Input feature selection for real-time transient stability assessment for Artificial Neural Network (ANN) using ANN sensitivity analysis

被引：8

作者：

Bahbah, AG ^{[1
]}

Girgis, AA ^{[1
]}

机构：

[1] Clemson Univ, Dept Elect & Comp Engn, Elect Power Res Assoc, Clemson, SC 29634 USA

来源：

PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON POWER INDUSTRY COMPUTER APPLICATIONS | 1999年

关键词：

power system transient stability; Neural Networks;

D O I：

10.1109/PICA.1999.779510

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 [计算机应用技术]; 0835 [软件工程];

摘要：

This paper presents a method for the selection of the input parameters, and their ranking for Feed Forward Artificial Neural Networks (FF-ANN) applications in transient stability assessment. The method utilizes Feed Forward Artificial Neural Networks to estimate the sensitivity of the output to all inputs. An evaluation of most of the common inputs used by the researchers is made. Sensitivity analysis using ANN is performed on key parameters to obtain the optimal ranking of the ANN input features. The critical clearing time (CCT) is used to assess the transient stability of the system. The proposed method is applied to a simple power system to illustrate the concept. The preliminary results show that the proposed sensitivity factors are converging to stable values.

引用

页码：295 / 300

页数：6

共 38 条

[1]

Stability-indicating liquid chromatographic determination of trifluoperazine hydrochloride in bulk form and tablets [J].