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A statistics-based genetic algorithm for quality improvements of power supplies

Author

Listed:
  • K.Y. Chan
  • K.W. Chan
  • Glory T.Y. Pong
  • M.E. Aydin
  • T.C. Fogarty
  • S.H. Ling

Abstract

This paper presents a new statistics-based evolutionary algorithm to improve the qualities of power supplies, in which operational costs and the stability of the power supply are optimised to provide a highly smooth but low-cost power supply service to customers. The proposed method is incorporated with the characteristics of the stochastic method, evolutionary algorithm and a more systematical statistical method, orthogonal design. It intends to compensate for the built-in randomness of the stochastic method and, at the same time, overcome the limitations of local search methods that are not suitable for handling multi-optima problems. Case studies on the WSCC 9-bus and New England 39-bus systems indicate that the proposed approach outperforms the existing method in terms of robustness in solution and convergence speed while the solution quality that can offer a more stable and cheaper power supply to customers is achieved. [Received 03 July 2008; Revised 29 December 2008; Revised 20 January 2009; Accepted 26 January 2009]

Suggested Citation

  • K.Y. Chan & K.W. Chan & Glory T.Y. Pong & M.E. Aydin & T.C. Fogarty & S.H. Ling, 2009. "A statistics-based genetic algorithm for quality improvements of power supplies," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 3(4), pages 468-492.
    • Handle: RePEc:ids:eujine:v:3:y:2009:i:4:p:468-492
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    Cited by:

    1. Z.H. Che, 2012. "A hybrid algorithm for fuzzy clustering," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 6(1), pages 50-67.

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