Time series power flow framework for the analysis of FIDVR using linear regression

Wenbo Wang, Marc Diaz-Aguilo, Kwok Ben Mak, Francisco De Leon, Dariusz Czarkowski, Resk Ebrahem Uosef

Research output: Contribution to journalArticlepeer-review


A comprehensive time series power flow (TSPF) framework is proposed for the analysis of fault-induced delayed voltage recovery (FIDVR). TSPF bridges the gap between static power flow simulations and time-domain simulations for FIDVR analysis. FIDVR events can be simulated faster with TSPF, while transient simulations normally require much longer time. In the TSPF framework, a random model for the disconnection of the induction motors is proposed to determine the load for different 'snapshots' during FIDVR events. Regression analysis is used to predict the parameters needed in the simulations. There is no need to simplify the network topology or aggregate loads into clusters as in measurement-based load modeling approaches. The techniques presented in this paper successfully reproduced two FIDVR events recorded in heavily meshed distribution networks in New York City in 2010 and 2015. The paper uncovers that the proper modeling of motor protections (thermal and under-voltage) is key to properly predict FIDVR events.

Original languageEnglish (US)
Article number8355767
Pages (from-to)2945-2955
Number of pages11
JournalIEEE Transactions on Power Delivery
Issue number6
StatePublished - Dec 2018


  • Fault induced delayed voltage recovery (FIDVR)
  • ZIP load model
  • load model
  • simulation tools
  • thermal protection
  • time series power flow

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Time series power flow framework for the analysis of FIDVR using linear regression'. Together they form a unique fingerprint.

Cite this