On-line voltage security assessment of power systems using core vector machines

This paper presents a core vector machine (CVM)-based algorithm for on-line voltage security assessment of power systems. To classify the system security status, a CVM has been trained for each contingency. The proposed CVM-based security assessment has very small training time and space in comparison with support vector machines (SVM) and artificial neural networks (ANNs)-based algorithms. The proposed algorithm produces less support vectors (SV) and therefore is faster than existing algorithms. In this paper, a new decision tree (DT)-based feature selection technique has been presented, too. The proposed CVM algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line voltage security assessment procedure of large-scale power system.