Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules

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dc.contributor.authorOtamendi, Urtzi
dc.contributor.authorMartinez, Iñigo
dc.contributor.authorQuartulli, Marco
dc.contributor.authorOlaizola, Igor G.
dc.contributor.authorViles, Elisabeth
dc.contributor.authorCambarau, Werther
dc.contributor.institutionSISTEMAS FOTOVOLTAICOS
dc.date.issued2021-05-15
dc.descriptionPublisher Copyright: © 2021 International Solar Energy Society
dc.description.abstractIn the operation & maintenance (O&M) of photovoltaic (PV) plants, the early identification of failures has become crucial to maintain productivity and prolong components’ life. Of all defects, cell-level anomalies can lead to serious failures and may affect surrounding PV modules in the long run. These fine defects are usually captured with high spatial resolution electroluminescence (EL) imaging. The difficulty of acquiring such images has limited the availability of data. For this work, multiple data resources and augmentation techniques have been used to surpass this limitation. Current state-of-the-art detection methods extract barely low-level information from individual PV cell images, and their performance is conditioned by the available training data. In this article, we propose an end-to-end deep learning pipeline that detects, locates and segments cell-level anomalies from entire photovoltaic modules via EL images. The proposed modular pipeline combines three deep learning techniques: 1. object detection (modified Faster-RNN), 2. image classification (EfficientNet) and 3. weakly supervised segmentation (autoencoder). The modular nature of the pipeline allows to upgrade the deep learning models to the further improvements in the state-of-the-art and also extend the pipeline towards new functionalities.en
dc.description.sponsorshipThis publication resulted (in part) from the PROMISE (Advances in Photovoltaic Solar Energy Operation and Maintenance Research) project (KK2019/00088), which is financed by the ELKARTEK program of the Basque Government and is a collaborative project between Tecnalia, Tekniker, Vicomtech, UPV-ISG, UPV-TIM and Koniker. W. Cambarau would like to acknowledge I.Aizpurua, J.M. Calama and I.Arrizabalaga for contributing to the generation of the TecnaliaPR dataset.
dc.description.statusPeer reviewed
dc.format.extent13
dc.identifier.citationOtamendi , U , Martinez , I , Quartulli , M , Olaizola , I G , Viles , E & Cambarau , W 2021 , ' Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules ' , Solar Energy , vol. 220 , pp. 914-926 . https://doi.org/10.1016/j.solener.2021.03.058
dc.identifier.doi10.1016/j.solener.2021.03.058
dc.identifier.issn0038-092X
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85104444224&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofSolar Energy
dc.relation.projectIDUPV-TIM
dc.relation.projectIDEusko Jaurlaritza
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsAnomaly detection
dc.subject.keywordsDeep autoencoder
dc.subject.keywordsDeep learning
dc.subject.keywordsElectroluminescence images
dc.subject.keywordsPhotovoltaic modules
dc.subject.keywordsWeakly supervised segmentation
dc.subject.keywordsRenewable Energy, Sustainability and the Environment
dc.subject.keywordsGeneral Materials Science
dc.subject.keywordsSDG 7 - Affordable and Clean Energy
dc.titleSegmentation of cell-level anomalies in electroluminescence images of photovoltaic modulesen
dc.typejournal article
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