Artificial neural networks for transient stability assessment of power system

Author

Tao Lan ; Jiang Jiguang ; Xiao Dachuarn

Author_Institution

Dept. of Electr. Eng., Qinghua Univ., Beijing, China

Volume

2

fYear

1993

fDate

19-21 Oct. 1993

Firstpage

889

Abstract

The paper explores the suitability of using two artificial neural network models (ANN) as tools for power system transient security assessment (TSA). Firstly, a TSA problem of a local power net is changed into a pattern recognition problem suitable for an ANN, and sample data are preprocessed. Then BPN and KNN models are used respectively for this TSA problem. The suitability and advantages of the two models are discussed and compared on mapping capability of the problem, estimation of certainty factor of interpolating results and ANN size. The results show that from the point of view of TSA application, the KNN model is better than BPN.<>

Keywords

backpropagation; pattern recognition; power system analysis computing; self-organising feature maps; stability; ANN; BPN; KNN models; TSA; artificial neural networks; certainty factor; interpolating results; local power net; mapping capability; pattern recognition problem; power system; power system transient security assessment; transient stability assessment; Artificial neural networks; Iterative algorithms; Neurons; Power measurement; Power system faults; Power system measurements; Power system modeling; Power system stability; Power system transients; Virtual colonoscopy;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on

Conference_Location

Beijing, China

Print_ISBN

0-7803-1233-3

Type

conf

DOI

10.1109/TENCON.1993.320223

Filename

320223