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

机译：使用球矢量机的电力系统在线小信号稳定性评估

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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.
机译：本文讨论了一种用于电力系统在线小信号稳定性评估的新方法。一种基于球矢量机的新方法已用于在线小信号稳定性评估。与支持向量机，人工神经网络和其他基于机器学习的算法相比，该方法的训练时间非常短，而且空间较小。而且，所提出的基于球向量机的算法具有较少的支持向量，因此比现有算法更快。本文还提出了一种新的基于决策树的特征选择算法。该算法已应用于新英格兰39总线和PST 16机测试电源系统。仿真结果表明了该方法在大规模电力系统在线小信号稳定性评估中的有效性。

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