-"Luminoscopy"-Novel Tool for the Diagnosis of Crystalline Silicon solar cells and Modules Utilizing Electroluminescence

Takahashi, Yasuhiro; Kaji, Y.; Ogane, Akiyoshi; Uraoka, Yukiharu; Fuyuki, Takashi

doi:10.1109/wcpec.2006.279607

Cited by 11 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Detailed descriptions of the physical principles underlying the camera-based DLIT and EL techniques can be found elsewhere. [44][45][46][47][48] Transmission electron microscopy ͑TEM͒ images were taken with a Philips CM 20 TWIN TEM, which is equipped with an energy dispersive X-ray ͑EDX͒ analysis system by Noran Instruments. The samples under test are 125ϫ 125 mm 2 alkalinetextured industrial mc-Si solar cells.…”

Section: A Measurement Details and Setupmentioning

confidence: 99%

Luminescence emission from forward- and reverse-biased multicrystalline silicon solar cells

Bothe

Ramspeck

Hinken

et al. 2009

Journal of Applied Physics

View full text Add to dashboard Cite

We study the emission of light from industrial multicrystalline silicon solar cells under forward and reverse biases. Camera-based luminescence imaging techniques and dark lock-in thermography are used to gain information about the spatial distribution and the energy dissipation at pre-breakdown sites frequently found in multicrystalline silicon solar cells. The pre-breakdown occurs at specific sites and is associated with an increase in temperature and the emission of visible light under reverse bias. Moreover, additional light emission is found in some regions in the subband-gap range between 1400 and 1700 nm under forward bias. Investigations of multicrystalline silicon solar cells with different interstitial oxygen concentrations and with an electron microscopic analysis suggest that the local light emission in these areas is directly related to clusters of oxygen.

show abstract

Section: A Measurement Details and Setupmentioning

confidence: 99%

Luminescence emission from forward- and reverse-biased multicrystalline silicon solar cells

Bothe

Ramspeck

Hinken

et al. 2009

Journal of Applied Physics

View full text Add to dashboard Cite

show abstract

“…The current issue and full text archive of this journal is available at www.emeraldinsight.com/0260-2288.htm responses. The most common crack inspection methods include dye inspection, eddy current inspection (Zenzinger et al, 2007), acoustic inspection (Hilmersson et al, 2007;Yagi et al, 2007), ultrasonic inspection (Reber and Beller, 2003), radiant heat thermography (RHT) (Pilla et al, 2002;Devitt et al, 1992), scanning acoustic microscopy (SAM) (Knauss et al, 1995;Connor et al, 1998), photoluminescence (PL) (Trupke et al, 2006a, b), electroluminescence (EL) (Takahashi et al, 2006;Dreckschmidt et al, 2007), and resonance ultrasonic vibration (RUV) (Polupan and Ostapenko, 2006;Dallas et al, 2008). It is easy to detect surface cracks by applying deep color dye to the surface of a solar wafer.…”

Section: Crack Inspection Methodsmentioning

confidence: 99%

Micro crack detection of multi‐crystalline silicon solar wafer using machine vision techniques

2011

View full text Add to dashboard Cite

Purpose -The detection of invisible micro cracks (m-cracks) in multi-crystalline silicon (mc-si) solar wafers is difficult because of the wafers' heterogeneously textured backgrounds. The difficulty is twofold. First, invisible m-cracks must be visualized to imaging devices. Second, an image processing sequence capable of extracting m-cracks from the captured images must be developed. The purpose of this paper is to reveal invisible m-cracks that lie beneath the surface of mc-si solar wafers. Design/methodology/approach -To solve the problems, the authors first set up a near infrared (NIR) imaging system to capture images of interior m-cracks. After being able to see the invisible m-cracks, a region-growing flaw detection algorithm was then developed to extract m-cracks from the captured images. Findings -The experimental results showed that the proposed m-cracks inspection system is effective in detecting m-cracks. In addition, the system can also be used for the inspection of silicon solar wafers for stain, pinhole, inclusion and macro cracks. The overall accuracy of the defect detection system is 99.85 percent. Research limitations/implications -At present, the developed prototype system can detect m-crack down to 13.4 mm. The inspection resolution is high but the speed is low. However, the limitation on inspection speed can easily be lifted by choosing a higher resolution NIR camera. Practical implications -Generally, this paper is a great reference for researchers who are interested in developing automatic optical inspection systems for inspecting solar wafer for invisible m-cracks. Originality/value -The research described in this paper makes a step toward developing an effective while low-cost approach for revealing invisible m-crack of mc-si solar wafers. The advantages provided by the proposed system include excellent crack detection sensitivity, capability of detecting hidden subsurface m-cracks, and low cost.

show abstract

“…Injection electroluminescence is commonly used in the photovoltaic industry. This method was first proposed by Y. Takahashi 12 . The specific step is to apply a forward bias to the solar cell.…”

Section: Defect and Data Setmentioning

confidence: 99%

“…This method was first proposed by Y. Takahashi. 12 The specific step is to apply a forward bias to the solar cell. The forward current will inject many unbalanced carriers into the solar cell, which will produce injection electroluminescence.…”

Section: Defect and Data Setmentioning

confidence: 99%

Defect object detection algorithm for electroluminescence image defects of photovoltaic modules based on deep learning

Meng

Wang

et al. 2022

Energy Science & Engineering

View full text Add to dashboard Cite

Visual inspection of photovoltaic modules using electroluminescence (EL) images is a common method of quality inspection. Because human inspection requires a lot of time, object detection algorithm to replace human inspection is a popular research direction in recent years. To solve the problem of low accuracy and slow speed in EL image detection, we propose a YOLO‐based object detection algorithm YOLO‐PV, which achieves 94.55% of AP (average precision) on the photovoltaic module EL image data set, and the interference speed exceeds 35 fps. The improvement of speed and accuracy benefits from the targeted design of the network architecture according to the characteristics of EL image. First, we weaken the backbone's ability to extract deep‐level information so that it can focus on extracting the low‐level defect information. Second, the PAN network is used for feature fusion in the Neck part. But, only the single‐size feature map output is retained, which significantly reduces the amount of calculation. Also, we analyze the impact of data enhancement methods on model overfitting and performance. Finally, we give effective data enhancement methods. The results show that the object detection algorithm in this paper can meet the requirements for high‐precision and real‐time processing on the PV module production line.

show abstract

-"Luminoscopy"-Novel Tool for the Diagnosis of Crystalline Silicon solar cells and Modules Utilizing Electroluminescence

Cited by 11 publications

References 1 publication

Luminescence emission from forward- and reverse-biased multicrystalline silicon solar cells

Luminescence emission from forward- and reverse-biased multicrystalline silicon solar cells

Micro crack detection of multi‐crystalline silicon solar wafer using machine vision techniques

Defect object detection algorithm for electroluminescence image defects of photovoltaic modules based on deep learning

Contact Info

Product

Resources

About