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

机译：球矢量机在大型电力系统在线暂态稳定性评估中的应用

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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.
机译：在本文中，球矢量机（BVM）已用于大型电力系统的在线瞬态稳定性评估。为了对系统瞬态安全状态进行分类，已经针对所有突发事件对BVM进行了培训。与人工神经网络（ANN），支持向量机（SVM）和其他基于机器学习的算法相比，基于BVM的安全评估算法的训练时间和空间非常小。另外，提出的算法具有较少的支持向量（SV），因此比用于在线应用的现有算法更快。应用机器学习方法的要点之一是特征选择。本文提出了一种新的基于决策树（DT）的特征选择技术。所提出的基于BVM的算法已应用于新英格兰39总线电力系统。仿真结果表明了该方法在大型电力系统在线暂态稳定评估过程中的有效性和稳定性。所提出的特征选择算法已经与不同的特征选择算法进行了比较。仿真结果证明了所提特征算法的有效性。

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