Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques

The health condition of hydraulic turbines is one of the most critical factors for the operation safety and financial benefits of a hydro power plant. After the massive entrance of intermittent renewable energies, hydropower units have to regulate their output much more frequently for the balancing...

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Autores: Zhao, Weiqiang, Egusquiza, Mónica, Estévez Urra, Aída, Presas, Alexandre, Valero, Carme, Valentín, David, Egusquiza, Eduard
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/137039
Acesso em linha:https://hdl.handle.net/11441/137039
https://doi.org/10.1177/1475921720981839
Access Level:acceso abierto
Palavra-chave:Condition monitoring
Pelton turbine
Damage detection
Condition indicator
Factor analysis
Principal component analysis
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spelling Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniquesZhao, WeiqiangEgusquiza, MónicaEstévez Urra, AídaPresas, AlexandreValero, CarmeValentín, DavidEgusquiza, EduardCondition monitoringPelton turbineDamage detectionCondition indicatorFactor analysisPrincipal component analysisThe health condition of hydraulic turbines is one of the most critical factors for the operation safety and financial benefits of a hydro power plant. After the massive entrance of intermittent renewable energies, hydropower units have to regulate their output much more frequently for the balancing of the power grid. Under these conditions, the components of the machine have to withstand harsher excitation forces, which are more likely to produce damage and eventual failure in the turbines. To ensure the reliability of these machines, improved condition monitoring techniques are increasingly demanded. In this article, the feasibility of upgrading condition monitoring of Pelton turbines using novel vibration indicators and data-driven techniques is discussed. The new indicators are selected after performing a detailed analysis of the dynamic behavior of the turbine using numerical models and field measurements. After that, factor analysis is carried out in order to assess which are the most informative indicators and to reduce the dimension of the input data. For the validation of the proposed method, monitoring data from an actual Pelton turbine that suffered from an important fatigue failure due to a crack propagation on the buckets have been used. The novel condition indicators as well as classical indicators based on the spectrum and harmonics levels have been obtained while the machine was in good operation, during different stages of damage and after repair. All of these have been used to train an artificial neural network model in order to predict the evolution of the crack until failure occurs. The results show that using the improved monitoring methodology enhances the ability to predict the appearance of damage in comparison to typical condition indicators.Horizonte 2020 (Unión Europea) 857832SAGE Publications LtdIngeniería Mecánica y FabricaciónGeneralitat de CatalunyaConsejo de Becas de China (CSC)2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/137039https://doi.org/10.1177/1475921720981839reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésStructural Health Monitoring, 20 (6), 3239-3251.857832https://journals.sagepub.com/doi/full/10.1177/1475921720981839info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1370392026-06-17T12:51:07Z
dc.title.none.fl_str_mv Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques
title Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques
spellingShingle Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques
Zhao, Weiqiang
Condition monitoring
Pelton turbine
Damage detection
Condition indicator
Factor analysis
Principal component analysis
title_short Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques
title_full Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques
title_fullStr Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques
title_full_unstemmed Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques
title_sort Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques
dc.creator.none.fl_str_mv Zhao, Weiqiang
Egusquiza, Mónica
Estévez Urra, Aída
Presas, Alexandre
Valero, Carme
Valentín, David
Egusquiza, Eduard
author Zhao, Weiqiang
author_facet Zhao, Weiqiang
Egusquiza, Mónica
Estévez Urra, Aída
Presas, Alexandre
Valero, Carme
Valentín, David
Egusquiza, Eduard
author_role author
author2 Egusquiza, Mónica
Estévez Urra, Aída
Presas, Alexandre
Valero, Carme
Valentín, David
Egusquiza, Eduard
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería Mecánica y Fabricación
Generalitat de Catalunya
Consejo de Becas de China (CSC)
dc.subject.none.fl_str_mv Condition monitoring
Pelton turbine
Damage detection
Condition indicator
Factor analysis
Principal component analysis
topic Condition monitoring
Pelton turbine
Damage detection
Condition indicator
Factor analysis
Principal component analysis
description The health condition of hydraulic turbines is one of the most critical factors for the operation safety and financial benefits of a hydro power plant. After the massive entrance of intermittent renewable energies, hydropower units have to regulate their output much more frequently for the balancing of the power grid. Under these conditions, the components of the machine have to withstand harsher excitation forces, which are more likely to produce damage and eventual failure in the turbines. To ensure the reliability of these machines, improved condition monitoring techniques are increasingly demanded. In this article, the feasibility of upgrading condition monitoring of Pelton turbines using novel vibration indicators and data-driven techniques is discussed. The new indicators are selected after performing a detailed analysis of the dynamic behavior of the turbine using numerical models and field measurements. After that, factor analysis is carried out in order to assess which are the most informative indicators and to reduce the dimension of the input data. For the validation of the proposed method, monitoring data from an actual Pelton turbine that suffered from an important fatigue failure due to a crack propagation on the buckets have been used. The novel condition indicators as well as classical indicators based on the spectrum and harmonics levels have been obtained while the machine was in good operation, during different stages of damage and after repair. All of these have been used to train an artificial neural network model in order to predict the evolution of the crack until failure occurs. The results show that using the improved monitoring methodology enhances the ability to predict the appearance of damage in comparison to typical condition indicators.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/137039
https://doi.org/10.1177/1475921720981839
url https://hdl.handle.net/11441/137039
https://doi.org/10.1177/1475921720981839
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Structural Health Monitoring, 20 (6), 3239-3251.
857832
https://journals.sagepub.com/doi/full/10.1177/1475921720981839
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv SAGE Publications Ltd
publisher.none.fl_str_mv SAGE Publications Ltd
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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