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The ANN Dynamic Security Regions of Power System Dynamic Security Analysis

机译:电力系统神经网络动态安全区域动态安全性分析

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摘要

PDSR imitated by ANN to judge the security of the power system is described in this paper. The two-kind and three-kind classifier are constructed by ANN to judge the stability of running points exactly, and the remains imitator built by ANN, the output of which is shown by CCT t_(cr), is used to decide the stability remains of running points of the power system. The emulation on the three-machine and nine-bus test system demonstrates that the DSR imitated by ANN is more accurate than the hyper-plane method. However, with the expansion of the injection elements, the 'dimensions misfortune' problem is caused easily, so optimal feature subset of decision tables is used, combined with element discretization of rough-set, to select a group of effective feature elements from larger dimensions and rebuild the ANN. The simulation performed on EPRI-36 system shows a good result.
机译:本文描述了人工神经网络用来判断电力系统安全性的PDSR。 ANN构造两类和三类分类器以准确判断运行点的稳定性,而ANN构造的残差模仿器的输出由CCT t_(cr)表示,用于确定残差的稳定性。电力系统的运行点。在三机九总线测试系统上的仿真表明,ANN模仿的DSR比超平面方法更准确。但是,随着注入元素的扩展,容易造成“维数不幸”问题,因此,使用决策表的最佳特征子集,结合粗糙集的元素离散化,从较大的维度中选择一组有效的特征元素并重建ANN。在EPRI-36系统上进行的仿真显示了良好的结果。

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