전기학회논문지 (The Transactions of The Korean Institute of Electrical Engineers)

  • 제66권8호
  • /
  • Pages.1163-1171
  • /
  • 2017
  • /
  • 1975-8359(pISSN)
  • /
  • 2287-4364(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지
DOI QR코드

DOI QR Code

Python을 이용한 유전 알고리즘 기반의 수도권 고장전류 저감을 위한 BTB HVDC 최적 위치 선정 기법에 관한 연구

A Study on Selecting the Optimal Location of BTB HVDC for Reducing Fault Current in Metropolitan Regions Based on Genetic Algorithm Using Python

  • Song, Min-Seok (Dept. of Electrical Engineering, Incheon National University) ;
  • Kim, Hak-Man (Dept. of Electrical Engineering, Incheon National University) ;
  • Lee, Byung Ha (Dept. of Electrical Engineering, Incheon National University)
  • 투고 : 2017.05.29
  • 심사 : 2017.07.12
  • 발행 : 2017.08.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The problem of fault current to exceed the rated capacity of a power circuit breaker can cause a serious accident to hurt the reliability of the power system. In order to solve this issue, current limiting reactors and circuit breakers with increased capacity are utilized but these solutions have some technical limitations. Back-to-back high voltage direct current(BTB HVDC) may be applied for reducing the fault current. When BTB HVDCs are installed for reduction in fault current, selecting the optimal location of the BTB HVDC without causing overload of line power becomes a key point. In this paper, we use genetic algorithm to find optimal location effectively in a short time. We propose a new methodology for determining the BTB HVDC optimal location to reduce fault current without causing overload of line power in metropolitan areas. Also, the procedure of performing the calculation of fault current and line power flow by PSS/E is carried out automatically using Python. It is shown that this optimization methodology can be applied effectively for determining the BTB HVDC optimal location to reduce fault current without causing overload of line power by a case study.

키워드

참고문헌

  1. KPX, A Study on Policy Directions for the Promotion of Distributed District Heat and Power Generation, 2014.
  2. KISTEP, Development project of Multi-Terminal DC transmission and distribution system, 2015.
  3. KPX, A study on optimal mix plan of long-term metropolitan power network, 2015.
  4. F. Nikolas, G. A. Vassilios, and D. D. Georgios, "VSC-based HVDC Power Transmission Systems: An Overview", IEEE Trans. on Power Energy, Vol. 23, No. 3, pp. 592-602, 2009.
  5. G. Chunyi and Z. Chengyong, "Supply of an Entirely Passive AC Network Through a Double-Infeed HVDC System", IEEE Trans. on Power Energy, Vol. 24, pp. 2835-2841, 2010.
  6. P. Manohar, and K. K. Dutta, "Effect of SCFCL on the Performance of BTB-HVDC System" 1st In Electrical Energy Systems (ICEES), Newport Beach, United States of America, pp. 288-293, 2011.
  7. E. T. Raymundo, and M. Marta, "Offshore Wind Farm Grid Integration by VSC Technology With LCC-Based HVDC Transmission", IEEE Trans. on Sustainable Energy, Vol. 3, No. 4, pp. 899-907, 2012. https://doi.org/10.1109/TSTE.2012.2200511
  8. C. Guo, C. Zhao, and X. Chen, "Analysis of Dual-Infeed HVDC with LCC-HVDC and VSC-HVDC", IEEE Power & Energy Society General Meeting Power Delivery, Vancouver, Canada, pp.1529-1537, 2013.
  9. S. S. Lee, and Y. T. Yoon, "VSC-HVDC Model-Based Power System Optimal Power Flow Algorithm and Analysis", IEEE Power & Energy Society General Meeting, Vancouver, Canada, pp.1-5, 2013.
  10. N. Rehan, S. C. Muhammad, A. Maaz, A. H. Saad and T. S. Umar, "Impact of HVDC Grid Segmentation Topology on Transient Stability of HVDC-Segmented Electric Grid", IEEE International Conference on Open Source Systems and Technologies (ICOSST), Lahore, Pakistan, pp.132-136, 2015.
  11. D. E. Goldberg, Genetic algorithms in search optimization and machine learning, Addison-wesley, Canada, 1989.
  12. G. Liu, J. Zhang, R. Gao, and Y. Sun, "A Coarse- Grained Genetic Algorithm for the Optimal Design of the Flexible Multi-body Model Vehicle Suspensions Based on Skeletons Implementing", First In Intelligent Networks and Intelligent Systems, Wuhan, China, pp. 139-142, 2008.
  13. J. Cheng, and Y. Yang, "Application of hybrid genetic algorithm in optimization formula system", 2nd In Education Technology and Computer (ICETC), Shanghai, China, Vol. 4, pp. 130-134, 2010.
  14. P. Guo, X. Wang, and Y. Han, "The enhanced genetic algorithms for the optimization design", 3rd International Conference on In Biomedical Engineering and Informatics (BMEI), Yantai, China, Vol. 7, pp. 2990-2994, 2010.
  15. W. Sheng, K.Y. Liu, Y. Liu, X. Meng, Y. Li, "Optimal Placement and Sizing of Distributed Generation via an Improved Nondominated Sorting Genetic Algorithm II", IEEE Transactions on Power Delivery, Vol. 30, pp. 569-578, 2015. https://doi.org/10.1109/TPWRD.2014.2325938
  16. L. Zhang, K. Zhang, G. Zhang, "Power distribution system reconfiguration based on genetic algorithm", 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 80-84, 2016.
  17. Z. Zhang, J. Liang, X. Wang, R. Ye, Z. Lin, "Transmission network including VSC-HVDC expansion planning based on genetic algorithm with shift operation", 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 722-726, 2016.
  18. KPX, Establishment of fault current analysis standard of power systems, 2013.
  19. M. U. Khalid, F. Rashid, W. Akhtar, M. H. Ghauri, and O. LATEEF, Python Based Power System Automation in PSS/E, Lulu.com, 2014.