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Penalty guided genetic search for redundancy optimization in multi-state series-parallel power system

Author

Listed:
  • Rashika Gupta

    (University of Delhi)

  • Manju Agarwal

    (University of Delhi)

Abstract

This paper presents a genetic algorithm (GA) for parallel redundancy optimization in series-parallel power systems exhibiting multi-state behavior, optimizing the reliability subject to constraints. The components are binary and chosen from a list of products available in the market, and are being characterized by their feeding capacity, reliability, cost and weight. System reliability is defined as the ability to satisfy consumer demand and is presented as a piecewise cumulative load curve. In GA, to handle infeasible solutions penalty strategies are used. Penalty technique keep a certain amount of infeasible solutions in each generation so as to enforce genetic search towards an optimal solution from sides of, both, feasible and infeasible regions. We here present a dynamic adaptive penalty function which helps the algorithm to search efficiently for optimal/near optimal solution. To evaluate system reliability, a fast procedure, based on universal generating function, is used. An example considering a multi-state series-parallel power system is solved considering both homogeneous and heterogeneous types of redundancy. Also an example considering price discounts is solved. The effectiveness of the penalty function and the proposed algorithm is studied and shown graphically.

Suggested Citation

  • Rashika Gupta & Manju Agarwal, 2006. "Penalty guided genetic search for redundancy optimization in multi-state series-parallel power system," Journal of Combinatorial Optimization, Springer, vol. 12(3), pages 257-277, November.
    • Handle: RePEc:spr:jcomop:v:12:y:2006:i:3:d:10.1007_s10878-006-9632-1
    DOI: 10.1007/s10878-006-9632-1
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    References listed on IDEAS

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    1. David W. Coit & Alice E. Smith & David M. Tate, 1996. "Adaptive Penalty Methods for Genetic Optimization of Constrained Combinatorial Problems," INFORMS Journal on Computing, INFORMS, vol. 8(2), pages 173-182, May.
    2. Richard E. Barlow & Alexander S. Wu, 1978. "Coherent Systems with Multi-State Components," Mathematics of Operations Research, INFORMS, vol. 3(4), pages 275-281, November.
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    Cited by:

    1. Zhang Huajun & Zhao Jin & Luo Hui, 2016. "A method combining genetic algorithm with simultaneous perturbation stochastic approximation for linearly constrained stochastic optimization problems," Journal of Combinatorial Optimization, Springer, vol. 31(3), pages 979-995, April.
    2. Zhou, Yifan & Liu, Libo & Li, Hao, 2022. "Reliability estimation and optimisation of multistate flow networks using a conditional Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. H A Taboada & J F Espiritu & D W Coit, 2008. "Design allocation of multistate series-parallel systems for power systems planning: A multiple objective evolutionary approach," Journal of Risk and Reliability, , vol. 222(3), pages 381-391, September.
    4. Sun, Mu-Xia & Li, Yan-Fu & Zio, Enrico, 2019. "On the optimal redundancy allocation for multi-state series–parallel systems under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    5. Coit, David W. & Zio, Enrico, 2019. "The evolution of system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    6. Sarita Devi & Deepika Garg, 2020. "Hybrid genetic and particle swarm algorithm: redundancy allocation problem," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 313-319, April.

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