• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권2호
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning 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. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM 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.

• 한국재료학회지
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• 제8권5호
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• pp.470-473
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• 1998

Co/Ti 이중막을 급속열처리하여 형성한 $CoSi_{2}$에 $As^+$을 이온주입한 후, 500~$1000^{\circ}C$에서 drive-in 열처리하여 매우얇은 $n_{+}$ p접합의 다이오드를 제작하고 I-V 특성을 측정하였다. $500^{\circ}C$에서 280초 drive-in 열처리하였을 때, 50nm정도의 매우 얇은접합이 형성되었고, 누설전류가 매우 낮아 가장 우수한 다이오드 특성을 나타내었다. 특히, Co 단일막을 사용한 다이오드에 비해 누설전류는 2order 이상 낮았으며, 이는 $CoSi_{2}$Si의 계면이 균일하였기 때문이다.

• 조명전기설비학회논문지
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• 제24권5호
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• pp.16-28
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• 2010

본 논문은 AIPI 및 ANN에 의한 SynRM 드라이브의 최대토크 제어를 제시한다. 본 논문은 인버터의 정격 전압과 전류의 한계 조건을 고려하여 전 속도영역에서 최대토크제어를 제시한다. 속도에 따라 각 제어모드에서 최대토크를 발생하기 위한 최적의 전류값을 계산하고. 계산된 최적전류를 이용하여 최대토크 제어를 수행한다. 제시된 최대토크 제어 알고리즘은 AIPI와 ANN 제어기와 함께 SynRM 드라이브에 적용하여 동작특성을 분석하고 그 타당성을 제시한다.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권3호
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• pp.110-114
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. 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. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM 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 experimental results to verify the effectiveness of AI controller.

• 한국산학기술학회논문지
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• 제7권3호
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• pp.338-343
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• 2006

본 논문에서는 직류전동기 구동 시스템의 전류 및 속도 제어를 위하여 dSPACE 1104 시스템을 이용하여 전류 궤환을 갖는 속도 제어시스템을 구현하였다. 전류 및 속도 제어기의 설계는 MATLAB/SIMULINK 프로그램을 사용하여 간편하고 손쉽게 구현하였으며, 직류전동기 속도제어의 안정성과 응답성을 향상시킬 수 있었다. 직류전동기의 전류제어 및 속도제어는 DSP 보드와 dSPACE 시스템을 사용하여 수행하였으며, 속도의 궤환은 속도센서인 엔코더 펄스를 이용해서 QEP로 처리하였고, 전류의 궤환은 전류센서인 홀센서를 통해서 A/D 변환기로 처리하였다. 제어기들은 각각 PI 전류제어기 및 PI 속도제어기를 설계하였고 시뮬레이션과 실험을 통해서 전류 및 속도 응답을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1986년도 하계학술대회논문집
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• pp.535-537
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• 1986

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.238-245
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• 2016

Inverters are considered the basic building blocks of industrial electrical drive systems that are widely used for various applications; however, the failure of electronic switches mainly affects the constancy of these inverters. For safe and reliable operation of an electrical drive system, faults in power electronic switches must be detected by an efficient system that is capable of identifying the type of faults. In this paper, an open switch fault identification technique for a three-phase inverter is presented. Single, double, and triple switching faults can be diagnosed using this method. The detection mechanism is based on stator current analysis. Discrete wavelet transform (DWT) using Daubechies is performed on the Clarke transformed (-) stator current and features are extracted from the wavelets. An artificial neural network is then used for the detection and identification of faults. To prove the feasibility of this method, a Simulink model of the DWT-based feature extraction scheme using a neural network for the proposed fault detection system in a three-phase inverter with an induction motor is briefly discussed with simulation results. The simulation results show that the designed system can detect faults quite efficiently, with the ability to differentiate between single and multiple switching faults.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1356-1357
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• 2011

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes on-line efficiency optimization of IPMSM drive using fuzzy logic control(FLC) and the loss minimization method. In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. The proposed control algorithm is applied to IPMSM drive system and the operating characteristics controlled by the loss minimization method and FLC control are examined in detail.

• 전기학회논문지P
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• 제56권2호
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• pp.74-82
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning control fuzzy neural network (AFLC-FNN) controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC-FNN controller. Also, this paper proposes speed control of IPMSM using AFLC-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled AFLC-FNN controller, the operating characteristics controlled by efficiency optimization control are examined in detail.

• Journal of Electrical Engineering and Technology
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• 제11권3호
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• pp.653-661
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• 2016

This paper proposes a capacitance estimation method of the dc-link capacitor for brushless DC motor (BLDCM) drive systems. In order to estimate the dc-link capacitance, the BLDCM is operated in quadrant-II or -IV among four-quadrant operation. Quadrant-II and -IV are called reverse braking and forward braking, respectively. During the braking operation of the BLDCM, the capacitor is charged by the phase current and then the voltage is increased during the braking operation time. The capacitor current and voltage can be obtained by using the phase current sensor of BLDCM and the dc-link voltage sensor. The capacitance and be easily obtained by the voltage equation of the capacitor. The proposed method guarantees the reliable and simple calculation of the dc-link capacitance without additional hardware system except several the sensors already installed for the motor control system. The effectiveness of the proposed method is verified through both the simulation and experimental results.