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Impact of Remediation-Based Maintenance on the Reliability of a Coal-Fired Power Plant Using Generalized Stochastic Petri Nets

Author

Listed:
  • Jakov Batelić

    (HEP Generation, Plomin Luka 50, 52234 Zagreb, Croatia)

  • Karlo Griparić

    (Department of Engineering, Juraj Dobrila University of Pula, Zagrebacka 30, 52000 Pula, Croatia)

  • Dario Matika

    (Mechanical Engineering, Zagreb University of Applied Sciences, Vrbik 8, 10000 Zagreb, Croatia)

Abstract

Rapid changes in electricity power markets have increased the production costs of coal-fired power plants and pushed their production to the limits of profitability. For power plants currently in operation, a possible approach to cope with this issue is to introduce novel methods that increase the plant’s reliability and availability. Coal mills are a subsystem that should ensure a plant’s availability without unexpected breakdowns. Remediation-based maintenance is defined as a set of actions performed after fault detection that do not require instant shutdown due to safety reasons. The aim of this paper was to provide a scientific confirmation that by implementing a novel remediation-based maintenance strategy, electricity production breakdowns can be significantly reduced. First, the performance of the proposed maintenance method was proved in simulation where coal mills were modeled by generalized stochastic Petri nets. The maintenance strategy was then experimentally verified in a 220 MW coal-fired power plant located in Croatia, where the plant’s availability, reliability and efficiency were increased.

Suggested Citation

  • Jakov Batelić & Karlo Griparić & Dario Matika, 2021. "Impact of Remediation-Based Maintenance on the Reliability of a Coal-Fired Power Plant Using Generalized Stochastic Petri Nets," Energies, MDPI, vol. 14(18), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5682-:d:632389
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    References listed on IDEAS

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    1. Muchiri, Peter & Pintelon, Liliane & Gelders, Ludo & Martin, Harry, 2011. "Development of maintenance function performance measurement framework and indicators," International Journal of Production Economics, Elsevier, vol. 131(1), pages 295-302, May.
    2. Arthur H.A. Melani & Carlos A. Murad & Adherbal Caminada Netto & Gilberto F.M. Souza & Silvio I. Nabeta, 2019. "Maintenance Strategy Optimization of a Coal-Fired Power Plant Cooling Tower through Generalized Stochastic Petri Nets," Energies, MDPI, vol. 12(10), pages 1-28, May.
    3. Piotr F. Borowski, 2021. "Digitization, Digital Twins, Blockchain, and Industry 4.0 as Elements of Management Process in Enterprises in the Energy Sector," Energies, MDPI, vol. 14(7), pages 1-20, March.
    4. Piotr F. Borowski, 2021. "Significance and Directions of Energy Development in African Countries," Energies, MDPI, vol. 14(15), pages 1-19, July.
    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. Melani, Arthur Henrique Andrade & Murad, Carlos Alberto & Caminada Netto, Adherbal & Souza, Gilberto Francisco Martha de & Nabeta, Silvio Ikuyo, 2018. "Criticality-based maintenance of a coal-fired power plant," Energy, Elsevier, vol. 147(C), pages 767-781.
    7. Yong Hu & Boyu Ping & Deliang Zeng & Yuguang Niu & Yaokui Gao, 2020. "Modeling of Coal Mill System Used for Fault Simulation," Energies, MDPI, vol. 13(7), pages 1-14, April.
    8. 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).
    9. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
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    1. Fausto Pedro García Márquez, 2022. "Advanced Analytics in Renewable Energy," Energies, MDPI, vol. 15(10), pages 1-5, May.
    2. Arthur Henrique de Andrade Melani & Miguel Angelo de Carvalho Michalski & Carlos Alberto Murad & Adherbal Caminada Netto & Gilberto Francisco Martha de Souza, 2022. "Generalized Stochastic Petri Nets for Planning and Optimizing Maintenance Logistics of Small Hydroelectric Power Plants," Energies, MDPI, vol. 15(8), pages 1-16, April.

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