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Lu, Lian. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 91-95). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.

Thesis--M. Phil., University of Hong Kong, 1978.

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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.

Thesis (M.S.)--Ohio University, August, 2006. / Title from PDF t.p. Includes bibliographical references.

Thesis (M.S. in electrical engineering)--Washington State University, December 2007. / Includes bibliographical references (p. 61-62).