Electric power systems are characterized by their immense complexity. The assessment of their security on-line has always been a challenging task. Many possibilities were investigated in the past in an attempt to characterize the secure operating region of a power system. Pattern recognition is thus far the only tool that can take various degrees of network complexity into consideration. / In the present study, an efficient algorithm which learns adaptively the secure operating region is proposed. At each iteration, training operating points are generated sequentially on a piecewise linearly approximated separation surface computed by the one-nearest-neighbor (1-NN) rule. The separation surface so estimated approaches the true one as the number of training points increases. The algorithm not only provides a consistent technique in learning an unknown region, it generates a highly efficient training set. It is found to be effective in reducing the size of the training set without adverse effect to the classifier. / Once the secure region of a power system is available, the task of on-line security monitoring reduces to one of determining whether the current operating point resides in the secure region. As demonstrated in the thesis, both the security status and the security margin of the operating point can be assessed very efficiently. By using the piecewise linearly approximated secure region, the thesis proceeds to give efficient ways of moving an insecure operating point into the secure region. This comprises the problem of security enhancement. / The regionwise methodology via the Voronoi diagram developed in the thesis is also applied to a wide range of problems, such as network planning, coordinating tuning of machine parameters and automatic contingency selection. The major merit is that the dynamics and the nonlinearity of the system no longer present a limitation to solving these problems.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.75354 |
| Date | January 1986 |
| Creators | Fok, Danny Sik-Kwan |
| 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: 000416948, proquestno: AAINL38265, Theses scanned by UMI/ProQuest. |
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