Optimisation of inverter placement for mass rapid transit systems by immune algorithm

Author(s): H.J. Chuang
DOI: 10.1049/ip-epa:20041143

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Author(s): H.J. Chuang ¹
- Affiliations: 1: Department of Electrical Engineering, Kao Yuan Institute of Technology, Lu-Chu, Republic of China
Source: Volume 152, Issue 1, January 2005, p. 61 – 71
DOI: 10.1049/ip-epa:20041143 , Print ISSN 1350-2352, Online ISSN 1359-7043

Optimal inverter substation planning is solved by minimising the overall cost of power consumption and inverter investment for mass rapid transit power systems with immune algorithm (IA). The objective function and constraints are expressed as antigens, and all feasible solutions are expressed as antibodies in the IA simulation process. The diversity of antibodies is then enhanced by considering the proximity of antigens so that the global optimisation during the solution process can be obtained. It is found that energy regeneration, which results from the braking operation of train sets approaching the next station, can be restored effectively by the optimal planning of inverters using the proposed immune algorithm.

References

1. 1)
  - Mitsumoto, N., Fukuda, T., Arai, F.: `The immune mechanism, adapttion, learning for the multi agent system', IEEE Symp. on Emerging technologies and factory automation, 1994, Tokyo, Japan.
2. 2)
  - Chen, C.S., Chuang, H.J., Chen, J.L.: `Analysis of dynamic load behavior for electrified mass rapid transit systems', Presented at IEEE/IAS Annual Meeting, 1999, Phoenix, AZ, USA.
3. 3)
  - C.S. Chang , W. Wang , A.C. Liew , F.S. Wen . Bicriterion optimisation for tractions in rapid transit systems using genetic algorithms. IEE Proc. Electr. Power Appl. , 1 , 49 - 54
4. 4)
  - C.S. Chen , H.J. Chuang , L.J. Fan . Unit commitment of main transformers for electrified mass rapid transit systems. IEEE Trans. Power Deliv. , 3 , 747 - 753
5. 5)
  - W. Spatny . Substations for the power supply to the Sao Paulo Metro. Brown Boveri Rev. , 840 - 847
6. 6)
  - Gondo P. T., Hoshino M., and Sawada K.: ‘Electrical power system computer simulation’, Singapore Mass Rapid Transit Reporting, pp. 377–396.
7. 7)
  - Rail System Center of Carnegie-Mellon University, Carnegie-Mellon University, Pittsburgh, PA, USA, `User manual for the transportation system energy management model (EMM)', July 1986.
8. 8)
  - S. Tzeng Y. , R.N. Wu , N. Chen . Electric network solutions of DC transit systems with inverting substations. IEEE Trans. Veh. Technol. , 4 , 1405 - 1412
9. 9)
  - S.J. Huang . An immune-based optimization method to capacitor placement in a radial distribution system. IEEE Trans. Power Deliv. , 2 , 744 - 749
10. 10)
  - J.D. Farmer , N.H. Packard , A.A. Perelson . The immune system adaption, and machine learning. Physica D , 187 - 204
11. 11)
  - C.S. Chang , J.S. Low , D. Srinivasan . Application of tabu search in optimal system design and operation of MRT power supply systems. IEE Proc. Electr. Power Appl. , 1 , 75 - 80
12. 12)
  - Lin, C.H., Chen, C.S., Wu, C.J.: `An immune-based optimization method for loading balance of main transformers and distribution feeders', Presented at IEEE Power Engineering Society Summer Meeting, 2001, 1, p. 48–53.
13. 13)
  - T. Suzuki , M. Tanaka . DC Railway Substations with Solid-State Thyristor Circuit Breakers. Mitsubishi Electr. Adv. , 14 - 17
14. 14)
  - Tsujimura, Y., Gen, M.: `Entropy-based genetic algorithm for solving TSP', Second Int. Conf. Knowledge-based intelligent electronic system, 1998, Adelaide, Australia, p. 285–290.
15. 15)
  - K. Mori , M. Tsukiyama , T. Fukuda . Immune algorithm with searching diversity and its application to resource allocation problem. Trans. Inst. Elect. Eng. Jpn. , 10 , 872 - 878
16. 16)
  - , : `Power and Traction Subsystem Running Maintenance Manual', July 1992.
17. 17)
  - J.S. Chun , J.P. Lim , J.S. Yoon . Optimal design of synchronous motor with parameter correction using immune algorithm. IEEE Trans. Energy Convers. , 3 , 610 - 615
18. 18)
  - J.S. Chun , M.K. Kim , H.K. Jung . Shape optimization of electromagnetic using immune algorithm. IEEE Trans. Magn. , 2 , 1876 - 1879
19. 19)
  - C.S. Chen , H.J. Chuang , H.M. Shiau . Stochastic harmonic analysis of mass rapid transit power systems with uncontrolled rectifiers. IEE Proc., Gener., Transm. Distrib. , 2 , 224 - 232
20. 20)
  - `Metro Taipei Tamshui/Hsintein lines contract 303', HV-Final-System StudyErlangen, 1991.
21. 21)
  - C.H. Lin , C.S. Chen , C.J. Wu , M.S. Kang . Application of immune algorithm to optimal switching operation for distribution-loss minimization and loading balance. IEE proc. Gener. Trans. Distrib. , 2 , 183 - 189

Optimisation of inverter placement for mass rapid transit systems by immune algorithm

Optimisation of inverter placement for mass rapid transit systems by immune algorithm

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