Implications of the Weibull K Factor in Resource Assessment
- Autores
- Thamhain, Mathias; Storm, Brandon
- Año de publicación
- 2012
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- This study investigates the implication of the site specific wind speed distribution on the energy production of a wind turbine generator. It will proof that the average wind speed and the power density are not a good measure on how well a turbine will perform; the performance depends on how well a power curve corresponds to the wind speed distribution. The Weibull distribution is commonly used to describe the probability distribution of wind speed at a given location. Two parameters, the scale factor (c, also sometimes referred to as A) and the shape factor (k) are sufficient to describe a curve which approximates the probability distribution of the wind speed.The Rayleigh distribution (defined as a Weibull distribution with the shape factor k = 2), is considered frequently as a reference frequency distribution. EAPC ́s practical experience in wind resource assessment in Southern Latin America has revealed a broad variety of shape factors beyond the standard k=2. We have also found that for a given mean wind speed, different shape factors lead to different magnitude of annual energy production on the other. This is especially the case for high wind speeds, where impact on annual energy production can be 15% or higher.
Laboratorio de Capa Límite y Fluidodinámica Ambiental
Grupo Fluidodinámica Computacional - Materia
-
Ingeniería Aeronáutica
Ingeniería Aeronáutica
Weibull wind speed distribution
Energy production of a wind turbine generator - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/100216
Ver los metadatos del registro completo
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Implications of the Weibull K Factor in Resource AssessmentThamhain, MathiasStorm, BrandonIngeniería AeronáuticaIngeniería AeronáuticaWeibull wind speed distributionEnergy production of a wind turbine generatorThis study investigates the implication of the site specific wind speed distribution on the energy production of a wind turbine generator. It will proof that the average wind speed and the power density are not a good measure on how well a turbine will perform; the performance depends on how well a power curve corresponds to the wind speed distribution. The Weibull distribution is commonly used to describe the probability distribution of wind speed at a given location. Two parameters, the scale factor (c, also sometimes referred to as A) and the shape factor (k) are sufficient to describe a curve which approximates the probability distribution of the wind speed.The Rayleigh distribution (defined as a Weibull distribution with the shape factor k = 2), is considered frequently as a reference frequency distribution. EAPC ́s practical experience in wind resource assessment in Southern Latin America has revealed a broad variety of shape factors beyond the standard k=2. We have also found that for a given mean wind speed, different shape factors lead to different magnitude of annual energy production on the other. This is especially the case for high wind speeds, where impact on annual energy production can be 15% or higher.Laboratorio de Capa Límite y Fluidodinámica AmbientalGrupo Fluidodinámica Computacional2012info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/100216enginfo:eu-repo/semantics/altIdentifier/url/http://www.aero.ing.unlp.edu.ar/cliv2/public/actas%20congreso/31.Thamhain.CLIV2.pdfinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:21:49Zoai:sedici.unlp.edu.ar:10915/100216Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:21:50.147SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Implications of the Weibull K Factor in Resource Assessment |
title |
Implications of the Weibull K Factor in Resource Assessment |
spellingShingle |
Implications of the Weibull K Factor in Resource Assessment Thamhain, Mathias Ingeniería Aeronáutica Ingeniería Aeronáutica Weibull wind speed distribution Energy production of a wind turbine generator |
title_short |
Implications of the Weibull K Factor in Resource Assessment |
title_full |
Implications of the Weibull K Factor in Resource Assessment |
title_fullStr |
Implications of the Weibull K Factor in Resource Assessment |
title_full_unstemmed |
Implications of the Weibull K Factor in Resource Assessment |
title_sort |
Implications of the Weibull K Factor in Resource Assessment |
dc.creator.none.fl_str_mv |
Thamhain, Mathias Storm, Brandon |
author |
Thamhain, Mathias |
author_facet |
Thamhain, Mathias Storm, Brandon |
author_role |
author |
author2 |
Storm, Brandon |
author2_role |
author |
dc.subject.none.fl_str_mv |
Ingeniería Aeronáutica Ingeniería Aeronáutica Weibull wind speed distribution Energy production of a wind turbine generator |
topic |
Ingeniería Aeronáutica Ingeniería Aeronáutica Weibull wind speed distribution Energy production of a wind turbine generator |
dc.description.none.fl_txt_mv |
This study investigates the implication of the site specific wind speed distribution on the energy production of a wind turbine generator. It will proof that the average wind speed and the power density are not a good measure on how well a turbine will perform; the performance depends on how well a power curve corresponds to the wind speed distribution. The Weibull distribution is commonly used to describe the probability distribution of wind speed at a given location. Two parameters, the scale factor (c, also sometimes referred to as A) and the shape factor (k) are sufficient to describe a curve which approximates the probability distribution of the wind speed.The Rayleigh distribution (defined as a Weibull distribution with the shape factor k = 2), is considered frequently as a reference frequency distribution. EAPC ́s practical experience in wind resource assessment in Southern Latin America has revealed a broad variety of shape factors beyond the standard k=2. We have also found that for a given mean wind speed, different shape factors lead to different magnitude of annual energy production on the other. This is especially the case for high wind speeds, where impact on annual energy production can be 15% or higher. Laboratorio de Capa Límite y Fluidodinámica Ambiental Grupo Fluidodinámica Computacional |
description |
This study investigates the implication of the site specific wind speed distribution on the energy production of a wind turbine generator. It will proof that the average wind speed and the power density are not a good measure on how well a turbine will perform; the performance depends on how well a power curve corresponds to the wind speed distribution. The Weibull distribution is commonly used to describe the probability distribution of wind speed at a given location. Two parameters, the scale factor (c, also sometimes referred to as A) and the shape factor (k) are sufficient to describe a curve which approximates the probability distribution of the wind speed.The Rayleigh distribution (defined as a Weibull distribution with the shape factor k = 2), is considered frequently as a reference frequency distribution. EAPC ́s practical experience in wind resource assessment in Southern Latin America has revealed a broad variety of shape factors beyond the standard k=2. We have also found that for a given mean wind speed, different shape factors lead to different magnitude of annual energy production on the other. This is especially the case for high wind speeds, where impact on annual energy production can be 15% or higher. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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http://sedici.unlp.edu.ar/handle/10915/100216 |
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http://sedici.unlp.edu.ar/handle/10915/100216 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/altIdentifier/url/http://www.aero.ing.unlp.edu.ar/cliv2/public/actas%20congreso/31.Thamhain.CLIV2.pdf |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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alira@sedici.unlp.edu.ar |
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