• Title/Summary/Keyword: SynRM drive

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Maximum Torque Control of SynRM Drive using LM-FNN Controller (LM-FNN 제어기를 이용한 SynRM 드라이브의 최대토크 제어)

  • Park, Byung-Sang;Choi, Jung-Sik;Park, Ki-Tae;Ko, Jae-Sub;Chung, Dong-Hwa
    • 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.

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High Performance Speed and Current Control of SynRM Drive with ALM-FNN and FLC Controller (ALM-FNN 및 FLC 제어기에 의한 SynRM 드라이브의 고성능 속도와 전류제어)

  • Choi, Jung-Sik;Ko, Jae-Sub;Chung, Dong-Hwa
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.58 no.3
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    • pp.249-256
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    • 2009
  • The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.

Maximum Torque Control of SynRM Using Multi-PI Controller (Multi-PI 제어기를 이용한 SynRM의 최대토크 제어)

  • Jung, Byung-Jin;Ko, Jae-Sub;Choi, Jung-Sik;Jung, Chul-Ho;Kim, Do-Yeon;Park, Ki-Tae;Chung, Dong-Hwa
    • 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.

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Maximum Torque Control of SynRM Drive with ALM-FNN Controller (ALM-FNN 제어기에 의한 SynRM 드라이브의 최대토크 제어)

  • Ko, Jae-Sub;Choi, Jung-Sik;Lee, Jung-Ho;Kim, Jong-Kwan;Park, Ki-Tae;Park, Byung-Sang;Chung, Dong-Hwa
    • 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.

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Maximum Torque Control of SynRM Drive with Adaptive FNN Controller (적응 FNN 제어기에 의한 SynRM 드라이브의 최대토크 제어)

  • Ko, Jae-Sub;Choi, Jung-Sik;Lee, Jung-Ho;Kim, Jong-Kwan;Park, Byung-Sang;Park, Ki-Tae;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
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    • 2006.07b
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    • pp.729-730
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    • 2006
  • The paper is proposed maximum torque control of SynRM drive using adaptive fuzzy neural network(A-FNN) controller and artificial neural network(ANN). 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 A-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 A-FNN and ANN controller.

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A Novel Efficiency Optimization Control of SynRM Considering Iron Loss with Neural Network (신경회로망에 의한 철손을 고려한 SynRM의 새로운 효율 최적화 제어)

  • Kang, Sung-Joon;Ko, Jae-Sub;Choi, Jung-Sik;Baek, Jung-Woo;Jang, Mi-Geum;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
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    • 2009.07a
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    • pp.776_777
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    • 2009
  • Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using neural network(NN). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism fuzzy-neural networks(ALM-FNN) controller that is implemented using fuzzy control and neural networks. The objective of the efficiency optimization control 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 control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

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High Performance Control of Container Crane using Adaptive-Fuzzy Control (적응 퍼지제어를 이용한 컨테이너 크레인의 고성능제어)

  • Jung, Dong-Hwo;Kim, Do-Yun;Jung, Byung-Jin
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.23 no.2
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    • pp.115-124
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    • 2009
  • This paper proposed an adaptive fuzzy controller for controlling speed and positions of a container crane. The motor used in container crane is installed as SynRM with variable-speed drive having the robustness on the problems of energy and environment. The conventional PI controller is not able to accurately track the position, speed and sway angle of trolley due to the factors of environment and the parameter variety. In the paper, we analyzed the performance of SynRM derive applied to the container crane by using an adaptive fuzzy control of SynRM in order to solve those problems. This paper analyzed the characteristics of position and speed response and compared the performance of PI controller with an adapative Fuzzy controller, proving the validity.

Maximum Torque Control of SynRM using AFNIS(Adaptive Fuzzy Neuro Inference) (AFNIS를 이용한 SynRM의 최대토크 제어)

  • Jung, Byung-Jin;Ko, Jae-Sub;Choi, Jung-Sik;Jung, Chul-Ho;Kim, Do-Yeon;Chung, Dong-Hwa
    • 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.

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High Performance Control of SynRM Drive using FNN based PI Controller (FNN 기반 PI 제어기를 이용한 SynRM 드라이브의 고성능 제어)

  • Jung, Byung-Jin;Ko, Jae-Sub;Choi, Jung-Sik;Kim, Do-Yeon;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
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    • 2008.10c
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    • pp.68-70
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    • 2008
  • This paper proposes FNN based PI controller for high performance control of SynRM Drive. Traditional PI controller can't be obtained good performance because it has fixed gain. Therefore, in this paper, FNN based PI controller that gain of PI controller is tuned use FNN proposes. FNN based PI controller proposed in this paper can be obtained excellent performance more than traditional PI controller. Algorithm proposed in this paper make a analysis and prove valid.

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Maximum Torque Control of SynRM Drive with Artificial Neural Network (인공 신경회로망에 의한 SynRM 드라이브의 최대토크 제어)

  • Ko, Jae-Sub;Nam, Su-Myeong;Chung, Dong-Hwa
    • 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.