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Artificial Neural Networks for Generator Scheduling

  • Michael P. Walsh
  • Mark J. O’Malley
Chapter
Part of the International Series on Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 20)

Abstract

The Hopfield neural network was first developed in 1982[10] and has since found applications in many optimisation problems. This chapter first describes the development of the Hopfield network and illustrates its applicability to optimisation problems. A selection of applications to the scheduling problem will then be discussed. The Augmented Hopfield Network is then described and its application to the generalised scheduling problem is illustrated. Finally an overview of other neural network approaches to generator scheduling is provided.

Keywords

Cost Function Schedule Problem Power System Energy Function Unit Commitment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Dillon J. D., 1998, “Scheduling of a hydrothermal power system” Masters thesis, Department of Electronic and Electrical Engineering, University College Dublin.Google Scholar
  2. 2.
    Djukanovi M., alovi M., Miloevi B., and Sobajic D. J., 1996, “Neural-net based real-time economic dispatch for thermal power plants”, IEEE Transactions on Energy Conversion, Vol. 11, No. 4, pp. 755–761.CrossRefGoogle Scholar
  3. 3.
    Fukuyama Y. and Ueki Y., 1994, “An application of neural network to dynamic dispatch using multi processors” IEEE Transactions on Power Systems, Vol. 9, No. 4, pp. 1759–1765.CrossRefGoogle Scholar
  4. 4.
    Funabiki N., and Nishikawa S., 1997, “A binary Hopfield neural-network approach for satellite broadcast scheduling problems”, IEEE Transactions on Neural Networks, Vol. 8, No. 2, pp. 441–445.CrossRefGoogle Scholar
  5. 5.
    Gee A. and Prager R., 1995, “Limitations of neural networks for solving travelling salesman problems”, IEEE Transactions on Neural Networks, Vol. 6, No. 1, pp. 280–282.CrossRefGoogle Scholar
  6. 6.
    Ghosh S. and Chowdhury B. H.,1996, “Security-constrained optimal rescheduling of real power using Hopfield neural network”, IEEE transactions on Power Systems,Vol. 11, No. 4, pp. 1743–1748.Google Scholar
  7. 7.
    Gill P. E., Murray W. and Wright M. H.,1993, “Practical Optimization”, London, Academic Press.Google Scholar
  8. 8.
    Guan X., Luh P. B., Yan H. and Amalfi J. A., 1992, “An optimisation-based method for unit commitment”, Electric Power and Energy Systems, Vol. 14, pp. 9–17.CrossRefGoogle Scholar
  9. 9.
    Hayashi Y., Iwamoto S., Furuya S. and Liu C. C., 1996, “Efficient determination of optimal radial power system structure using Hopfield neural network with constrained noise”, IEEE Transactions on Power Delivery, Vol. 11, No. 3, pp. 1529–1535.CrossRefGoogle Scholar
  10. 10.
    Hopfield, J. J., 1982, “Neural networks and physical systems with emergent collective computational abilities”, Proceedings of the National Academy of Science, USA, Vol. 79, pp. 2554–2558.Google Scholar
  11. 11.
    Hopfield, J. J., 1984, “Neurons with graded response have collective computational properties like those of two state neurons”, Proceedings of the National Academy of Science, USA, Vol. 81, pp. 3088–3092.Google Scholar
  12. 12.
    Hopfield J. J. and Tank D.W., 1985, “Neural computation of decisions in optimisation problems,” Biological Cybernetics, Vol. 52, pp. 141–152.MathSciNetzbMATHGoogle Scholar
  13. 13.
    Kasangaki V. B. A., Sendaula H. M. and Biswas S.K., 1995, “Stochastic Hopfield artificial neural network for electric power production costing”, IEEE Transactions on Power systems, Vol. 10, No. 3, pp. 1525–1533.CrossRefGoogle Scholar
  14. 14.
    Kennedy M. P. and Chua L., 1987, “Unifying the Hopfield linear programming circuit and the canonical non-linear programming circuit of Chua and Lin”, IEEE Transactions on Circuits and Systems, Vol. 34, No. 2, pp. 210–214.zbMATHCrossRefGoogle Scholar
  15. 15.
    King T., El-Hawary M., and El-Hawary F., 1996, “Optimal environmental dispatching of electric power systems via an improved Hopfield neural network model”, IEEE Transactions on Power Systems, Vol. 11, No. 3, pp. 1146–1158.CrossRefGoogle Scholar
  16. 16.
    Kumar J. and Sheblé G., 1995, “Clamped state solution of artificial neural network for real time economic dispatch,” IEEE Transactions on Power Systems, Vol. 10, No. 2, pp. 925–931.CrossRefGoogle Scholar
  17. 17.
    Lee F. N., Lemonidis L. and Liu K. C., 1994, “Price-based ramp-rate model for dynamic dispatch and unit commitment,” IEEE Transactions on Power Systems, Vol. 9, No. 3, pp. 1233–1241.CrossRefGoogle Scholar
  18. 18.
    Lee K. Y., Sode–Yome A. and Park J. H., 1997, “Adaptive Hopfield networks for economic load dispatch” to appear in IEEE Transactions on Power Systems, IEEE paper No. PE–217–PWRS–0–01–1997.Google Scholar
  19. 19.
    Lippmann, R. P., 1987, “An introduction to computing with neural nets”, IEEE ASSP Magazine, April, pp. 4–22.Google Scholar
  20. 20.
    Liu Z. J., Villaseca F. E., Renovich F., 1992, “Neural networks for generation scheduling in power systems”, Proceedings of the International Joint Conference on Neural Networks, Baltimore, MD, pp. 233–238.Google Scholar
  21. 21.
    Ouyang Z., Shahidehpour S. M., 1992b, “A multi stage intelligent system for unit commitment”, IEEE Transactions on Power Systems, Vol. 7, No. 2, pp. 639–645.CrossRefGoogle Scholar
  22. 22.
    Park J. H., Kim Y. S., Eom I. K. and Lee K. Y., 1993, “Economic load dispatch for piecewise quadratic cost function using Hopfield neural network”, IEEE Transactions on Power Systems, Vol. 8, No. 3, pp. 10301038.Google Scholar
  23. 23.
    Pipes L. A. and Harvill L. R., 1985, “Applied Mathematics for engineers and physicists”, McGraw-Hill.Google Scholar
  24. 24.
    Sasaki H., Watanabe M., Kubokawa J., Yorino N., and Yokoyama R, 1992, “A solution method of unit commitment by artificial neural networks”, IEEE Transactions on Power Systems, Vol. 7, No. 3, pp. 974–981.CrossRefGoogle Scholar
  25. 25.
    Su C. and Chiou G., 1997, “A fast computation Hopfield Method to economic dispatch of power systems”, IEEE Transactions on Power Systems, Vol. 12, No. 4, pp. 1759–1764.CrossRefGoogle Scholar
  26. 26.
    Tank D. and Hopfield J. J., 1986, “Simple neural optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit”, IEEE Transactions on circuits and systems, Vol. 33, No. 5, pp. 533–541.CrossRefGoogle Scholar
  27. 27.
    Walsh M. P. and O’Malley M. J., 1997, “Augmented Hopfield network for unit commitment and economic dispatch”, IEEE Transactions on Power Systems, Vol. 12, No. 4, pp. 1765–1774.CrossRefGoogle Scholar
  28. 28.
    Walsh M. P., 1998, “A novel neural network for power system scheduling”, Ph.D. Thesis, Department of Electrical and Electronic Engineering, University College Dublin, Ireland.Google Scholar
  29. 29.
    Walsh M. P., Flynn M. E. and O’Malley M. J., 1998, “Augmented Hopfield network for mixed integer programming”, to appear in IEEE Transactions on Neural Networks.Google Scholar
  30. 30.
    Wang C. and Shahidehpour S., 1993, “Effects of ramp-rate limits on unit commitment and economic dispatch”, IEEE Transactions on Power Systems, Vol. 8, No. 3, pp. 1341–1350.CrossRefGoogle Scholar
  31. 31.
    Wang Y. and Wahl F. M., 1997, “Vector-entropy optimization-based neural-network approach to image reconstruction from projections”, IEEE Transactions on Neural Networks, Vol. 8, No. 5, pp. 1008–1014.CrossRefGoogle Scholar
  32. 32.
    Watta P. B. and Hassoun M. H., 1996, “A coupled gradient approach for static and temporal Mixed Integer Optimization,” IEEE Transactions on Neural Networks, Vol. 7, No. 3, pp. 578–593.CrossRefGoogle Scholar
  33. 33.
    Wood A. J. and Wollenberg B. F., 1996, “Power generation operation and control”, Wiley.Google Scholar
  34. 34.
    Yalcinoz T. and Short M. J., 1997, “Neural networks for solving economic dispatch problem with transmission capacity constraints”, to appear in IEEE Transactions on Power Systems, IEEE paper No. PE–807–PWRS–0–07–1997.Google Scholar
  35. 35.
    Yan H., Luh P. B., Guan X., and Rogan P. M., 1993, “Scheduling of hydrothermal power systems”, IEEE Transactions on Power Systems, Vol. 8, pp. 1358–1365.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • Michael P. Walsh
    • 1
  • Mark J. O’Malley
    • 1
  1. 1.University CollegeDublinIreland

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