【作者】 李军；

【导师】 顾雪平；

【作者基本信息】 华北电力大学（河北） ， 电力系统及其自动化， 2005， 硕士

【摘要】 神经网络以其固有的高执行速度在电力系统暂态稳定评估领域具有良好的应用前景,但是神经网络存在的两个缺点限制了其在线应用。其一,随着系统规模的扩大,训练样本数目的增加,其训练速度慢的缺点显露出来。针对这个问题,本文提出基于信息熵的属性离散化算法用于训练样本集的压缩,从而大大提高了神经网络的训练速度。其二,由于暂态稳定评估问题输入空间的复杂性以及神经网络本身的局限性,神经网络的分类结果中不可避免地会存在误分类。本文针对误分类的问题,提出在特征矩阵的基础上,将神经网络和粗糙集理论相结合,构造新型的神经网络集成分类器,从而有效地提高了神经网络用于暂态稳定评估时的分类准确率。更多还原

【Abstract】 Transient stability assessment based on artificial neural networks (ANNs) has shown much potential for online application in view of the computation efficiency of ANNs. However, two shortcomings of ANNs hinder them to be employed in an online environment. Firstly, with the increases of the system size, the training samples increase dramatically. The training burden, therefore, becomes too heavy. To this problem, a method for continuous attribute discretization based entropy is proposed to compress the training sample set in this thesis. Secondly, misclassifications in the boundary region between the two classes are in fact unavoidable due to the complexity of TSA input dimension and the limitation of ANN. To this problem, an assembling classification schemes is proposed by integrating different ANN classifiers based attribute matrix in rough set theory to improve the classification reliability. The application examples of the 10-machine 39-bus power system show the validity of the proposed methods for transient stability assessment.更多还原