Stream data cleaning for dynamic line rating application

The maximum current that an overhead transmission line can continuously carry depends on external weather conditions, most commonly obtained from real-time streaming weather sensors. The accuracy of the sensor data is very important in order to avoid problems such as overheating. Furthermore, faulty...

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Detalles Bibliográficos
Autores: Nemati, Hassan M., Laso Pérez, Alberto|||0000-0003-3751-7305, Mañana Canteli, Mario|||0000-0001-6886-8170, Sant'Anna, Anita, Nowaczyk, Slawomir
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/14792
Acceso en línea:http://hdl.handle.net/10902/14792
Access Level:acceso abierto
Palabra clave:Smart grids
Dynamic line rating
Stream data cleaning
Data mining
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repository_id_str
spelling Stream data cleaning for dynamic line rating applicationNemati, Hassan M.Laso Pérez, Alberto|||0000-0003-3751-7305 Mañana Canteli, Mario|||0000-0001-6886-8170Sant'Anna, AnitaNowaczyk, SlawomirSmart gridsDynamic line ratingStream data cleaningData miningThe maximum current that an overhead transmission line can continuously carry depends on external weather conditions, most commonly obtained from real-time streaming weather sensors. The accuracy of the sensor data is very important in order to avoid problems such as overheating. Furthermore, faulty sensor readings may cause operators to limit or even stop the energy production from renewable sources in radial networks. This paper presents a method for detecting and replacing sequences of consecutive faulty data originating from streaming weather sensors. The method is based on a combination of (a) a set of constraints obtained from derivatives in consecutive data, and (b) association rules that are automatically generated from historical data. In smart grids, a large amount of historical data from different weather stations are available but rarely used. In this work, we show that mining and analyzing this historical data provides valuable information that can be used for detecting and replacing faulty sensor readings. We compare the result of the proposed method against the exponentially weighted moving average and vector autoregression models. Experiments on data sets with real and synthetic errors demonstrate the good performance of the proposed method for monitoring weather sensors.This research was partially funded by Spanish Government under Spanish R+D initiative with reference ENE2013-42720-R and RETOS RTC-2015-3795-3.MDPIUniversidad de Cantabria20182018-08-02journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttp://hdl.handle.net/10902/14792Energies, 2018, 11(8), 2007reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/147922026-06-02T12:39:31Z
dc.title.none.fl_str_mv Stream data cleaning for dynamic line rating application
title Stream data cleaning for dynamic line rating application
spellingShingle Stream data cleaning for dynamic line rating application
Nemati, Hassan M.
Smart grids
Dynamic line rating
Stream data cleaning
Data mining
title_short Stream data cleaning for dynamic line rating application
title_full Stream data cleaning for dynamic line rating application
title_fullStr Stream data cleaning for dynamic line rating application
title_full_unstemmed Stream data cleaning for dynamic line rating application
title_sort Stream data cleaning for dynamic line rating application
dc.creator.none.fl_str_mv Nemati, Hassan M.
Laso Pérez, Alberto|||0000-0003-3751-7305
Mañana Canteli, Mario|||0000-0001-6886-8170
Sant'Anna, Anita
Nowaczyk, Slawomir
author Nemati, Hassan M.
author_facet Nemati, Hassan M.
Laso Pérez, Alberto|||0000-0003-3751-7305
Mañana Canteli, Mario|||0000-0001-6886-8170
Sant'Anna, Anita
Nowaczyk, Slawomir
author_role author
author2 Laso Pérez, Alberto|||0000-0003-3751-7305
Mañana Canteli, Mario|||0000-0001-6886-8170
Sant'Anna, Anita
Nowaczyk, Slawomir
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Smart grids
Dynamic line rating
Stream data cleaning
Data mining
topic Smart grids
Dynamic line rating
Stream data cleaning
Data mining
description The maximum current that an overhead transmission line can continuously carry depends on external weather conditions, most commonly obtained from real-time streaming weather sensors. The accuracy of the sensor data is very important in order to avoid problems such as overheating. Furthermore, faulty sensor readings may cause operators to limit or even stop the energy production from renewable sources in radial networks. This paper presents a method for detecting and replacing sequences of consecutive faulty data originating from streaming weather sensors. The method is based on a combination of (a) a set of constraints obtained from derivatives in consecutive data, and (b) association rules that are automatically generated from historical data. In smart grids, a large amount of historical data from different weather stations are available but rarely used. In this work, we show that mining and analyzing this historical data provides valuable information that can be used for detecting and replacing faulty sensor readings. We compare the result of the proposed method against the exponentially weighted moving average and vector autoregression models. Experiments on data sets with real and synthetic errors demonstrate the good performance of the proposed method for monitoring weather sensors.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-08-02
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10902/14792
url http://hdl.handle.net/10902/14792
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Energies, 2018, 11(8), 2007
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.198674

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