Statistical and clustering analysis for disturbances: A case study of voltage dips in wind farms

Garcia-Sanchez, Tania; Gomez-Lazaro, Emilio; Muljadi, Eduard; Kessler, Mathieu; Molina-Garcia, Angel

doi:10.1109/TPWRD.2016.2522946

Title: Statistical and clustering analysis for disturbances: A case study of voltage dips in wind farms

Abstract

This study proposes and evaluates an alternative statistical methodology to analyze a large number of voltage dips. For a given voltage dip, a set of lengths is first identified to characterize the root mean square (rms) voltage evolution along the disturbance, deduced from partial linearized time intervals and trajectories. Principal component analysis and K-means clustering processes are then applied to identify rms-voltage patterns and propose a reduced number of representative rms-voltage profiles from the linearized trajectories. This reduced group of averaged rms-voltage profiles enables the representation of a large amount of disturbances, which offers a visual and graphical representation of their evolution along the events, aspects that were not previously considered in other contributions. The complete process is evaluated on real voltage dips collected in intense field-measurement campaigns carried out in a wind farm in Spain among different years. The results are included in this paper.

Authors:

Garcia-Sanchez, Tania ^[1]; Gomez-Lazaro, Emilio ^[1]; Muljadi, Eduard ^[2]; Kessler, Mathieu ^[3]; Molina-Garcia, Angel ^[3]

Renewable Energy Research Institute and DIEEAC/EDII-AB Univ. de Castilla-La Mancha, Albacete (Spain)
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Univ. Politecnica de Cartagena, Cartagena (Spain)

Publication Date:: Thu Jan 28 00:00:00 EST 2016

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)

OSTI Identifier:: 1330934

Report Number(s):: NREL/JA-5D00-65751
Journal ID: ISSN 0885-8977

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Power Delivery

Additional Journal Information:: Journal Volume: 31; Journal Issue: 6; Journal ID: ISSN 0885-8977

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; voltage dip; principal component analysis; clustering methods

Citation Formats


                    Garcia-Sanchez, Tania, Gomez-Lazaro, Emilio, Muljadi, Eduard, Kessler, Mathieu, and Molina-Garcia, Angel. Statistical and clustering analysis for disturbances: A case study of voltage dips in wind farms.  United States: N. p., 2016. 
Web.  doi:10.1109/TPWRD.2016.2522946.

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                    Garcia-Sanchez, Tania, Gomez-Lazaro, Emilio, Muljadi, Eduard, Kessler, Mathieu, & Molina-Garcia, Angel. Statistical and clustering analysis for disturbances: A case study of voltage dips in wind farms.  United States.  https://doi.org/10.1109/TPWRD.2016.2522946

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                    Garcia-Sanchez, Tania, Gomez-Lazaro, Emilio, Muljadi, Eduard, Kessler, Mathieu, and Molina-Garcia, Angel. Thu .  
"Statistical and clustering analysis for disturbances: A case study of voltage dips in wind farms".  United States.  https://doi.org/10.1109/TPWRD.2016.2522946.  https://www.osti.gov/servlets/purl/1330934.

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@article{osti_1330934,

  title        = {Statistical and clustering analysis for disturbances: A case study of voltage dips in wind farms},

  author       = {Garcia-Sanchez, Tania and Gomez-Lazaro, Emilio and Muljadi, Eduard and Kessler, Mathieu and Molina-Garcia, Angel},

  abstractNote = {This study proposes and evaluates an alternative statistical methodology to analyze a large number of voltage dips. For a given voltage dip, a set of lengths is first identified to characterize the root mean square (rms) voltage evolution along the disturbance, deduced from partial linearized time intervals and trajectories. Principal component analysis and K-means clustering processes are then applied to identify rms-voltage patterns and propose a reduced number of representative rms-voltage profiles from the linearized trajectories. This reduced group of averaged rms-voltage profiles enables the representation of a large amount of disturbances, which offers a visual and graphical representation of their evolution along the events, aspects that were not previously considered in other contributions. The complete process is evaluated on real voltage dips collected in intense field-measurement campaigns carried out in a wind farm in Spain among different years. The results are included in this paper.},

  doi          = {10.1109/TPWRD.2016.2522946},

  journal      = {IEEE Transactions on Power Delivery},

  number       = 6,

  volume       = 31,

  place        = {United States},

  year         = {Thu Jan 28 00:00:00 EST 2016},

  month        = {Thu Jan 28 00:00:00 EST 2016}

}

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Works referencing / citing this record:

Wind power within European grid codes: Evolution, status and outlook
journal, January 2018

Vrana, Til Kristian; Flynn, Damian; Gomez‐Lazaro, Emilio
WIREs Energy and Environment, Vol. 7, Issue 3
DOI: 10.1002/wene.285

Online Recognition Method for Voltage Sags Based on a Deep Belief Network
journal, December 2018

Mei, Fei; Ren, Yong; Wu, Qingliang
Energies, Vol. 12, Issue 1
DOI: 10.3390/en12010043

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Abstract

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