• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.293-300
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• 2011

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 and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. 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 and AFLC is confirmed by comparing to conventional algorithm.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.332-337
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• 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
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• v.13 no.5
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• pp.429-433
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• 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
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• 2006.04a
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• pp.207-209
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• 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.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.4
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• pp.193-199
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• 2011

The next generation domestic high speed railway system is a power distributed type and uses vector control method for motor speed control. Nowadays, inverter driven induction motor system is widely used. However, recently PMSM drives are deeply considered as a alternative candidate instead of an induction motor driven system due to their advantages in efficiency, noise reduction and maintenance. The next-generation high-speed train is composed of 2 converter units, 4 inverter units, and 4 Traction Motor units. Each motor is connected to the inverter directly. In this paper, the effects of IPMSM parameter variation to the system operation characteristics of the multi inverter driven high speed train system are investigated. The parallel connected inverter input-output characteristics are analyzed to the parameter mismatches of the IPMSM in 1C1M control using Matlab/Simulink, then the reliability of the simulation results are verified through experimental results.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.3133-3141
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• 2011

The next generation domestic high speed railway system is a power distributed type and uses vector control method for motor speed control. Nowadays, inverter driven induction motor system is widely used. However, recently PMSM drives are deeply considered as a alternative candidate instead of an induction motor drive system due to their advantages in efficiency, noise reduction and maintenance. The next-generation high speed train is composed of 2 converter units, 4 inverter units, and 4 Traction Motor units. Each motor is connected to the inverter directly. In this paper, the effect of IPMSM parameter variations to the system operation characteristics of the multi inverter drive high speed train system are investigated. The parallel connected inverter input-output characteristics are analyzed to the parameter mismatches of IPMSM using the 1C1M control simulator based on Matlab/Simulink.

• Proceedings of the KIEE Conference
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• 2003.10b
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• pp.197-199
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• 2003

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.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2276-2283
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• 2018

A 50kW-4000rpm interior permanent magnet synchronous machine (IPMSM) applied to the high-performance electric vehicle (EV) is introduced in this paper. The main work of this paper is that a 2-D T-type lumped-parameter thermal network (LPTN) model is presented for IPMSM temperature rise calculation. Thermal conductance matrix equation is generated based on calculated thermal resistance and loss. Thus the temperature of each node is obtained by solving thermal conductance matrix. Then a 3-D liquid-solid coupling model is built to compare with the 2-D T-type LPTN model. Finally, an experimental platform is established to verify the above-mentioned methods, which obtains the measured efficiency map and current wave at rated load case and overload case. Thermocouple PTC100 is used to measure the temperature of the stator winding and iron core, and the FLUKE infrared-thermal-imager is applied to measure the surface temperature of IPMSM and controller. Test results show that the 2-D T-type LPTN model have a high accuracy to predict each part temperature.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.3
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• pp.213-221
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• 2012

In a PWM inverter-fed IPMSM (Interior Permanent Magnet Synchronous Motor) drive, a dead time is inserted to prevent a breakdown of switching device caused by the short-circuit of DC link. This distorts the inverter output voltage resulting in a current distortion and torque ripple. In addition to the dead time, nonlinearity exists in switching devices of the PWM inverter, which is generally dependent on operating conditions such as the temperature, DC link voltage, and current. The voltage disturbance caused by the dead time and inverter nonlinearity directly influences on the inverter output performance, and it is known to be more severe at low speed. In this paper, a new compensation scheme for the dead time and inverter nonlinearity under the parameter variation is proposed for a PWM inverter-fed IPMSM drive. The overall system is implemented using DSP TMS320F28335 and the validity of the proposed algorithm is verified through the simulation and experiments.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.289-297
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• 2014

This paper proposed a novel high performance sensorless control scheme for IPMSM based on an extended nonlinear state observer. The gain-matrix of the observer has been derived by using state linearization method. Steady state errors in estimated rotor position and speed due to parameter inaccuracy have been analyzed, and an equivalent flux error is defined to represent the overall effect of parameter errors contributing to the wrong convergence of the estimated rotor speed as well as rotor position. Then, an online compensation strategy was proposed to limit the estimation errors in rotor position and speed. The effectiveness of the extended nonlinear state observer is validated through simulation and experimental test.