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In the post-fault dynamic analysis of interconnected power systems, the critical fault clearing time (CCT) is one of the parameters of paramount importance. It constitutes a complex function of the pre-fault system condition, fault type and location, and protective relaying strategy. The evaluation of CCT involves elaborate computations that often include time-consuming solutions of nonlinear on-fault system equations. This paper describes an adaptive pattern recognition approach based on highly parallel information processing using artificial neural networks (ANN). High adaptation capabilities of these networks make them able to synthesize the complex mappings that carry the input attributesfeatures into the single valued space of the CCT's. Appropriate input feature selection makes this approach a candidate for successfully handling toDologicallv indeoendent dynamic security assessment process.

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