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UBC Theses and Dissertations

Online power system security classifier and enhancement control De Arizon, Paloma


Today, the stable and reliable operation of power systems is becoming increasingly difficult. While, on the one hand, due to regulatory reforms, an increasing number of merchant plants and co-generators are being connected to the network, on the other, the expansion of transmission systems has been increasingly difficult due to environmental and land issues. As a result, thousands of power flow schedules are changed hourly, complicating the systems operation. This scenario has rendered the traditional system operation criteria, based on off-line studies, inadequate to cope with the constantly changing nature, and online assessment techniques are becoming increasingly important. In this work, an approach based on "approximate reasoning techniques" is presented for the classification o f dynamic security conditions in the power system and for the selection of dynamic security enhancement strategies (preventive control actions). The algorithm proposed combines energy functions and sensitivities to find the "membership" of the system to the subsets that determine its security status, as well as, the "membership" of the different generators to specific control related subsets. The algorithm proposed combines energy functions and sensitivities, together with physical equipment limitations, to select the generators voltage and power output such that the required security level is met. The procedure uses optimisation methods for tuning the threshold values that describe the membership functions in order to obtain optimum preventive control strategies. The developed procedure was also designed to produce a simultaneous preventive control for a set of non-disjoint contingencies. Finally to enhance the speed of the algorithm a new and time-saving technique for efficient [Y[sub bus]] matrix evaluations was developed in this work. The results presented in the work show that the proposed method achieves the following objectives: a) A fast and accurate classification of the system security conditions. b) A classification that is flexible enough to allow the inclusion of other important parameters. c) The development of a stability enhancement strategy that produces a system that is secure for a wide range of credible contingencies. d) The development of a method that combines "firm" knowledge based on analytical solutions with "soft" expert system knowledge.

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