Title:
Optimal allocation of reactive power to mitigate fault delayed voltage recovery

Simple item page
dc.contributor.advisor Meliopoulos, A. P. Sakis
dc.contributor.author Madan, Sandhya en_US
dc.contributor.committeeMember M. Begovic
dc.contributor.committeeMember R. Harley
dc.contributor.committeeMember S. Deng
dc.contributor.department Electrical and Computer Engineering en_US
dc.date.accessioned 2010-09-15T18:55:11Z
dc.date.available 2010-09-15T18:55:11Z
dc.date.issued 2010-07-09 en_US
dc.description.abstract The Masters Thesis research focuses on reactive power and voltage control during and following major power system disturbances such as faults and subsequent loss of transmission line(s) or generator(s), voltage recovery phenomena following successful fault clearing, dynamic swings of power systems and local voltage suppression, etc. During these events, load and other system dynamics may cause reactive power deficiencies and system voltage issues such as delayed voltage recovery. These phenomena may lead to secondary events such as tripping of loads and/or circuits. Dynamic VAr sources such as generators, static VAr compensators (SVCs), STATCOMs etc and to a lesser degree static VAr sources such as capacitor or reactor banks, can help the system recover from these contingencies by providing fast modulation of the reactive power. Because of the higher cost of dynamic VAr resources, it is important to optimize the deployment of these devices by minimizing the total installed capacity of dynamic VAR resources while meeting the technical requirement and achieving the necessary performance of the system. We refer to this problem as the optimal allocation of dynamic VAR sources (OAODVARS). OAODVARS has been addressed with traditional analytic methods as well as with Artificial Intelligence methods such as genetic algorithms and Tabu search using mostly power flow type models. Both type of methods, as reported in the literature, have not provided satisfactory solutions because they ignore system dynamics and especially load dynamics, in other words they are based on power flow type models. In addition the AI methods have been proved to be extremely inefficient. We propose a new approach that has the following two advantages: (a) it is based on a realistic model that captures system dynamics and (b) it is based on the efficient successive approximation dynamic programming. The solution is provided as a sequence of planning decisions over the planning horizon. The proposed method will be demonstrated on the IEEE 24-bus reliability test system. en_US
dc.description.degree M.S. en_US
dc.identifier.uri http://hdl.handle.net/1853/34749
dc.publisher Georgia Institute of Technology en_US
dc.subject Power system en_US
dc.subject Voltage stability en_US
dc.subject Reactive power en_US
dc.subject.lcsh Electric power system stability
dc.subject.lcsh Voltage regulators
dc.subject.lcsh Genetic algorithms
dc.subject.lcsh Electric power-plants Load
dc.title Optimal allocation of reactive power to mitigate fault delayed voltage recovery en_US
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.advisor Meliopoulos, A. P. Sakis
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication e6c102d1-d39c-4d1d-9070-a8743be93cb5
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
madan_sandhya_201008_mast.pdf
Size:
2.15 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Theses and Dissertations