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Large scale power system dynamic security assessment using the growing hierarchical self-organizing feature maps

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2 Author(s)
Boudour, M. ; Dept. of Electr. Eng., Sci. & Technol. Univ., Algiers, Algeria ; Hellal, A.

This paper proposes a new methodology which combines supervised and unsupervised learning for evaluating power system dynamic security. Based on the concept of stability margin, pre-fault power system conditions are assigned to the output neurons on the two-dimensional grid with the growing hierarchical self-organizing map technique (GHSOM) via supervised ANNs which performs an estimation of post-fault power system state. The technique estimates the dynamic stability index that corresponds to the most critical value of synchronizing and damping torques of multimachine power systems. ANN-based pattern recognition is carried out with the growing hierarchical self-organizing feature mapping in order to provide an adaptive neural net architecture during its unsupervised training process. Numerical tests, carried out on a IEEE 9 bus power system are presented and discussed. The analysis using such method provides accurate results and improves the effectiveness of system security evaluation.

Published in:

Industrial Technology, 2004. IEEE ICIT '04. 2004 IEEE International Conference on  (Volume:1 )

Date of Conference:

8-10 Dec. 2004