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Evolutionary programming approach to reactive power planning

Evolutionary programming approach to reactive power planning

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The paper proposes an application of evolutionary programming (EP) to reactive power planning (RPP). RPP is a nonsmooth and nondifferentiable optimisation problem for a multiobjective function. Several techniques to make EP practicable have been developed. The proposed approach is demonstrated with the IEEE 30-bus system. The comprehensive simulation results show that EP is a suitable method to solve the RPP problem. A conventional optimisation method is used as the comparison method. The comparison shows that EP is better than the conventional method in the RPP problem.

References

    1. 1)
      • D.B. Fogel . A comparison of evolutionary programming and genetic algorithms onselected constrained optimization problems. Simulation , 397 - 404
    2. 2)
      • Mutalik, P.P., Knight, L.R., Blanton, J.L., Wainwright, R.L.: `Solving combinatorialoptimization problems using parallel simulated annealing and parallel geneticalgorithms', Proceedings of the 1992 ACM/SIGAPP symposium on Appliedcomputing, 1992, USA, p. 1031–1038.
    3. 3)
      • P.J. Angeline . Evolution revolution: an introduction to the special track on geneticandevolutionary programming. IEEE Expert , 6 - 10
    4. 4)
      • S.S. Sachdeva , R. Billington . Optimum network VAR planning by nonlinear programming. IEEE Trans. , 1217 - 1225
    5. 5)
      • L.J. Fogel . Autonomous automata. Ind. Res. , 14 - 19
    6. 6)
      • W.-S. Jwo , C.-W. Liu , C.-C. Liu , Y.-T. Hsiao . Hybrid expert system and simulatedannealing approach to optimal reactive power planning. IEE Proc. Gener. Transm. Distrib. , 381 - 385
    7. 7)
      • Swarup, K.S., Yoshimi, M., Izui, Y.: `Genetic algorithm approach to reactive powerplanning in power systems', Proceedings of the 5th Annual Conference of Power and EnergySociety IEE Japan, 1994, p. 119–124.
    8. 8)
      • M.S. Bazaraa , H.D. Sherali , C.M. Shetty . (1993) Nonlinear programming: theory andalgorithms.
    9. 9)
      • K. Iba . Reactive power optimization by genetic algorithm. IEEE Trans. , 685 - 692
    10. 10)
      • D.B. Fogel . An introduction to simulated evolutionary optimization. IEEE Trans. Neural Netw. , 3 - 14
    11. 11)
      • K.H. Abdul-Rahman , S.M. Shahidehpour , M. Daneshdoost . AI approach to optimalVAR control with fuzzy reactive loads. IEEE Trans. , 88 - 97
    12. 12)
      • A. Kishore , E.F. Hill . Static optimization of reactive power sources by use ofsensitivity parameters. IEEE Trans. , 1166 - 1173
    13. 13)
      • D.B. Fogel . (1991) System identification through simulated evolution: a machine learningapproach to modeling.
    14. 14)
      • R.A. Fernandes , F. Lange , R.C. Burchett , H.H. Happ , K.A. Wirgau . Large scale reactive power planning. IEEE Trans. , 1083 - 1088
    15. 15)
      • Y.Y. Hong , D.I. Sun , S.Y. Lin , C.J. Lin . Multi-year multi-case optimal VARplanning. IEEE Trans. , 1294 - 1301
    16. 16)
      • O. Alsac , B. Stott . Optimal load flow with steady-state security. IEEE Trans. , 745 - 751
    17. 17)
      • D.B. Fogel . (2000) Evolutionary computation: toward a new philosophy of machine intelligence.
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