摘要
基于神经网络的暂态稳定评估技术提出了一种新思路。使用半监督学习算法来训练反向传播神经网络 ,得到一个连续分布的暂态稳定指标 ,该指标可用来指明相对稳定度和确定类间边界区。一种数据结构分析方法被用来观察输入空间的可分性及边界区。在此基础上 ,提出一种新的分类方法 ,即将边界区样本分为不确定类 ,以避免误分类。在两个系统中的应用结果表明 ,该方法对暂态稳定评估问题的有效性。
This paper presents a new framework for arificial neural networks (ANN) based transient stability assessment(TSA).The ANN\|based TSA problems may be treated as a two\|pattern classification problem separating the stable class from the unstable class.The cases close to the classification boundary are often liable to be misclassified.This paper proposes to train a back\|propagation ANN using a novel semisupervised learning algorithm for deriving a continuous\|spread stability index.The derived stability index is used to indicate the relative stability degree and define a boundary zone between the two different classes.A new classification scheme is hence proposed to group the boundary\|zone cases into an extra indeterminate class to avoid misclassifications.A nonlinear mapping for data structure analysis is employed as a convenient tool to observe the separability of the input space.Two well\|studied power systems are employed to demonstrate the validity of the proposed approach.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2000年第4期77-82,共6页
Proceedings of the CSEE
关键词
电力系统
暂态稳定评估
神经网络
反向传播
neural networks
back propagation
semi supervised learning
pattern classification
transient stability
