• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.238-239
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• 2013

It is a crucial requirement to utilize an economical battery capacity for the wind energy conversion system. In this paper, the optimal BESS capacity is determined for the wind farm whose dispatched power is assigned by the min-max dispatching method. Based on a lifetime cost function that indicates the BESS cost spent to dispatch 1kWh wind energy into grid, the battery capacity can be optimized so as to obtain the minimum system operation cost. Moreover, the battery state of charge (SOC) is also managed to be in a safe operating range to ensure the system undamaged. In order to clarify the proposed optimizing method, a 3MW permanent magnet synchronous generator (PMSG) wind turbine model and real wind speed data measured each minute are investigated.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1994.04a
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• pp.38-45
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• 1994

The general conceptual constitution of structural optimization is formulated. The algorithm using the gradient projection method and design sensitivity analysis is discussed. Examples of minimum-weight design for six-story steel plane frame are taken to illustrate the application of this algorithm. The advantages of this algorithm such as marginal cost and design sensitivity analysis as well as system analysis are explained.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.227-242
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• 2015

This paper proposes a weighting factor optimization method in predictive control algorithm for torque ripple reduction in an induction motor fed by an indirect matrix converter (IMC). In this paper, the torque ripple behavior is analyzed to validate the proposed weighting factor optimization method in the predictive control platform and shows the effectiveness of the system. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponds to minimum torque ripple and is compared with the results of conventional weighting factor based predictive control algorithm. The predictive control algorithm selects the optimum switching state that minimizes a cost function based on optimized weighting factor to actuate the indirect matrix converter. The conventional and introduced weighting factor optimization method in predictive control algorithm are validated through simulations and experimental validation in DS1104 R&D controller platform and show the potential control, tracking of variables with their respective references and consequently reduces the torque ripple.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1410-1420
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• 2006

In this paper, a replica update propagation method for cost optimization of request forwarding in the Grid database is proposed,. In the Grid database, the data is replicated for performance and availability. In the case of data update, update information is forwarded to the neighbor nodes to synchronize with the others replicated data. There are two kinds of update propagation method that are the query based scheme and the log based scheme. And, only one of them is commonly used. But, because of dynamically changing environment through property of update query and processing condition, strategies that using one propagation method increases transmission cost in dynamic environment. In the proposed method, the three classes are defined from two cost models of query and log based scheme. And, cost functions and update propagation method is designed to select optimized update propagation scheme from these three classes. This paper shows a proposed method has an optimized performance through minimum transmission cost in dynamic processing environment.

• Structural Engineering and Mechanics
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• v.76 no.5
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• pp.561-578
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• 2020

The present work addresses the surface integrity and chip morphology in finish hard turning of AISI D3 steel under nanofluid assisted minimum quantity lubrication (NFMQL) condition. The surface integrity aspects include microhardness, residual stress, white layer formation, machined surface morphology, and surface roughness. This experimental investigation aims to explore the feasibility of low-cost multilayer (TiCN/Al2O3/TiN) coated carbide tool in hard machining applications and to assess the propitious role of minimum quantity lubrication using graphene nanoparticles enriched eco-friendly radiator coolant based nano-cutting fluid for machinability improvement of hardened steel. Combined approach of central composite design (CCD) - analysis of variance (ANOVA), desirability function analysis, and response surface methodology (RSM) have been subsequently employed for experimental investigation, predictive modelling and optimization of surface roughness. With a motivational philosophy of "Go Green-Think Green-Act Green", the work also deals with economic analysis, and sustainability assessment under environmental-friendly NFMQL condition. Results showed that machining with nanofluid-MQL provided an effective cooling-lubrication strategy, safer and cleaner production, environmental friendliness and assisted to improve sustainability.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.56-63
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• 1997

This paper presents a hybrid network model(HY-PIPENET) implementing a minimum cost spanning tree(MCST) network algorithm to be able to determine optimum path and constrained derivative(CD) method to select optimum Pipe diameter. The HY-PIPENET has been validated with the published data of 6-node/7-pipe network. Networking system and also this system has been optimized with MCST-CD method. As a result, it was found that the gas can be sufficiently supplied at the lower pressure with the smaller diameters of pipe compared to the original system in 6-node/7-pipe network. Hence, the construction cost was reduced about $40\%$ in the optimized system. The hybrid networking model has been also applied to a complicated domestic gas pipeline network in metropolitan area, Korea. In this simulation, parametric study was peformed to understand the role of each individual parameter such as source pressure, flow rate, and pipe diameter on the optimized network. From the results of these simulations, we have proposed the optimized network as tree-type structure with optimum pipe diameter and source pressure in metropolitan area, Korea, however, this proposed system does not consider the environmental problems or safety concerns.

• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.431-438
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• 2016

Large energy consumption is one of the main weaknesses of RO desalination. A new method is proposed to reduce the energy consumption of RO desalination which depends on the salinity of the feedwater. Low salinity feedwater can be obtained using groundwater wells which extracts both fresh groundwater and subsurface sea water. Subsurface feedwater is advantageous in overcoming other problems associated with surface seawater intakes. Salinities of groundwater depend on a number of factors. In this work a new simulation-optimization model is proposed to identify well locations and pumping rates with would provide the required design flow rate with the minimum salinity. When groundwater is developed in a coastal area, the saltwater wedge advances inland and may contaminate existing groundwater wells, which must be prevented. The model can protect existing wells while developing minimum salinity feedwater. Examples are provided to demonstrate the usage of the model.

• Journal of Navigation and Port Research
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• v.32 no.3
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• pp.223-228
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• 2008

As most of small fishing boats made of FRP have been constructed by experience in Korea, some structural safety problems have been occurred occasionally. To improve the structural strength and reduce the costs for construction and operation, optimum design for small fishing boat was carried out in this study. The weight of fishing boat and the main dimensions of structural members are chosen as objective function and design variables, respectively. By the combination of global and local search methods, a hybrid optimization algorithm was developed to escape the local minima and reduce CPU time in analysis procedure, and finite element analysis was performed to determine the constraint parameters at each iteration step in optimization loop. Optimization results were compared with the real existing fishing boat, and the effects of optimum design were examined from points of view; structural strength, material cost, etc.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.5
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• pp.272-281
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• 1988

The problem of optimal transmission system planning is to find the most economical locations and time of transmission line construction under the various constraints such as available rights-of-way, finances, the technical characteristics of power system, and the reliability criterion of power supply, and so on. In this paper the constraint of right-of-way is represented as a finite set of available rights-of-way. And the constructed for a unit period. The electrical constraints are represented in terms of line overload and steady state stability margin. And the reliability criterion is dealt with the suppression of failure cost and with single-contingency analysis. In general, the transmission planning problem requires integer solutions and its objective function is nonlinear. In this paper the objective function is defined as a sum of the present values of construction cost and the minimum operating cost of power system. The latter is represented as a sum of generation cost and failure cost considering the change of yearly load, economic dispatch, and the line contingency. For the calculation of operating cost linear programming is adopted on the base of DC load flow calculation, and for the optimization of main objective function nonlinear Branch-and-Bound algorithm is used. Finally, for improving the efficiency of B & B algorithm a new sensitivity analysis algorithm is proposed.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.261-268
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• 2020

Machine learning is achieved by making a cost function from a given digital signal data and optimizing the cost function. The cost function here has local minimums in the cost function depending on the amount of digital signal data and the structure of the neural network. These local minimums make a problem that prevents learning. Among the many ways of solving these methods, our proposed method is to change the learning step-size. Unlike existed methods using the learning rate (step-size) as a fixed constant, the use of multivariate function as the cost function prevent unnecessary machine learning and find the best way to the minimum value. Numerical experiments show that the results of the proposed method improve about 3%(88.8%→91.5%) performance using the proposed method rather than the existed methods.