Feature reduction techniques for power system security assessment

Mingoo Kim, Sung Kwan Joo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


Neural Networks (NN) have been applied to the security assessment of power systems and have shown great potential for predicting the security of large power systems. The curse of dimensionality states that the required size of the training set for accurate NN increases exponentially with the size of input dimension. Thus, an effective feature reduction technique is needed to reduce the dimensionality of the operating space and create a high correlation of input data with the decision space. This paper presents a new feature reduction technique for NN-based power system security assessment. The proposed feature reduction technique reduces the computational burden and the NN is rapidly trained to predict the security of power systems. The proposed feature reduction technique was implemented and tested on IEEE 50-generator, 145-bus system. Numerical results are presented to demonstrate the performance of the proposed feature reduction technique.

Original languageEnglish
Title of host publicationAdvances in Natural Computation - Second International Conference, ICNC 2006, Proceedings,
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540459014, 9783540459019
Publication statusPublished - 2006
Event2nd International Conference on Natural Computation, ICNC 2006 - Xi'an, China
Duration: 2006 Sept 242006 Sept 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4221 LNCS - I
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2nd International Conference on Natural Computation, ICNC 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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