On-line transient stability assessment of large-scale power systems by using ball vector machines

In this paper ball vector machine (BVM) has been used for on-line transient stability assessment of large-scale power systems. To classify the system transient security status, a BVM has been trained for all contingencies. The proposed BVM based security assessment algorithm has very small training time and space in comparison with artificial neural networks (ANN), support vector machines (SVM) and other machine learning based algorithms. In addition, the proposed algorithm has less support vectors (SV) and therefore is faster than existing algorithms for on-line applications. One of the main points, to apply a machine learning method is feature selection. In this paper, a new Decision Tree (DT) based feature selection technique has been presented. The proposed BVM based 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 transient stability assessment procedure of large-scale power system. The proposed feature selection algorithm has been compared with different feature selection algorithms. The simulation results demonstrate the effectiveness of the proposed feature algorithm.