On the transient behavior of large-scale distribution networks during automatic feeder reconfiguration

Vitaly Spitsa, Xuanchang Ran, Reynaldo Salcedo, Juan F. Martinez, Resk Ebrahem Uosef, Francisco De León, Dariusz Czarkowski, Zivan Zabar

Research output: Contribution to journalArticlepeer-review


The paper presents an in-depth analysis of the automatic reconfiguration and self-healing principles of the next generation (3G) smart grid of a real metropolitan distribution network. The large network is to be divided dynamically and remotely controlled into three smaller subnetworks to further increase the reliability of electrical power distribution secondary networks. When one subsection is experiencing difficulties, there is no longer the need to de-energize the entire network. A time-domain (EMTP) model has been developed and validated by comparing simulations with recordings of actual transient events. Different switching and fault scenarios are investigated using this model. Analysis of the results provides important conclusions on equipment rating, relay protection coordination, voltage regulation, switching and operation strategies which are discussed in the paper. A subset of these results is presented for illustration. This extensive study of a complex urban network suggests that: 1) before implementation of smart grid principles, it would be prudent to supplement steady-state analysis with time-domain analysis to avoid problems, such as installation of improperly rated equipment, and improper relay-protection coordination; and 2) EMTP-type programs may be used to conduct the time-domain analysis, despite the enormous number of elements contained in an urban network.

Original languageEnglish (US)
Article number6204243
Pages (from-to)887-896
Number of pages10
JournalIEEE Transactions on Smart Grid
Issue number2
StatePublished - 2012


  • Distribution automation
  • power distribution
  • power system modeling
  • smart grid
  • switching transients
  • time-domain analysis

ASJC Scopus subject areas

  • General Computer Science


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