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A new approach to determine maintenance periods of the most critical hydroelectric power plant equipment

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  • BULUT, Merve
  • ÖZCAN, Evrencan

Abstract

Power plants are large-scale generation facilities with high investment costs established to ensure sustainable energy supply. Maintenance-Repair activities constitute the most critical process to be considered in these facilities. Because, these activities cause planned or unplanned generation interruptions. For this reason, it is of great importance to calculate the maintenance periods determined on the basis of equipment health in the power plants. From this point of view in this study, the problem of determining maintenance periods is handled in the big scale hydroelectric power plant in Turkey. Due to the complexity of problem environment particularly including 16 equipment groups with the highest level of criticality on the power plant, proposing combined multi criteria decision making model (MCDM) has become invaluable for the purpose of reaching convincing outcomes. In order to obtain maximum benefit, investigation for appropriate probability distributions of 11-year failure and repair data belonging to the equipment group and analyses for reliability, availability and maintainability analyses are performed for 16 critical equipment groups. Finally, maintenance periods for 16 equipment groups were calculated using the curve family formed by the cost coefficient of Weibull distribution parameters. On the specified maintenance days based on system reliability and maintenance costs, it was observed that 16 critical equipment had no failures during the 1-year monitoring process. This indicates a 100% improvement.

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  • BULUT, Merve & ÖZCAN, Evrencan, 2021. "A new approach to determine maintenance periods of the most critical hydroelectric power plant equipment," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:reensy:v:205:y:2021:i:c:s0951832020307389
    DOI: 10.1016/j.ress.2020.107238
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    7. Rokhforoz, Pegah & Montazeri, Mina & Fink, Olga, 2023. "Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units," Reliability Engineering and System Safety, Elsevier, vol. 232(C).

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