• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.4
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• pp.185-191
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• 2005

In this paper, a new approach for the Synchronous Reluctance Motor control which ensures producing Maximum Torque per Ampere(MTPA) over the entire field weakening region is presented. In addition, This paper presents a speed sensorless control scheme of SynRM using artificial neural network. Also, by adjusting the base speed for the field weakening operation according to the flux level, the current and voltage limit, the smooth and precise transition into the field weakening operation can be achieved. The proposed scheme is verified validity through simulation.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.563-565
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• 2005

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor(SynRM) which minimizes the copper and iron losses. ALso, this paper presents a sensorless control scheme of SynRM using artificial neural network(ANN). The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of ANN is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2103-2108
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• 2007

This study investigates the dynamic characteristics of Synchronous Reluctance Motor (SynRM), with segmental rotor structure, using finite element method in which the moving mesh technique is considered. The focus of this paper is the efficiency of on-line parameter identification system for position sensorless control of a SynRM considering saturation and iron loss. Comparisons are given with angle of the observer and those of proposed FEM & Preisach model of synchronous reluctance motor, respectively. The position sensorless control using identified motor parameters is realized, and the efficiency of the on-line parameter identification system is verified by experimental results.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.956-957
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• 2008

The paper is proposed maximum torque control of SynRM drive using Multi-PI controller. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current ids for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled Multi-PI controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the Multi-PI controller.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.902-903
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• 2015

현재 세계는 각종 에너지에 대한 연구가 치열하게 진행되고 있는 가운데 각 기계들의 에너지 효율등급이 엄격하게 규제되고 있다. 전동기 분야에서는 사용 용도에 따라서는 영구자석을 쓰지 않는 유도기가 주목을 받았다. 하지만 유도전동기는 가공이 쉽지 않고 동일 사이즈에서 출력이 동기 릴럭턴스 전동기(Synchronous Reluctance Motor; SynRM)보다 낮기 때문에 현재 국내외 전동기 시장에서는 SynRM이 주목받고 있으며 현재 많은 연구가 진행되고 있다. 본 논문은 30kW급 팬 부하용 SynRM 설계에 관해 논문이다. 기존에 사용되었던 유도 전동기를 대체하기 위해 전동기의 자속장벽의 형상에 따라서 어떠한 효과가 있는지에 대해 연구하고 앞으로의 SynRM의 회전자 형상 설계 방법을 제시한다.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.11a
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• pp.309-314
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• 2005

The paper is proposed maximum torque control of SynRM drive using learming mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $^i{_d}$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.159-162
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• 2001

This paper is proposed an efficiency optimization operation algorithm for synchronous reluctance motor (SynRM) using current phase angle control technique. The SynRM has to controlled with the optimal current phase angles with load and operation speed variation, to obtain high efficiency over the wide speed ranges. An efficiency optimization condition in SynRM which minimizes the copper and iron losses is derived based on the equivalent circuit model of the machine. The objective of the efficiency optimization control algorithm compensating the optimum current angle, is to seek a combination of d and q-axis current components which provides minimum losses at a certain operating point in steady state. The usefulness of the proposed efficiency optimization control is verified through vector-controlled inverter system with the SynRM.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1011-1012
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• 2007

The paper is proposed maximum torque control of SynRM drive using learning mechanism-fuzzy neural network(LM-FNN) controlle. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current ${^{i}}_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled LM-FNN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN controller.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.219-220
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• 2008

The paper is proposed maximum torque control of SynRM drive using adaptive fuzzy neuro inference system(AFNIS) and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled AFNIS and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the AFNIS and ANN controller.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.155-157
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive learning mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.