Anomaly detection in photovoltaic systems

Detalhes bibliográficos
Autor(a) principal: Branco, Pedro Miguel Mayer
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/72312
Resumo: Internship report presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with a specialization in Information Analysis and Management
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spelling Anomaly detection in photovoltaic systemsAnomaly detectionPhotovoltaic systemsRenewable energyTime-seriesInternship report presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with a specialization in Information Analysis and ManagementPhotovoltaic (PV) solar energy is the fastest-growing renewable source of energy, and poised to become the world’s largest source of electricity by 2050. To maximize efficiency and remain a viable alternative energy source, PV systems should ideally operate seamlessly without anomalies. In reality, however, several kinds of anomalies may occur that prevent PV systems from operating at their full capacity. Here, we address this problem by developing five algorithms for the detection of several PV-system anomalies, and establishing metrics to determine the severity of daytime shading and suboptimal orientation. Specifically, our algorithms are used to detect brief and sustained daytime zero-production, daytime and sunrise/sunset shading, low maximum production and suboptimal orientation. We apply these detection algorithms to several time-series of electricity production, which were obtained for two periods with contrasting weather conditions. When weather conditions were favorable, our algorithms successfully detected the majority of time-series labeled with either sustained or brief daytime zero-production, and either daytime or sunrise/sunset shading. Furthermore, these algorithms also produced a relatively low percentage of false positives, which indicates that most anomaly detections are correct. When weather conditions were adverse, the detection rate of our algorithms was similarly high, if not higher, than when weather conditions were favorable. However, the percentage of false positive anomaly detections is also substantially higher under adverse weather conditions, which indicates that the algorithms are generally more robust under favorable weather conditions. Our results suggest that, on the one hand, daytime shading is a relatively rare anomaly, although it may have a severe impact on PV-system efficiency that warrants its detection. On the other hand, suboptimal orientation appears to be relatively common, and our orientation index can therefore be useful to determine the severity of this prevalent type of anomaly.Costa, Ana Cristina Marinho daGonçalves, FranciscoRUNBranco, Pedro Miguel Mayer2022-06-01T00:31:31Z2019-05-312019-05-31T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/72312TID:202253856enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:33:48Zoai:run.unl.pt:10362/72312Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:35:15.501360Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Anomaly detection in photovoltaic systems
title Anomaly detection in photovoltaic systems
spellingShingle Anomaly detection in photovoltaic systems
Branco, Pedro Miguel Mayer
Anomaly detection
Photovoltaic systems
Renewable energy
Time-series
title_short Anomaly detection in photovoltaic systems
title_full Anomaly detection in photovoltaic systems
title_fullStr Anomaly detection in photovoltaic systems
title_full_unstemmed Anomaly detection in photovoltaic systems
title_sort Anomaly detection in photovoltaic systems
author Branco, Pedro Miguel Mayer
author_facet Branco, Pedro Miguel Mayer
author_role author
dc.contributor.none.fl_str_mv Costa, Ana Cristina Marinho da
Gonçalves, Francisco
RUN
dc.contributor.author.fl_str_mv Branco, Pedro Miguel Mayer
dc.subject.por.fl_str_mv Anomaly detection
Photovoltaic systems
Renewable energy
Time-series
topic Anomaly detection
Photovoltaic systems
Renewable energy
Time-series
description Internship report presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with a specialization in Information Analysis and Management
publishDate 2019
dc.date.none.fl_str_mv 2019-05-31
2019-05-31T00:00:00Z
2022-06-01T00:31:31Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/72312
TID:202253856
url http://hdl.handle.net/10362/72312
identifier_str_mv TID:202253856
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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