• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1837_1838
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• 2009

본 논문에서는 Particle Swarm Optimization(PSO)을 이용하여 지능형 로봇의 주행 제어 방법을 제안한다. 지능형 로봇의 주행을 위한 퍼지 제어기를 설계하고 퍼지 규칙을 구성하고 있는 멤버쉽 함수의 매개 변수를 PSO를 이용하여 동정한다. 최종 PSO로 동정된 매개변수를 이용하여 벽면 주행을 수행한다. 마지막으로, 실험을 통해 그 응용 가능성을 증명한다.

• The Korean Journal of Pesticide Science
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• v.4 no.4
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• pp.87-92
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• 2000

Recently, a new developed petroleiun spray oil(PSO; D-C Tron $Plus^{(R)}$) has been used to control key pests in integrated pest management (IPM) system of citrus orchards in Australia. The efficacy and phytotoxicity of the PSO against the citrus red mite (Panonychus citri) were compared with conventional pesticides (e.g.Tebufenpyrad, Bifenazate and Sun spray oil) in field condition in Jeju. And under PSO mixing spray with some fungicides, the occurrence of phytotoxic symptoms on citrus leaf was investigated. All concentrations of PSO spraying (0.25%, 0.33%, 0.5% and 1.0%) were significantly suppressed the citrus red mite to similar levels of other conventional pesticides. And the spraying of PSO levels ${\geq}0.5%$ was induced not only occurrence of some oil-sucked symptoms on leaf, but also increase of the dropping leaf and fruit rates. As results from mixing PSO spraying test with other fungicides, little burning on new flush shoot was founded only in PSO 1% plus Fluazinam treatment. From the results of this study, with the consideration of pesticidal efficacy and phytotoxicity, 0.25% and 0.33% PSO spraying level will be recommended for the control of citrus red mite during summer season in Jeju.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1699-1708
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• 2007

The optimal power flow(OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, the OPF problem has been intensively studied and widely used in power system operation and planning. In these days, OPF is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. To solve OPF problem, many heuristic optimization methods have been developed, such as Genetic Algorithm(GA), Evolutionary Programming(EP), Evolution Strategies(ES), and Particle Swarm Optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallel processing of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.181-182
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• 2008

본 논문에서는 연산 최적화 알고리듬 중 PSO(Particle Swarm Optimization) 알고리듬을 NVIDIA사(社)에서 제공한 CUDA(Compute Unified Device Architecture)를 이용하여 새롭게 구현하였다. CUDA는 CPU가 아닌 GPU(Graphic Processing Unit)의 다양한 병렬 처리 능력을 사용해 복잡한 컴퓨팅 문제를 해결하는 소프트웨어 개발을 가능케 하는 기술이다. 이 기술을 연산 최적화 알고리듬 중 PSO에 적용함으로써 알고리듬의 수행 속도를 개선하였다. CUDA를 적용한 PSO 알고리듬의 검증을 위해 언어 기반으로 프로그래밍하고 다양한 Test Function을 통해 시뮬레이션 하였다. 그리고 기존의 PSO 알고리듬과 비교 분석하였다. 또한 알고리듬의 성능 향상으로 여러 가지 최적화 분야에 적용 할 수 있음을 보인다.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.261-263
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• 2008

In power systems operation, state estimation takes an important role in security control. For the state estimation problem, the weighted least squares(WLS) method and the fast decoupled method have been widely used at present. But these algorithms have disadvantage of converging local optimal solution. In these days, a modern heuristic optimization method such as Particle Swarm Optimization(PSO), are introduced to overcome the problems of classical optimization. In this paper, we proposed particle swarm optimization (PSO) to search an optimal solution of state estimation in power systems. To demonstrate the usefulness of the proposed method, PSO algorithm was tested in the IEEE-57 bus systems. From the simulation results, we can find that the PSO algorithm is applicable for power system state estimation.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.196-198
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• 2007

전력계통에서 상태추정은 신뢰도 높은 제어를 위해 매우 중요한 역할을 한다. 최근에는 상태추정 문제를 위해 가중최소자승법과 fast decoupled 방법이 많이 쓰이고 있다. 그러나 이런 알고리즘들은 지역해로 수렴하기 쉬운 단점이 있다. 이런 문제점을 극복하기 위해 PSO와 같은 heuristic한 방법을 이용한다. 본 논문에서는 전력계통에서 상태추정의 최적해를 구하기 위한 방법으로 PSO 알고리즘을 제안한다. 제안한 PSO알고리즘의 유용함을 증명하기 위해 IEEE-118모선 시스템에 적용해 보고, 그 결과로부터 전력계통의 상태 추정에 PSO 알고리즘의 적용가능성을 증명하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2536-2543
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• 2013

In this paper, we proposed the modeling in one zone buildings and the energy efficient temperature control algorithm using particle swarm optimization (PSO). A control horizon switching method with PSO is used for optimal control, and the TOU tariff is included to calculate the energy costs. Simulation results show that the reductions of energy cost and peak power can be obtained using proposed algorithms.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.381-383
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• 2009

본 논문에서는 대표적인 시스템 모델링 도구중의 하나인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)를 설계하고 모델을 최적화하기 위하여 최적화 알고리즘인 PSO(Particle Swarm Optimization) 알고리즘을 이용하였다. 즉, 모델의 최적화에 주요한 영향을 미치는 모델의 파라미터들을 PSO 알고리즘을 이용하여 동정한다. 제안된 RBF 뉴럴 네트워크는 은닉층에서의 활성함수로서 일반적으로 많이 사용되어지는 가우시안 커널함수를 사용한다. 더 나아가 모델의 최적화를 위하여 각 커널함수의 중심값은 HCM 클러스터링에 기반을 두어 중심값을 결정하고, PSO 알고리즘을 통하여 가우시안 커널함수의 분포상수, 은닉층에서의 노드 수 그리고 다수의 입력을 가질 경우 입력의 종류를 동정한다. 제안한 모델의 성능을 평가하기 위해 Mackey-Glass 시계열 공정 데이터를 적용하였으며 제안된 모델의 근사화와 일반화 능력을 분석한다.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.303-304
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• 2008

For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.