Automatic solar cell diagnosis and treatment

UDC.coleccionInvestigaciónes_ES
UDC.departamentoCiencias da Computación e Tecnoloxías da Informaciónes_ES
UDC.endPage1172es_ES
UDC.grupoInvRedes de Neuronas Artificiais e Sistemas Adaptativos -Informática Médica e Diagnóstico Radiolóxico (RNASA - IMEDIR)es_ES
UDC.issue4es_ES
UDC.journalTitleJournal of Intelligent Manufacturinges_ES
UDC.startPage1163es_ES
UDC.volume32es_ES
dc.contributor.authorRodríguez, Álvaro
dc.contributor.authorGonzález-Val, Carlos
dc.contributor.authorFernández, Andrés
dc.contributor.authorRodríguez, Francisco
dc.contributor.authorDelgado, Tamara
dc.contributor.authorBellman, Martin
dc.date.accessioned2023-11-28T09:32:41Z
dc.date.available2023-11-28T09:32:41Z
dc.date.issued2021-04
dc.description.abstract[Abstract]: Solar cells represent one of the most important sources of clean energy in modern societies. Solar cell manufacturing is a delicate process that often introduces defects that reduce cell efficiency or compromise durability. Current inspection systems detect and discard faulty cells, wasting a significant percentage of resources. We introduce Cell Doctor, a new inspection system that uses state of the art techniques to locate and classify defects in solar cells and performs a diagnostic and treatment process to isolate or eliminate the defects. Cell Doctor uses a fully automatic process that can be included in a manufacturing line. Incoming solar cells are first moved with a robotic arm to an Electroluminescence diagnostic station, where they are imaged and analysed with a set of Gabor filters, a Principal Component Analysis technique, a Random Forest classifier and different image processing techniques to detect possible defects in the surface of the cell. After the diagnosis, a laser station performs an isolation or cutting process depending on the detected defects. In a final stage, the solar cells are characterised in terms of their I–V Curve and I–V Parameters, in a Solar Simulator station. We validated and tested Cell Doctor with a labelled dataset of images of monocrystalline silicon cells, obtaining an accuracy and recall above 90% for Cracks, Area Defects and Finger interruptions; and precision values of 77% for Finger Interruptions and above 90% for Cracks and Area Defects. Which allows Cell Doctor to diagnose and repair solar cells in an industrial environment in a fully automatic way.es_ES
dc.description.sponsorshipThis project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 679692.es_ES
dc.identifier.citationRodriguez, A., Gonzalez, C., Fernandez, A. et al. Automatic solar cell diagnosis and treatment. J Intell Manuf 32, 1163–1172 (2021). https://doi.org/10.1007/s10845-020-01642-6es_ES
dc.identifier.issn1572-8145
dc.identifier.urihttp://hdl.handle.net/2183/34352
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/679692es_ES
dc.relation.urihttps://doi.org/10.1007/s10845-020-01642-6es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectSolar cell manufacturinges_ES
dc.subjectAutomatic inspectiones_ES
dc.subjectDefect classificationes_ES
dc.subjectElectroluminescence imaginges_ES
dc.subjectRandom forestes_ES
dc.subjectRandom forestes_ES
dc.subjectGabor filterses_ES
dc.titleAutomatic solar cell diagnosis and treatmentes_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublication9512bc94-e8ae-428a-ac56-5768b866995f
relation.isAuthorOfPublication.latestForDiscovery9512bc94-e8ae-428a-ac56-5768b866995f

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