Snail Trails and Cell Microcrack Impact on PV Module Maximum Power and Energy Production

Dolara, Alberto; Lazaroiu, George Cristian; Leva, Sonia; Manzolini, Giampaolo; Votta, Luca

doi:10.1109/jphotov.2016.2576682

Cited by 82 publications

(27 citation statements)

References 12 publications

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“…In 2016, an interesting article done by D. Alberto et al [17] which is one of the highly cited articles in the field of PV micro cracks detection and analysis, proposed a suitable detection method to analyse the PV micro cracks and its output power degradation on PV modules. The technique uses EL imaging to inspect micro cracks, however, authors tried to improve the detection accuracy using a thermal imaging for the cracked PV modules.…”

Section: Discussionmentioning

confidence: 99%

“…8. Output images of the detection technique proposed by [17] Therefore, the proposed technique in [17] does not necessary reflect to the PV micro cracks, in which we have tried to solve this problem through the proposed detection technique discussed in section III and IV, where the original EL image is captured and analysed with crack free solar cell sample, then bit-by-bit structure is obtained for both images, whereas the final stage is to process the ORing between the bits from each captured image. This technique ensures that no additional cost is required in the micro crack detection process, and it improves the output image quality, thus it is easier to identify the PV solar cells micro crack size, location and orientation.…”

Section: Discussionmentioning

confidence: 99%

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Novel Photovoltaic Micro Crack Detection Technique

Dhimish

Holmes

Mather

2019

IEEE Trans. Device Mater. Relib.

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This paper presents a novel detection technique for inspecting solar cells micro cracks. Initially, the solar cell is captured using Electroluminescence (EL) method, then processed by the proposed technique. The technique consist of three stages, the first stage combines two images, the first image is the crackfree (healthy) solar cell, whereas the second is the cracked solar cell image. Both output images processed into a bit-by-bit gridding technique, which enables the detection of all bits in the considered area of the cracked solar cell. The second stage uses an OR gate between each of the examined bits for both healthy and cracked solar cells. The final calibrated image presents a high quality, and low noise structure, thus easier to identify the micro crack size, location and its orientation. In order to examine the effectiveness of the proposed technique, three different cracked PV solar cells have been examined. The results show that the micro cracks size, orientation, and location is more visible using the proposed technique. In addition, the developed technique has been validated using a full scale PV module, and compared with up to date available PV micro crack detection methods.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Novel Photovoltaic Micro Crack Detection Technique

Dhimish

Holmes

Mather

2019

IEEE Trans. Device Mater. Relib.

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“…As a result of these factors, there is a Pmax degradation of 22% per year. Another study [10] on PV modules with 20 years of age also found degradation factors as revealed [17].…”

Section: Introductionmentioning

confidence: 89%

“…Degradation due to temperature was quantified further in another study in Africa [16] which found that, in addition to dust, an Organic PV (OPV) system experienced a 20% decrease in performance due to temperature measured after the system operated for 2.5 years. Dolara [17] in his research in Italy investigating the degradation of PV modules found that the PV had snail trails, micro-cracks on PV cells. As a result of these factors, PV modules that have been installed for 3 years have decreased Pmax by 10% to 30% from initial capacity.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Snail Trails and Cracks to Energy Output of Photovoltaic Modules at Lotas Off-Grid PV System

Sinaga¹,

Tanesab²,

Beily³

2019

Proceedings of the Proceedings of the 1st International Conference on Engineering, Science, and Commerce, ICESC 2019, 18-19 Oct

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This paper analyzes the impact of the snail trails and cracks phenomena in 200 Wp photovoltaic (PV) modules to energy output after five years of operation. Data collection was carried out through string voltage measurement, visual inspection, and Real Operating Conditions (ROC) test for permanently degraded PV especially the type of snail trail and crack degradation conducted at the Lotas Off-Grid PV System, South Central Timor Indonesia. For this paper, we applied quantitative methods to analyze data. The result shows that the permanent degradation caused by the snail trail will experience a maximum power decrease of 67 Watt per m 2 , while permanent degradation caused by crack will experience a power decrease of 551 Watt/m 2 .

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“…This increasing attention is mainly due to the increasing shares of RES quota in power systems, which involve novel technical challenges for the efficiency of the electrical grid [3]. In particular, predictive tools based on historical data can generally provide advantages in PV plant operation [4,5], reduce excess production, and take advantage of incentives for RES production [6].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

et al. 2018

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Abstract:The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present a comparison of the artificial neural network's main characteristics used in a hybrid method, focusing in particular on the training approach. In particular, the influence of different data-set composition affecting the forecast outcome have been inspected by increasing the training dataset size and by varying the training and validation shares, in order to assess the most effective training method of this machine learning approach, based on commonly used and a newly-defined performance indexes for the prediction error. The results will be validated over a one-year time range of experimentally measured data. Novel error metrics are proposed and compared with traditional ones, showing the best approach for the different cases of either a newly deployed PV plant or an already-existing PV facility.

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Snail Trails and Cell Microcrack Impact on PV Module Maximum Power and Energy Production

Cited by 82 publications

References 12 publications

Novel Photovoltaic Micro Crack Detection Technique

Novel Photovoltaic Micro Crack Detection Technique

The Impact of Snail Trails and Cracks to Energy Output of Photovoltaic Modules at Lotas Off-Grid PV System

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

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