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Combined use of unsupervised and supervised learning for large scale power system static security mapping

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

This paper presents an artificial neural-net based technique which combines supervised and unsupervised learning for evaluating on-line power system static security. It automatically scans contingencies of a power system. The proposed approach allows the on-line security evaluation of (N -1) contingencies by considering the pre-fault state vector. ANN-based pattern recognition is carried out with the growing hierarchical self-organizing feature mapping (GHSOM) in order to provide an adaptive neural net architecture during its unsupervised training process. Numerical tests, carried out on a IEEE 14 buses power system are presented and discussed. The analysis using such method provides accurate results with a great saving in computation time.

Published in:

Industrial Electronics, 2004 IEEE International Symposium on  (Volume:2 )

Date of Conference:

4-7 May 2004

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