TY - JOUR
T1 - Intelligent Maximum Power Factor Searching Control Using Recurrent Chebyshev Fuzzy Neural Network Current Angle Controller for SynRM Drive System
AU - Chen, Shih Gang
AU - Lin, Faa Jeng
AU - Liang, Chia Hui
AU - Liao, Chen Hao
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - To develop a high-performance synchronous reluctance motor (SynRM) drive system, a novel maximum power factor control (MPFC) using a current angle controller with stator resistance and stator flux estimators is proposed. First, a traditional maximum power factor control system using a saliency ratio of the SynRM to generate a fixed current angle command is described. Since the saliency ratio requires offline prepreparation and cannot be adjusted automatically, it is difficult to improve the performance of the MPFC in different operating regions because of the increasing of manufacturing cost and time-consumption. Therefore, an intelligent-maximum power factor searching control (MPFSC) using a recurrent Chebyshev fuzzy neural network (RCFNN) current angle controller is developed for the speed control of a SynRM. In order to search the online optimal power factor (PF) points of the SynRM under different operating conditions, the RCFNN current angle controller is designed to produce the compensated current angle command. Moreover, a proportional-integral speed controller is adopted to generate the stator current magnitude command, and the proposed intelligent-MPFSC is employed to generate the current angle command. Furthermore, the proposed intelligent-MPFSC system is implemented in a 32-bit floating-point TMS320F28075 digital signal processor. Finally, from the experimental results, the current angle commands of the optimal PF can be effectively obtained online at different speed operating commands with varied load torque.
AB - To develop a high-performance synchronous reluctance motor (SynRM) drive system, a novel maximum power factor control (MPFC) using a current angle controller with stator resistance and stator flux estimators is proposed. First, a traditional maximum power factor control system using a saliency ratio of the SynRM to generate a fixed current angle command is described. Since the saliency ratio requires offline prepreparation and cannot be adjusted automatically, it is difficult to improve the performance of the MPFC in different operating regions because of the increasing of manufacturing cost and time-consumption. Therefore, an intelligent-maximum power factor searching control (MPFSC) using a recurrent Chebyshev fuzzy neural network (RCFNN) current angle controller is developed for the speed control of a SynRM. In order to search the online optimal power factor (PF) points of the SynRM under different operating conditions, the RCFNN current angle controller is designed to produce the compensated current angle command. Moreover, a proportional-integral speed controller is adopted to generate the stator current magnitude command, and the proposed intelligent-MPFSC is employed to generate the current angle command. Furthermore, the proposed intelligent-MPFSC system is implemented in a 32-bit floating-point TMS320F28075 digital signal processor. Finally, from the experimental results, the current angle commands of the optimal PF can be effectively obtained online at different speed operating commands with varied load torque.
KW - Current angle controller
KW - maximum power factor searching control (MPFSC)
KW - power factor (PF)
KW - recurrent Chebyshev fuzzy neural network (RCFNN)
KW - synchronous reluctance motor (SynRM)
UR - http://www.scopus.com/inward/record.url?scp=85095699417&partnerID=8YFLogxK
U2 - 10.1109/TPEL.2020.3016709
DO - 10.1109/TPEL.2020.3016709
M3 - 期刊論文
AN - SCOPUS:85095699417
SN - 0885-8993
VL - 36
SP - 3496
EP - 3511
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
IS - 3
M1 - 9167461
ER -