• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.1
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• pp.14-17
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• 2015

This paper presents a analysis method for power system stability using a Modified Genetic-based Power System Stabilizer(MGPSS). The proposed MGPSS parameters are optimized using Modified Genetic Algorithm(MGA) in order to maintain optimal operation of generator under the various operating conditions. To improve the convergence characteristics, real variable string is adopted. The results tested on a single machine infinite bus system verify that the proposed controller has better power system stability than conventional controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1314-1323
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• 2011

The maintenance of generating units is implicitly related with power system reliability and has a tremendous bearing on the operation of the power system. A technique using a fuzzy search method which is based on fuzzy multi-criteria function has been proposed for GMS (generator maintenance scheduling) in order to consider multi-objective function. In this study, a new technique using combined fuzzy set theory and genetic algorithm(GA) is proposed for generator maintenance scheduling. The genetic algorithm(GA) is expected to make up for that fuzzy search method might search the local solution. The effectiveness of the proposed approach is demonstrated by the simulation results on a practical size test systems.

• KIEE International Transactions on Power Engineering
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• v.5A no.1
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• pp.9-15
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• 2005

In the operation of distribution systems, DGs (Distributed Generations) are installed as an alternative to extension and the establishment of substations, transmission and distribution lines according to the increasing power demand. In the operation planning of DGs, determining optimal capacity and allocation achieves economical profitability and improves the reliability of power distribution systems. This paper proposes a determining method for the optimal number, size and allocation of DGs in order to minimize the operation costs of distribution systems. Capacity and allocation of DGs for economical operation planning duration are determined to minimize total cost composed with power buying cost, operation cost of DGs, loss cost and outage cost using the GA (Genetic Algorithm).

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.6
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• pp.242-248
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• 2006

This paper proposes a coordinated control method for under-load tap changers (ULTCs) with shunt capacitors to reduce the operation numbers of both devices. The proposed method consists of two stages. In the first stage, the dispatch schedule is determined using a genetic algorithm with forecasted loads to reduce the power loss and to improve the voltage profile during a day. In the second stage, each capacitor operates according to this dispatch schedule and the ULTCs are controlled in real time with the modified reference voltages considering the dispatch schedule of the capacitors. The performance of the method is evaluated for the modified IEEE 14-bus system. Simulation results show that the proposed method performs better than a conventional control method.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1123-1125
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• 1999

This paper proposes an optimization technique using Genetic Algorithm(GA) for service restoration in the distribution system. Restoration planning problem can be treated as a combinatorial optimization problem. So GA is appropriate to solve the service restoration problem in the distribution network. But searching capabilities of the GA can be enhanced by developing relevant repairing operation and modifying GA operations. In this paper, we aimed at finding appropriate open sectionalizing switch position for the restoration of distribution networks after disturbances using enhanced GA with repairing operation and modified mutation. Simulation results show that proposed method found the open sectionalizing switches with less out of service area and minimize transmission line losses and voltage drop.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.123.2-123
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• 2001

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the neuro-fuzzy system and the genetic-fuzzy system were used in determining the feeding rate of the coagulant. The fuzzy system is excellently robust in multi-variables and nonlinear problems. Therefore it uses basic algorithm, but it is difficult to construct of the fuzzy parameter such as the rule table and the membership function, Therefore we made the neuro-fuzzy system and the genetic-fuzzy system with the fusion of learning algorithms and compared the performance of the two fuzzy systems. To apply these algorithms, we made the rule table, membership function from the actual operation data of the water treatment plant. We determined optimized feeding rate of coagulant using the fuzzy operation, and also compared ...

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.164-171
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• 2008

This paper proposes the planning method for placement of capacitors in a distribution system. The main objectives in the planning for capacitor allocation are the reduction of installation costs and electric power loss. In the proposed method, the life time of each device is considered in calculating installation costs, and the optimal operation status of devices is found by genetic algorithm. Then, the optimal numbers and locations are determined based on the optimal operation status. Simulation results in the 69-bus distribution system show that the proposed method performs better than conventional methods.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.123-133
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• 1998

A cell design model based on operation sequence is proposed for maximizing the total parts flow within cells considering the data of Process plans for parts, Production volume, and cell size. A relationship between machines is calculated on the basis of the process plans for parts obtained from process plan sheets. Then the machines are classified into machine cells using the relationship. The model is formulated as a 0-1 integer programming and a genetic algorithm approach is developed to solve the model. The developed approach is tested and Proved using actual industrial data. Experimental results indicate that the approach is appropriate for large-size cell design problems efficiently.

• IE interfaces
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• v.20 no.1
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• pp.58-68
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• 2007

This research deals with a vehicle scheduling problem for the sludge collection strategies which might be solved via quantitative analysis and cost evaluations schemes. This problem can be modeled as a kind of capacitated vehicle routing pick-up problems. With the aim of establishing operation schedule of vehicles and analyzing the total cost under considering various assumptions and realistic restrictions of the sludge collection problem, we propose a heuristic method based on the genetic algorithm in conjunction with the sweep algorithm and the 4-opt algorithm. Finally, we present the cost effective operation schedule that can be used as the managing tool for the sludge treatment plant of the multi-purpose dam.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.29-32
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• 2001

In this paper, we applied the high-performance PGAP(Parallel Genetic Algorithm Processor) to recognizing rotated pattern. In order to perform this research efficiently, we used Multi-PGAP system consisted of four PGAP. In addition, we used mental rotation based on the rotated pattern recognition mechanism of human to reduce the number of operation. Also, we experimented with distinguishing specific pattern from similar coin patterns and determine rotated angle between patterns. The result showed that the development of future artificial recognition system is feasible by employing high performance PGAPS.