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 journalArticlepeer-review

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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 journalArticlepeer-review

Harvard

Cai, L, Thornhill, N, Kuenzel, S & Pal, B 2018, 'Wide-Area Monitoring of Power Systems Using Principal Component Analysis and k-Nearest Neighbor Analysis', IEEE Transactions on Power Systems , vol. 33, no. 5, pp. 4913-4923. https://doi.org/10.1109/TPWRS.2017.2783242

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Vancouver

Author

Cai, Lianfang ; Thornhill, Nina ; Kuenzel, Stefanie ; Pal, Bikash. / Wide-Area Monitoring of Power Systems Using Principal Component Analysis and k-Nearest Neighbor Analysis. In: IEEE Transactions on Power Systems . 2018 ; Vol. 33, No. 5. pp. 4913-4923.

BibTeX

@article{0432da18e09247aabfcef459271f24fb,
title = "Wide-Area Monitoring of Power Systems Using Principal Component Analysis and k-Nearest Neighbor Analysis",
abstract = "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.",
keywords = "Wide-area monitoring , real time, principal component analysis, k-nearest neighbor , localization, detection, stability, security, power system disturbances, electrical measurements",
author = "Lianfang Cai and Nina Thornhill and Stefanie Kuenzel and Bikash Pal",
year = "2018",
month = sep,
doi = "10.1109/TPWRS.2017.2783242",
language = "English",
volume = "33",
pages = "4913--4923",
journal = "IEEE Transactions on Power Systems ",
issn = "0885-8950",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "5",

}

RIS

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 -