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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26001
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dc.contributor.authorZgraggen, Jannik-
dc.contributor.authorGuo, Yuyan-
dc.contributor.authorNotaristefano, Antonio-
dc.contributor.authorGoren Huber, Lilach-
dc.date.accessioned2022-11-11T10:09:10Z-
dc.date.available2022-11-11T10:09:10Z-
dc.date.issued2022-10-28-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/26001-
dc.description.abstractOne of the main challenges for fault detection in commercial fleets of machines is the lack of annotated data from the faulty condition. The use of supervised algorithms for anomaly detection or fault diagnosis is often unrealistic in this case. One approach to overcome this challenge is to augment the available normal data by generating synthetic anomalous data that represents faulty conditions. In this paper we apply this approach to the detection of faults in the tracking system of solar panels in utility-scale photovoltaic (PV) power plants. We develop a physical model in order to augment the training data for a deep convolutional neural network. We show that the physics informed learning algorithm is capable of detecting faults in an accurate and robust manner under diverse weather conditions, outperforming a purely data-driven approach. Developing and testing the algorithm with real operational data ensures its efficient deployment for PV power plants that are monitored at string level. This in turn enables the early detection of root causes for power losses, thereby contributing to the accelerated adoption of solar energy at utility scale.de_CH
dc.language.isoende_CH
dc.publisherPHM Societyde_CH
dc.rightshttp://creativecommons.org/licenses/by/3.0/de_CH
dc.subjectPredictive maintenancede_CH
dc.subjectDeep learningde_CH
dc.subjectAnomalieerkennungde_CH
dc.subjectSolarkraftanlagede_CH
dc.subjectPhysics informed DLde_CH
dc.subjectCondition based maintenancede_CH
dc.subjectMachine learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc620: Ingenieurwesende_CH
dc.titlePhysics informed deep learning for tracker fault detection in photovoltaic power plantsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.36001/phmconf.2022.v14i1.3235de_CH
dc.identifier.doi10.21256/zhaw-26001-
zhaw.conference.details14th Annual Conference of the Prognostics and Health Management Society, Nashville, USA, 1-4 November 2022de_CH
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.parentwork.editorKulkarni, Chetan-
zhaw.parentwork.editorSaxena, Abhinav-
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume14de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the Annual Conference of the PHM Society 2022de_CH
zhaw.webfeedDatalabde_CH
zhaw.funding.zhawIntelligente Diagnostik von Leistungseinbussen in Solarkraftwerkende_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Zgraggen, J., Guo, Y., Notaristefano, A., & Goren Huber, L. (2022). Physics informed deep learning for tracker fault detection in photovoltaic power plants [Conference paper]. In C. Kulkarni & A. Saxena (Eds.), Proceedings of the Annual Conference of the PHM Society 2022 (Vol. 14, Issue 1). PHM Society. https://doi.org/10.36001/phmconf.2022.v14i1.3235
Zgraggen, J. et al. (2022) ‘Physics informed deep learning for tracker fault detection in photovoltaic power plants’, in C. Kulkarni and A. Saxena (eds) Proceedings of the Annual Conference of the PHM Society 2022. PHM Society. Available at: https://doi.org/10.36001/phmconf.2022.v14i1.3235.
J. Zgraggen, Y. Guo, A. Notaristefano, and L. Goren Huber, “Physics informed deep learning for tracker fault detection in photovoltaic power plants,” in Proceedings of the Annual Conference of the PHM Society 2022, Oct. 2022, vol. 14, no. 1. doi: 10.36001/phmconf.2022.v14i1.3235.
ZGRAGGEN, Jannik, Yuyan GUO, Antonio NOTARISTEFANO und Lilach GOREN HUBER, 2022. Physics informed deep learning for tracker fault detection in photovoltaic power plants. In: Chetan KULKARNI und Abhinav SAXENA (Hrsg.), Proceedings of the Annual Conference of the PHM Society 2022. Conference paper. PHM Society. 28 Oktober 2022
Zgraggen, Jannik, Yuyan Guo, Antonio Notaristefano, and Lilach Goren Huber. 2022. “Physics Informed Deep Learning for Tracker Fault Detection in Photovoltaic Power Plants.” Conference paper. In Proceedings of the Annual Conference of the PHM Society 2022, edited by Chetan Kulkarni and Abhinav Saxena. Vol. 14. PHM Society. https://doi.org/10.36001/phmconf.2022.v14i1.3235.
Zgraggen, Jannik, et al. “Physics Informed Deep Learning for Tracker Fault Detection in Photovoltaic Power Plants.” Proceedings of the Annual Conference of the PHM Society 2022, edited by Chetan Kulkarni and Abhinav Saxena, vol. 14, no. 1, PHM Society, 2022, https://doi.org/10.36001/phmconf.2022.v14i1.3235.


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