Community detection in power grids by an evolutionary method

Guerrero, Manuel; Gil, C.; Montoya, Francisco G.; Alcayde, Alfredo

doi:10.1145/3067695.3075606

Cited by 1 publication

(1 citation statement)

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“…For this reason, clustering recently received a lot of attention in the power systems literature. A very diverse set of methodologies have been considered, ranging from spectral clustering [135] to various heuristics based on the modularity score [50,88]. A substantial effort has been devoted to expand and augment classical clustering methods in order to account for specific features of power systems, for instance using the notion of electrical distance [10,26] or conductances [93,92].…”

Section: Related Literaturementioning

confidence: 99%

A Spectral Representation of Power Systems with Applications to Adaptive Grid Partitioning and Cascading Failure Localization

Zocca¹,

Chen²,

Guo³

et al. 2021

Preprint

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Transmission line failures in power systems propagate and cascade non-locally. This well-known yet counter-intuitive feature makes it even more challenging to optimally and reliably operate these complex networks. In this work we present a comprehensive framework based on spectral graph theory that fully and rigorously captures how multiple simultaneous line failures propagate, distinguishing between non-cut and cut set outages. Using this spectral representation of power systems, we identify the crucial graph sub-structure that ensures line failure localization -the network bridge-block decomposition. Leveraging this theory, we propose an adaptive network topology reconfiguration paradigm that uses a two-stage algorithm where the first stage aims to identify optimal clusters using the notion of network modularity and the second stage refines the clusters by means of optimal line switching actions. Our proposed methodology is illustrated using extensive numerical examples on standard IEEE networks and we discussed several extensions and variants of the proposed algorithm.

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