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University of Tennessee at Chattanooga

A two step predictor-corrector method for voltage collapse point estimation

Abstract

dc:description.abstract

Voltage Collapse is the system failure to obtain acceptable voltage levels in significant part of the power system, and it is often due to system failure to satisfy reactive power demand. Voltage Collapse can lead to blackout like the one occurred in 2003 in North America. Methods for on-line voltage stability monitoring were established, and indices to quantify it were proposed. However, estimations of voltage collapse point based on these indices are often inaccurate or time consuming. A well-established method of voltage collapse point estimation is the Continuation Power Flow (CPF). CPF is considered accurate but, it is very computationally expensive for large systems. This work aims to speed up the predictor-corrector process by using a VSI called P-index. An initial prediction is made, corrected using a continuation technique, and then updated after correction. The results are relatively accurate and it makes a significant improvement to the CPF computational time.

Degree

thesis:*
Grantor dc:publisher
University of Tennessee at Chattanooga
Year dc:date.available
2020

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Ali, Anas Yousif
Contributors dc:contributor
  • Karrar, Abdelrahman A.
  • Eltom, Ahmed H.; Kobet, Gary L.
  • College of Engineering and Computer Science

Subjects

dc:subject × 2

Rights

dc:rights
Language dc:language
English, eng

Identifiers

dc:identifier.*
Repository record dc:identifier
https://scholar.utc.edu/theses/591
OAI identifier oai:identifier
oai:scholar.utc.edu:theses-1753

Chain of custody

source
Harvested from
University of Tennessee - Chattanooga
Base URL
scholar.utc.edu/do/oai/
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Ali, Anas Yousif. A two step predictor-corrector method for voltage collapse point estimation. University of Tennessee at Chattanooga, 2020. https://scholar.utc.edu/theses/591