Accurate model identification and fault detection are necessary for reliable motor control. Motor-characterizing parameters experience substantial changes due to aging, motor operating conditions, and faults. Conseque...Accurate model identification and fault detection are necessary for reliable motor control. Motor-characterizing parameters experience substantial changes due to aging, motor operating conditions, and faults. Consequently, motor parameters must be estimated accurately and reliably during operation. Based on enhanced model structures of electric motors that accommodate both normal and faulty modes, this paper introduces bias-corrected least-squares （LS） estimation algorithms that incorporate functions for correcting estimation bias, forgetting factors for capturing sudden faults, and recursive structures for efficient real-time implementation. Permanent magnet motors are used as a benchmark type for concrete algorithm development and evaluation. Algorithms are presented, their properties are established, and their accuracy and robustness are evaluated by simulation case studies under both normal operations and inter-turn winding faults. Implementation issues from different motor control schemes are also discussed.展开更多
Torque ripple is one of the most important specification indexes of brushless DC torque motors. This paper analyzes the torque ripple of a sinusoidal-driven three-phase permanent magnet(PM) brushless DC (BLDC) torque ...Torque ripple is one of the most important specification indexes of brushless DC torque motors. This paper analyzes the torque ripple of a sinusoidal-driven three-phase permanent magnet(PM) brushless DC (BLDC) torque motor and approaches the expression of torque ripple. An indirect method using armature currents to measure torque ripple and its implementation by hardware are presented. The torque ripple of the motor is measured by the indirect and the direct methods respecively. The validity of the indirect method is demonstrated by the comparative analysis of the experimental results.展开更多
文摘Accurate model identification and fault detection are necessary for reliable motor control. Motor-characterizing parameters experience substantial changes due to aging, motor operating conditions, and faults. Consequently, motor parameters must be estimated accurately and reliably during operation. Based on enhanced model structures of electric motors that accommodate both normal and faulty modes, this paper introduces bias-corrected least-squares （LS） estimation algorithms that incorporate functions for correcting estimation bias, forgetting factors for capturing sudden faults, and recursive structures for efficient real-time implementation. Permanent magnet motors are used as a benchmark type for concrete algorithm development and evaluation. Algorithms are presented, their properties are established, and their accuracy and robustness are evaluated by simulation case studies under both normal operations and inter-turn winding faults. Implementation issues from different motor control schemes are also discussed.
文摘Torque ripple is one of the most important specification indexes of brushless DC torque motors. This paper analyzes the torque ripple of a sinusoidal-driven three-phase permanent magnet(PM) brushless DC (BLDC) torque motor and approaches the expression of torque ripple. An indirect method using armature currents to measure torque ripple and its implementation by hardware are presented. The torque ripple of the motor is measured by the indirect and the direct methods respecively. The validity of the indirect method is demonstrated by the comparative analysis of the experimental results.
