Optimal micro-PMU placement and virtualization for distribution network changing topologies

L. Ibarra, J. Avilés, D. Guillen, J. C. Mayo-Maldonado, J. E. Valdez-Resendiz, P. Ponce

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Distribution networks are currently the main focus of modernization in electric power systems. For instance, to deal with emerging and increasingly challenging scenarios (e.g., high penetration of renewables, distributed generation, electric vehicle proliferation, etc.), new smart grid technologies are being deployed over distribution networks. This is the case of micro phasor measurement units (μPMUs), which can be seen as a modern high-precision version of the legacy transmission system PMU. Motivated by the benefits, as well as the costs associated with μPMU deployment, in this work we introduce a new methodology to determine optimal μPMU placement, considering grid topology changes. Firstly, a technique to minimize power losses across the grid by changing its topology is presented. Afterward, a technique to estimate the grid states – full numerical observability – with the minimum number of μPMUs is disclosed, introducing the concept of bus “virtualization” through pseudo-measurements. This work is based on a multi-objective approach, acting on genetic algorithms, and validated over a distribution network with a variant structure, which is optimally instrumented. It is shown that the proposed methodology can maintain low approximation errors despite topology variations. Also, the virtualization approach enables full numerical observability even when using fewer measurement units, i.e., the observability constraint is eliminated and the number of μPMUs can be solely related to the estimation error.

Original languageEnglish
Article number100510
JournalSustainable Energy, Grids and Networks
StatePublished - Sep 2021
Externally publishedYes

Bibliographical note

Funding Information:
This work has been sponsored by seed program University of Alberta-Tecnologico de Monterrey : Development of smart grid and energy storage management technologies for distribution networks.

Publisher Copyright:
© 2021 Elsevier Ltd


  • Distribution network
  • Genetic algorithms
  • Grid reconfiguration
  • Monitoring
  • Optimal placement


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