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Title: Acceleration of power system small signal stability analysis
Author: McIlhagger, David
ISNI:       0000 0001 3624 7396
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2007
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Abstract:
Electric power networks comprise large complex interconnections of generation and loads. The generators, and their controllers, are non-linear dynamic systems which on interconnection form a very complex control problem. Traditionally the models used to capture the small signal Rtability of these systems were not highly detailed. This waR jURtified Rince the generation was provided by large centralized power Rtations, however with the current trend towards small scale and diRtributed generation, as provided by.wind farmR and diesel genRetR, the power system modelR require a greater level of detail. This means that the stability assessment of theRe models involves greater detail and requires greater computation time, thus rendering near future predictions obsolete. ThiR thesis studies the methods that are Ilsed to determine • power system small signal stability, in order to provide acceleration to this analysis. A method based on wavelet approximations to provide an approximate solution was developed and its effectiveneRs against the traditional QR algorithm waR investigated. The method was applied to a four generator RyRtem and the IEEE New England 39 bus Rystem. Alternative methods to form accelerating polynomials for eigenvalue methodR were developed and evaluated against the IEEE New England 39 bus system. A new algorithm, called the polygon polynomial Arnoldi method (PPAM) was developed and tested against the implicitly restarted Arnoldi method (IRAM), from the linear algebra literature. The effectiveness of both theRe methods was tested against the IEEE New England 39 bus system and the one area IEEE reliability test system along with that for the QR algorithm.
Supervisor: Not available Sponsor: Not available
Qualification Name: Queen's University Belfast, 2007 Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.486529  DOI: Not available
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