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Stochastic modeling and parameter estimation of turbogenerator unit of a thermal power plant under classical and Bayesian inferential framework

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  • Ashish Kumar
  • Ravi Chaudhary
  • Kapil Kumar
  • Monika Saini
  • Dinesh Kumar Saini
  • Punit Gupta

Abstract

The work reported in present study deals with the development of a novel stochastic model and estimation of parameters to assess reliability characteristics for a turbogenerator unit of thermal power plant under classical and Bayesian frameworks. Turbogenerator unit consists of five components namely turbine lubrication, turbine governing, generator oil system, generator gas system and generator excitation system. The concepts of cold standby redundancy and Weibull distributed random variables are used in development of stochastic model. The shape parameter for all the random variables is same while scale parameter is different. Regenerative point technique and semi-Markov approach are used for evaluation of reliability characteristics. Sufficient repair facility always remains available in plant as well as repair done by the repairman is considered perfect. As the life testing experiments are time consuming, so to highlight the importance of proposed model Monte Carlo simulation study is carried out. A comparative analysis is done between true, classical and Bayesian results of MTSF, availability and profit function.

Suggested Citation

  • Ashish Kumar & Ravi Chaudhary & Kapil Kumar & Monika Saini & Dinesh Kumar Saini & Punit Gupta, 2023. "Stochastic modeling and parameter estimation of turbogenerator unit of a thermal power plant under classical and Bayesian inferential framework," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-22, October.
    • Handle: RePEc:plo:pone00:0292154
    DOI: 10.1371/journal.pone.0292154
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    References listed on IDEAS

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    1. Jae-Hak Lim & Sang Wook Shin & Dae Kyung Kim & Dong Ho Park, 2004. "Bootstrap Confidence Intervals For Steady-State Availability," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 21(03), pages 407-419.
    2. V. S. S. Yadavalli & A. Bekker & J. Pauw, 2005. "Bayesian Study Of A Two-Component System With Common-Cause Shock Failures," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 105-119.
    3. Anoop Chaturvedi & Manaswini Pati & Sanjeev Tomer, 2014. "Robust Bayesian analysis of Weibull failure model," METRON, Springer;Sapienza Università di Roma, vol. 72(1), pages 77-95, April.
    4. Hsu, Ying-Lin & Lee, Ssu-Lang & Ke, Jau-Chuan, 2009. "A repairable system with imperfect coverage and reboot: Bayesian and asymptotic estimation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(7), pages 2227-2239.
    5. Jagtap, Hanumant P. & Bewoor, Anand K. & Kumar, Ravinder & Ahmadi, Mohammad Hossein & Chen, Lingen, 2020. "Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Kapil Kumar & Indrajeet Kumar, 2019. "Estimation in Inverse Weibull Distribution Based on Randomly Censored Data," Statistica, Department of Statistics, University of Bologna, vol. 79(1), pages 47-74.
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