Network centrality measures for classifying important components in electrical power systems based on linegraph transformation

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José A. Moronta R.
Claudio M. Rocco S.


Network theory techniques have recently contributed to the analysis of electrical power systems, enabling faster computational solutions. Taking advantage of the topological information of a network, it becomes possible to characterize its elements both locally (individual network components) and globally (interactions and behavior of the components). Identifying the crucial elements within an electrical system involves classifying each component based on its interaction with the entire network, considering, possibly, various operating conditions. Current network centrality measures predominantly focus on nodes, which represent connection buses in the system, to quantify the significance of individual elements. In this study, we employ the linegraph technique to transform links into nodes. Subsequently, we calculate and categorize the links (representing lines and transformers) of different electrical networks found in the literature using three centrality measures. Moreover, our methodology allows for the aggregation or combination of the indices from each measure, leading to a unified classification based on the importance of links in the considered electrical power systems. Analyzing diverse networks reveals a consistent empirical distribution of centrality indices, resulting in similar classifications of significant elements regardless of network size.