The present thesis tackles the problem of on-line steady state security assessment in electric power transmission networks. The contingencies examined include generation shift as well as line (transformer) outages. / The methodology developed is Pattern Recognition-motivated although not entirely within the frame of traditional statistical Pattern Recognition. / Due to the fact that training samples are rather expensive to obtain in electric power engineering, our first concern was to develop and implement algorithms carrying out the task of intelligently acquiring training points. It is found that these algorithms, permit to substantially reduce the amount of off-line computational effort while, at the same time, the coherency and impartiality of the information contained in the training sets is enhanced. / A new scheme for security assessment (equally applicable for real time security screening) was developed based on the concept of the hyperellipsoids of confidence. It is shown that by proper utilization of the hyperellipsoids of confidence, uncertainty in real time decision making (directly related to the misclassification error) is circumvented. The results of the new methodology were verified by full scale ac simulations. / Finally, the usefulness and potential applicability of the new scheme is demonstrated for EHV equivalents. Its merits are simplicity and reliability in real time environment.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.71995 |
| Date | January 1984 |
| Creators | Rodolakis, Anthony J. |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Doctor of Philosophy (Department of Electrical Engineering.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 000218905, proquestno: AAINL20858, Theses scanned by UMI/ProQuest. |
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