• 검색어에 아래의 연산자를 사용하시면 더 정확한 검색결과를 얻을 수 있습니다.
  • 검색연산자
검색연산자 기능 검색시 예
() 우선순위가 가장 높은 연산자 예1) (나노 (기계 | machine))
공백 두 개의 검색어(식)을 모두 포함하고 있는 문서 검색 예1) (나노 기계)
예2) 나노 장영실
| 두 개의 검색어(식) 중 하나 이상 포함하고 있는 문서 검색 예1) (줄기세포 | 면역)
예2) 줄기세포 | 장영실
! NOT 이후에 있는 검색어가 포함된 문서는 제외 예1) (황금 !백금)
예2) !image
* 검색어의 *란에 0개 이상의 임의의 문자가 포함된 문서 검색 예) semi*
"" 따옴표 내의 구문과 완전히 일치하는 문서만 검색 예) "Transform and Quantization"
쳇봇 이모티콘
ScienceON 챗봇입니다.
궁금한 것은 저에게 물어봐주세요.

논문 상세정보


This paper presents an application of the parallel Genetic Algorithm-Tabu Search (GA- TS) algorithm, and that is to search for an optimal solution of a reconfiguration in distribution systems. The aim of the reconfiguration of distribution systems is to determine the appropriate switch position to be opened for loss minimization in radial distribution systems, which is a discrete optimization problem. This problem has many constraints and it is very difficult to solve the optimal switch position because of its numerous local minima. This paper develops a parallel GA- TS algorithm for the reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10$\%$ of the population to enhance the local searching capabilities. With migration operation, the best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based rapid Ethernet. To demonstrate the usefulness of the proposed method, the developed algorithm was tested and is compared to a distribution system in the reference paper From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution quality, speedup, efficiency, and computation time.

참고문헌 (15)

  1. Gunther Brauner and Manfred Zabel, 'Knowledge based planning of Distribution networks,' IEEE Trans. on Power Systems, vol. 9, no. 2, pp. 942-948, May 1994 
  2. Young-Jae Jeon, Jae-Chul Kim, Jin-O Kim, Joong- Rin Shin, Kwang Y. Lee, 'An Efficient Simulated Annealing Algorithm for Network Reconfiguration in Large-Scale Distribution Systems,' IEEE Trans. on Power Delivery, vol. 17, no. 4, pp. 1070-1078, Oct. 2002 
  3. K. Nara, A. Shiose, M. Kitagawa, and T.Tshihara, 'Implementation of genetic algorithm for distribution system loss minimum reconfiguration,' IEEE Trans. on Power Systems, vol. 7, no. 3, pp. 1044-1051, Aug. 1992 
  4. H. Mori and K. Takeda, 'Parallel simulated annealing for power system decomposition,' IEEE Proc. of PICA 93, pp. 366-372, May 1993 
  5. R. Tanese, 'Parallel genetic algorithm for a hypercube,' Proc. of 2th ICGA'87, pp. 177-183, 1987 
  6. J. Arabas, Z. Michalewicz, and J. Mulawka, 'GAVaPS-a Genetic Algorithm with Varying Population Size,' IEEE International Conference on Evolutionary Computation, pp. 73-78, 1994 
  7. Z. Michalewicz, Genetic Algorithms+Data Structures = Evolution Programs, Springer-Verlag, 1992 
  8. M. E. Baran and F. F. Wu, 'Network reconfiguration in Distribution systems for loss reduction and load balancing,' IEEE Trans. on Power Delivery, vol. 4, no. 2, pp. 1401-1407, April 1989 
  9. Tim Taylor and David Lubkeman, 'Implementation of heuristic search strategies for distribution feeder reconfiguration,' IEEE Trans. on Power Delivery, vol. 5, no. 1, pp. 239-246, Jan. 1990 
  10. D. Schlierkamp-Voosen and H. Muhlenbein, 'Adaptation of Population Sizes by Competing Subpopulations,' IEEE International Conference on Evolutionary Computation, pp. 330-335, 1996 
  11. D. B. Fogel, L. J. Fogel and J. W. Atmas, 'Meta- Evolutionary Programming,' Proceedings 2sth Asilomar Conference on Systems, Signals, and Computers, pp. 540-545, 1991 
  12. D. E. Goldberg, Genetic Algorithms in Search, optimization, and Machine Learning, Addison- Wesley publishing Company, INC., 1989 
  13. D. Shirmohammadi and H. W. Hong, 'Reconfiguration of electric distribution networks for resistive losses reduction,' IEEE Trans. on Power Delivery, vol. 4, no. 2, pp. 1492-1498, April 1989 
  14. D. B. Fogel, 'An Introduction to Simulated Evolutionary Optimization,' IEEE Trans. on Neural Networks, vol. 5, no. 1, Jan. 1994 
  15. K. Nara, Y. Mishima, A. Gojyo, T. Ito and H. Kaneda, 'Loss minimum reconfiguration of distribution system by tabu search,' Proc. of IEEE PES T&D Conference and Exhibition 2002 Asia Pacific, vol. 1, pp. 232-236, Oct. 2002 

이 논문을 인용한 문헌 (0)

  1. 이 논문을 인용한 문헌 없음


원문 PDF 다운로드

  • ScienceON :

원문 URL 링크

원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다. (원문복사서비스 안내 바로 가기)

상세조회 0건 원문조회 0건

DOI 인용 스타일