Detection of Typical Defects in Silicon Photovoltaic Modules and Application for Plants with Distributed MPPT Configuration

Ahmad, Jawad; Ciocia, Alessandro; Fichera, Stefania; Murtaza, Ali Faisal; Spertino, Filippo

doi:10.3390/en12234547

Cited by 24 publications

(9 citation statements)

References 74 publications

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“…Specifically, the inclusion of δ I and F S improved the accuracy of the 1D CNNs by 30.9% and reduced the associated standard deviation by 26.6% compared to the 1D CNN with only the I CS and I F S parameters ( Table 3). The F S was constructed to capture changes in slope values near the I sc (an important diagnostic tool [8]) while the δ I was constructed based on knowledge of mismatch profiles in IV curves. These results show that calculating parameters based on domain knowledge allows for more stable and accurate results.…”

Section: Resultsmentioning

confidence: 99%

“…After processing the data, additional parameters were calculated to explicitly capture the mismatch of profiles between the CS and FS IV curves and through associated first order differences (i.e., between consecutive observations) ( Table 2). For example, the F S parameter, which evaluates consecutive, pairwise differences in I F S , was inspired by a calculation of shunt resistance R sh , an important diagnostic feature [8].…”

Section: B Data Filtering and Processingmentioning

confidence: 99%

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Neural Network-Based Classification of String-Level IV Curves From Physically-Induced Failures of Photovoltaic Modules

et al. 2020

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Accurate diagnosis of failures is critical for meeting photovoltaic (PV) performance objectives and avoiding safety concerns. This analysis focuses on the classification of field-collected string-level current-voltage (IV) curves representing baseline, partial soiling, and cracked failure modes. Specifically, multiple neural network-based architectures (including convolutional and long short-term memory) are evaluated using domain-informed parameters across different portions of the IV curve and a range of irradiance thresholds. The analysis identified two models that were able to accurately classify the relatively small dataset (~400 samples) at a high accuracy (99%+). Findings also indicate optimal irradiance thresholds and opportunities for improvements in classification activities by focusing on portions of the IV curve. Such advancements are critical for expanding accurate classification of PV faults, especially for those with low power loss (e.g., cracked cells) or visibly similar IV curve profiles.

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Section: Resultsmentioning

confidence: 99%

Section: B Data Filtering and Processingmentioning

confidence: 99%

Neural Network-Based Classification of String-Level IV Curves From Physically-Induced Failures of Photovoltaic Modules

et al. 2020

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“…Reversible up to 85% loss [74] PID-s Degradation < 40%: Increase R s , R sh and FF decreases, I sc and V oc not significantly affected. [73,74] Degradation > 40%: Increase R s , R sh and FF decreases, I sc and V o c drop significantly [73,74] Normally up to 30% [74] 50% power lossunder front side illumination 68% power lossunder rear side illumination [74] Indoors: Increase module's temperature [74,79] Reverse bias the cell compared to the grounded frame [74] Increasing refractive index of anti-reflective coat [83] Use of Borosilicate glass or POE or TPO encapsulants [64] PID-p Drop I sc and V oc but very low effect on FF. [79] Table 4.…”

Section: Discussionmentioning

confidence: 99%

“…The presence of micro fissures on the cells can cause a reduction of the short circuit current and shunt resistance. When the cracking of the cells is not uniform throughout the module, the I-V curve is commonly stepped [73]. Strategies to overcome and reduce the occurrence of microcracks on the solar cell have been studied at the cell, module, and system levels.…”

Section: Microcracksmentioning

confidence: 99%

Review on the Sources of Power Loss in Monofacial and Bifacial Photovoltaic Technologies

2021

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An evaluation of the degradation effects on photovoltaic modules is essential to minimise uncertainties in the system operation. Bifacial photovoltaic technology is attracting attention due to the capacity of generating energy from the front and rear sides. This paper presents a review of degradation factors, for both conventional monofacial and bifacial photovoltaic modules, to highlight how the current and voltage characteristics of these technologies are affected by degradation. Microcracking, encapsulant discoloration, and light induced degradation seem to have similar effects on both modules. Contrarily, bifacial modules are more prone to potential induced degradation as the electromagnetic shielding is affected by the bifaciality. Bifacial devices are less affected by light and elevated temperature induced degradation. The degradation (1.3%) is similar for both technologies, up to 40 kWh/m2 of solar radiation. Above this value, monofacial degradation increases faster, reaching values of 7%. For tilted systems, the front side soiling degradation of 0.30% per day is similar for both technologies. For vertical systems, soiling loss for bifacial is considerably lower with values of 0.02% per day.

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“…For each cell, an average (spatial) temperature was calculated in order to evaluate the electrical efficiency by using the Shockley one-diode model, in which it depends on the solar irradiance G and the ambient temperature T a . The current I and the voltage V are related by the following equation [28]:…”

Section: The Pvfc Module: Structural Thermal and Electrical Modelmentioning

confidence: 99%

Theoretical and Numerical Study of a Photovoltaic System with Active Fluid Cooling by a Fully-Coupled 3D Thermal and Electric Model

et al. 2020

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The paper deals with the three-dimensional theoretical and numerical investigation of the electrical performance of a Photovoltaic System (PV) with active fluid cooling (PVFC) in order to increase its efficiency in converting solar radiation into electricity. The paper represents a refinement of a previous study by the authors in which a one-dimensional theoretical model was presented to evaluate the best compromise, in terms of fluid flow rate, of net power gain in a cooled PV system. The PV system includes 20 modules cooled by a fluid circulating on the bottom, the piping network, and the circulating pump. The fully coupled thermal and electrical model was developed in a three-dimensional geometry and the results were discussed with respect to the one-dimensional approximation and to experimental tests. Numerical simulations show that a competitive mechanism between the power gain due to the cell temperature reduction and the power consumption of the pump exists, and that a best compromise, in terms of fluid flow rate, can be found. The optimum flow rate can be automatically calculated by using a semi-analytical approach in which irradiance and ambient temperature of the site are known and the piping network losses are fully characterized.

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Detection of Typical Defects in Silicon Photovoltaic Modules and Application for Plants with Distributed MPPT Configuration

Cited by 24 publications

References 74 publications

Neural Network-Based Classification of String-Level IV Curves From Physically-Induced Failures of Photovoltaic Modules

Neural Network-Based Classification of String-Level IV Curves From Physically-Induced Failures of Photovoltaic Modules

Review on the Sources of Power Loss in Monofacial and Bifacial Photovoltaic Technologies

Theoretical and Numerical Study of a Photovoltaic System with Active Fluid Cooling by a Fully-Coupled 3D Thermal and Electric Model

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