• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.2
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• pp.164-172
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• 2001

This paper describes common MPPT(Maximum Power Point Tracking) control algorithm; Constant Voltage Control, P&O(Perturbation and Observation), IncCond(Incremental Conductance), and investigated it\`s efficiency. Though simulation and efficiency evaluation, the steady/transient states characteristics and efficiency of control algorithms are analyzed respectively. Also, two-mode MPPT control to improve on the existing control algorithm. Moreover, is proposed for high efficiency this paper suggests a topology for MPPT measuring efficiency and a method of examination.

• Journal of the Korean Solar Energy Society
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• v.21 no.1
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• pp.11-20
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• 2001

In this paper described common MPPT(Maximum Power Point Tracking) control algorithm; Constant Voltage Control, P&O(Perturbation and Observation), IncCond(Incremental Conductance), and investigated it's efficiency. Through simulation and efficiency evaluation, analyze the steady/transient states characteristics and efficiency of control algorithms respectively. Also, To high-efficiency proposed Two-mode MPPT control for improve on the existing control algorithm. Moreover, this paper suggested a topology for MPPT measuring efficiency and a method of examination.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.3
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• pp.133-140
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• 2006

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 speed estimated control scheme of SynRM using artificial neural network(ANN). There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. 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.62 no.3
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• pp.342-348
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• 2013

It proposes an efficient control algorithm to increase electric power transmission efficiency between photovoltaic power generating system and the grid. The main controller finds a maximum efficiency condition by considering the quantity of power generated from PV arrays, the number of inverters, and efficiency of PV inverter. According to the condition, a relay board arranges a point of contract of PV arrays. By the disposition of PV arrays, it assigns the optimized power on each PV inverter. Operational principle of the proposed maximum efficiency point tracking algorithm is given in detail. To verify the validity of the proposed approach, computer-aided simulation and experiment carried out.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1132-1136
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• 2014

In the context of increasing electric energy consumption in a data center, energy efficiency improvement is strongly emphasized. In a data center, electric energy is largely consumed by DC power supply system, which is based on a rectifier composed by multiple parallel converters. Therefore, rectifier efficiency must be improved for minimizing loss of DC power supply system. Rectifier efficiency can be modulated by load allocation to converters because converter efficiency depends on input AC power. In this paper, we propose a new control method to maximize rectifier efficiency. The method can control load allocation to converters by introducing active power converter control scheme and start-and-stop of converters. In order to illustrate optimal load allocations in a rectifier, a maximization problem of rectifier efficiency is formulated as a nonlinear optimization one. The problem is solved by Lagrangian relaxation method and the computation results provide the validity of proposed method.

• 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.

• Journal of Power Electronics
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• v.2 no.1
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• pp.67-75
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• 2002

This paper presents the control algorithm for maximum efficiency drives of an induction motor system with the high dynamic performance. This system uses a simple model of the induction motor that includes equations of the iron losses. The model, which only requires the parameters of the induction motor, is referred to a field-oriented frame. The minimum point of the input power can be obtained at the steady state condition. The proposed optimal efficiency control algorithm calculates the reference torque and flux currents for the vector control of the induction motors. A 32 bit floating point TMS320C32 DSP chip implements the drive system with the efficiency optimization controller. The results show the effectiveness of the control strategy Proposed for the induction motor drive.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.114-121
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• 2013

High efficiency operation is required for motors of vehicle to increase fuel efficiency due to the regulation of exhaust gas. This paper presents a control method of fuel pressure to increase fuel efficiency and a sensorless control method of BLDC motor to get higher efficiency than conventional brushed DC motor. Initial rotor position of BLDC motor is detected from current value that is occurred by test voltage pulse and rotor is accelerated by defined sequence to enter sensorless operation mode. The algorithm to control flow rate of fuel pump uses PI controller that is control motor speed to maintain the target fuel pressure commanded by ECU.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.3
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• pp.279-287
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• 2010

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 efficiency optimization control of IPMSM drive using adaptive fuzzy learning controller(AFLC). 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 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. 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. Also, this paper proposes speed control of IPMSM using AFLC1, current control of AFLC2 and AFLC3, and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled AFLC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.10a
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• pp.355-358
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• 2009

This paper proposes efficiency optimization control of IPMSM drive using multi hybrid fuzzy controller(HFC). 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 HFC 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 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 multi HFC. Also, this paper proposes speed control of IPMSM using HFC1, current control of HFC2-HFC3 and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled HFC, the operating characteristics controlled by efficiency optimization control are examined in detail.