A CPF based neural approach for critical contingency evaluation and weak bus identfication

Author

Kumar, Ravindra ; Kumar, B.U. ; Chauhan, Shubhika

Author_Institution

Dept. of Electr. Eng., NIT, Hamirpur, India

fYear

2013

fDate

10-12 April 2013

Firstpage

1154

Lastpage

1158

Abstract

In this paper, artificial neural network (ANN) based approach is used to quickly estimate the margin to voltage collapse. A new index “Post Contingency Loading Margin” (PCLM) has been proposed to estimate the loadability margin of the system leading to evaluation of critical contingencies and identification of weak buses under any operating condition. The proposed method has been applied and tested on IEEE 30 bus test system and corresponding results have been presented.

Keywords

load flow; neural nets; power engineering computing; power system dynamic stability; ANN; CPF based neural approach; IEEE 30 bus test system; PCLM; artificial neural network; continuation power flow; critical contingency evaluation; post contingency loading margin index; voltage collapse; voltage stability; weak bus identfication; Algorithm design and analysis; Artificial neural networks; Loading; Power system stability; Stability analysis; Training; Artificial neural network; CPF; Critical contingency; Post Contingency loadability Margin; Voltage stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Energy Efficient Technologies for Sustainability (ICEETS), 2013 International Conference on

Conference_Location

Nagercoil

Print_ISBN

978-1-4673-6149-1

Type

conf

DOI

10.1109/ICEETS.2013.6533549

Filename

6533549