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  • DG system condition monitoring paper - Lancaster University - final

    Rights statement: This is the author’s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy, 97, 2016 DOI: 10.1016/j.renene.2016.06.006

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Reducing sensor complexity for monitoring wind turbine performance using principal component analysis

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Reducing sensor complexity for monitoring wind turbine performance using principal component analysis. / Wang, Yifei; Ma, Xiandong; Joyce, Malcolm John.

In: Renewable Energy, Vol. 97, 11.2016, p. 444–456.

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@article{c1b066d3f4a24c34b905694e6173f475,
title = "Reducing sensor complexity for monitoring wind turbine performance using principal component analysis",
abstract = "Availability and reliability are among the priority concerns for deployment of distributed generation (DG) systems, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by large amounts of data needing to be processed in real time due to the large number of sensors being deployed. This paper proposes an optimal sensor selection method based on principal component analysis (PCA) for condition monitoring of a DG system oriented to wind turbines. The research was motivated by the fact that salient patterns in multivariable datasets can be extracted by PCA in order to identify monitoring parameters that contribute the most to the system variation. The proposed method is able to correlate the particular principal component to the corresponding monitoring variable, and hence facilitate the right sensor selection for the first time for the condition monitoring of wind turbines. The algorithms are examined with simulation data from PSCAD/EMTDC and SCADA data from an operational wind farm in the time, frequency, and instantaneous frequency domains. The results have shown that the proposed technique can reduce the number of monitoring variables whilst still maintaining sufficient information to detect the faults and hence assess the system{\textquoteright}s conditions.",
keywords = "Principal component analysis (PCA), Feature extraction, Condition monitoring, Wind turbine, Distributed generation",
author = "Yifei Wang and Xiandong Ma and Joyce, {Malcolm John}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy, 97, 2016 DOI: 10.1016/j.renene.2016.06.006",
year = "2016",
month = nov,
doi = "10.1016/j.renene.2016.06.006",
language = "English",
volume = "97",
pages = "444–456",
journal = "Renewable Energy",
issn = "0960-1481",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Reducing sensor complexity for monitoring wind turbine performance using principal component analysis

AU - Wang, Yifei

AU - Ma, Xiandong

AU - Joyce, Malcolm John

N1 - This is the author’s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy, 97, 2016 DOI: 10.1016/j.renene.2016.06.006

PY - 2016/11

Y1 - 2016/11

N2 - Availability and reliability are among the priority concerns for deployment of distributed generation (DG) systems, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by large amounts of data needing to be processed in real time due to the large number of sensors being deployed. This paper proposes an optimal sensor selection method based on principal component analysis (PCA) for condition monitoring of a DG system oriented to wind turbines. The research was motivated by the fact that salient patterns in multivariable datasets can be extracted by PCA in order to identify monitoring parameters that contribute the most to the system variation. The proposed method is able to correlate the particular principal component to the corresponding monitoring variable, and hence facilitate the right sensor selection for the first time for the condition monitoring of wind turbines. The algorithms are examined with simulation data from PSCAD/EMTDC and SCADA data from an operational wind farm in the time, frequency, and instantaneous frequency domains. The results have shown that the proposed technique can reduce the number of monitoring variables whilst still maintaining sufficient information to detect the faults and hence assess the system’s conditions.

AB - Availability and reliability are among the priority concerns for deployment of distributed generation (DG) systems, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by large amounts of data needing to be processed in real time due to the large number of sensors being deployed. This paper proposes an optimal sensor selection method based on principal component analysis (PCA) for condition monitoring of a DG system oriented to wind turbines. The research was motivated by the fact that salient patterns in multivariable datasets can be extracted by PCA in order to identify monitoring parameters that contribute the most to the system variation. The proposed method is able to correlate the particular principal component to the corresponding monitoring variable, and hence facilitate the right sensor selection for the first time for the condition monitoring of wind turbines. The algorithms are examined with simulation data from PSCAD/EMTDC and SCADA data from an operational wind farm in the time, frequency, and instantaneous frequency domains. The results have shown that the proposed technique can reduce the number of monitoring variables whilst still maintaining sufficient information to detect the faults and hence assess the system’s conditions.

KW - Principal component analysis (PCA)

KW - Feature extraction

KW - Condition monitoring

KW - Wind turbine

KW - Distributed generation

U2 - 10.1016/j.renene.2016.06.006

DO - 10.1016/j.renene.2016.06.006

M3 - Journal article

VL - 97

SP - 444

EP - 456

JO - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

ER -