• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.780_781
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• 2009

This paper proposes efficiency optimization control of IPMSM drive using hybrid intelligent controller(HIC). The design of the speed controller based on fuzzy-neural network that is implemented using fuzzy control and neural network. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The optimal current can be decided according to the operating speed and the load conditions. This paper proposes speed control of IPMSM using ALM-FNN, current control of model reference adaptive fuzzy control(MTC) and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled HIC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• 조명전기설비학회논문지
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• 제26권12호
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• pp.9-20
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• 2012

This paper proposes the efficiency optimization control of interior permanent magnet synchronous motor(IPMSM) drive using series connected-fuzzy neural network PI(SC-FNPI) controller. The PI controller is generally used to control IPMSM drive in industrial field. However, the PI controller has problem which is falling control performance about parameter variation such as command speed, load torque and inertia due to fixed gain of PI controller. Therefore, to improve performance of PI controller, this paper proposes SC-FNPI controller adjusted input of PI controller by FNN controller according to operating conditions. Also, this paper proposes efficiency optimization control which is improving efficiency with minimize loss. The SC-FNPI controller proposed in this paper is compared control performance with conventional FNN and PI controller about command speed, load torque and inertia variation. And the efficiency optimization control is compared with $i_d=0$ control about loss and efficiency. The SC-FNPI controller proposed in this paper shows more excellent control performance for rising time, overshoot and steady-state error. Also efficiency optimization control is increased efficiency by reducing loss.

• 전자공학회논문지SC
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• 제44권2호
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• pp.24-31
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• 2007

본 논문은 신경회로망을 이용한 IPMSM 드라이브의 자기동조 PI 제어기를 제시한다. 일반적으로 수치제어장치 처리는 고정된 이득값을 가진 PI 제어기를 이용한다. 고정된 이득값을 가진 PI 제어기는 어떠한 환경에서는 양호하게 동작할 수 도 있다. 고정된 이득값을 가진 PI 제어기의 강인성을 증가시키기 위하여 신경회로망을 기반으로한 새로운 방법인 STPI 제어기를 제시하였다. STPI 제어기는 속도, 부하토크, 관성과 같은 파라비터가 갑자기 변화하였을 때 오버슈트, 상승시간, 안정화시간을 최소화한다. 또한 본 논문에서는 신경회로망을 이용하여 속도를 제어하고 ANN 제어기를 이용하여 속도를 추정한다. 신경회로망의 역전파 알고리즘 기법은 전동기 속도의 실시간 추정을 제시한다. IPMSM의 속도제어의 결과는 이득값 동조의 효용성을 보여준다. 그리고 STPI 제어기는 고정된 이득값을 가진 PI 제어기에 비하여 강인성 광범위한 운전영역 부하 왜란등에 대하여 우수한 성능을 나타낸다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
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• pp.309-314
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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. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md 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 adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. 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 adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1105-1106
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• 2011

본 논문에서는 EV 구동용 유도전동기와 매입형 영구자석 동기전동기(IPMSM)의 동작특성과 효율을 비교하기 위하여 농형구조 유도전동기와 단층구조 IPMSM을 설계 하였다. EV 구동용 전동기는 정속운전 뿐만 아니라 가변속 운전이 필수적이므로 유도전동기는 V/f 제어, IPMSM은 약계자 제어를 사용하여 가변속 상태에서의 운전토크와 효율을 유한요소법을 이용하여 해석하였다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2007년도 추계학술대회 논문집
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• pp.313-318
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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 maximum torque control of IPMSM drive using adaptive fuzzy neural network controller and artificial neural network(ANN). This control method is applicable over the entire speed range which 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 Adaptive-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 reposes speed control of IPMSM using Adaptive-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 a lied to IPMSM drive system controlled Adaptive-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the Adaptive-FNN and ANN controller.

• Journal of Electrical Engineering and Technology
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• 제13권4호
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• pp.1558-1565
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• 2018

Three-phase current is reconstructed from the dc-link current in an AC machine drive with a single current sensor. Switching pattern modification methods, in which the magnitude of the effective voltage vector is secured over its minimum, are investigated to accurately reconstruct the three-phase current. However, the existing methods that modify the switching pattern cause voltage and current distortions that degrade sensorless performance. This paper proposes a variable-magnitude voltage signal injection method based on a high frequency voltage signal injection. The proposed method generates a voltage reference vector that ensures the minimum magnitude of the effective voltage vector by varying the magnitude of the injection signal. This method can realize high quality current reconstruction without switching pattern modification. The proposed method is verified by experiments in a 600W Interior permanent magnet synchronous machine (IPMSM) drive system.

• 전력전자학회논문지
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• 제18권3호
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• pp.225-231
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• 2013

This paper presents an improved signal processing technique in the square-wave-type voltage injection method for IPMSM sensorless drives. Since the sensorless method based on the square-wave voltage injection does not use low-pass filters to get an error signal for estimating rotor position and allows the frequency of the injected voltage signal to be high, the sensorless drive system may achieve an enhanced control bandwidth and reduced acoustic noise. However, this sensorless method still requires low-pass and band-pass filters to extract the fundamental component current and the injected frequency component current from the motor current, respectively. In this paper, these filters are replaced by simple arithmetic operations so that the time delay for estimating the rotor position can be effectively reduced to only one current sampling. Hence, the proposed technique can simplify its whole signal process for the IPMSM sensorless control using the square-wave-type voltage injection. The proposed technique is verified by the experiment on the 800W IPMSM drive system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.146-148
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications. This paper proposes efficiency optimization control of IPMSM drive using AFLC-FNN(Adaptive Fuzzy Learning Control Fuzzy Neural Network)controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The optimal current can be decided according to the operating speed and the load conditions. 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.

• 조명전기설비학회논문지
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• 제25권9호
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• pp.14-25
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• 2011

The solar vehicle is in the spotlight in the eco-friendly aspect of photovoltaic system using unlimited solar energy. The solar vehicle uses energy of photovoltaic and battery. The solar vehicle uses stored energy in battery when photovoltaic power is lower than consumption power by solar vehicle and if photovoltaic power is higher than consumption power by solar vehicle then photovoltaic power is stored to battery. To improve use efficiency of photovoltaic, the researches about MPPT method to operate maximum power point and interior permanent magnet synchronous motor(IPMSM)drive system using photovoltaic is necessary. This paper proposes MPPT control algorithm for solar vehicle using new fuzzy control(NFC). In this paper, to reduce switching loss, the DC-DC converter is omitted. The NFC controller can be use instead of PO. The NFC controller is performed MPPT control using solar cell voltage and q -axis current of IPMSM. The output of NFC is command q -axis current of IPMSM and this current is operated IPMSM. The response characteristics of algorithm proposed in this paper is compared response characteristics of conventional PO method by PSIM program and validity of this paper prove using this result.