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Generational and Steady-State Genetic Algorithms for Generator Maintenance Scheduling Problems

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Artificial Neural Nets and Genetic Algorithms

Abstract

The aim of generator maintenance scheduling (GMS) in an electric power system is to allocate a proper maintenance timetable for generators while maintaining a high system reliability, reducing total production cost, extending generator life time etc. In order to solve this complex problem a genetic algorithm technique is proposed here. The paper discusses the implementation of GAs to GMS problems with two approaches: generational and steady state. The results of applying these GAs to a test GMS problem based on a practical power system scenario are presented and analysed. The effect of different GA parameters is also studied.

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References

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  5. Z. Yamayee and S. Kathleen. A computationally efficient optimal maintenance scheduling method. IEEE Transactions on Power Apparatus and Systems, PAS-102(2):330–338, February 1983.

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Authors and Affiliations

  1. Centre for Electrical Power Engineering, University of Strathclyde, UK

    K. P. Dahal & J. R. McDonald

Authors
  1. K. P. Dahal
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  2. J. R. McDonald
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© 1998 Springer-Verlag Wien

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Dahal, K.P., McDonald, J.R. (1998). Generational and Steady-State Genetic Algorithms for Generator Maintenance Scheduling Problems. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_57

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  • DOI: https://doi.org/10.1007/978-3-7091-6492-1_57

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83087-1

  • Online ISBN: 978-3-7091-6492-1

  • eBook Packages: Springer Book Archive

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