Tailored algorithms for anomaly detection in photovoltaic systems

Detalhes bibliográficos
Autor(a) principal: Branco, Pedro
Data de Publicação: 2020
Outros Autores: Gonçalves, Francisco, Costa, Ana Cristina
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/91211
Resumo: Branco, P., Gonçalves, F., & Costa, A. C. (2020). Tailored algorithms for anomaly detection in photovoltaic systems. Energies, 13(1), [225]. https://doi.org/10.3390/en13010225
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spelling Tailored algorithms for anomaly detection in photovoltaic systemsAnomaly detectionOrientationPV systemsShadingTime-seriesRenewable Energy, Sustainability and the EnvironmentEnergy Engineering and Power TechnologyEnergy (miscellaneous)Control and OptimizationElectrical and Electronic EngineeringSDG 7 - Affordable and Clean EnergyBranco, P., Gonçalves, F., & Costa, A. C. (2020). Tailored algorithms for anomaly detection in photovoltaic systems. Energies, 13(1), [225]. https://doi.org/10.3390/en13010225The fastest-growing renewable source of energy is solar photovoltaic (PV) energy, which is likely to become the largest electricity source in the world by 2050. In order to be a viable alternative energy source, PV systems should maximise their efficiency and operate flawlessly. However, in practice, many PV systems do not operate at their full capacity due to several types of anomalies. We propose tailored algorithms for the detection of different PV system anomalies, including suboptimal orientation, daytime and sunrise/sunset shading, brief and sustained daytime zero-production, and low maximum production. Furthermore, we establish simple metrics to assess the severity of suboptimal orientation and daytime shading. The proposed detection algorithms were applied to a set of time-series of electricity production in Portugal, which are based on two periods with distinct weather conditions. Under favourable weather conditions, the algorithms successfully detected most of the time-series labelled with either daytime or sunrise/sunset shading, and with either sustained or brief daytime zero-production. There was a relatively low percentage of false positives, such that most of the anomaly detections were correct. As expected, the algorithms tend to be more robust under favourable rather than under adverse weather conditions. The proposed algorithms may prove to be useful not only to research specialists, but also to energy utilities and owners of small- and medium-sized PV systems, who may thereby effortlessly monitor their operation and performance.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNBranco, PedroGonçalves, FranciscoCosta, Ana Cristina2020-01-14T23:37:52Z2020-01-022020-01-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/91211eng1996-1073PURE: 16328200https://doi.org/10.3390/en13010225info: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:RCAAP2024-03-11T04:40:30Zoai:run.unl.pt:10362/91211Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:37:17.414151Repositório 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 Tailored algorithms for anomaly detection in photovoltaic systems
title Tailored algorithms for anomaly detection in photovoltaic systems
spellingShingle Tailored algorithms for anomaly detection in photovoltaic systems
Branco, Pedro
Anomaly detection
Orientation
PV systems
Shading
Time-series
Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Energy (miscellaneous)
Control and Optimization
Electrical and Electronic Engineering
SDG 7 - Affordable and Clean Energy
title_short Tailored algorithms for anomaly detection in photovoltaic systems
title_full Tailored algorithms for anomaly detection in photovoltaic systems
title_fullStr Tailored algorithms for anomaly detection in photovoltaic systems
title_full_unstemmed Tailored algorithms for anomaly detection in photovoltaic systems
title_sort Tailored algorithms for anomaly detection in photovoltaic systems
author Branco, Pedro
author_facet Branco, Pedro
Gonçalves, Francisco
Costa, Ana Cristina
author_role author
author2 Gonçalves, Francisco
Costa, Ana Cristina
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Branco, Pedro
Gonçalves, Francisco
Costa, Ana Cristina
dc.subject.por.fl_str_mv Anomaly detection
Orientation
PV systems
Shading
Time-series
Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Energy (miscellaneous)
Control and Optimization
Electrical and Electronic Engineering
SDG 7 - Affordable and Clean Energy
topic Anomaly detection
Orientation
PV systems
Shading
Time-series
Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Energy (miscellaneous)
Control and Optimization
Electrical and Electronic Engineering
SDG 7 - Affordable and Clean Energy
description Branco, P., Gonçalves, F., & Costa, A. C. (2020). Tailored algorithms for anomaly detection in photovoltaic systems. Energies, 13(1), [225]. https://doi.org/10.3390/en13010225
publishDate 2020
dc.date.none.fl_str_mv 2020-01-14T23:37:52Z
2020-01-02
2020-01-02T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/91211
url http://hdl.handle.net/10362/91211
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1996-1073
PURE: 16328200
https://doi.org/10.3390/en13010225
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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