A methodology for the calculation of typical gas concentration values and sampling intervals in the power transformers of a distribution system operator†

Predictive maintenance strategies in power transformers aim to assess the risk through the calculation and monitoring of the health index of the power transformers. The parameter most used in predictive maintenance and to calculate the health index of power transformers is the dissolved gas analysis...

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Authors: Bustamante Sánchez, Sergio|||0000-0001-7691-6187, Mañana Canteli, Mario|||0000-0001-6886-8170, Arroyo Gutiérrez, Alberto|||0000-0002-6045-2610, Martínez Torre, Raquel|||0000-0002-0278-2785, Laso Pérez, Alberto|||0000-0003-3751-7305
Format: article
Publication Date:2020
Country:España
Institution:Universidad de Cantabria (UC)
Repository:UCrea Repositorio Abierto de la Universidad de Cantabria
Language:English
OAI Identifier:oai:repositorio.unican.es:10902/20536
Online Access:http://hdl.handle.net/10902/20536
Access Level:Open access
Keyword:Asset management
Dissolved gas analysis
Maintenance management
Oil insulation
Power transformers
Predictive maintenance
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spelling A methodology for the calculation of typical gas concentration values and sampling intervals in the power transformers of a distribution system operator†Bustamante Sánchez, Sergio|||0000-0001-7691-6187Mañana Canteli, Mario|||0000-0001-6886-8170Arroyo Gutiérrez, Alberto|||0000-0002-6045-2610Martínez Torre, Raquel|||0000-0002-0278-2785Laso Pérez, Alberto|||0000-0003-3751-7305 Asset managementDissolved gas analysisMaintenance managementOil insulationPower transformersPredictive maintenancePredictive maintenance strategies in power transformers aim to assess the risk through the calculation and monitoring of the health index of the power transformers. The parameter most used in predictive maintenance and to calculate the health index of power transformers is the dissolved gas analysis (DGA). The current tendency is the use of online DGA monitoring equipment while continuing to perform analyses in the laboratory. Although the DGA is well known, there is a lack of published experimental data beyond that in the guides. This study used the nearest-rank method for obtaining the typical gas concentration values and the typical rates of gas increase from a transformer population to establish the optimal sampling interval and alarm thresholds of the continuous monitoring devices for each power transformer. The percentiles calculated by the nearest-rank method were within the ranges of the percentiles obtained using the R software, so this simple method was validated for this study. The results obtained show that the calculated concentration limits are within the range of or very close to those proposed in IEEE C57.104-2019 and IEC 60599:2015. The sampling intervals calculated for each transformer were not correct in all cases since the trend of the historical DGA samples modified the severity of the calculated intervals.This work was partially financed by the EU Regional Development Fund (FEDER) and the Spanish Government under RETOS-COLABORACIÓN RTC-2017-6782-3 and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 864579 (FLEXIGRID).MDPIUniversidad de Cantabria20202020-11-12journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttp://hdl.handle.net/10902/20536Energies, 2020, 13(22), 5891reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)InglésengEuropean Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 864579open 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:10902/205362026-06-02T12:39:31Z
dc.title.none.fl_str_mv A methodology for the calculation of typical gas concentration values and sampling intervals in the power transformers of a distribution system operator†
title A methodology for the calculation of typical gas concentration values and sampling intervals in the power transformers of a distribution system operator†
spellingShingle A methodology for the calculation of typical gas concentration values and sampling intervals in the power transformers of a distribution system operator†
Bustamante Sánchez, Sergio|||0000-0001-7691-6187
Asset management
Dissolved gas analysis
Maintenance management
Oil insulation
Power transformers
Predictive maintenance
title_short A methodology for the calculation of typical gas concentration values and sampling intervals in the power transformers of a distribution system operator†
title_full A methodology for the calculation of typical gas concentration values and sampling intervals in the power transformers of a distribution system operator†
title_fullStr A methodology for the calculation of typical gas concentration values and sampling intervals in the power transformers of a distribution system operator†
title_full_unstemmed A methodology for the calculation of typical gas concentration values and sampling intervals in the power transformers of a distribution system operator†
title_sort A methodology for the calculation of typical gas concentration values and sampling intervals in the power transformers of a distribution system operator†
dc.creator.none.fl_str_mv Bustamante Sánchez, Sergio|||0000-0001-7691-6187
Mañana Canteli, Mario|||0000-0001-6886-8170
Arroyo Gutiérrez, Alberto|||0000-0002-6045-2610
Martínez Torre, Raquel|||0000-0002-0278-2785
Laso Pérez, Alberto|||0000-0003-3751-7305
author Bustamante Sánchez, Sergio|||0000-0001-7691-6187
author_facet Bustamante Sánchez, Sergio|||0000-0001-7691-6187
Mañana Canteli, Mario|||0000-0001-6886-8170
Arroyo Gutiérrez, Alberto|||0000-0002-6045-2610
Martínez Torre, Raquel|||0000-0002-0278-2785
Laso Pérez, Alberto|||0000-0003-3751-7305
author_role author
author2 Mañana Canteli, Mario|||0000-0001-6886-8170
Arroyo Gutiérrez, Alberto|||0000-0002-6045-2610
Martínez Torre, Raquel|||0000-0002-0278-2785
Laso Pérez, Alberto|||0000-0003-3751-7305
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Asset management
Dissolved gas analysis
Maintenance management
Oil insulation
Power transformers
Predictive maintenance
topic Asset management
Dissolved gas analysis
Maintenance management
Oil insulation
Power transformers
Predictive maintenance
description Predictive maintenance strategies in power transformers aim to assess the risk through the calculation and monitoring of the health index of the power transformers. The parameter most used in predictive maintenance and to calculate the health index of power transformers is the dissolved gas analysis (DGA). The current tendency is the use of online DGA monitoring equipment while continuing to perform analyses in the laboratory. Although the DGA is well known, there is a lack of published experimental data beyond that in the guides. This study used the nearest-rank method for obtaining the typical gas concentration values and the typical rates of gas increase from a transformer population to establish the optimal sampling interval and alarm thresholds of the continuous monitoring devices for each power transformer. The percentiles calculated by the nearest-rank method were within the ranges of the percentiles obtained using the R software, so this simple method was validated for this study. The results obtained show that the calculated concentration limits are within the range of or very close to those proposed in IEEE C57.104-2019 and IEC 60599:2015. The sampling intervals calculated for each transformer were not correct in all cases since the trend of the historical DGA samples modified the severity of the calculated intervals.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-11-12
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/10902/20536
url http://hdl.handle.net/10902/20536
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 864579
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, 2020, 13(22), 5891
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
_version_ 1869418934790258688
score 15.301629

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