A new BVM based approach to transient security assessment

Machine Learning techniques have been extensively used in Power Systems Analysis during the last years. This paper describes a ball vector machine based algorithm for on-line transient security assessment of large-scale power systems. The proposed ball vector machine based security assessment reduces the training time and space complexities in comparison with support vector machines, artificial neural networks, and other machine learning based algorithms. In addition, the proposed algorithm has less support vectors and therefore is faster than existing algorithms for -line applications. A new decision tree based feature selection technique has been presented, too. The proposed ball vector machine based,algorithm has been applied to New England 39-bus test system. The simulation results show the effectiveness of the proposed algorithm for on-line transient security assessment procedure. Copyright (C) 2010 John Wiley & Sons, Ltd.