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논문 기본 정보

자료유형
학술저널
저자정보
Iraj Kheirizad (Islamic Republic of Iran Broadcasting) Amir Mohammadi (Azad University of Science and Research) Mohammad Hadi Varahram (Ministry of Science Research and Technology)
저널정보
대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.3 No.2
발행연도
2008.6
수록면
177 - 183 (7page)

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초록· 키워드

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The particle swarm optimization (PSO) has been shown to converge rapidly during the initial stages of a global search, but around global optimum, the search pro~ess becomes very slow. On the other hand, the genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient and can find the optimal solution more accurately and with less computational time. Optimal location of SVC using this hybrid PSO-GA algorithm is found. We have also found the optimal place of SVC using GA and PSO separately and have compared the results. It has been shown that the new algorithm is more effective and efficient. An IEEE 68 bus test system is used for simulation.

목차

Abstract
1. Introduction
2. SVC Model and the Fitness Function
3. Particle Swarm Optimization (PSO)
4. Genetic Algorithm (GA)
5. The PSO-GA Algorithm for Finding the Optimal Placement of SVC
6. Numerical Results and Discussion
7. Conclusion
References

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