Buradasınız

Anasayfa » Content

IMMUNE GENETIC ALGORITHM PERFORMANCE IN OPTIMIZATION OF POWER FLOW IN POWER SYSTEMS

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2010

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
In this paper two conventional random search methods that are Genetic Algorithm and Immune Genetic Algorithm has been compared in optimal power flow problem in power system. The IEEE 14-bus test system has been selected as case study. This comparison has been done in equal conditions for two algorithms. Objective function in this problem is the minimization of cost of network losses and the cost of reactive power injection in the period of five years. Control variables are voltage magnitude of generator buses, active power of generators and reactive power injection of load buses. The results show that the IGA is more accurate than the GA and global optimal solutions can be found by the IGA.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-immune-genetic-algorithm-performance-optimization-power-flow-power-systems.pdf
  • 2
1229-1234
  • English

REFERENCES

References: 

[1] W. Huang, C. Xu, J. Zhang, S. Hu, "Study of Reactive
Power Optimization Based on Immune Genetic
Algorithm", Transmission and Distribution Conference
and Exposition, 2003 IEEE PES, Vol. 1, pp. 186–190,
Sept. 2003.
[2] W. Lei, J. Licheng, "The Immune Genetic Algorithm and
Its Convergence", Fourth International Conference on
Signal Proceedings, Vol. 2, pp. 1347–1350, Oct. 1998.
[3] Z. Haibo, Z, Lizi, M. Fanling, "Reactive Power
Optimization Based on Genetic Algorithm",
International Conference on Power System Technology,
Vol. 2, pp. 1448-1453, Aug. 1998.
[4] H. Shyh-Jier, "An Immune Based Optimization Method to
Capacitor Placement in a Radial Distribution System",
IEEE Transaction on Power Delivery, Vol. 15, Issue 2,
pp. 744–749.
[5] Z. Junmin, H. Tinghua, Z. Honggang, "The Reactive
Power Optimization of Distribution Network Based on
an Improved Genetic Algorithm", Asia and Pacific
Transmission and Distribution Conference and
Exhibition, pp. 1–4, Aug. 2005.
[6] J. Licheng, W. Lei, "A Novel Genetic Algorithm Based
on Immunity", IEEE Transaction on Systems, Man and
Cybernetics, Part A, Vol. 30, Issue 5, pp. 552–561, Sept.
2000.
[7] J. Timmis, C. Edmonds, J. Kelsey, "Assessing the
Performance of Two Immune Inspired Algorithms and a
Hybrid Genetic Algorithm for Function Optimization",
Congress on Evolutionary Computation, Vol. 1, pp.
1044–1051, June 2004.
[8] Q. H. Wu, J.T. Ma, "Genetic Search for Optimal Reactive
Power Dispatch ff Power Systems", International
Conference on Control, vol. 1, pp. 717–722, April 2000.
[9] Y. Malachi, S. Singer, "A Genetic Algorithm for the
Corrective Control of Voltage and Reactive Power",
IEEE Transaction on Power Systems, Vol. 21, pp. 295–
300, Feb. 2006.
Hadi Hosseinian: He received the B.Sc. degree in
electrical engineering from university of Tabriz, Iran in
2005 and the M.Sc. degree from university of Zanjan,
Iran in 2008. His research interests include power
system protections, power quality and application of
artificial intelligent in power systems.
Amin Lafzi: He received the B.Sc. and M.Sc. degree
in electrical engineering from university of Tabriz, Iran
in 2005 and 2008 respectively. His research interests
include power system operating and control.
Sadjad Galvani: He received the B.Sc. degree in
electrical engineering from university of Tabriz, Iran in
2005 and the M.Sc. degree from university of Zanjan,
Iran in 2008. His research interests include power
system operating, reliability and application of artificial
intelligent in power systems.

Thank you for copying data from http://www.arastirmax.com