Gene expression programming for power system static security assessment

Abstract

In this paper, a novel gene expression programming (GEP) algorithm is presented 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 and achieve high classification accuracy. The proposed methodology introduces the GEP based classifier for the first time in static security assessment problems. The proposed algorithm is examined using different IEEE standard test systems available in the literature. Different contingency case studies have been used to test the proposed methodology performance. The GEP based algorithm formulates the problem as a multi-class classification problem using the one-against-all binarization method. The algorithm classifies the static security of the power system into three classes, namely normal, alert and emergency. Performance of the algorithm is compared with other probabilistic and deterministic algorithms including different neural network based classifiers. Simulation results show the superiority of the proposed technique in static security assessment.

Keywords: static security, gene expression programming, probabilistic neural network, radial basis function neural network, power system classifier.