• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.64-66
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• 1995

This paper presents a new approach to evaluate optimal basic reliability indices of electric distribution systems using genetic algorithm. The use of optimal reliability evaluation is an important aspect of distribution system planning and operation to determine adequacy reliability level of each area. In this paper, the reliability model is based on the analytical method, connecting component failure to load point outage in each section. The proposed method applies genetic algorithm to calculate the optimal values of basic reliability indices, ie. failure rate and repair time, for a load point in the power distribution system, subject to minimizing interruption cost. Test results for the model system are reported in the paper compared with a direct optimization method(gradient projection).

• 대한전기학회논문지:전력기술부문A
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• 제51권10호
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• pp.483-494
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• 2002

In this thesis an optimal design technique of fuzzy logic controller using the real variable elitist genetic algorithm(RVEGA) as a supplementary control to Static Var Compensator(SVC) in order to damp oscillation in an AC-DC Dower system was proposed. Fuzzy logic controller is designed self-tuning shape of fuzzy rule and fuzzy variable using genetic algorithm based on natural selection and natural genetics. To verify the robustness of the proposed method, considered dynamic response of system by applying a load fluctuation.

• 한국자동차공학회논문집
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• 제18권4호
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• pp.68-73
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• 2010

Welding conditions in process of spot welding must become optimum and need to guarantee stability and reliability of vehicle body considering dynamic load history. Because welding parameters in process of spot welding are various, it is difficult that the quality of spot weldment was included in the optimum levels. In this paper, we found the optimum welding conditions satisfying KS spot welding qualities using genetic algorithm and spot welding experiments of high tensile strength steel and galvanized steel. Those experiments were dissimilar weld and 2-lap spot welding. Genetic algorithm created random welding condition after that, selected optimum individuals by probability concept.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.446-448
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• 1997

The aim of this paper is to introduce a method designing the optimal model following boiler-turbine control system using genetic algorithms. This boiler-turbine control system is designed by applying genetic algorithms with reference model to the optimal determination of weighting matrices Q, R that are given by LQ regulator problem. These weighting matrices are optimized simultaneously in the search domain selected adequately. The effectiveness of this boiler-turbine control system is verified by computer simulation.

• International Journal of Fluid Machinery and Systems
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• 제2권2호
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• pp.102-109
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• 2009

A design optimization system for Francis turbine was developed. The system consists of design program and CFD solver. Flow passage shapes are optimized automatically by using the system with Multi-Objective Genetic Algorithm (MOGA). In this study, the system was applied to a high specific speed Francis turbine (nSP = 250m-kW). The runner profile and the draft tube shape were optimized to decrease hydraulic losses. As the results, it was shown that the turbine efficiency was improved in wide operating range, furthermore, the height of draft tube was reduced with the hydraulic performance kept.

• 조명전기설비학회논문지
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• 제29권2호
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• pp.104-112
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• 2015

This paper deals with an methodology for acquiring optimal order of rational function model in FDNE(frequency dependent network equivalents) with GA(genetic Algorithm). In order to analyze the modern power system with huge complexity, an practical and efficient equivalent model is needed which represents the system's characteristics of transient phenomenon. this paper shows developing a z domain rational function model which have the resultant coefficient from proposed GA simulation. To demonstrate this methodology, some simulations are performed with practical power system of NZ which applied with fault condition and nonlinear converter load.

• 동력기계공학회지
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• 제2권1호
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• pp.52-58
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• 1998

An immune system has powerful abilities such as memory, recognition and learning to respond to invading antigens, and is applied to many engineering algorithm recently. In this paper, the combined optimization algorithm is proposed for multi-objective problem by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The optimizing ability of the proposed algorithm is identified by using multi-peak function which have many local optimums and identification of the flexural rigidity for wire rope model.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2001년도 춘계학술대회 논문집
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• pp.243-248
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• 2001

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Thus, in this paper, an Evolving Neural Network Controller(ENNC) without Error Back Propagation(EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC) are compared with the ones of conventional optimal controller and the conventional evolving neural network controller(CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Nuclear Engineering and Technology
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• 제42권4호
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• pp.414-425
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• 2010

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into "families". On the basis of the decision maker's preferences, each family is then synthetically represented by a "head of the family" solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 춘계학술대회 논문집
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• pp.561-564
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• 2002

With Rapid Prototyping system, the efficient packing in a fixed work volume reduces build time when multiple parts are built in a process. In this paper, an efficient and rapid packing algorithm is developed for SLS system that has cylindrical workspace. A genetic algorithm is implemented to place as many part as possible in a vat. For fast computation, a collision detection algorithm "k-DOPs Tree" is implemented.