Title :
A Neuro-fuzzy approach for stator resistance estimation of induction motor
Author :
Jaalam, N. ; Haidar, A.M.A. ; Ramli, N.L. ; Ismail, N.L. ; Sulaiman, A.S.M.
Author_Institution :
Fac. of Electr. & Electron. Eng., Univ. Malaysia Pahang, Kuantan, Malaysia
Abstract :
An accurate estimation of stator resistance is very important especially during operation of an induction motor due to variation in the stator resistance and temperature of the working machine. This paper proposes a Neuro-fuzzy Technique (NFT) for an online estimation of the stator resistance under steady state operating conditions of an induction motor. The proposed technique is compared with the Proportional Integral (PI) estimator to see the effectiveness. The obtained results of applying the NFT for resistance estimation give better performance and high robustness than those obtained by the application of PI.
Keywords :
electric machine analysis computing; fuzzy neural nets; fuzzy reasoning; induction motors; stators; NFT; induction motor; neurofuzzy technique; proportional integral estimator; stator resistance estimation; Estimation; Fuzzy logic; Induction motors; Resistance; Rotors; Stator windings; PI estimator; induction motor; neuro-fuzzy; stator resistance;
Conference_Titel :
Electrical, Control and Computer Engineering (INECCE), 2011 International Conference on
Conference_Location :
Pahang
Print_ISBN :
978-1-61284-229-5
DOI :
10.1109/INECCE.2011.5953913