dc.creator | Ruiz-Moreno, Sara | es |
dc.creator | Gallego Len, Antonio Javier | es |
dc.creator | Sánchez, Adolfo J. | es |
dc.creator | Camacho, Eduardo F. | es |
dc.date.accessioned | 2022-10-07T12:09:18Z | |
dc.date.available | 2022-10-07T12:09:18Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Ruiz-Moreno, S., Gallego Len, A.J., Sánchez, A.J. y Camacho, E.F. (2022). Fault Detection and Isolation Based on Deep Learning for a Fresnel Collector Field. En 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2022. IFAC-PapersOnLine, Volume 55, Issue 6 (563-568), Pafos, Cyprus: IFAC Publisher ; Elsevier. | |
dc.identifier.issn | 2405-8963 | es |
dc.identifier.uri | https://hdl.handle.net/11441/137722 | |
dc.description | 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2022: Pafos, Cyprus, 8-10 June 2022 | es |
dc.description.abstract | With the advancement of new technologies, power systems are increasingly equipped with more sensors and actuators, heightening the risk of failure. This fact, together with the vulnerability of solar plants -not only to internal faults but also to the action of the sun, rain, wind, and animals, among others- gives rise to the need for detecting and identifying faults to deal with them. Methods that detect and diagnose faults play a crucial role in solar plants, allowing the systems to cope with them as soon as they occur and before they lead to large-scale problems. This work proposes using neural networks to detect and distinguish mirror and flow rate faults in a Fresnel plant. In addition, a defocusing stage is added to access hard-to-isolate faults, increasing the accuracy of 89.61% to 97.43%. These results contribute to the problem of isolability in thermal solar plants. The simulations for obtaining the neural networks and the results were conducted on a model of the Fresnel plant located at the Engineering School of Seville, Spain (ETSI). | es |
dc.description.sponsorship | Unión Europea, Horizon 2020, No. 789051 | es |
dc.format | application/pdf | es |
dc.format.extent | 6 p. | es |
dc.language.iso | eng | es |
dc.publisher | IFAC Publisher ; Elsevier | es |
dc.relation.ispartof | 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2022. IFAC-PapersOnLine, Volume 55, Issue 6 (2022), pp. 563-568. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Fresnel | es |
dc.subject | Artificial Intelligence | es |
dc.subject | Fault Detection | es |
dc.subject | Fault Diagnosis | es |
dc.subject | Solar Energy | es |
dc.title | Fault Detection and Isolation Based on Deep Learning for a Fresnel Collector Field | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | No. 789051 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S2405896322005730?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.ifacol.2022.07.188 | es |
dc.contributor.group | Universidad de Sevilla. TEP116. Automática y Robótica Industrial | es |
dc.publication.initialPage | 563 | es |
dc.publication.endPage | 568 | es |
dc.eventtitle | 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2022. IFAC-PapersOnLine, Volume 55, Issue 6 | es |
dc.eventinstitution | Pafos, Cyprus | es |