Wide-Area Monitoring of Power Systems Using Principal Component Analysis and k-Nearest Neighbor Analysis. / Cai, Lianfang; Thornhill, Nina; Kuenzel, Stefanie; Pal, Bikash.
In: IEEE Transactions on Power Systems , Vol. 33, No. 5, 09.2018, p. 4913-4923.Research output: Contribution to journal › Article › peer-review
Wide-Area Monitoring of Power Systems Using Principal Component Analysis and k-Nearest Neighbor Analysis. / Cai, Lianfang; Thornhill, Nina; Kuenzel, Stefanie; Pal, Bikash.
In: IEEE Transactions on Power Systems , Vol. 33, No. 5, 09.2018, p. 4913-4923.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Wide-Area Monitoring of Power Systems Using Principal Component Analysis and k-Nearest Neighbor Analysis
AU - Cai, Lianfang
AU - Thornhill, Nina
AU - Kuenzel, Stefanie
AU - Pal, Bikash
PY - 2018/9
Y1 - 2018/9
N2 - Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate analysis known as Principal Component Analysis (PCA) and time series analysis known as k-Nearest Neighbor (kNN) analysis. Ad-vantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method.
AB - Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate analysis known as Principal Component Analysis (PCA) and time series analysis known as k-Nearest Neighbor (kNN) analysis. Ad-vantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method.
KW - Wide-area monitoring
KW - real time
KW - principal component analysis
KW - k-nearest neighbor
KW - localization
KW - detection
KW - stability
KW - security
KW - power system disturbances
KW - electrical measurements
U2 - 10.1109/TPWRS.2017.2783242
DO - 10.1109/TPWRS.2017.2783242
M3 - Article
VL - 33
SP - 4913
EP - 4923
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
IS - 5
ER -