A survey on application of swarm intelligence computation to electric power system

Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

Volumn 2, Issue , 2006, Pages 7587-7591

  Bai, Hua ^a   Zhao, Bo ^b  

^a ZHEJIANG UNIVERSITY   (China)

^b Jiangsu Electric Power Research Institute Corporation Limited   (China)

Author keywords

Ant colony optimization; Particle swarm optimization; Population based immune algorithm; Power system; Swarm intelligence computation

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CAPACITORS; COMPUTER SIMULATION; GLOBAL OPTIMIZATION;

ANT COLONY OPTIMIZATION; PARTICLE SWARM OPTIMIZATION; POPULATION-BASED IMMUNE ALGORITHM; SWARM INTELLIGENCE ALGORITHMS;

ELECTRIC POWER SYSTEMS;

EID: 34047211536     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WCICA.2006.1713441     Document Type: Conference Paper

References (44)

1. Q. Kang, L. Wang, J. An, et al, Swarm intelligent computation [J]. Journal of Shanghai University (Natural Science), 2004, 10(10):73-76.
2. E. Bonabeau, M. Dorigo, G. Theraulaz, Inspiration for optimization from social insect behavior [J]. Nature, 2002, 406(6):39-42.
3. X. Peng, Y. Peng, Y. F. Dai, Swarm intelligence theory and applications [J]. Acta Electronica Sinca, 2003, 31(12A): 1982-1988.
4. Y. Lu, Ant colony optimization and its applications in power system [J]. Transactions of China Electrotechnical Society, 2003, 18(4): 70-74.
5. X. H. Yuan, A survey on application of particle swarm optimization to electric power system [J]. Power System Technology, 2004, 28(19): 14-19.
6. J. Kennedy, R. C. Eberhart, Swarm intelligence [M]. San Francisco: Morgan Kaufmann Publisher, 2001.
7. E. Bonabeau, M. Dorigo, G. Theraulaz, Inspiration for optimization from social insect behavior [J]. Nature, 2002, 406(6):39-42.
8. M. M. Millonas, Swarms, phase transitions, and collective intelligence [A]. Proceedings of Artificial Life III [C] (C. G. Langton, ed.) Santa Fe Institute: Addison-Wesley, 1994: 417-445.
9. M. Dorigo, V. Maniezzo, A. Colorni, The ant system: optimization by a colony of cooperating agents [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 1996, 26(1): 29-41.
10. R. C. Eberhart, Y. Shi, Particle swarm optimization: developments, applications and resources [A]. Proceedings of the 2001 Congress on Evolutionary Computation [C], Seoul, South Korea, 2001: 81-86.
11. T. Li, Computer immunology [M].Publishing House of Electronics Industry, Beijing, China. 2004
12. M. Dorigo, L. M. Gambardella, Ant colony system: a cooperative learning approach to the traveling salesman problem [J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53-66.
13. S. Y. Li, Progresses in ant colony optimization algorithm with applications [J]. Computer Measurement & Control, 2003, 11(12): 911-913.
14. W. Yang, Q. Q. Li, Survey on particle swarm optimization algorithm [J]. Engineering Science,2004, 6(5): 87-94.
15. Y. H. Hou, Economiic dispatch of power systems based on generalized ant colony optimization method[J]. Proceedings of the CSEE, 2003, 23(3): 59-64.
16. Y. H. Hou, Application of generalized ant colony optimization algorithm integrated with particle swarm optimization algorithm in economic dispatch power system [J]. Power System Technology, 2004, 28(21): 34-38.
17. Y. H. Hou, L. J. Lu, X. Y. Xiong, et al, Enhanced particle swarm optimization algorithm and its application on economic dispatch of power systems[J]. Proceedings of the CSEE, 2004, 24(7): 95-100.
18. J. B. Park, A particle swarm optimization for economic dispatch with nonsmooth cost functions[J]. IEEE Trans. Power Systems, 2005, 20(1):34-42.
19. M. T. Tsay, C. Y. Chang, H. J. Gow, The operational strategy of cogeneration plants in a competitive market [A]. IEEE Region 10 Conference TENCON[C], Hong Kong, China, 2004: 520-523.
20. B. Zhao, Dynamic economic dispatch in electricity market using particle swarm optimization algorithm [A]. Fifth World Congress on Intelligence Control and Automation[C], Hangzhou, China, 2004, 3: 2219-2221.
21. J. W. Zhang, C. Z. Hou, Immune algorithm based unit commitment optimization method [J]. Electric Power, 2005, 38(4): 36-40.
22. J. Hao, L. B. Shi, J. Q. Zhou, An ant colony optimization algorithm with random perturbation behavior for unit commitment problem[J]. Automation of Electric Power Systems, 2002, 26(23):1-6.
23. B. Zhao, An improved particle swarm optimization for power system unit commitment [J]. Power System Technology, 2004,28(21):6-10.
24. X. H. Yuan, A new enhanced particle swarm optimization method for unit commitment [J]. Automation of Electric Power Systems, 2005, 29(1): 34-38.
25. P. Sriyanyong, Y. H. Song, Unit commitment using particle swarm optimization combined with lagrange relaxation [A]. IEEE Power Engineering Society General Meeting [C], San Francisco, United States, 2005: 1522-1529.
26. G. C. Liao, T. P. Tsao, Hybrid immune genetic algorithm approach for short-term unit commitment problem [A]. IEEE Power Engineering Society General Meeting[C], Denver, United States, 2004, 1: 1075-1081.
27. W. Zhang, Y. T. Liu, Reactive power optimization based on PSO in a practical power system [A]. IEEE Power Engineering Society General Meeting [C]. Denver, United States, 2004, 1: 239-243.
28. A. A. Esmin, A hybrid particle swarm optimization applied to loss power minimization [J]. IEEE Trans. Power Systems, 2005, 20(2):859-866.
29. H. M. Zhong, Z. Ren, Y. G. Zhang, et al, Immune algorithm and its application in power system reactive power optimization [J]. Power System Technology, 2004, 28(3): 16-19.
30. J. N. Chen, G. X. He, Application of adaptive ant colony algorithm with hybrid and variation factors in power system reactive optimization [J]. Relay, 2003, 31(11): 36-39.
31. B. Zhao, C. X. Guo, Y. J. Cao, A multiagent-based particle swarm optimization approach for optimal reactive power dispatch [J]. IEEE Trans. Power Systems, 2005, 20(2):1070-1078.
32. C. X. Guo, C. Z. Zhu, B. Zhao, et al, Power system reactive power optimization based on an improved immune algorithm[J]. Automation of Electric Power Systems, 2005, 29(15)
33. Z. F. Liu, S. Y. Ge, Y. X. Yu, Optimal reactive power dispatch using chaotic particle swarm optimization algorithm [J]. Automation of Electric Power Systems, 2005, 29(7): 53-57.
34. S. Kannan, M. R. Slochanal, N. P. Padhy, Application and comparison of metaheuristic techniques to generation expansion planning problem [J]. IEEE Trans. Power Systems, 2005, 20(1):466-475.
35. Y. X. Jin, H. Z. Cheng, J. Y. Yan, et al, Improved particle swarm optimization method and its application in power transmission network planning [J]. Proceedings of the CSEE, 2005, 25(4): 46-50.
36. C. W. Gao, H. Z. Cheng, X. Wang, Multi-population central solution searching algorithm based on computation resource distributing for electric network planning [J]. Proceedings of the CSEE, 2004, 24(10): 8-14.
37. G. J. Chen, G. Q. Tang, Tabu search-ant colony optimization hybrid algorithm based distribution network planning [J]. Power System Technology, 2005, 29(1): 23-27.
38. S. J. Huang, An immune-based optimization method to capacitor placement in a radial distribution system [J]. IEEE Trans. Power Systems, 2000, 15(2):744-749.
39. X. M. Yu, Y. Li, Optimal shunt capacitor placement using particle swarm optimization algorithm with harmonic distortion consideration[J]. Proceedings of the CSEE, 2003, 23 (2): 26-31.
40. J. P. Chiou, C. F. Chang, T. S. Ching, Ant direction hybrid differential evolution for solving large capacitor placement problems [J]. IEEE Trans. Power Systems, 2004, 19(4):1794-1800.
41. H. X. Wu, A short-term load forecasting approach based on immune support vector machines [J]. Power System Technology, 2004 28(23): 47-51.
42. C. M. Huang, C. J. Huang, M. L. Wang, A particle swarm optimization to identifying the ARMAX model for short-term load forecasting [J]. IEEE Trans. Power Systems, 2005, 20(2): 1126-1133.
43. Z. L. Gaing, A particle swarm optimization approach for optimum design of PID controller in AVR system [J]. IEEE Trans. Power Systems, 2004, 19(2):384-391.
44. S. C. Zhan, J. Wu, The optimization design based on ant colony algorithm for designing parameters of PID controller [J]. Measurement & Control Technology, 2004, 23(1): 69-71.