New method for generators' angles and angular velocities prediction for transient stability assessment of multimachine power systems using recurrent artificial neural network

被引：79

作者：

Bahbah, AG ^{[1
]}

Girgis, AA ^{[1
]}

机构：

[1] Clemson Univ, CUEPRA, Dept Elect & Comp Engn, Clemson, SC 29634 USA

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2004年 / 19卷 / 02期

基金：

美国国家科学基金会;

关键词：

instability detection; multilayer perceptron; power system transient stability assessment; radial basis function; recurrent neural networks;

D O I：

10.1109/TPWRS.2004.826765

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 [电气工程]; 0809 [电子科学与技术];

摘要：

Recurrent radial basis function (RBF) and multilayer perceptron (MLP) artificial neural network (ANN) schemes are proposed for dynamic system modeling, and generators' angles and angular velocities prediction for transient stability assessment. The method is presented for multimachine power systems. In this scheme, transient stability is assessed based on monitoring generators' angles and angular velocities with time, and checking whether they exceed the specified limits for system stability or not. Data generation schemes have been proposed. The proposed recurrent ANN scheme is not sensitive to fault locations. It is only dependent on the postfault system configuration.

引用

页码：1015 / 1022

页数：8

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