Transient stability Assessment using Artificial Neural Network Considering Fault Location

P.K.Olulope --- K.A.Folly --- S.Chowdhury --- S.P.Chowdhury

Iraqi Journal for Electrical And Electronic Engineering المجلة العراقية للهندسة الكهربائية والالكترونية
ISSN: 18145892 Year: 2010 Volume: 6 Issue: 1 Pages: 67-72
Publisher: Basrah University جامعة البصرة


This paper describes the capability ofartificial neural network for predicting the criticalclearing time of power system. It combines theadvantages of time domain integration schemes withartificial neural network for real time transientstability assessment. The training of ANN is done usingselected features as input and critical fault clearingtime (CCT) as desire target. A single contingency wasapplied and the target CCT was found using timedomain simulation. Multi layer feed forward neuralnetwork trained with Levenberg Marquardt (LM)back propagation algorithm is used to provide theestimated CCT. The effectiveness of ANN, the methodis demonstrated on single machine infinite bus system(SMIB). The simulation shows that ANN can providefast and accurate mapping which makes it applicable toreal time scenario


Artificial neural network --- Critical clearing time --- Regression analysis