A kernel plus method for quantifying wind turbine performance upgrades

Handle URI:
http://hdl.handle.net/10754/566063
Title:
A kernel plus method for quantifying wind turbine performance upgrades
Authors:
Lee, Giwhyun; Ding, Yu; Xie, Le; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
Power curves are commonly estimated using the binning method recommended by the International Electrotechnical Commission, which primarily incorporates wind speed information. When such power curves are used to quantify a turbine's upgrade, the results may not be accurate because many other environmental factors in addition to wind speed, such as temperature, air pressure, turbulence intensity, wind shear and humidity, all potentially affect the turbine's power output. Wind industry practitioners are aware of the need to filter out effects from environmental conditions. Toward that objective, we developed a kernel plus method that allows incorporation of multivariate environmental factors in a power curve model, thereby controlling the effects from environmental factors while comparing power outputs. We demonstrate that the kernel plus method can serve as a useful tool for quantifying a turbine's upgrade because it is sensitive to small and moderate changes caused by certain turbine upgrades. Although we demonstrate the utility of the kernel plus method in this specific application, the resulting method is a general, multivariate model that can connect other physical factors, as long as their measurements are available, with a turbine's power output, which may allow us to explore new physical properties associated with wind turbine performance. © 2014 John Wiley & Sons, Ltd.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Wiley-Blackwell
Journal:
Wind Energy
Issue Date:
21-Apr-2014
DOI:
10.1002/we.1755
Type:
Article
ISSN:
10954244
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLee, Giwhyunen
dc.contributor.authorDing, Yuen
dc.contributor.authorXie, Leen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2015-08-12T09:26:42Zen
dc.date.available2015-08-12T09:26:42Zen
dc.date.issued2014-04-21en
dc.identifier.issn10954244en
dc.identifier.doi10.1002/we.1755en
dc.identifier.urihttp://hdl.handle.net/10754/566063en
dc.description.abstractPower curves are commonly estimated using the binning method recommended by the International Electrotechnical Commission, which primarily incorporates wind speed information. When such power curves are used to quantify a turbine's upgrade, the results may not be accurate because many other environmental factors in addition to wind speed, such as temperature, air pressure, turbulence intensity, wind shear and humidity, all potentially affect the turbine's power output. Wind industry practitioners are aware of the need to filter out effects from environmental conditions. Toward that objective, we developed a kernel plus method that allows incorporation of multivariate environmental factors in a power curve model, thereby controlling the effects from environmental factors while comparing power outputs. We demonstrate that the kernel plus method can serve as a useful tool for quantifying a turbine's upgrade because it is sensitive to small and moderate changes caused by certain turbine upgrades. Although we demonstrate the utility of the kernel plus method in this specific application, the resulting method is a general, multivariate model that can connect other physical factors, as long as their measurements are available, with a turbine's power output, which may allow us to explore new physical properties associated with wind turbine performance. © 2014 John Wiley & Sons, Ltd.en
dc.publisherWiley-Blackwellen
dc.subjectBinning methoden
dc.subjectKernel estimationen
dc.subjectNon-parametric methodsen
dc.subjectPower curveen
dc.subjectWind turbineen
dc.titleA kernel plus method for quantifying wind turbine performance upgradesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalWind Energyen
dc.contributor.institutionKorea Army Academy Yeongcheon Koreaen
dc.contributor.institutionDepartment of Industrial and Systems Engineering Texas A and M University College Station, Texas 77843 USAen
dc.contributor.institutionDepartment of Electrical and Computer Engineering Texas A and M University College Station, Texas 77843 USAen
kaust.authorGenton, Marc G.en
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