Towards effective clustering techniques for the analysis of electric power grids

  • Authors:

  • Emilie Hogan;Eduardo Cotilla-Sanchez;Mahantesh Halappanavar;Shaobu Wang;Patrick Mackey;Paul Hines;Zhenyu Huang

  • Affiliations:

  • Pacific Northwest National Laboratory;Oregon State University;Pacific Northwest National Laboratory;Pacific Northwest National Laboratory;Pacific Northwest National Laboratory;University of Vermont;Pacific Northwest National Laboratory

  • Venue:
  • HiPCNA-PG '13 Proceedings of the 3rd International Workshop on High Performance Computing, Networking and Analytics for the Power Grid
  • Year:
  • 2013

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Abstract

Clustering is an important data analysis technique with numerous applications in the analysis of electric power grids. Standard clustering techniques are oblivious to the rich structural and dynamic information available for power grids. Therefore, by exploiting the inherent topological and electrical structure in the power grid data, we propose new methods for clustering with applications to model reduction, locational marginal pricing, phasor measurement unit (PMU or synchrophasor) placement, and power system protection. We focus our attention on model reduction for analysis based on time-series information from synchrophasor measurement devices, and spectral techniques for clustering. By comparing different clustering techniques on two instances of realistic power grids we show that the solutions are related and therefore one could leverage that relationship for a computational advantage. Thus, by contrasting different clustering techniques we make a case for exploiting structure inherent in the data with implications for several domains including power systems.