@inproceedings{88e935c242e14201b6159f53f0dbe6b0,
title = "Gene expression programming for static security assessment of power systems",
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.",
keywords = "gene expression programming, line outage, power system classifier, probabilistic neural network, radial basis function neural network, Static security, voltage level",
author = "Khattab, \{H. M.\} and Abdelaziz, \{A. Y.\} and Mekhamer, \{S. F.\} and Badr, \{M. A.L.\} and El-Saadany, \{E. F.\}",
year = "2012",
doi = "10.1109/PESGM.2012.6345123",
language = "British English",
isbn = "9781467327275",
series = "IEEE Power and Energy Society General Meeting",
booktitle = "2012 IEEE Power and Energy Society General Meeting, PES 2012",
note = "2012 IEEE Power and Energy Society General Meeting, PES 2012 ; Conference date: 22-07-2012 Through 26-07-2012",
}