Power system reduced model by artificial neural networks

被引：0
|
作者：
Ramirez, JM [1 ]
机构：
[1] Univ Guadalajara, CINVESTAV, Dept Elect Engn, Guadalajara 44430, Jalisco, Mexico
来源：
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5 | 2005年
关键词：
artificial neural networks; power systems; reduced order models; power system transient stability;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper is aimed to the application of artificial neural networks (ANN) for constructing a power system reduced model, also termed dynamic equivalent. ANN are trained to help in constructing dynamic equivalents, which is considered a bard task in the context of electrical power systems. The main objective is to reproduce the complex voltage at some relevant nodes. The simulation results prove the applicability and robustness of this innovative approach.
引用
收藏
页码：2607 / 2612
页数：6
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