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Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system

机译：比较大型电力系统瞬态稳定性评估中的最小二乘支持向量机和概率神经网络

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This paper presents transient stability assessment of a large practical power system using two artificial neural network techniques which are the probabilistic neural network (PNN) and the least squares support vector machine (LS-SVM). The large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the number of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the test system considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN and LS-SVM. Both networks are used as classifiers to determine whether the power system is stable or unstable. Classification results show that the PNN gives faster and more accurate transient stability assessment compared to the LS-SVM.
机译：本文呈现使用两个人工神经网络技术，其是概率神经网络（PNN）和最小二乘支持向量机（LS-SVM）具有很大的实用电力系统暂态稳定评估。根据对受扰动区域的相干性，大型电力系统分为五个较小的区域。这是为了减少为各个区域收集的数据集的数量。首先基于从时域模拟输出获得的发电机相对转子角度来确定电力系统的瞬态稳定性。考虑到不同负载条件下的三相故障，对测试系统进行了模拟。然后将从时域仿真收集的数据用作PNN和LS-SVM的输入。两个网络用作分类器，以确定电力系统是否稳定或不稳定。分类结果表明，与LS-SVM相比，PNN提供更快，更准确的瞬态稳定性评估。

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