Wind Turbine Static Errors Related to Yaw, Pitch or Anemometer Apparatus: Guidelines for the Diagnosis and Related Performance Assessment.
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| Title: | Wind Turbine Static Errors Related to Yaw, Pitch or Anemometer Apparatus: Guidelines for the Diagnosis and Related Performance Assessment. |
|---|---|
| Authors: | Astolfi, Davide1 (AUTHOR) antony.vasile@unibs.it, Iuliano, Silvia2 (AUTHOR) s.iuliano@studenti.unisannio.it, Vasile, Antony1 (AUTHOR) marco.pasetti@unibs.it, Pasetti, Marco1 (AUTHOR) salvatore.delloiacono@unibs.it, Iacono, Salvatore Dello1 (AUTHOR), Vaccaro, Alfredo2 (AUTHOR) vaccaro@unisannio.it |
| Source: | Energies (19961073). Dec2024, Vol. 17 Issue 24, p6381. 34p. |
| Subjects: | Wind turbine efficiency, Absolute pitch, Wind turbines, Wind power, Production losses, Wind power plants, Offshore wind power plants |
| Abstract: | The optimization of the efficiency of wind turbine systems is a fundamental task, from the perspective of a growing share of electricity produced from wind. Despite this, and given the complex multivariate dependence of the power of wind turbines on environmental conditions and working parameters, the literature is lacking studies specifically devoted to a careful characterization of wind farm performance. In particular, in the literature, it is overlooked that there are several types of faults which have similar manifestations and that can be defined as static errors. This kind of error manifests as a static bias occurring from a certain time onward, which can affect the anemometer, the absolute or relative pitch of the blades, or the yaw system. Static or systematic errors typically do not cause the functional failure of the wind turbine system, but they deserve attention due to the fact that they cause power production loss throughout the operation time. Based on this, the first objective of the present study is a critical review of the recent papers devoted to three types of wind turbine static errors: anemometer bias, static yaw error, and pitch misalignment. As a result, a comprehensive viewpoint, enhancing the state of the art in the literature, is developed in this study. Given that the use of data collected by Supervisory Control And Data Acquisition (SCADA) systems has, up to now, been prevailing for the diagnosis of systematic errors compared to the use of further specific sensors, particular attention in the present study is thus devoted to the discussion of the phenomena which can be observable through SCADA data analysis. Based on this, finally, a rigorous work flow is formulated for detecting static errors and discriminating among them through SCADA data analysis. Nevertheless, methods based on additional information sources (like further sensors or meteorological data) are also discussed. An important aspect of this study is that, for each considered type of systematic error, some previously unpublished results based on real-world SCADA data are reported in order to corroborate the proposed framework. Summarizing, then, the present is the first paper which considers and discusses several types of wind turbine static errors in a unified viewpoint, correctly interprets apparently controversial results collected in the literature, and finally provides guidelines for the diagnosis of this kind of error and for the quantification of the performance drop associated with their presence. [ABSTRACT FROM AUTHOR] |
| Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 181915124 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Wind Turbine Static Errors Related to Yaw, Pitch or Anemometer Apparatus: Guidelines for the Diagnosis and Related Performance Assessment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Astolfi%2C+Davide%22">Astolfi, Davide</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> antony.vasile@unibs.it</i><br /><searchLink fieldCode="AR" term="%22Iuliano%2C+Silvia%22">Iuliano, Silvia</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> s.iuliano@studenti.unisannio.it</i><br /><searchLink fieldCode="AR" term="%22Vasile%2C+Antony%22">Vasile, Antony</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> marco.pasetti@unibs.it</i><br /><searchLink fieldCode="AR" term="%22Pasetti%2C+Marco%22">Pasetti, Marco</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> salvatore.delloiacono@unibs.it</i><br /><searchLink fieldCode="AR" term="%22Iacono%2C+Salvatore+Dello%22">Iacono, Salvatore Dello</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vaccaro%2C+Alfredo%22">Vaccaro, Alfredo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> vaccaro@unisannio.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Dec2024, Vol. 17 Issue 24, p6381. 34p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Wind+turbine+efficiency%22">Wind turbine efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Absolute+pitch%22">Absolute pitch</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+turbines%22">Wind turbines</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+power%22">Wind power</searchLink><br /><searchLink fieldCode="DE" term="%22Production+losses%22">Production losses</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+power+plants%22">Wind power plants</searchLink><br /><searchLink fieldCode="DE" term="%22Offshore+wind+power+plants%22">Offshore wind power plants</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The optimization of the efficiency of wind turbine systems is a fundamental task, from the perspective of a growing share of electricity produced from wind. Despite this, and given the complex multivariate dependence of the power of wind turbines on environmental conditions and working parameters, the literature is lacking studies specifically devoted to a careful characterization of wind farm performance. In particular, in the literature, it is overlooked that there are several types of faults which have similar manifestations and that can be defined as static errors. This kind of error manifests as a static bias occurring from a certain time onward, which can affect the anemometer, the absolute or relative pitch of the blades, or the yaw system. Static or systematic errors typically do not cause the functional failure of the wind turbine system, but they deserve attention due to the fact that they cause power production loss throughout the operation time. Based on this, the first objective of the present study is a critical review of the recent papers devoted to three types of wind turbine static errors: anemometer bias, static yaw error, and pitch misalignment. As a result, a comprehensive viewpoint, enhancing the state of the art in the literature, is developed in this study. Given that the use of data collected by Supervisory Control And Data Acquisition (SCADA) systems has, up to now, been prevailing for the diagnosis of systematic errors compared to the use of further specific sensors, particular attention in the present study is thus devoted to the discussion of the phenomena which can be observable through SCADA data analysis. Based on this, finally, a rigorous work flow is formulated for detecting static errors and discriminating among them through SCADA data analysis. Nevertheless, methods based on additional information sources (like further sensors or meteorological data) are also discussed. An important aspect of this study is that, for each considered type of systematic error, some previously unpublished results based on real-world SCADA data are reported in order to corroborate the proposed framework. Summarizing, then, the present is the first paper which considers and discusses several types of wind turbine static errors in a unified viewpoint, correctly interprets apparently controversial results collected in the literature, and finally provides guidelines for the diagnosis of this kind of error and for the quantification of the performance drop associated with their presence. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=181915124 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en17246381 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 6381 Subjects: – SubjectFull: Wind turbine efficiency Type: general – SubjectFull: Absolute pitch Type: general – SubjectFull: Wind turbines Type: general – SubjectFull: Wind power Type: general – SubjectFull: Production losses Type: general – SubjectFull: Wind power plants Type: general – SubjectFull: Offshore wind power plants Type: general Titles: – TitleFull: Wind Turbine Static Errors Related to Yaw, Pitch or Anemometer Apparatus: Guidelines for the Diagnosis and Related Performance Assessment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Astolfi, Davide – PersonEntity: Name: NameFull: Iuliano, Silvia – PersonEntity: Name: NameFull: Vasile, Antony – PersonEntity: Name: NameFull: Pasetti, Marco – PersonEntity: Name: NameFull: Iacono, Salvatore Dello – PersonEntity: Name: NameFull: Vaccaro, Alfredo IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 17 – Type: issue Value: 24 Titles: – TitleFull: Energies (19961073) Type: main |
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