Feature reduction techniques for power system security assessment

Mingoo Kim, Sung-Kwan Joo

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

3 Citations (Scopus)

Abstract

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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages525-534
Number of pages10
Volume4221 LNCS - I
Publication statusPublished - 2006 Oct 31
Event2nd International Conference on Natural Computation, ICNC 2006 - Xi'an, China
Duration: 2006 Sep 242006 Sep 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)03029743
ISSN (Electronic)16113349

Other

Other2nd International Conference on Natural Computation, ICNC 2006
CountryChina
CityXi'an
Period06/9/2406/9/28

Fingerprint

Motor Vehicles
Security systems
Power System
Neural Networks
Neural networks
Curse of Dimensionality
Dimensionality
Generator
Predict
Numerical Results
Demonstrate

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Kim, M., & Joo, S-K. (2006). Feature reduction techniques for power system security assessment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4221 LNCS - I, pp. 525-534). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4221 LNCS - I).

Feature reduction techniques for power system security assessment. / Kim, Mingoo; Joo, Sung-Kwan.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4221 LNCS - I 2006. p. 525-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4221 LNCS - I).

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

Kim, M & Joo, S-K 2006, Feature reduction techniques for power system security assessment. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4221 LNCS - I, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4221 LNCS - I, pp. 525-534, 2nd International Conference on Natural Computation, ICNC 2006, Xi'an, China, 06/9/24.
Kim M, Joo S-K. Feature reduction techniques for power system security assessment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4221 LNCS - I. 2006. p. 525-534. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Kim, Mingoo ; Joo, Sung-Kwan. / Feature reduction techniques for power system security assessment. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4221 LNCS - I 2006. pp. 525-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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