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Critical Contingencies Ranking for Dynamic Security Assessment Using Neural Networks



Author(s)

Gustavo Schweickardt¹ and Juan Manuel Gimenez-Alvarez²

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DOI:10.17265/1934-8975/2012.10.016

1. Energetic Economy Institute, Bariloche Atomic Center, Bariloche CP 8400, Argentina
2. Faculty of Engineering, National University of San Juan, San Juan CP 5400, Argentina

A number of contingencies simulated during dynamic security assessment do not generate unacceptable values of power system state variables, due to their small influence on system operation. Their exclusion from the set of contingencies to be simulated in the security assessment would achieve a significant reduction in computation time. This paper defines a critical contingencies selection method for on-line dynamic security assessment. The selection method results from an off-line dynamical analysis, which covers typical scenarios and also covers various related aspects like frequency, voltage, and angle analyses among others. Indexes measured over these typical scenarios are used to train neural networks, capable of performing on-line estimation of a critical contingencies list according to the system state.

Critical contingencies, dynamic security assessment, neural networks.