• Journal of Electrical Engineering and Technology
• /
• v.9 no.5
• /
• pp.1577-1581
• /
• 2014

This paper presents a method to minimize torque ripple of a V-type IPMSM using the PSO (Particle Swarm Optimization) method with FEM. The proposed algorithm includes one objective function and three design variables for a notch on the surface of a rotor. The simulation model of the V-type IPMSM has 3-phases, 8-poles and 48 slots with 2 notches on the one-pole rotor surface. The arc-angle, length and width of the notch are optimized to minimize the torque ripple of the motor. The cogging torque of the model is reduced by 55.6% and the torque ripple is decreased by 15.5 %. Also, the efficiency of the motor is increased by 15.5 %.

• Proceedings of the KIEE Conference
• /
• 1999.11b
• /
• pp.369-371
• /
• 1999

A torque ripple minimization technique is proposed for high power induction motors driven by 3-level inverters with switching frequency of inverter switching elements limited around 500Hz level. It is noted that conventional DTC algorithms with torque ripple minimization scheme are devised for applications with relatively high switching frequency above 2-3kHz. A new DTC algorithm, especially for low switching frequency inverter system, illustrates relatively reduced torque ripple characteristics all over the operating speed region. Simulation results show effectiveness of the proposed control algorithm, and associated experimental works will be presented in the final paper.

• Proceedings of the KIEE Conference
• /
• 2005.07b
• /
• pp.1291-1293
• /
• 2005

Switched reluctance motors have the advantage a high torque/weight ratio, as the large reluctance torque is made by salient poles of both stator and rotor, and a high reliability. On the other hand, the switched reluctance motors have the disadvantage of a large ripple torque which is made by salient poles. So the application for the industrial fields have been limited to special cases. This paper describes the design of a 12/8 switched reluctance motor using a enemy layer method of the asymmetry rotor. The design is focussed to reduce the torque ripple and radial force in the demanded value. The three dimension finite element analysis method(3D-FEM) was used for decides a enemy layer angle of the asymmetry rotor. This paper presents modifications of the rotor pole shape which reduces the torque ripple.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 1999.11a
• /
• pp.279-284
• /
• 1999

Although the switched reluctance motor (SRM) has a several advantages such as simple magnetic structure, robustness, wide range of speed characteristics and simple driving, it has a considerable inherent torque ripple and speed variation duet to the driving characteristics of pulse current waveform and the nonlinear inductance profile. The high torque ripple and speed variation inhibits wide application. The minimization of the torque ripple is very important in high performance servo drive applications, which require smooth operation with minimum torque pulsations. This paper presents the new SRM torque modeling technique for the control of instantaneous torque. The SRM is modeled by the database of torque profiles for every small variation in currents and rotor angles, which is inferred from the several measured data by the adaptive neuro-fuzzy inference technique. Simulation results demonstrating the effectiveness of proposed torque modeling technique are presented.

• Journal of Magnetics
• /
• v.21 no.4
• /
• pp.544-553
• /
• 2016

Torque ripple minimization without decrease in average torque is a vital attribute in the design of Synchronous Reluctance (SynRel) motor. As the design of SynRel motor is an arduous task, which encompasses many design variables, this work first analyses the significance of the effect of varying the geometrical parameters on average torque and torque ripple and then proposes an extensive optimization procedure to obtain configurations with improved average torque and minimized torque ripple. A hardware prototype is fabricated and tested. The Finite Element Analysis (FEA) software tool used for validating the test results is MagNet 7.6.0.8. Multi Objective Particle Swarm Optimization (MOPSO) is used to determine the various designs meeting the requirements of reduced torque ripple and improved torque performance. The results indicate the efficacy of the proposed methodology and substantiate the utilization of MOPSO as a significant tool for solving design problems related to SynRel motor.

• Proceedings of the KIEE Conference
• /
• 2001.04a
• /
• pp.33-36
• /
• 2001

Torque ripple control of brush less DC motor has been the main issue of the servo drive systems in which the speed fluctuation, vibration and acoustic noise should be minimized. Most methods for suppressing the torque ripples require Fourier series analysis and either the iterative or least mean square minimization. In this paper, the novel approach to achieve the ripple-tree torque control with maximum efficiency based on the d-q-0 reference frame is presented. The proposed method optimize the reference phase current waveforms including even the case of 3 phase unbalanced condition, and the motor winding currents are controlled to follow up the optimized current waveforms by delta modulation technique. As a result, the proposed approach provides a simple and clear way to obtain the optimal motor excitation currents. The validity and practical applications of the proposed control scheme are verified through the simulations and experimental results.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.10
• /
• pp.1384-1392
• /
• 2014

This paper presents a torque ripple reduction algorithm for the switched reluctance motor drives using the fuzzy logic and the sliding mode control. A turn-on angle controller based on the fuzzy logic determines the optimal turn-on angle. In addition, a sliding mode torque control (SMTC) methods reduces torque ripples instantaneously in the commutation region. The proposed algorithm does not require complex system models considering nonlinear magnetizing or demagnetizing periods of the phase current. According to the rotor speed and torque, the proposed controller changes the turn-on angle and reference torque instantaneously until the torque ripples are minimized. The simulation and experimental results verify the validity of minimizing the torque ripple performance.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.53 no.5
• /
• pp.332-337
• /
• 2004

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying. parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.5
• /
• pp.429-433
• /
• 2007

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Proceedings of the KIEE Conference
• /
• 2006.04a
• /
• pp.207-209
• /
• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.