Analysis of different microcracks shapes and the effect of each shape on performance of PV modules

Bdour, Mathhar; Al-Sadi, A M Ginan

doi:10.1088/1757-899x/876/1/012005

Cited by 3 publications

(7 citation statements)

References 3 publications

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“…This is also evident in Figure 7. In general, vertical and multiple orientation microcracks have been deemed by the literature as the most critical types causing significant power output drops [54,55].…”

Section: Literature On the Performance Modelling Of Pv Systemsmentioning

confidence: 99%

A Review of Models for Photovoltaic Crack and Hotspot Prediction

2022

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The accurate prediction of the performance output of photovoltaic (PV) installations is becoming ever more prominent. Its success can provide a considerable economic benefit, which can be adopted in maintenance, installation, and when calculating levelized cost. However, modelling the long-term performance output of PV modules is quite complex, particularly because multiple factors are involved. This article investigates the available literature relevant to the modelling of PV module performance drop and failure. A particular focus is placed on cracks and hotspots, as these are deemed to be the most influential. Thus, the key aspects affecting the accuracy of performance simulations were identified and the perceived relevant gaps in the literature were outlined. One of the findings demonstrates that microcrack position, orientation, and the severity of a microcrack determines its impact on the PV cell’s performance. Therefore, this aspect needs to be categorized and considered accordingly, for achieving accurate predictions. Additionally, it has been identified that physical modelling of microcracks is currently a considerable challenge that can provide beneficial results if executed appropriately. As a result, suggestions have been made towards achieving this, through the use of methods and software such as XFEM and Griddler.

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Section: Literature On the Performance Modelling Of Pv Systemsmentioning

confidence: 99%

A Review of Models for Photovoltaic Crack and Hotspot Prediction

2022

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“…The impact of those cracks depends on their orientation and the shape which is created by the cause type; see Figure 1 [14]. [15]. Figure 1 shows an example of µ cracks that is classified as major, since the damage reaches 20% of the module.…”

Section: Micro-cracksmentioning

confidence: 99%

“…However, if the mechanical loading Figure 1 shows an example of µcracks that is classified as major, since the damage reaches 20% of the module. Moreover, µcracks can affect the power drop up to 3.2%, this percentage affects the overall produced power from economical and energy yield for large projects, taking into consideration that PV projects are long life projects [15]. Interestingly, µcracks' orientation plays an important role in power degradation, for instance, vertical µcracks are crucial, as well as dendritic ones and multiple orientations.…”

Section: Micro-cracksmentioning

confidence: 99%

“…Figure 13 describes the classifications of µ cracks influence on the severity of output power yield. This conclusion is very important in order to reject the major affected modules or replace them, in addition to monitoring others in case their effect is converted to major [15]. The sample in Figure 12 shows different locations of vertical µcracks, but the length of each one is small, except in one cell, which is located in the third column from the right; it looks larger than the others and cuts three bus bars.…”

Section: Group 4: Mono-crystalline 5bbmentioning

confidence: 99%

Section: Group 4: Mono-crystalline 5bbmentioning

confidence: 99%

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A Comprehensive Evaluation on Types of Microcracks and Possible Effects on Power Degradation in Photovoltaic Solar Panels

et al. 2020

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Photovoltaic (PV) panels installation has become one of the major technologies used for energy production worldwide. Knowledge and competitive prices are the main reasons for the spread usage and expanded exploiting of PV systems. Accordingly, this creates several challenges for manufacturers and customers, mainly, the quality of PV panels to withstand environmental conditions during service lifetime. Hence, the quality of PV panels is a vital aspect. By thinking of PV power plants, it appears that some factors should be considered, like the developing microcracks (µcracks). An issue like that increases the chances of having power loss during the operation phase. Notably, µcracks develop in different shapes and orientations; the variation depends on what causes them. This study is a presentation and summary of data collected from different projects in Jordan to describe the effect of each µcracks shape on power loss, aiming to give decision makers an indication to decide whether to replace the faulty panels or not, depending on their own conditions and projects sizes. Hence, in this study, it was found that the µcracks have impacted power loss differently and recorded power reduction of percentages of 0.82–3.21% for poly-crystalline technology. Variation in power degradation depends on the module situation; whether it is stocked in facility or operated on-site. In the mono-crystalline technology case, the power losses varied between 0.55% and 0.9%, with the exception of some samples from both technologies that have effects other than microcracks, which affected power severely. Furthermore, a general overview is provided for µcracks before installation.

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Application of Machine Learning to Predict I-V Characteristics of PV Modules Based on Steady-State Solar Simulator

Porowski,

Kowalik,

Gorzelnik

et al. 2024

Preprint

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Photovoltaic modules should pass series of tests and examinations, verifying the electrical and thermal characteristics of the module, before being released to the market, examining the properties of the modules should undergo appropriate durability and efficiency tests under real outdoor conditions with exposure to climatic conditions and under laboratory conditions. The performance of photovoltaic cells depends on many factors, such as solar irradiance, module operating temperature, installation location, weather conditions and module shading. In this paper, three selected photovoltaic modules were examined using a large-scale steady-state solar simulator. The current-voltage (I-V) characteristics of the photovoltaic modules were experimentally tested and analyzed. The next step was to implement a three-layer artificial neural network model (MLP). The experimental data obtained in the previous step coupled with output data from MLP were then used in global sensitivity analysis (GSA). Experiments carried out on a large-scale stationary solar simulator showed differences between the values declared by the manufacturer and the values obtained from measurements of PV modules. The first module tested achieved the maximum power point greater than that specified by the manufacturer, while the other two showed power drops; it was 85-87% for the second module, and 95-98% for the third, respectively. The performed global sensitivity analysis (GSA) for the MLP model showed that the parameters: eff (22.9) and Voc/V (14.19) have the largest effect on the power-voltage relationship, while U (7.29) has the smallest effect. The usefulness of machine learning (ML) methods in the comparative analysis of PV modules has been proved.

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Analysis of different microcracks shapes and the effect of each shape on performance of PV modules

Cited by 3 publications

References 3 publications

A Review of Models for Photovoltaic Crack and Hotspot Prediction

A Review of Models for Photovoltaic Crack and Hotspot Prediction

A Comprehensive Evaluation on Types of Microcracks and Possible Effects on Power Degradation in Photovoltaic Solar Panels

Application of Machine Learning to Predict I-V Characteristics of PV Modules Based on Steady-State Solar Simulator

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