Application of core vector machines for on-line voltage security assessment using a decisiontree-based feature selection algorithm

This study 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 algorithm has a very small training time and space in comparison with support vector machines (SVMs) and artificial neural networks (ANNs)-based algorithms. The proposed algorithm produces less support vectors (SVs). Therefore is faster than existing algorithms. One of the main points to apply a machine learning method is feature selection. In this study, a new decision tree (DT)-based feature selection algorithm has been presented. 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. The effectiveness of the proposed feature selection algorithm has also been investigated. The proposed feature selection algorithm has been compared with different feature selection algorithms. The simulation results demonstrate the effectiveness of the proposed feature algorithm.