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On-line Small-signal Stability Assessment of Power Systems Using Ball Vector Machines

Abstract This article deals with a new method for on-line small signal stability assessment of power systems. A new, ball-vector-machine-based method has been used for on-line small-signal stability assessment. The proposed method has a very short training time and a small space in comparison with support vector machines, artificial neural networks, and other machine-learning-based algorithms. Also, the proposed ball-vector-machine-based algorithm has fewer support vectors and, therefore, is faster than existing algorithms. In this article, a new decision-tree-based feature-selection algorithm has also been presented. The proposed algorithm has been applied to New England 39-bus and PST 16-machine test power systems. The simulation results show the effectiveness of the proposed method for on-line small-signal stability assessment of large-scale power system.