@inproceedings{d1059582843b4ef9bf7581a333d4abd4,
title = "Intentional islanding of power grids with data depth",
abstract = "A new method for intentional islanding of power grids is proposed, based on a data-driven and inherently geometric concept of data depth. The utility of the new depth-based islanding is illustrated in application to the Italian power grid. It is found that spectral clustering with data depths outperforms spectral clustering with k-means in terms of k-way expansion. Directions on how the k-depths can be extended to multilayer grids in a tensor representation are outlined.",
keywords = "Controlled islanding, clustering, complex networks, data depth, power grids, probabilistic geometry, tensor projection depth",
author = "Asim Dey and Gel, {Yulia R.} and Poor, {H. Vincent}",
note = "Funding Information: ∗Yulia R. Gel has been partially supported by NSF DMS 1736368 and NSF IIS 1633331, and H. Vincent Poor has been partially supported by NSF DMS 1736417, NSF CMMI 1435778 and NSF ECCS-1549881. This material is also based in part upon work supported by DARPA. This work was initiated while the second two authors were Visiting Scholars at the Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, under the support of EPSRC grant no EP/K032208/1. The authors would like to thank Mart{\'i}Rosas-Casals, for providing the data for European power grid networks and Yongwan Chun for help with GIS data extraction. Publisher Copyright: {\textcopyright} 2017 IEEE.; 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 ; Conference date: 10-12-2017 Through 13-12-2017",
year = "2018",
month = mar,
day = "9",
doi = "10.1109/CAMSAP.2017.8313149",
language = "English (US)",
series = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017",
address = "United States",
}