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

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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.

Original languageEnglish
Title of host publication20th Power Systems Computation Conference (PSCC)
Number of pages8
ISBN (Electronic)978-1-910963-10-4
ISBN (Print)978-1-5386-1583-6
Publication statusPublished - 2018
Event20th Power Systems Computation Conference, PSCC 2018 - Dublin, Ireland
Duration: 11 Jun 201815 Jun 2018


Conference20th Power Systems Computation Conference, PSCC 2018
Internet address

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Graph outlier detection
  • Outliers
  • Power network partitioning
  • Power system analysis computing


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