Fast and robust detection of solar modules in electroluminescence images

Fast, non-destructive and on-site quality control tools, mainly high sensitive imaging techniques, are important to assess the reliability of photovoltaic plants. To minimize the risk of further damages and electrical yield losses, electroluminescence (EL) imaging is used to detect local defects in an early stage, which might cause future electric losses. For an automated defect recognition on EL measurements, a robust detection and rectification of modules, as well as an optional segmentation into cells is required. This paper introduces a method to detect solar modules and crossing points between solar cells in EL images. We only require 1-D image statistics for the detection, resulting in an approach that is computationally efficient. In addition, the method is able to detect the modules under perspective distortion and in scenarios, where multiple modules are visible in the image. We compare our method to the state of the art and show that it is superior in presence of perspective distortion while the performance on images, where the module is roughly coplanar to the detector, is similar to the reference method. Finally, we show that we greatly improve in terms of computational time in comparison to the reference method

Segmentation of Photovoltaic Module Cells in Electroluminescence Images

High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kinds of defects, which is time-consuming and expensive. Automated segmentation of cells is therefore a key step in automating the visual inspection workflow. In this work, we propose a robust automated segmentation method for extraction of individual solar cells from EL images of PV modules. This enables controlled studies on large amounts of data to understanding the effects of module degradation over time-a process not yet fully understood. The proposed method infers in several steps a high-level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 94.47% and an $F_1$ score of 97.54%, both indicating a very high similarity between automatically segmented and ground truth solar cell masks

Cross-characterization for imaging parasitic resistive losses in thin-film photovoltaic modules

Thin-film photovoltaic (PV) modules often suffer from a variety of parasitic resistive losses in transparent conductive oxide (TCO) and absorber layers that significantly affect the module electrical performance. This paper presents the holistic investigation of resistive effects due to TCO lateral sheet resistance and shunts in amorphous-silicon (a-Si) thin-film PV modules by simultaneous use of three different imaging techniques, electroluminescence (EL), lock-in thermography (LIT) and light beam induced current (LBIC), under different operating conditions. Results from individual techniques have been compared and analyzed for particular type of loss channel, and combination of these techniques has been used to obtain more detailed information for the identification and classification of these loss channels. EL and LIT techniques imaged the TCO lateral resistive effects with different spatial sensitivity across the cell width. For quantification purpose, a distributed diode modeling and simulation approach has been exploited to estimate TCO sheet resistance from EL intensity pattern and effect of cell width on module efficiency. For shunt investigation, LIT provided better localization of severe shunts, while EL and LBIC given good localization of weak shunts formed by the scratches. The impact of shunts on the photocurrent generation capability of individual cells has been assessed by li-LBIC technique. Results show that the cross-characterization by different imaging techniques provides additional information, which aids in identifying the nature and severity of loss channels with more certainty, along with their relative advantages and limitations in particular cases

Mot monitorering av fotovoltaiske kraftverk med fotoluminescensavbildning

It is predicted that the photovoltaic energy conversion will be the largest installed power capacity by 2027. The least costly option for new electricity generation in many of the world’s countries will be the utility-scale solar photovoltaic electricity generation. Accurate monitoring of solar plants for localizing and detecting faults is expected to be one of the critical tasks facing the energy industry. Imaging of photovoltaic modules for the purpose of fault detection can be more efficient and accurate compared to measurements of electrical parameters. Different spectral regions provide different types of information about a faulty module. Detection of photoluminescence, that is, radiation emitted upon band-to-band recombination after charge carrier excitation with an illumination source, has shown a great potential in the laboratory setting. In the recent years, the first approaches in the outdoor setting have been conducted on silicon modules with the Sun as an excitation source. However, the reflected sunlight overlaps spectrally with the emitted photoluminescence. The imaging apparatus detects the total signal out of which only a few percent represent the emitted photoluminescence. Several approaches for elimination of more than 95% of the total signal have been suggested in the recent years. They are either based on controlling the emission of photoluminescence during imaging to achieve a variation in signal strength and, thus, a separation from the reflected solar irradiance, or on filtering of reflected solar irradiance with specially designed, narrow band-pass filters. The former requires interfering with the production to modulate the operating point of the modules between two operating conditions. This has been implemented by using additional equipment connected physically to a certain number of modules it is dimensioned for and by moving it during imaging. We have tried to develop an approach for photoluminescence imaging which would enable imaging of as many modules as possible with as little interference as possible for an easier implementation on a utility-scale photovoltaic power plant. This has been done by using the capabilities of a string inverter to change the operating point of a string. The first approach is based on remote control of the operating point between two conditions. The second approach is far less invasive and takes the advantage of the string inverter’s built-in functionality to conduct current-voltage curve sweeps. Both approaches enable variation of the operating point on more than one string. The approach with current-voltage curve sweeps implies that a string undergoes an entire range of operating conditions, which results in a continuously changing photoluminescence signal. From such a data set one can obtain more information about modules’ defects than what is possible from an image set obtained during controlled modulation between two conditions. Therefore, it is more timeconsuming to process an image set acquired during a current-voltage curve sweep. We propose an alternative algorithm which performs better in case of unsupervised image processing in real time. This way of imaging and data processing is also applicable in irradiance conditions below 100 Wm−2. The mentioned aspects of our photoluminescence imaging approach and the novel algorithm make this technique promising for large-scale inspections.Ifølge prognoser vil fotovoltaisk energikonvertering vare den største installerte effektkapasiteten innen 2027. I mange land vil storskala solcelleanlegg være den rimeligste løsningen for ny energiproduksjon. Presis monitorering av fotovoltaiske kraftverk med mål om lokalisering og detektering av feil forventes å være én av energiindustriens kritiske oppgaver. Avbildning av fotovoltaiske moduler for å detektere feil kan vare mer effektivt og gi mer nøyaktige resultater enn målinger av elektriske parametere. Detektering av fotoluminescens med kamera, dvs. stråling avgitt fra halvledermaterialet silisium i forbindelse med bånd-til-bånd-rekombinasjon etter eksitasjon av elektroner med en lyskilde, har vist stort potensiale. De første forsøkene med sola som eksitasjonskilde har blitt gjennomført på silisium moduler i de siste årene. Det reflekterte sollyset i det samme bølgelengdeområdet som det fotoluminescerende signalet blir også detektert av kamerautstyret. Fotoluminescens utgjør kun noen få prosent av det totale signalet. Flere metoder for å skille fotoluminescens fra det reflekterte sollyset har blitt foreslått. De baserer seg enten på kontrollert emisjon av fotoluminescenssignalet i løpet av avbildningen for å oppnå en variasjon i signalet som skal gjøre det mulig å separere det fra det reflekterte sollyset, eller på detektering av kun fotoluminescens gjennom spesiallagde, smale bånd-pass filtre. Førstnevnte krever inngrep i modulenes strømproduksjon for å styre operasjonspunktet mellom to tilstander. Dette kan gjennomføres ved at tilleggsutstyr kobles på modulene og flyttes i løpet av avbildningen. Vi har jobbet med å utvikle en tilnærming for fotoluminescensavbildning av så mange moduler så mulig med så lite inngrep som mulig. Formålet har vært å utvikle en avbildningsmetode som vil kunne gjennomføres på storskala solcelleanlegg. Dette har blitt gjort ved å utnytte funksjonalitetene til en strenginverter. Den ene tilnærmingen har vært kontaktløs styring av operasjonspunktet gjennom strenginverteren og dermed uten tilleggsutstyr som må flyttes i løpet av avbildningen. En forbedring av denne metoden baserer seg på utnyttelse av strenginverterens innebygde egenskap til å skanne strøm-spenningsskarakteristikken til en gitt streng og er derfor betraktelig mindre inngripende. Begge tilnærmingene muliggjør en endring i operasjonspunktet på mer enn ´en streng av gangen. Tilnærmingen med skanningen av strøm-spenningsskarakteristikken innebærer at strengen(e) går gjennom en hel rekke av operasjonstilstander som resulterer i et fotoluminescenssignal i kontinuerlig endring. En slik bildeserie gir mer informasjon om modulenes defekter enn en bildeserie tatt i løpet av den kontrollerte styringen av operasjonspunktet mellom to tilstander. Det er derfor mer tidkrevende å prosessere en bildeserie samlet med den førstnevnte metoden. I den forbindelse foreslår vi en alternativ algoritme som gir bedre resultater med ikke-styrt bildebehandling i sanntid. Metoden er også anvendelig ved veldig lave irradiansnivåer, under 100 Wm−2. Metoden for fotoluminescensavbildning mens skanningen av strøm-spenningskarakteristikken pågår i kombinasjon med den nye algoritmen for bildebehandling er lovende for videre utvikling med hensyn på storskala avbildning.The Research Center for Sustainable Solar Cell Technolog

1st International round robin on EL imaging: automated camera calibration and image normalisation

Results from the first international Round Robin on electroluminescence (EL) imaging of PV devices are presented. 17 Laboratories across Europe, Asia and the US measured EL images of ten commercially available modules and five single-cell modules. This work presents a novel automated camera calibration and image scaling routine. Its performance is quantified through comparing intensity deviation of corrected images and their cell average. While manual calibration includes additional measurement of lens distortion and flat field, the automated calibration extracts camera calibration parameters (here: lens distortion, and vignetting) exclusively from EL images. Although it is shown that the presented automated calibration outperforms the manual one, the method proposed in this work uses both manual and automated calibration. 501 images from 24 cameras are corrected. Intensity deviation of cell averages of every measured device decreased from 10.3 % (results submitted by contributing labs) to 2.8 % (proposed method), For three images the image correction produced insufficient results and vignetting correction failed for one camera, known of having a non-linear camera sensor. Surprisingly, largest image quality improvements are achieved by spatially precise image alignment of the same device and not by correcting for vignetting and lens distortion. This is due to overall small lens distortion and the circumstance that, although vignetting caused intensity reduction of more than 50%, PV devices are generally positioned in the image centre in which vignetting distortion is lowest

Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network

Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, withnon-destructive inspection and traceability of 100 % of produced parts. Developing robust fault detection and classification modelsfrom the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patternsand the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting thesepeculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomalydetection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localizationof anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without anymanual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automaticallygenerated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types offaults. The experimental results using 1873 EL images of monocrystalline cells show that (a) the anomaly detection scheme can beused to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order totrain a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels arecomparable to the ones obtained from the models trained with manual labels.Comment: 20 pages, 10 figures, 6 tables. This article is part of the special issue "Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems" Published in MDPI - Sensors: see https://www.mdpi.com/journal/sensors/special_issues/Condition_Monitoring_Field_Inspection_and_Fault_Diagnostic_Methods_for_Photovoltaic_System

A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation

Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectiveness, convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells. However, the parameters of these CNN-based models are very large, which require stringent hardware resources and it is difficult to be applied in actual industrial projects. To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in electroluminescence(EL) images based on neural architecture search and knowledge distillation. To auto-design an effective lightweight model, we introduce neural architecture search to the field of PV cell defect classification for the first time. Since the defect can be any size, we design a proper search structure of network to better exploit the multi-scale characteristic. To improve the overall performance of the searched lightweight model, we further transfer the knowledge learned by the existing pre-trained large-scale model based on knowledge distillation. Different kinds of knowledge are exploited and transferred, including attention information, feature information, logit information and task-oriented information. Experiments have demonstrated that the proposed model achieves the state-of-the-art performance on the public PV cell dataset of EL images under online data augmentation with accuracy of 91.74% and the parameters of 1.85M. The proposed lightweight high-performance model can be easily deployed to the end devices of the actual industrial projects and retain the accuracy.Comment: 12 pages, 7 figure