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Genetic algorithm optimisation of the maintenance scheduling of generating units in a power system

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  • Volkanovski, Andrija
  • Mavko, Borut
  • BoÅ¡evski, Tome
  • ÄŒauÅ¡evski, Anton
  • ÄŒepin, Marko

Abstract

A new method for optimisation of the maintenance scheduling of generating units in a power system is developed. Maintenance is scheduled to minimise the risk through minimisation of the yearly value of the loss of load expectation (LOLE) taken as a measure of the power system reliability. The proposed method uses genetic algorithm to obtain the best solution resulting in a minimal value of the annual LOLE value for the power system in the analysed period. The operational constraints for generating units are included in the method. The proposed algorithm was tested on a Macedonian power system and the obtained results were compared with the results received from the approximate methodology. The results show the improved reliability of a power system with the maintenance schedule obtained by the new method compared to the results from the approximate methodology.

Suggested Citation

  • Volkanovski, Andrija & Mavko, Borut & BoÅ¡evski, Tome & ÄŒauÅ¡evski, Anton & ÄŒepin, Marko, 2008. "Genetic algorithm optimisation of the maintenance scheduling of generating units in a power system," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 779-789.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:6:p:779-789
    DOI: 10.1016/j.ress.2007.03.027
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    References listed on IDEAS

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    1. Kralj, Branimir & Petrovic, Radivoj, 1995. "A multiobjective optimization approach to thermal generating units maintenance scheduling," European Journal of Operational Research, Elsevier, vol. 84(2), pages 481-493, July.
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    Cited by:

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    2. Volkanovski, Andrija & ÄŒepin, Marko & Mavko, Borut, 2009. "Application of the fault tree analysis for assessment of power system reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1116-1127.
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    7. Ye, Zhisheng & Li, Zhizhong & Xie, Min, 2010. "Some improvements on adaptive genetic algorithms for reliability-related applications," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 120-126.
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    15. Laggoune, Radouane & Chateauneuf, Alaa & Aissani, Djamil, 2010. "Impact of few failure data on the opportunistic replacement policy for multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 108-119.
    16. 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).
    17. Pavel Y. Gubin & Vladislav P. Oboskalov & Anatolijs Mahnitko & Roman Petrichenko, 2020. "Simulated Annealing, Differential Evolution and Directed Search Methods for Generator Maintenance Scheduling," Energies, MDPI, vol. 13(20), pages 1-26, October.
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    19. Eryilmaz, Serkan & Navarro, Jorge, 2022. "A decision theoretic framework for reliability-based optimal wind turbine selection," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    20. Do, Phuc & Vu, Hai Canh & Barros, Anne & Bérenguer, Christophe, 2015. "Maintenance grouping for multi-component systems with availability constraints and limited maintenance teams," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 56-67.
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