Spatial-Temporal Estimation for Nontechnical Losses
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TPWRD.2015.2469135 http://hdl.handle.net/11449/172766 |
Resumo: | This paper presents a novel method for estimating the spatial distribution in geographical space of the nontechnical losses over time. The method progresses in two stages: in the first stage, a generalized additive model is used to generate a map of current loss probabilities. The second stage employs the Markov chain to generate a map that indicates possible future changes in loss probabilities. The method yields an assessment of the location of the nontechnical losses now and in the future at the city subarea level, even indicating the variables that have greater statistical correlation with the nontechnical losses. We apply the method to a city with approximately 81 000 consumers, and the results are compared with those obtained through inspections carried out by a Brazilian power utility. The detection rate surpasses 78% in inspected subareas. The method we propose offers improved estimation of distribution of the nontechnical losses in urban regions. |
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Repositório Institucional da UNESP |
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Spatial-Temporal Estimation for Nontechnical LossesElectricity theftgeneralized additive modelsnontechnical lossesspatial-point pattern analysisThis paper presents a novel method for estimating the spatial distribution in geographical space of the nontechnical losses over time. The method progresses in two stages: in the first stage, a generalized additive model is used to generate a map of current loss probabilities. The second stage employs the Markov chain to generate a map that indicates possible future changes in loss probabilities. The method yields an assessment of the location of the nontechnical losses now and in the future at the city subarea level, even indicating the variables that have greater statistical correlation with the nontechnical losses. We apply the method to a city with approximately 81 000 consumers, and the results are compared with those obtained through inspections carried out by a Brazilian power utility. The detection rate surpasses 78% in inspected subareas. The method we propose offers improved estimation of distribution of the nontechnical losses in urban regions.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Electrical Enginnering São Paulo State University (UNESP) Ilha Solteira Campus Ilha SolteiraDepartment of Electrical Enginnering São Paulo State University (UNESP) Ilha Solteira Campus Ilha SolteiraCAPES: 2014/06629-0CNPq: 2014/06629-0Universidade Estadual Paulista (Unesp)Faria, Lucas Teles [UNESP]Melo, Joel David [UNESP]Padilha-Feltrin, Antonio [UNESP]2018-12-11T17:02:05Z2018-12-11T17:02:05Z2016-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article362-369application/pdfhttp://dx.doi.org/10.1109/TPWRD.2015.2469135IEEE Transactions on Power Delivery, v. 31, n. 1, p. 362-369, 2016.0885-8977http://hdl.handle.net/11449/17276610.1109/TPWRD.2015.24691352-s2.0-849623769152-s2.0-84962376915.pdf0437995235427473Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Transactions on Power Delivery1,814info:eu-repo/semantics/openAccess2024-07-04T19:06:36Zoai:repositorio.unesp.br:11449/172766Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:06:42.274963Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Spatial-Temporal Estimation for Nontechnical Losses |
title |
Spatial-Temporal Estimation for Nontechnical Losses |
spellingShingle |
Spatial-Temporal Estimation for Nontechnical Losses Faria, Lucas Teles [UNESP] Electricity theft generalized additive models nontechnical losses spatial-point pattern analysis |
title_short |
Spatial-Temporal Estimation for Nontechnical Losses |
title_full |
Spatial-Temporal Estimation for Nontechnical Losses |
title_fullStr |
Spatial-Temporal Estimation for Nontechnical Losses |
title_full_unstemmed |
Spatial-Temporal Estimation for Nontechnical Losses |
title_sort |
Spatial-Temporal Estimation for Nontechnical Losses |
author |
Faria, Lucas Teles [UNESP] |
author_facet |
Faria, Lucas Teles [UNESP] Melo, Joel David [UNESP] Padilha-Feltrin, Antonio [UNESP] |
author_role |
author |
author2 |
Melo, Joel David [UNESP] Padilha-Feltrin, Antonio [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Faria, Lucas Teles [UNESP] Melo, Joel David [UNESP] Padilha-Feltrin, Antonio [UNESP] |
dc.subject.por.fl_str_mv |
Electricity theft generalized additive models nontechnical losses spatial-point pattern analysis |
topic |
Electricity theft generalized additive models nontechnical losses spatial-point pattern analysis |
description |
This paper presents a novel method for estimating the spatial distribution in geographical space of the nontechnical losses over time. The method progresses in two stages: in the first stage, a generalized additive model is used to generate a map of current loss probabilities. The second stage employs the Markov chain to generate a map that indicates possible future changes in loss probabilities. The method yields an assessment of the location of the nontechnical losses now and in the future at the city subarea level, even indicating the variables that have greater statistical correlation with the nontechnical losses. We apply the method to a city with approximately 81 000 consumers, and the results are compared with those obtained through inspections carried out by a Brazilian power utility. The detection rate surpasses 78% in inspected subareas. The method we propose offers improved estimation of distribution of the nontechnical losses in urban regions. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-02-01 2018-12-11T17:02:05Z 2018-12-11T17:02:05Z |
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://dx.doi.org/10.1109/TPWRD.2015.2469135 IEEE Transactions on Power Delivery, v. 31, n. 1, p. 362-369, 2016. 0885-8977 http://hdl.handle.net/11449/172766 10.1109/TPWRD.2015.2469135 2-s2.0-84962376915 2-s2.0-84962376915.pdf 0437995235427473 |
url |
http://dx.doi.org/10.1109/TPWRD.2015.2469135 http://hdl.handle.net/11449/172766 |
identifier_str_mv |
IEEE Transactions on Power Delivery, v. 31, n. 1, p. 362-369, 2016. 0885-8977 10.1109/TPWRD.2015.2469135 2-s2.0-84962376915 2-s2.0-84962376915.pdf 0437995235427473 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IEEE Transactions on Power Delivery 1,814 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
362-369 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129285471338496 |