Intentional islanding of power grids with data depth

Asim Dey, Yulia R. Gel, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538612514
DOIs
StatePublished - Mar 9 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: Dec 10 2017Dec 13 2017

Publication series

Name2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Volume2017-December

Conference

Conference7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Control and Optimization
  • Instrumentation

Keywords

  • Controlled islanding
  • clustering
  • complex networks
  • data depth
  • power grids
  • probabilistic geometry
  • tensor projection depth

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  • Cite this

    Dey, A., Gel, Y. R., & Poor, H. V. (2018). Intentional islanding of power grids with data depth. In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 (pp. 1-5). (2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAMSAP.2017.8313149