• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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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.

• Journal of Power Electronics
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• 제6권4호
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• pp.340-346
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• 2006

A high efficiency drive technique for synchronous reluctance motors (SynRM) using a neural network (NN) is presented in this paper. High efficiency drive condition depends on the mathematical model of SynRM. A NN is employed as an adaptive model of SynRM. The proposed high efficiency drive technique does not require an accurate mathematical model of SynRM. Moreover, the proposed method shows robustness against machine parameter variations because the training algorithm of the NN is executed on-line. The usefulness of the proposed method is confirmed through experimentation.

• 전기학회논문지P
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• 제54권1호
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• pp.20-29
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• 2005

This paper presents an implementation of efficiency optimization of reluctance synchronous motor (RSM) using a neural network (NN) with a direct torque control (DTC). The equipment circuit considered with iron losses in RSM is analyzed theoretically, and the optimal current ratio between torque current and exiting current component are derived analytically. For the RSM driver, torque dynamic can be maintained with DTC using TMS320F2812 DSP Controller even with controlling the flux level because a torque is directly proportional to the stator current unlike induction motor. In order to drive RSM at maximum efficiency and good dynamics response, the Backpropagation Neural Network is adapted. The experimental results are presented to validate the applicability of the proposed method. The developed control system show high efficiency and good dynamic response features with 1.0 [kW] RSM having 2.57 inductance ratio of d/q.

• 산업경영시스템학회지
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• 제36권4호
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• pp.59-63
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• 2013

In biomass gasification, efficiency of energy quantification is a difficult part without finishing the process. In this article, a radial basis function neural network (RBFN) is proposed to predict biomass efficiency before gasification. RBFN will be compared with a principal component regression (PCR) and a multilayer perceptron neural network (MLPN). Due to the high dimensionality of data, principal component transform is first used in PCR and afterwards, ordinary regression is applied to selected principal components for modeling. Multilayer perceptron neural network (MLPN) is also used without any preprocessing. For this research, 3 wood samples and 3 other feedstock are used and they are near infrared (NIR) spectrum data with high-dimensionality. Ash and char are used as response variables. The comparison results of two responses will be shown.

• Journal of Mechanical Science and Technology
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• 제20권7호
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• pp.929-940
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• 2006

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.

• 정보학연구
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• 제8권1호
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• pp.119-124
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• 2005

Learning procedure in the neural network is updating of weights between neurons. Unadequate initial learning coefficient causes excessive iterations of learning process or incorrect learning results and degrades learning efficiency. In this paper, adaptive learning algorithm is proposed to increase the efficient in the learning algorithms of Kohonens Self-Organization Neural networks. The algorithm updates the weights adaptively when learning procedure runs. To prove the efficiency the algorithm is experimented to clustering of the random weight. The result shows improved learning rate about 42~55% ; less iteration counts with correct answer.

• Journal of Power Electronics
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• 제2권1호
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• pp.19-24
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• 2002

The torque of SRM depends on a phase current and the inductance of the motor. But the inductance is saturated nonlinearly according to the position and current. To drive the motor effectively, the control scheme should take into account the nonlinear characteristics of the magnetic material. This paper proposes an optimal control scheme for high efficiency drive of SRM by adjusting both the turn-on and turn-off angle. The high efficiency drive points are simulated and searched by using GA-Neural Network The switching angles are nonlinearly varied with rotor speed and load.

• 전력전자학회논문지
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• 제4권2호
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• pp.166-174
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• 1999

최적효율제어를 통한 유도전동기의 효율향상은 에너지 절감측면에서 매우 중요하며 인공신경망을 사용하면 시스템의 특성이 충분히 해석되지 않은 상태에서도 우수한 제어특성을 얻을 수 있다. 본 논문은 유도전동기 구동시스템에서 최적 슬립주파수를 추종하는 실시간 인공신경망 회로를 구성하여 운전효율을 최적화하는 제어방법을 제안한다. 제안된 최적 효율제어기는 인공신경망 제어기에 의해 시스템의 비선형성을 포함하여 전동기의 내부손실이 최소가 되는 운전점을 추종한다. 시뮬레이션과 실험을 통하여 기존의 일정v/f 방식에 비하여 고속 경부하시 경제성 있는 에너지 절감효과를 충분히 확보할 수 있었다.

• 대한전기학회논문지
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• 제45권3호
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• pp.444-450
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• 1996

A digital hardware architecture for artificial neural network with learning capability is described in this paper. It is a modified hardware architecture known as HANNIBAL(Hardware Architecture for Neural Networks Implementing Back propagation Algorithm Learning). For implementing an efficient neural network hardware, we analyzed various type of multiplier which is major function block of neuro-processor cell. With this result, we design a efficient digital neural network hardware using serial/parallel multiplier, and test the operation. We also analyze the hardware efficiency with logic level simulation. (author). refs., figs., tabs.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.480-480
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• 2000

In this paper, we proposed a fuzzy-neuro controller to control the speed of wound rotor induction motor with slip energy recovery. The speed is limited at some range of sub-synchronous speed of the rotating magnetic field. Control speed by adjusting resistance value in the rotor circuit that occurs the efficiency of power are reduced, because of the slip energy is lost when it passes through the rotor resistance. The control system is designed to maintain efficiency of motor. Recently, the emergence of artificial neural networks has made it conductive to integrate fuzzy controllers and neural models for the development of fuzzy control systems, Fuzzy-neuro controller has been designed by integrating two neural network models with a basic fuzzy logic controller. Using the back propagation algorithm, the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the control speed of a wound rotor induction motor process. The control system is designed to maintain efficiency of motor and compensate power factor of system. That is: the proposed controller gives the controlled system by keeping the speed constant and the good transient response without overshoot can be obtained.