dataset size and an automated preprocessing pipeline for on-site measurements showing multiple modules at once.

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New monitoring method to characterize individual modules in large photovoltaic systems

Cited by 14 publications

References 39 publications

Improving the Efficiency of Stand-Alone Solar PV Power Plants

Improving the Efficiency of Stand-Alone Solar PV Power Plants

Comprehensive Review of Methods and Instruments for Photovoltaic–Thermoelectric Generator Hybrid System Characterization

Deep‐learning‐based pipeline for module power prediction from electroluminescense measurements

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