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 journalArticle

Abstract

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 modelling approaches. The techniques presented in this paper successfully reproduced two FIDVR events recorded in heavily-meshed distribution net-works in New York City in 2010 and 2015. The paper uncovers that the proper modeling of motor protections (thermal and un-der-voltage) is key to properly predict FIDVR events.

Original languageEnglish (US)
JournalIEEE Transactions on Power Delivery
DOIs
StateAccepted/In press - May 5 2018

Fingerprint

Linear regression
Time series
Recovery
Electric potential
Flow simulation
Regression analysis
Induction motors
Topology

Keywords

  • Fault Induced Delayed Voltage Recovery (FIDVR)
  • load model
  • simulation tools
  • thermal protection
  • time series power flow
  • ZIP load model

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

Time Series Power Flow Framework for the Analysis of FIDVR Using Linear Regression. / Wang, Wenbo; Diaz-Aguilo, Marc; Mak, Kwok Ben; De Leon, Francisco; Czarkowski, Dariusz; Uosef, Resk Ebrahem.

In: IEEE Transactions on Power Delivery, 05.05.2018.

Research output: Contribution to journalArticle

@article{f395147daee64911a7a3c3134537c809,
title = "Time Series Power Flow Framework for the Analysis of FIDVR Using Linear Regression",
abstract = "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 modelling approaches. The techniques presented in this paper successfully reproduced two FIDVR events recorded in heavily-meshed distribution net-works in New York City in 2010 and 2015. The paper uncovers that the proper modeling of motor protections (thermal and un-der-voltage) is key to properly predict FIDVR events.",
keywords = "Fault Induced Delayed Voltage Recovery (FIDVR), load model, simulation tools, thermal protection, time series power flow, ZIP load model",
author = "Wenbo Wang and Marc Diaz-Aguilo and Mak, {Kwok Ben} and {De Leon}, Francisco and Dariusz Czarkowski and Uosef, {Resk Ebrahem}",
year = "2018",
month = "5",
day = "5",
doi = "10.1109/TPWRD.2018.2832852",
language = "English (US)",
journal = "IEEE Transactions on Power Delivery",
issn = "0885-8977",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Time Series Power Flow Framework for the Analysis of FIDVR Using Linear Regression

AU - Wang, Wenbo

AU - Diaz-Aguilo, Marc

AU - Mak, Kwok Ben

AU - De Leon, Francisco

AU - Czarkowski, Dariusz

AU - Uosef, Resk Ebrahem

PY - 2018/5/5

Y1 - 2018/5/5

N2 - 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 modelling approaches. The techniques presented in this paper successfully reproduced two FIDVR events recorded in heavily-meshed distribution net-works in New York City in 2010 and 2015. The paper uncovers that the proper modeling of motor protections (thermal and un-der-voltage) is key to properly predict FIDVR events.

AB - 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 modelling approaches. The techniques presented in this paper successfully reproduced two FIDVR events recorded in heavily-meshed distribution net-works in New York City in 2010 and 2015. The paper uncovers that the proper modeling of motor protections (thermal and un-der-voltage) is key to properly predict FIDVR events.

KW - Fault Induced Delayed Voltage Recovery (FIDVR)

KW - load model

KW - simulation tools

KW - thermal protection

KW - time series power flow

KW - ZIP load model

UR - http://www.scopus.com/inward/record.url?scp=85046455965&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046455965&partnerID=8YFLogxK

U2 - 10.1109/TPWRD.2018.2832852

DO - 10.1109/TPWRD.2018.2832852

M3 - Article

JO - IEEE Transactions on Power Delivery

JF - IEEE Transactions on Power Delivery

SN - 0885-8977

ER -