Online Distributed Measurement of Dark I-V Curves in Photovoltaic Plants

Morales-Aragonés, José Ignacio; Alonso-García, María del Carmen; Gallardo-Saavedra, Sara; Alonso‐Gómez, Víctor; Balenzategui, J.L.; Redondo-Plaza, Alberto; Hernández-Callejo, Luís

doi:10.3390/app11041924

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…The dark I-V characterization was also measured for all the devices shown in figure S5. In figures S5(a) and (b), R sh decreases with the reduction in the thickness of Ag with both ZnO and PEIE ETL based devices, resulting in decreased performance [59]. However, R sh is higher with PEIE compared to ZnO based opaque and semitransparent devices, as shown in figure.…”

Section: Solar Cell Characterizationmentioning

confidence: 89%

Semitransparent organic solar cells based on low temperature processed PEIE as electron transport layer with enhanced charge transfer ability

Khanam¹,

Singh²

2023

Phys. Scr.

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In inverted structure-based semitransparent organic solar cells (OSCs), the electron transport layer (ETL) plays a crucial role in the improvement of the transparent cathode efficiency in collecting and extracting negative charge carriers. Zinc oxide (ZnO) thin film prepared by zinc acetate dihydrate precursor with various benefits is generally used as ETL. However, high temperature, less charge transfer ability, and irregular film surface due to fiber-like domain formation limit the device performance. In this work, a new approach is presented by using low-temperature processed polyethylenimine ethoxylated (PEIE) as ETL in semitransparent OSCs fabricated in an ambient environment with a blend of low-bandgap donor polymer PTB7-Th, and fullerene acceptor, PC71BM, based active layer. For semitransparent OSCs, the thickness of the silver electrode has been varied from 55 nm to 25 nm to investigate its effect on the electrical and optical properties of the devices. The power conversion efficiencies (PCE) of 5.1% and 4.6% were achieved for semitransparent devices (25 nm thickness of silver electrode) for PEIE and ZnO ETLs, respectively. Similarly, PCE of 7% and 6.7% have been achieved for opaque devices (85 nm thickness of silver electrode) using PEIE and ZnO ETLs, respectively. PEIE based devices with 25 nm Ag demonstrate about 25-30% transparency. The impedance spectroscopy measurements indicate low interfacial contact resistance and fast charge transfer capability for PEIE interlayer-based devices compared to the ZnO based devices. The encapsulated semitransparent devices processed and stored in ambient condition with PEIE and ZnO ETLs were found to retain ∽80% performance for up to 45 days.

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Section: Solar Cell Characterizationmentioning

confidence: 89%

Semitransparent organic solar cells based on low temperature processed PEIE as electron transport layer with enhanced charge transfer ability

Khanam¹,

Singh²

2023

Phys. Scr.

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“…For modules on the other hand, the utilization of dark-IVs has been rather sparse, i.e., mostly limited to module development and quality testing [12] or research outdoor studies [13], despite the valuable information they can provide. However, with advancing remote wireless monitoring and automated AI supported data analysis tools, dark current characterization of modules has been suggested recently as a valuable tool for every-day plant diagnostics [14].…”

Section: Introductionmentioning

confidence: 99%

Alternative preconditioning by utilization of a thin film module's dark diode fingerprint

Friedel,

Winter

2024

EPJ Photovolt.

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Although the continuously advancing silicon wafer-based modules dominate the commercial PV landscape, thin film technologies have not lost any of their attraction, especially in areas where their advantages count, like light weight, flexibility, and easy manufacturing. This has been the case for chalcogenides in the past and it will be for coming perovskite-based materials, whether as stand-alone, in multi- or heterojunction devices. Unfortunately, many thin film technologies suffer from metastability, i.e., their physical properties change temporarily with storage, transport or operating conditions, on time scales from hours to months. For this reason, preconditioning is crucial, before reliably evaluating such a module's performance. Presently, the respective preconditioning standards are exclusively focused on illumination-induced stabilization of the module's power at the maximum power point (PMPP). However, using PMPP as the only marker might not be the wisest choice. First, the PMPP is basically a black box, i.e., a module may show the same temporary power value at times, while being in very different condition if one looked closely on its device physics then. This may lead to false assumptions about the module's quality. Second, aiming for the highest stable PMPP of a module might not always be the desired goal, e.g., in warranty cases where the actual field performance of a module is in question and not how it would behave in perfect state after standard preconditioning. To overcome these limitations of present preconditioning standards, an alternative additional approach is required. In this report, we give a brief view on the inevitable shortcomings of present methods for thin film modules and demonstrate how the dark current characteristic of a thin film module can be used like a fingerprint instead, representing its device physics that define its actual state. Whereas in PV research, dark IV curves are commonly analyzed in detail for hints on charge transport mechanisms, interface properties or semiconductor degradation in the device, such effort would be inconvenient and unnecessary for fast-track commercial module testing. Here, we suggest focusing merely on the effective device properties, which are reflected quantitatively in the diode-parameters. The goal is to feed a recorded module dark current curve into an automated mathematical procedure, which fits the data to the double-diode model, enabling the extraction of the diode parameter-set. With this as a marker, instead of using solely PMPP during preconditioning treatments, it is much more likely that the desired previous physical state of a module is really reinstated. Additionally, the described dark current approach is conveniently independent of a light source's properties and insensitive to module soiling. The results presented here, give a first impression on the potential that such a method could have, showcasing effects of dark storage degradation and their recovery by illumination or bias-induced preconditioning on the dark current characteristics of individual CdTe and CIGS commercial PV-modules of different generations and manufacturers.

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“…Additionally, it is also possible to perform online dark I-V curves of modules in PV plants without the need to disconnect them from the string. For this, a combination of electronic boards in the PV modules and a bidirectional inverter is required, as presented in [12].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells

et al. 2021

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Solar Photovoltaic (PV) energy has experienced an important growth and prospect during the last decade due to the constant development of the technology and its high reliability, together with a drastic reduction in costs. This fact has favored both its large-scale implementation and small-scale Distributed Generation (DG). PV systems integrated into local distribution systems are considered to be one of the keys to a sustainable future built environment in Smart Cities (SC). Advanced Operation and Maintenance (O&M) of solar PV plants is necessary. Powerful and accurate data are usually obtained on-site by means of current-voltage (I-V) curves or electroluminescence (EL) images, with new equipment and methodologies recently proposed. In this work, authors present a comparison between five AI-based models to classify PV solar cells according to their state, using EL images at the PV solar cell level, while the cell I-V curves are used in the training phase to be able to classify the cells based on its production efficiency. This automatic classification of defective cells enormously facilitates the identification of defects for PV plant operators, decreasing the human labor and optimizing the defect location. In addition, this work presents a methodology for the selection of important variables for the training of a defective cell classifier.

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Online Distributed Measurement of Dark I-V Curves in Photovoltaic Plants

Cited by 8 publications

References 20 publications

Semitransparent organic solar cells based on low temperature processed PEIE as electron transport layer with enhanced charge transfer ability

Semitransparent organic solar cells based on low temperature processed PEIE as electron transport layer with enhanced charge transfer ability

Alternative preconditioning by utilization of a thin film module's dark diode fingerprint

Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells

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