Title :
Synchronous Machine steady-State parameter estimation using neural networks
Author :
Calvo, M. ; Malik, O.P.
Author_Institution :
Nortel Networks, Calgary, Alta., Canada
fDate :
6/1/2004 12:00:00 AM
Abstract :
An online steady-state parameter estimation technique using the ability of the neural networks to recognize patterns is presented in this paper. The method is nonintrusive. Studies on a salient pole and on a round rotor synchronous machine illustrate the effectiveness of the proposed technique. Results indicate that the steady-state parameters can be obtained without the use of rotor position.
Keywords :
electric machine analysis computing; neural nets; parameter estimation; pattern recognition; rotors; synchronous machines; neural networks; pattern recognition; round rotor synchronous machines; salient pole; steady-state parameter estimation; Costs; Neural networks; Parameter estimation; Pattern recognition; Performance evaluation; Power generation; Samarium; Steady-state; Synchronous machines; Testing; Neural networks; pattern recognition; steady-state parameter estimation; synchronous machine;
Journal_Title :
Energy Conversion, IEEE Transactions on
DOI :
10.1109/TEC.2004.827041