Simplified methods for evaluating the degradation of photovoltaic module and modeling considering partial shading

Hemza, Arar; Haouam, Abdeslam; Chenni, Rachid; Aoun, Nouar

doi:10.1016/j.measurement.2019.01.098

Cited by 22 publications

(9 citation statements)

References 29 publications

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“…Figure 14 shows I-V and P-V curves of the panel that has a hot spot and a panel without faults as a comparison. It is observed that the voltage of the panel with failure falls at around 16Vdc, showing a typical behavior of a damaged or "shaded" cell [14][15][16][17] which matches the thermography image. Reviewing the P-V curve of the panels, the decrease in the maximum power point (MPP) is observed, power decreases from 94.16W on the panel without anomalies (see Figure 14a), to 69.97W on the panel with anomalies (with a hotspot, see Figure 14b) obtaining a 25.69% less power in the panel with the hot spot compared to a panel without failures.…”

Section: I-v/p-v Curves Analysis As First Validationmentioning

confidence: 71%

Low-cost infrared thermography in aid of photovoltaic panels degradation research

Dávila-Sacoto

Hernández-Callejo

Alonso‐Gómez

et al. 2020

redin

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The analysis of PV solar panels deterioration allows researchers to know the health status of a panel in order to determine the overall functioning of a PV solar farm. A part of this analysis is performed by thermography, generally using professional and expensive equipment. This article presents a validation for the use of low-cost thermal imaging cameras, reviewing the relative error that can be obtained through scattering, contour analysis and three-dimensional meshes. The procedure is validated by analysis of I-V/P-V curves and a temperature sensor matrix, reaching errors less than 10% with cameras with less than 500USD.

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Section: I-v/p-v Curves Analysis As First Validationmentioning

confidence: 71%

Low-cost infrared thermography in aid of photovoltaic panels degradation research

Dávila-Sacoto

Hernández-Callejo

Alonso‐Gómez

et al. 2020

redin

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“…Visual inspections present the swiftest and the first option of identifying PV modules degradation modes and signatures that are visible to human eyes [78,79,106,107]. Visual inspection of PV modules is typically performed before and after the modules have been subjected to environmental, electrical, or various laboratory stress tests.…”

Section: Visual Inspectionmentioning

confidence: 99%

Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review

2022

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The current geometric increase in the global deployment of solar photovoltaic (PV) modules, both at utility-scale and residential roof-top systems, is majorly attributed to its affordability, scalability, long-term warranty and, most importantly, the continuous reduction in the levelized cost of electricity (LCOE) of solar PV in numerous countries. In addition, PV deployment is expected to continue this growth trend as energy portfolio globally shifts towards cleaner energy technologies. However, irrespective of the PV module type/material and component technology, the modules are exposed to a wide range of environmental conditions during outdoor deployment. Oftentimes, these environmental conditions are extreme for the modules and subject them to harsh chemical, photo-chemical and thermo-mechanical stress. Asides from manufacturing defects, these conditions contribute immensely to PV module’s aging rate, defects and degradation. Therefore, in recent times, there has been various investigations into PV reliability and degradation mechanisms. These studies do not only provide insight on how PV module’s performance degrades over time, but more importantly, they serve as meaningful input information for future developments in PV technologies, as well as performance prediction for better financial modelling. In view of this, prompt and efficient detection and classification of degradation modes and mechanisms due to manufacturing imperfections and field conditions are of great importance towards minimizing potential failure and associated risks. In the literature, several methods, ranging from visual inspection, electrical parameter measurements (EPM), imaging methods, and most recently data-driven techniques have been proposed and utilized to measure or characterize PV module degradation signatures and mechanisms/pathways. In this paper, we present a critical review of recent studies whereby solar PV systems performance reliability and degradation were analyzed. The aim is to make cogent contributions to the state-of-the-art, identify various critical issues and propose thoughtful ideas for future studies particularly in the area of data-driven analytics. In contrast with statistical and visual inspection approaches that tend to be time consuming and require huge human expertise, data-driven analytic methods including machine learning (ML) and deep learning (DL) models have impressive computational capacities to process voluminous data, with vast features, with reduced computation time. Thus, they can be deployed for assessing module performance in laboratories, manufacturing, and field deployments. With the huge size of PV modules’ installations especially in utility scale systems, coupled with the voluminous datasets generated in terms of EPM and imaging data features, ML and DL can learn irregular patterns and make conclusions in the prediction, diagnosis and classification of PV degradation signatures, with reduced computation time. Analysis and comparison of different models proposed for solar PV degradation are critically reviewed, in terms of the methodologies, characterization techniques, datasets, feature extraction mechanisms, accelerated testing procedures and classification procedures. Finally, we briefly highlight research gaps and summarize some recommendations for the future studies.

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“…and 𝑉 𝑡ℎ = 𝑘 𝑇 𝑞 is the thermal voltage of the module, α0 temperature correction coefficient for current (°C -1 ), β0 temperature correction coefficient for voltage (°C -1 ), I0 reverse saturation current (A), n1 first diode ideality factor, and RS series resistance (Ω). So, the current supplied by the solar cell in 1 diode model is given by the following relation [13].…”

Section: Solar Cell Mathematical Modelmentioning

confidence: 99%

Benchmarking study between capacitive and electronic load technic to track I-V and P-V of a solar panel

Asbayou

Aamoume

Elyaqouti

et al. 2022

IJECE

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<p>To detect defects of solar panel and understand the effect of external parameters such as fluctuations in illumination, temperature, and the effect of a type of dust on a photovoltaic (PV) panel, it is essential to plot the Ipv=f(Vpv) characteristic of the PV panel, and the simplest way to plot this I-V characteristic is to use a variable resistor. This paper presents a study of comparison and combination between two methods: capacitive and electronic loading to track I-V characteristic. The comparison was performed in terms of accuracy, response time and instrumentation cost used in each circuit, under standard temperature and illumination conditions by using polycrystalline solar panel type SX330J and monocrystalline solar panels type ET-M53630. The whole system is based on simple components, less expensive and especially widely used in laboratories. The results will be between the datasheet of the manufacturer with the experimental data, refinements and improvements concerning the number of points and the trace time have been made by combining these two methods.</p>

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Simplified methods for evaluating the degradation of photovoltaic module and modeling considering partial shading

Cited by 22 publications

References 29 publications

Low-cost infrared thermography in aid of photovoltaic panels degradation research

Low-cost infrared thermography in aid of photovoltaic panels degradation research

Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review

Benchmarking study between capacitive and electronic load technic to track I-V and P-V of a solar panel

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