• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1721-1724
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• 2017

This paper presents a numerical optimization technique to reduce input current and torque ripple of the low voltage BLDC motor using core, coil and switching angle optimization. The optimization technique is employed using the generalized response surface method(RSM) and sampling minimization technique with FEM. A 50W 24V BLDC motor is used to verify the proposed algorithm. As optimizing results, the input current is reduced from 2.46 to 2.11[A], and the input power is reduced from 59 [W] to 51 [W] at the speed of 1000 [rpm]. Also, applied the same optimization algorithm, the torque ripple is reduced about 7.4 %. It is confirmed that the proposed technique is a reasonably useful tool to reduce the consuming current and torque ripple of the low voltage BLDC motor for a compact and efficient design.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.85-92
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• 2007

This paper presents a design optimization of a new Advanced Active Blade Twist (AATR-II) blade incorporating single crystal Macro Fiber Composites (MFC) and conducts vibratory loads reduction analysis using an obtained optimal blade configuration. Due to the high actuation performance of the single crystal MFC, the AATR blade may reduce the helicopter vibration more efficiently even with a lower input-voltage as compared with the previous ATR blades. The design optimization provides the optimal cross-sectional configuration to maximize the tip twist actuation when a certain input-voltage is given. In order to maintain the properties of the original ATR blade, various constraints and bounds are considered for the design variables selected. After the design optimization is completed successfully, vibratory load reduction analysis of the optimized AATR-II blade in forward flight condition is conducted. The numerical result shows that the hub vibratory loads are reduced significantly although 20% input-voltage of the original ATR blade is used.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1108-1121
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• 2019

The non-inverting buck-boost converter (NIBB) is a step-up and step-down DC-DC converter suitable for wide-input-voltage-range applications. However, when the input voltage is close to the output voltage, the NIBB needs to operate in the buck-boost mode, causing a significant efficiency reduction since all four switches operates in the PWM mode. Considering both the current stress limitation and the efficiency optimization, a novel design methodology for the optimal phase-shift modulation of a NIBB in the buck-boost mode is proposed in this paper. Since the four switches in the NIBB form two bridges, the shifted phase between the two bridges can serve as an extra degree of freedom for performance optimization. With general phase-shift modulation, the analytic current expressions for every duty ratio, shifted phase and input voltage are derived. Then with the two key factors in the NIBB, the converter efficiency and the switch current stress, taken into account, an objective function with constraints is derived. By optimizing the derived objective function over the full input voltage range, an offline design methodology for the optimal modulation scheme is proposed for efficiency optimization on the premise of current stress limitation. Finally, the designed optimal modulation scheme is implemented on a DSPs and the design methodology is verified with experimental results on a 300V-1.5kW NIBB prototype.

• International Journal of Korean Welding Society
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• v.3 no.2
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• pp.15-23
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed, root opening and the output variables were bead height, bead width, penetration and back bead width. The number of level for each input variable is 8, 16, 8 and 3, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 3,072 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 48 experiments.

• Journal of Welding and Joining
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• v.18 no.5
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• pp.63-69
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• 2000

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables was wire feed rate, welding voltage, and welding speed and the output variables were bead height, bead width, and penetration. The number of level for each input variable is 16, 16, and 8, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 2048 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 40 experiments.

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.44-50
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• 2001

A genetic algorithm was applied to the arc welding process as to determine the near-optimal settings of welding process parameters that produce the good weld quality. This method searches for optimal settings of welding parameters through the systematic experiments without the need for a model between the input and output variables. It has an advantage of being capable to find the optimal conditions with a fewer number of experiments rather than conventional full factorial designs. A genetic algorithm was applied to the optimization of the weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed. The output variables were the bead height bead width, and penetration. The number of levels for each input variable is 16, 16, and 8, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions,2048 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions in less than 40 experiments.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.643-653
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• 2017

The paper presents controller optimization algorithm for a 12-pulse voltage source converter (VSC) based high voltage direct current (HVDC) system. To get an optimum algorithm, three methods namely conventional-Zeigler-Nichols, linear-golden section search (GSS) and stochastic-particle swarm optimization (PSO) are applied to control of 12 pulse VSC based HVDC system and simulation results are presented to show the best among the three. The performance results are obtained under various dynamic conditions such as load perturbation, non-linear load condition, and voltage sag, tapped load fault at points-of-common coupling (PCC) and single-line-to ground (SLG) fault at input AC mains. The conventional GSS and PSO algorithm are modified to enhance their performances under dynamic conditions. The results of this study show that modified particle swarm optimization provides the best results in terms of quick response to the dynamic conditions as compared to other optimization methods.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.4
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• pp.419-424
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• 2012

This paper presents a tubular reciprocating translational motion permanent magnet synchronous motor for percussive drilling applications for offshore oil & gas industry. The motor model and rock model are built up by doing force analysis of the motor and analyzing the physical procesof impact. The optimization of input voltage waveforms to maximize the rate of penetration is done by simulations. The simulation results show that the motor can be utilized in percussive drilling applications and achieve a very large impact force. Simulation results for optimization also show that second harmonic input voltage produces a higher rate of penetration than the sine wave and fourth harmonic input voltages.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.4
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• pp.286-289
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• 2019

In this paper, we proposed a method to control input powers and receiver loads for maximum efficiency in multiple-input multiple-output(MIMO) wireless power transfer(WPT) systems. The input voltage ratio between transmitters and receiver loads for maximum transfer efficiency is derived in terms of figure of merits. The theoretically derived input voltages for the transmitters and optimum loads for the receivers were found to be similar to those obtained by a genetic algorithm. We demonstrate the effectiveness of the theory using a few design examples. Using the results obtained from this study, effective and simplified designs of MIMO WPT systems will be possible.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.6
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• pp.826-833
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• 2010

Electric-thermal-structural actuated compliant mechanisms are mechanisms onto which electric voltage drop is applied as input instead of force. This mechanism is based on thermal expansion of material while being heated. Compliant mechanisms are designed subjected to electric charge input using BESO(bi-directional evolutionary structural optimization) method. Reliability-based topology optimization (RBTO) is applied to the topology design of actuators. performance measure approach (PMA), which has probabilistic constraints that are formulated in terms of the reliability index, is adopted to evaluate the probabilistic constraints. In this study, BESO method is used to obtain optimal topology of compliant mechanisms from initial design domain. PMA approach is used to evaluate reliability index. The procedure has been tested in numerical applications and compared with the results obtained by other methods to validate these approaches.