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  • PCA fault detection and identification paper-TSTE-00148-2017-final

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Wind turbine fault detection and identification through PCA-based optimal variable selection

Research output: Contribution to journalJournal article

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Wind turbine fault detection and identification through PCA-based optimal variable selection. / Wang, Yifei; Ma, Xiandong; Qian, Peng.

In: IEEE Transactions on Sustainable Energy, 06.02.2018.

Research output: Contribution to journalJournal article

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Wang Y, Ma X, Qian P. Wind turbine fault detection and identification through PCA-based optimal variable selection. IEEE Transactions on Sustainable Energy. 2018 Feb 6. Available from, DOI: 10.1109/TSTE.2018.2801625

Author

Wang, Yifei; Ma, Xiandong; Qian, Peng / Wind turbine fault detection and identification through PCA-based optimal variable selection.

In: IEEE Transactions on Sustainable Energy, 06.02.2018.

Research output: Contribution to journalJournal article

Bibtex

@article{1929ce240dcb4c55be141347f71f2e02,
title = "Wind turbine fault detection and identification through PCA-based optimal variable selection",
keywords = "Variable selection , Principal component analysis, Fault detection, Condition monitoring , Wind turbines",
author = "Yifei Wang and Xiandong Ma and Peng Qian",
note = "©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2018",
month = "2",
doi = "10.1109/TSTE.2018.2801625",
journal = "IEEE Transactions on Sustainable Energy",
issn = "1949-3029",
publisher = "IEEE",

}

RIS

TY - JOUR

T1 - Wind turbine fault detection and identification through PCA-based optimal variable selection

AU - Wang,Yifei

AU - Ma,Xiandong

AU - Qian,Peng

N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2018/2/6

Y1 - 2018/2/6

N2 - An effective condition monitoring system of wind turbines generally requires installation of a high number of sensors and use of a high sampling frequency in particular for monitoring of the electrical components within a turbine, resulting in a large amount of data. This can become a burden for condition monitoring and fault detection systems. This paper aims to develop algorithms that will allow a reduced dataset to be used in wind turbine fault detection. The paper firstly proposes a variable selection algorithm based on principal component analysis (PCA) with multiple selection criteria in order to select a set of variables to target fault signals while still preserving the variation of data in the original dataset. With the selected variables, the paper then describes fault detection and identification algorithms, which can identify faults, determine the corresponding time and location where the fault occurs, and estimate its severity. The proposed algorithms are evaluated with simulation data from PSCAD/EMTDC, SCADA (Supervisory control and data acquisition) data from an operational wind farm, and experimental data from a wind turbine test rig. Results show that the proposed methods can select a reduced set of variables with minimal information lost whilst detecting faults efficiently and effectively.

AB - An effective condition monitoring system of wind turbines generally requires installation of a high number of sensors and use of a high sampling frequency in particular for monitoring of the electrical components within a turbine, resulting in a large amount of data. This can become a burden for condition monitoring and fault detection systems. This paper aims to develop algorithms that will allow a reduced dataset to be used in wind turbine fault detection. The paper firstly proposes a variable selection algorithm based on principal component analysis (PCA) with multiple selection criteria in order to select a set of variables to target fault signals while still preserving the variation of data in the original dataset. With the selected variables, the paper then describes fault detection and identification algorithms, which can identify faults, determine the corresponding time and location where the fault occurs, and estimate its severity. The proposed algorithms are evaluated with simulation data from PSCAD/EMTDC, SCADA (Supervisory control and data acquisition) data from an operational wind farm, and experimental data from a wind turbine test rig. Results show that the proposed methods can select a reduced set of variables with minimal information lost whilst detecting faults efficiently and effectively.

KW - Variable selection

KW - Principal component analysis

KW - Fault detection

KW - Condition monitoring

KW - Wind turbines

U2 - 10.1109/TSTE.2018.2801625

DO - 10.1109/TSTE.2018.2801625

M3 - Journal article

JO - IEEE Transactions on Sustainable Energy

T2 - IEEE Transactions on Sustainable Energy

JF - IEEE Transactions on Sustainable Energy

SN - 1949-3029

ER -