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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 - 2018/10
Y1 - 2018/10
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
VL - 9
SP - 1627
EP - 1635
JO - IEEE Transactions on Sustainable Energy
JF - IEEE Transactions on Sustainable Energy
SN - 1949-3029
IS - 4
ER -