DocumentCode :
2721037
Title :
Self-organizing feature maps for power system dynamic security assessment using synchronizing and damping torques technique
Author :
Boudour, M. ; Hellal, A.
Author_Institution :
Electr. Eng. Dept., Univ. of Algiers, Algeria
Volume :
1
fYear :
2003
fDate :
2-6 Nov. 2003
Firstpage :
752
Abstract :
This paper proposes a new methodology of the power system dynamic security assessment. Based on the concept of stability margin, the method 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 self-organization feature mapping developed by Kohonen. Numerical results, carried out on a IEEE 9 buses power system are presented and discussed. The analysis using such method provides accurate results with a great saving in computation time.
Keywords :
IEEE standards; pattern recognition; power system analysis computing; power system dynamic stability; power system security; self-organising feature maps; synchronisation; unsupervised learning; ANN based pattern recognition; IEEE 9 buses power system; Kohonen self organizing feature maps; artificial neural networks; computation time; damping torque technique; dynamic security assessment; dynamic stability index; multimachine power systems; power systems; stability margin; synchronization; unsupervised learning; Damping; Neurons; Pattern recognition; Power system analysis computing; Power system dynamics; Power system interconnection; Power system modeling; Power system security; Power system stability; Torque;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics Society, 2003. IECON '03. The 29th Annual Conference of the IEEE
Print_ISBN :
0-7803-7906-3
Type :
conf
DOI :
10.1109/IECON.2003.1280077
Filename :
1280077
Link To Document :
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