Back to results

University of Tennessee at Chattanooga

A novel optimization formulation for the direct computation of the voltage collapse point

Abstract

dc:description.abstract

The knowledge of the critical loading point of a power system is essential to assess its voltage stability condition. This point is traditionally obtained through continuation power flows which are relatively accurate but requires a considerable amount of processing time. In this work, the search for the critical loading point of a certain power system is formulated as a constrained optimization problem which is solved using a robust scheme known as the Dog-leg Trust Region. This technique is mainly characterized by its reliability and fast convergence as it makes advantage of the merits of classical optimization techniques and gets rid of their limitations. The proposed method was examined on several test systems namely the IEEE14, IEEE39, IEEE57, and IEEE118-bus systems. Besides successfully identifying the maximum loading point of these systems the algorithm achieved a considerable saving in processing time when compared to the continuation power flow analysis of PSAT.

Degree

thesis:*
Grantor dc:publisher
University of Tennessee at Chattanooga
Year dc:date.available
2018

Author and committee

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

Subjects

dc:subject × 1

Rights

dc:rights
Language dc:language
English, eng

Identifiers

dc:identifier.*
Repository record dc:identifier
https://scholar.utc.edu/theses/518
OAI identifier oai:identifier
oai:scholar.utc.edu:theses-1670

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

Elballa, Wafa E.. A novel optimization formulation for the direct computation of the voltage collapse point. University of Tennessee at Chattanooga, 2018. https://scholar.utc.edu/theses/518