Open AccessComparative Study of Backpropagation Algorithms in Neural Network Based Identification of Power System
Author(s) -
Sanjay Tiwari,
Ram Naresh,
Rakesh Kumar Jha,
Babasaheb Ambedkar
Publication year - 2013
Publication title -
international journal of computer science and information technology/international journal of computer science and information technology (chennai. print)
Language(s) - English
Resource type - Journals
eISSN - 0975-4660
pISSN - 0975-3826
DOI - 10.5121/ijcsit.2013.5407
Subject(s) - computer science , backpropagation , artificial neural network , identification (biology) , algorithm , rprop , artificial intelligence , time delay neural network , machine learning , types of artificial neural networks , botany , biology
This paperexplores theapplicationof artificial neural networksfor online identification of a multimachinepower system.Arecurrent neural networkhas been proposedas the identifier of the two area, four machinesystemwhich is a benchmark system for studying electromechanical oscillations in multimachine powersystems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of thepaper is on investigating the performance of the variants of the Backpropagation algorithm in training theneural identifier. The paper also compares the performances of the neural identifiers trained usingvariantsof the Backpropagation algorithmover a wide range of operating conditions.The simulation resultsestablish a satisfactory performance of the trained neural identifiers in identification of the test powersyste