2010
DOI: 10.1049/iet-gtd.2009.0452
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Extended topological approach for the assessment of structural vulnerability in transmission networks

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Cited by 113 publications
(104 citation statements)
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“…In the particular case of power grids, the simplicity embedded in purely topological measures have made them useless for practical purposes. Instead, this approach has been recently extended by considering the following electrical properties [6][7][8]:…”
Section: Extended Topological Measuresmentioning
confidence: 99%
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“…In the particular case of power grids, the simplicity embedded in purely topological measures have made them useless for practical purposes. Instead, this approach has been recently extended by considering the following electrical properties [6][7][8]:…”
Section: Extended Topological Measuresmentioning
confidence: 99%
“…But this approach has failed when it has been applied to power systems with different topological characteristics [5], mainly due to the poor definition of purely topological measures, away from the real physical and electrical definition of the system. In order to overcome this limitation, more specific topological measures have been defined [6,7]. Among these better suited to electrical systems extended measures, entropy degree (ED) and electrical betweenness (EB) have been presented as useful means to characterize the topology of the nodes of a power network [8].…”
Section: Introductionmentioning
confidence: 99%
“…Multiple methods were introduced in [4][5][6][7][8][9][10][11] to assess the vulnerability of conventional power systems. These studies ignore the cyber layer and focus on physical layer vulnerability assessment.…”
Section: Introductionmentioning
confidence: 99%
“…Traditional power system security analysis involves voltage stability [3,4], rotor angle stability, topological structure vulnerability [5], risk evaluation [6,7], extreme contingencies [8], etc. To accurately and efficiently assess power system security, large amounts of machine learning tools such as decision trees [9], extreme learning machine [10], and support vector machines [11] provide promising solutions.…”
Section: Introductionmentioning
confidence: 99%