首页 | 本学科首页   官方微博 | 高级检索  
     

基于统计学习理论的电力系统暂态稳定评估
引用本文:许涛,贺仁睦,王鹏,徐东杰.基于统计学习理论的电力系统暂态稳定评估[J].中国电机工程学报,2003,23(11):51-55.
作者姓名:许涛  贺仁睦  王鹏  徐东杰
作者单位:华北电力大学电力系统控制研究所,北京,102206
摘    要:该文利用基于结构风险最小化原理的支持向量机,结合装袋和近似推理,提出了电力系统暂态稳定评估模型的构造方法。该方法充分发挥支持向量机在解决有限样本、非线性及高维识别中体现出的优势,有效地提高了暂稳评估模型的泛化能力,并通过训练样本集重构解决了暂稳评估的多类识别问题,在该评估模型中利用样本规范化、装袋和近似推理提高了训练速度和预测结果的精度及稳定性。在IEEE39节点测试系统中的应用结果证明了该方法对暂态稳定评估的有效性。

关 键 词:电力系统  暂态稳定评估  统计学习理论  支持向量机  人工智能  神经网络
文章编号:0258-8013(2003)11-0051-05
修稿时间:2003年4月10日

POWER SYSTEM TRANSIENT STABILITY ASSESSMENT BASED ON STATISTICAL LEARNING THEORY
XU Tao,HE Ren-mu,WANG Peng,XU Dong-jie.POWER SYSTEM TRANSIENT STABILITY ASSESSMENT BASED ON STATISTICAL LEARNING THEORY[J].Proceedings of the CSEE,2003,23(11):51-55.
Authors:XU Tao  HE Ren-mu  WANG Peng  XU Dong-jie
Abstract:This paper presents a method of model construction for the power system transient stability assessment based on statistical learning theory integrated with the bagging and the approximate reasoning. Support vector machines (SVM) operate on the principle of structure risk minimization. This paper takes full advantage of its ability to solve the problem with small sample, nonlinear and high dimension. Hence better generalization ability is guaranteed. The multi-class identification for power system transient stability assessment is solved by the data set reconstruction. The assessment model uses the data set regulation, bagging and approximate reasoning to improve the training speed, the accuracy and stability of the estimation result. The IEEE 39-Bus test system is employed to demonstrate the validity of the proposed approach.
Keywords:Transient stability assessment  Bagging  Support vector machine  Data set reconstruction
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号