Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization

Hanumant P. Jagtap, Anand K. Bewoor, Ravinder Kumar, Mohammad Hossein Ahmadi and Lingen Chen

Reliability Engineering and System Safety, 2020, vol. 204, issue C

Abstract: The work reported in this paper evaluates the performance of the turbo-generator subsystem of a thermal power plant by employing a particle swarm optimization method. The majorly selected systems considered for the study are turbine governing, turbine lubrication, generator oil system, generator gas system, and generator excitation system. The Markov based availability modeling is adopted for the turbo-generator system. The Markov probabilistic approach based availability simulation model is reported. Through this study, the Markov based results reveal that the turbine lubrication and generator excitation subsystems affect the system availability at most. Therefore, these critical subsystems are on higher priority from a maintenance perspective. Furthermore, system availability is optimized to get the optimized availability parameters using the particle swarm optimization method. The PSO based results revealed that the maximum optimized availability of 98.9394 % is obtained for 30 particle size. The results reveal that with an increase in particle size of more than 30, the system availability remains constant. Further, the optimized availability parameters are used to recommend a suitable maintenance strategy for the plant's turbo-generator system.

Keywords: Availability analysis; Performance evaluation; Thermal power plant; Maintenance schedule; Particle swarm optimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306311

DOI: 10.1016/j.ress.2020.107130

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