Optimal power flow using biogeography based optimisation

International Journal of Power and Energy Conversion

Volumn 2, Issue 3, 2010, Pages 216-249

  Roy, Provas Kumar ^a   Ghoshal, Sakti Prasad ^a   Thakur, Siddhartha Sankar ^a  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

BBO; biogeography; biogeography based optimisation; constriction factor approach; evolutionary programming; GA; genetic algorithm; inertia weight approach; migration; mutation; OPF; optimal power flow; particle swarm optimisation; PSO

Indexed keywords

EID: 84902086398     PISSN: 17571154     EISSN: 17571162     Source Type: Journal    
DOI: 10.1504/IJPEC.2010.037630     Document Type: Article

References (24)

1. Abido, M.A. (2002) ‘Optimal power flow using particle swarm optimization’, International Journal of Electrical Power & Energy Systems., Vol. 24, No. 7 pp.563–571.
2. AlRashidi, M.R. and El-Hawary, M.E. (2007) ‘Hybrid particle swarm optimization approach for solving the discrete OPF problem considering the valve point effect’, IEEE Transactions on Power Systems, Vol. 22, No. 4, pp.2030–2038.
3. Dommel, H.W. and Tinney, W.F. (1968) ‘Optimal power flow solutions’, IEEE Transactions on Power System Apparatus and Systems, PAS-87, No. 10, pp.1866–1876.
4. Ela, A.A.A.E., Abido, M.A. and Spea, S.R. (2010) ‘Optimal power flow using differential evolution algorithm’, Electric Power System Research, Article in press.
5. Gaing, Z. and Huang, H.S. (2004) ‘Real-coded mixed integer genetic algorithm for constrained optimal power flow’, in IEEE TENCON Region 10 Conference, 2004, Vol. 3, pp.323–326.
6. Gaing, Z.L. (2005) ‘Constrained optimal power flow by mixed-integer particle swarm optimization’, IEEE Power Engineering Society General Meeting, 2005, Vol. 1, pp.243–250.
7. Gaing, Z.L. and Chang, R.F. (2006) ‘Sequirity-constrained optimal power flow by mixed-integer genetic algorithm with arithmetic operators’, Power Engineering Society General Meeting, 2006, IEEE, Montreal, Quebec.
8. Ghoshal, S.P., Chatterjee, A. and Mukherjee, V. (2009) ‘Bio-inspired fuzzy logic based tuning of power system stabilizer’, Expert System with Applications, Vol. 36, No. 5, pp.9281–9292.
9. He, S., Wen, J.Y., Prempain, E., Wu, Q.H., Fitch, J. and Mann, S. (2004) ‘An improved particle swarm optimization for optimal power flow’, in International Conference of Power System Technology, 2004 Powercon, Vol. 2, pp.1633–1637.
10. Huneault, M. and Galiana, F.D. (1991) ‘A survey of the optimal power flow literature’, IEEE Transactions on Power Systems, Vol. 6, No. 2, pp.762–770.
11. Lai, L.L., Ma, J.T., Yokoyama, R. and Zhao, M. (1997) ‘Improved genetic algorithm for optimal power flow under both normal and contingent operation states’, International Journal of Electrical Power and Energy Systems, Vol. 19, No. 5, pp.287–292.
12. MacArthur, R. and Wilson, E. (1997) The Theory of Biogeography, Princeton University Press, Princeton, NJ.
13. Momoh, J.A., Adapa, R. and El-Hawary, M.E. (1999a) ‘A review of selected optimal power flow literature to 1993.I. Nonlinear and quadratic programming approaches’, IEEE Transactions on Power Systems, Vol. 14, No. 1, pp.96–104.
14. Momoh, J.A., EI-Hawary, M.E. and Adapa, R. (1999b) ‘A review of selected optimal power flow literature to 1993.II. Newton, linear programming and interior point methods’, IEEE Transactions on Power Systems, Vol. 14, No. 1, pp.105–111.
15. Paranjothi, S.R. and Anburaja, K. (2002) ‘Optimal power flow using refined genetic algorithm’, Electric Power Components and Systems, Vol. 30, No. 10, pp.1055–1063.
16. Roa-Sepulveda, C.A. and Pavez-Lazo, B.J. (2001) ‘A solution to the optimal power flow using simulated annealing’, Power Tech Proceedings, 2001 IEEE Porto, Vol. 2.
17. Roy, R. and Ghoshal, S.P. (2008) ‘A novel crazy swarm optimized economic load dispatch for various types of cost functions’, International Journal of Electric Power & Energy System, Vol. 30, No. 4, pp.242–253.
18. Simon, D. (2008) ‘Biogeography-based optimization’, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 6, pp.702–713.
19. Song, Y.H., Chou, C.S. and Stonham, T.J. (1999) ‘Combined heat and power economic dispatch by improved ant colony search algorithm’, Electric Power System Research, Vol. 52, No. 2, pp.115–121.
20. Swain, A.K. and Morris, A.S. (2000) ‘A novel hybrid evolutionary programming method for function optimization’, in Proc. 2000 Congress on Evolutionary Computation, Vol. 1, pp.699–705.
21. The IEEE 118-Bus Test System, Available at http://www.ee.washington.edu/research/pstca/pf118/pg_tca118bus.htm.
22. Vlachogiannis, J.G. and Lee, K.Y. (2006) ‘A comparative study on particle swarm optimization for optimal steady-state performance of power systems’, IEEE Transactions on Power Systems, Vol. 21, No. 4, pp.1718–1728.
23. Yuryevich, J. and Wong, K.P. (1999) ‘Evolutionary programming based optimal power flow algorithm’, IEEE Transactions on Power Systems, Vol. 14, No. 4, pp.1245–1250.
24. Zhao, B., Guo, C.X. and Cao, Y.J. (2004) ‘An improved particle swarm optimization for OPF problems’, in IEEE/PES Power System Conference and Exposition 2004, pp.233–238.