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Spectral MST-Based Graph Outlier Detection With Application to Clustering of Power Networks. / Tyuryukanov, Ilya; Popov, Marjan; Van Der Meijden, Mart A.M.M.; Terzija, Vladimir.

20th Power Systems Computation Conference (PSCC). IEEE, 2018. p. 1-8 8442671.

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Tyuryukanov, I, Popov, M, Van Der Meijden, MAMM & Terzija, V 2018, Spectral MST-Based Graph Outlier Detection With Application to Clustering of Power Networks. in 20th Power Systems Computation Conference (PSCC)., 8442671, IEEE, pp. 1-8, 20th Power Systems Computation Conference, PSCC 2018, Dublin, Ireland, 11/06/18. https://doi.org/10.23919/PSCC.2018.8442671

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@inproceedings{82afae5a151d4a3b9a53b533f70b9ef1,
title = "Spectral MST-Based Graph Outlier Detection With Application to Clustering of Power Networks",
abstract = "An increasing number of methods for control and analysis of power systems relies on representing power networks as weighted undirected graphs. Unfortunately, the presence of outliers in power system graphs may have a negative impact on many of these methods. In addition, detecting outliers can be a relevant task on its own. Motivated by the low number of outlier detection algorithms focusing on weighted undirected graphs, this paper proposes an efficient and effective method to detect loosely connected graph clusters below a certain number of nodes. The essence of the method lies in the efficient examination of the spectral minimal spanning tree of the input graph. The obtained results on several large test power networks validate the high outlier detection performance of the proposed method and its high computational efficiency.",
keywords = "Graph outlier detection, Outliers, Power network partitioning, Power system analysis computing",
author = "Ilya Tyuryukanov and Marjan Popov and {Van Der Meijden}, {Mart A.M.M.} and Vladimir Terzija",
year = "2018",
doi = "10.23919/PSCC.2018.8442671",
language = "English",
isbn = "978-1-5386-1583-6 ",
pages = "1--8",
booktitle = "20th Power Systems Computation Conference (PSCC)",
publisher = "IEEE",
address = "United States",
note = "20th Power Systems Computation Conference, PSCC 2018 ; Conference date: 11-06-2018 Through 15-06-2018",
url = "http://www.pscc2018.net/",

}

RIS

TY - GEN

T1 - Spectral MST-Based Graph Outlier Detection With Application to Clustering of Power Networks

AU - Tyuryukanov, Ilya

AU - Popov, Marjan

AU - Van Der Meijden, Mart A.M.M.

AU - Terzija, Vladimir

PY - 2018

Y1 - 2018

N2 - An increasing number of methods for control and analysis of power systems relies on representing power networks as weighted undirected graphs. Unfortunately, the presence of outliers in power system graphs may have a negative impact on many of these methods. In addition, detecting outliers can be a relevant task on its own. Motivated by the low number of outlier detection algorithms focusing on weighted undirected graphs, this paper proposes an efficient and effective method to detect loosely connected graph clusters below a certain number of nodes. The essence of the method lies in the efficient examination of the spectral minimal spanning tree of the input graph. The obtained results on several large test power networks validate the high outlier detection performance of the proposed method and its high computational efficiency.

AB - An increasing number of methods for control and analysis of power systems relies on representing power networks as weighted undirected graphs. Unfortunately, the presence of outliers in power system graphs may have a negative impact on many of these methods. In addition, detecting outliers can be a relevant task on its own. Motivated by the low number of outlier detection algorithms focusing on weighted undirected graphs, this paper proposes an efficient and effective method to detect loosely connected graph clusters below a certain number of nodes. The essence of the method lies in the efficient examination of the spectral minimal spanning tree of the input graph. The obtained results on several large test power networks validate the high outlier detection performance of the proposed method and its high computational efficiency.

KW - Graph outlier detection

KW - Outliers

KW - Power network partitioning

KW - Power system analysis computing

UR - http://www.scopus.com/inward/record.url?scp=85054006227&partnerID=8YFLogxK

U2 - 10.23919/PSCC.2018.8442671

DO - 10.23919/PSCC.2018.8442671

M3 - Conference contribution

AN - SCOPUS:85054006227

SN - 978-1-5386-1583-6

SP - 1

EP - 8

BT - 20th Power Systems Computation Conference (PSCC)

PB - IEEE

T2 - 20th Power Systems Computation Conference, PSCC 2018

Y2 - 11 June 2018 through 15 June 2018

ER -

ID: 48042625