• 전기학회논문지
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• 제59권5호
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• pp.882-889
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• 2010

This paper presents a method of finding the optimal operating point minimizing a given objective function with 3 phase power flow equations and operational constraints, called 3 phase optimal power flow (3POPF). 3 phase optimal power flow can provide operation and control strategies for the distribution systems with distributed generation assets, which might be frequently in unbalanced conditions assuming that high penetration rate of renewable energy sources in the systems. As the solution technique for 3POPF, this paper adopts a simulation-based method of particle swarm optimization (PSO). In the PSO based 3POPF, a utility function needs to be defined for evaluation of the degree in operational improvement of each particle's current position. To evaluate the utility function, in this paper, NR-based 3 phase power flow algorithm was developed which can deal with looped distributed generation systems. In this paper, illustrative examples with a 5-bus and a modified IEEE 37-bus test systems are given.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권8호
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• pp.415-422
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• 2000

This paper presents a new algorithm for hierarchical optimal control of nonlinear systems. The proposed method is simple because the solutions are obtained by only exchanging informations of coefficient vector based on interaction prediction principle and FWT(fast Walsh transform) in upper and lower level. Since we solve two point boundary problem with Picard's iterative method and the backward integral operational matrix of Walsh function to obtain the optimal vector of each independent subsystem, the algorithm is simple and its operation is fast without inverse matrix and kronecker product operation. In simulation, the proposed algorithm's usefulness is proved by comparison with the global optimal control methods.

• 품질경영학회지
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• 제36권1호
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• pp.40-48
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• 2008

The purpose of this study is to rationally manage service facilities of convention center. This study is to develop the algorithm to consider optimal assignment and optimal operation system planning for convention center. The scheduling algorithm of this study develops through constructing the mathematical model and analyzing the mathematical structure of variables and constraints in model. The scheduling algorithm develops to consist eight stage of optimal operation planning and five stage of optimal assignment planning. Especially, this study indicates that optimum answer through mathematical model and results of algorithm is nondiscrimination.

• 전기학회논문지
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• 제58권11호
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• pp.2079-2088
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• 2009

The optimal operation in distribution system is to select tie switches considering important elements(Load balance, Loss minimization, Voltage drop, Restoration index..) in distribution system. Optimal Tie Switches Selection is very important in operation of distribution system because that is closely related with efficiency and reliability. In this paper, a new algorithm considering important elements is proposed to find optimal location of tie switches. In the case study, the proposed algorithm has been testified using real distribution network of KEPCO for verifying algorithm and complex network for applying future distribution network.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권9호
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• pp.694-705
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• 2010

진화 프로그래밍은 실수형 최적화 문제에 널리 사용되는 알고리즘으로 돌연변이 연산이 중요한 연산이다. 일반적으로 돌연변이 연산은 확률 분포와 이에 따른 매개변수를 사용하여 변수값을 변화시키는데, 이 때 매개변수 역시 돌연변이 연산의 대상이 됨으로 이를 위한 또 다른 매개변수가 필요하다. 그러나 최적의 매개변수 값은 주어진 문제에 전적으로 의존하기 때문에 매개변수 개수가 많은 경우 매개변수값들에 대한 최적 조합을 찾기 어렵다. 이러한 문제를 부분적으로나마 해결하기 위하여 본 논문에서는 변수의 돌연변이 연산을 위한 매개변수를 자기 적응적 관점에서 이론적으로 추정한 돌연변이 연산을 제안하였다. 제안한 알고리즘에서는 코시 확률 분포의 축척 매개변수를 추정하여 돌연변이 연산에 적용함으로 축척 매개변수에 대한 돌연변이 연산이 필요하지 않다는 장점이 있다. 제안한 알고리즘을 벤치마킹 문제에 적용한 실험 결과를 통해 볼 때, 최적값 측면에서는 제안한 알고리즘의 상대적 우수성은 벤치마킹 문제에 의존하였으나 계산 시간 측면에서는 모든 벤치마킹 문제에 대하여 제안한 알고리즘이 우수하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.541-543
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• 2004

An optimal soot blowing system has been developed for an optimal operation of power utility boilers by both minimization of the use of steam and the number of soot blowers worked during soot blowing. Traditionally, the soot blowing system has been operated manually by operators. However, it causes the reduction of power and thermal performance degradation because all soot blowers installed in the plant should be worked simultaneously even there are lots of tubes those are not contaminated by slagging or fouling. Heat transfer area is divided into four groups, furnace, convection area including superheater, reheater and economizer, and air preheater in the present study. The condition of cleanness of the tubes is calculated by several parameters obtained by sensors. Then, a part of soot blowers works automatically where boiler tubes are contaminated. This system has been applied in a practical power plant. Therefore, comparison has been done between this system and manual operation and the results are discussed.

• 대한전기학회논문지:전력기술부문A
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• 제48권12호
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• pp.1498-1506
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• 1999

In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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• pp.310-313
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• 1996

Mathematical programming method for finding optimal solution of job shop scheduling is inadequate to real situation because fo too much computation time. In contrast, dispatching rule is helpful for reducing compuation time but is not guaranted to find optimal solution. The purpose of this paper is to develop a new dispatching rule and procedure to minimize mean flow time whose result is near the optimal solution for job shop scheduling. First step is to select machine which have shortest finishing operation time among the schedulable operations. Second step is to select operation with regard to estimated remaining operation time. The suggested rule is compared with nondelay and MWKR rule for three examples, and is confirmed to be most effective to minimize mean flow time.

• 한국시뮬레이션학회논문지
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• 제3권1호
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• pp.167-178
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• 1994

For the identification of the optimal operating conditions of phosphoric acid plant, an intelligent simulation was performed based on the dissolution reaction of phosphate rock. A phosphoric acid plant consists of three main processes : ball-mill grinding process, rock reaction process and slurry filteration process. The grinding and filteration processes are relatively simple processes and most of the simulation works are on the reaction process. The practical operation data of phosphoric acid plant at Namhae Chemical Corp. were utilized in the simulation. The operation of the phosphoric acid plant is highly dependent on the heuristics of operators and so the expert system technology was employed. The operation of phosphoric acid plant varies with the origin of phosphate rock. Results of the simulation showed the optimal values of major process variables and optimal operating conditions. The knowledgebase for the expert system was constructed based on the interview with the experienced plant operators.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1279-1281
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• 1999

The learning automaton is an automaton to update systematically the strategy for enhancing the performance in response to the output results, and several schemes of learning automata have been presented. In this paper, S-model learning automata are applied to achieving a best compromise solution between an optimal solution for economic operation and an optimal solution for stable operation of the power system under the circumstance that the loads vary randomly. It is shown that learning automata are applied satisfactorily to the multiobjective optimization problem for obtaining the best tradeoff among the conflicting economy and stability objectives of power systems.