It is highly desirable that the security and stability of electric power systems after exposure to large disturbances be assessable. In this connection, the critical clearing time (CCT) is an attribute which provides significant information about the quality of the post-fault system behavior. It may be regarded as a complex mapping of the prefault, fault-on, and post-fault system conditions in the time domain. Y.-H. Pao and D.J. Solajic (1989) showed that a feedforward neural network can be used to learn this mapping and successfully perform under variable system operating conditions and topologies. In that work the system was described in terms of some conventionally used parameters. In contrast to using those pragmatic features selected on the basis of the engineering understanding of the problem, the possibility of using unsupervised and supervised learning paradigms to discover what combination of raw measurements are significant in determining CCT is considered. Correlation analysis and Euclidean metric are used to specify interfeature dependencies. An example of a 4-machine power system is used to illustrate the suggested approach.
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