Title :
Rotor position estimation of high-speed SRM drive using neural networks
Author :
Cai, Jun ; Deng, Zhiquan ; Liu, Zeyuan ; Zeng, Wenyu ; Guo, Honghao
Author_Institution :
Aero-Power Sci-Tech Center, Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
Abstract :
Based on the artificial neural networks(ANN), a new rotor position estimation method for switched reluctance motor(SRM) drives is investigated in this paper. The nonlinear magnetic characteristics of SRM, obtained by finite element analysis(FEA), are used as the training data. After sufficient training, the correlation among flux linkage, phase current and rotor position can be built up with the ANN. In this way, the rotor position of SRM can be estimated with the ANN from the calculated flux linkage and phase current. In order to verify the validity of this method, a 7.5 kw 12/8 structure high-speed SRM is designed as the study object and the corresponding simulation model is developed via Matlab. Simulation results testify the validity of this algorithm.
Keywords :
finite element analysis; neural nets; power engineering computing; reluctance motor drives; rotors; artificial neural networks; finite element analysis; flux linkage; high-speed SRM drive; nonlinear magnetic characteristics; phase current; power 7.5 kW; rotor position estimation; switched reluctance motor drives; training data; Artificial neural networks; Couplings; Finite element methods; Magnetic analysis; Neural networks; Nonlinear magnetics; Reluctance machines; Reluctance motors; Rotors; Training data; Switched reluctance motor; finite element analysis; neural networks; sensorless;
Conference_Titel :
Electrical Machines and Systems, 2009. ICEMS 2009. International Conference on
Conference_Location :
Tokyo
Print_ISBN :
978-1-4244-5177-7
Electronic_ISBN :
978-4-88686-067-5
DOI :
10.1109/ICEMS.2009.5382658
