Transient stability Assessment using Artificial Neural Network Considering Fault Location - P.K.Olulope, nan; K.A.Folly, nan; S.Chowdhury, nan; S.P.Chowdhury, nan (Iraqi Journal for Electrical and Electronic Engineering, 2010)
This paper describes the capability of artificial neural network for predicting the critical clearing time of power system. It combines the advantages of time domain integration schemes with artificial neural network for real time transient stability assessment. The training of ANN is done using selected features as input and critical fault clearing time (CCT) as desire target. A single contingency was applied and the target CCT was found using time domain simulation. Multi layer feed forward neural network trained with Levenberg Marquardt (LM) back propagation algorithm is used to provide the estimated CCT. The effectiveness of ANN, the method is demonstrated on single machine infinite bus system (SMIB). The simulation shows that ANN can provide fast and accurate mapping which makes it applicable to real time scenario.