Gene expression programming for static security assessment of power systems

H. M. Khattab, A. Y. Abdelaziz, S. F. Mekhamer, M. A.L. Badr, E. F. El-Saadany

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

In this paper, a novel gene expression programming (GEP) algorithm is introduced for power system static security assessment. The GEP algorithms as evolutionary algorithms for pattern classification have recently received attention for classification problems because they can perform global searches. The proposed methodology introduces the GEP for the first time in static security assessment problems. The proposed algorithm is examined using different IEEE standard test systems. Different contingency case studies have been used to test the proposed methodology. The GEP based algorithm formulates the problem as a multi-class classification problem using the one-against-all binarization method. The algorithm classifies the security of the power system into three classes, normal, alert and emergency. Performance of the algorithm is compared with other neural network based algorithm classifiers to show its superiority in static security assessment.

Original languageBritish English
Title of host publication2012 IEEE Power and Energy Society General Meeting, PES 2012
DOIs
StatePublished - 2012
Event2012 IEEE Power and Energy Society General Meeting, PES 2012 - San Diego, CA, United States
Duration: 22 Jul 201226 Jul 2012

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2012 IEEE Power and Energy Society General Meeting, PES 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period22/07/1226/07/12

Keywords

  • gene expression programming
  • line outage
  • power system classifier
  • probabilistic neural network
  • radial basis function neural network
  • Static security
  • voltage level

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