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Novel approach for improving power-plant availability using advanced engine diagnostics

Author

Listed:
  • Ogaji, Stephen
  • Sampath, Suresh
  • Singh, Riti
  • Probert, Douglas

Abstract

Technological advances and high cost of ownership have resulted in considerable interest in advanced maintenance techniques. Quantifying fault and consequently availability requires the use of gas-turbine and combined-cycle models able to undertake appropriate diagnostics and life-cycle costing. These are complex processes as they include the simulation of such issues as performance and assessment of degraded gas-turbines, life usage and risk analysis. This report describes how the recent developments in engine diagnostics using advanced techniques like Artificial Neural Network (ANN) and Genetic Algorithm (GA) based techniques have provided new opportunities in the field of engine-fault diagnostics. It also discusses the potential of advanced engine-diagnostics, employing such features as ANN and GA for contributing to the management of availability of industrial gas-turbines.

Suggested Citation

  • Ogaji, Stephen & Sampath, Suresh & Singh, Riti & Probert, Douglas, 2002. "Novel approach for improving power-plant availability using advanced engine diagnostics," Applied Energy, Elsevier, vol. 72(1), pages 389-407, May.
    • Handle: RePEc:eee:appene:v:72:y:2002:i:1:p:389-407
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    Citations

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    Cited by:

    1. Paweł Ziółkowski & Marta Drosińska-Komor & Jerzy Głuch & Łukasz Breńkacz, 2023. "Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence," Energies, MDPI, vol. 16(17), pages 1-28, August.
    2. Orme, George J. & Venturini, Mauro, 2011. "Property risk assessment for power plants: Methodology, validation and application," Energy, Elsevier, vol. 36(5), pages 3189-3203.
    3. Finn, Joshua & Wagner, John & Bassily, Hany, 2010. "Monitoring strategies for a combined cycle electric power generator," Applied Energy, Elsevier, vol. 87(8), pages 2621-2627, August.
    4. Iskin, Ibrahim & Daim, Tugrul & Kayakutlu, Gulgun & Altuntas, Mehmet, 2012. "Exploring renewable energy pricing with analytic network process — Comparing a developed and a developing economy," Energy Economics, Elsevier, vol. 34(4), pages 882-891.
    5. Nikula, Riku-Pekka & Ruusunen, Mika & Leiviskä, Kauko, 2016. "Data-driven framework for boiler performance monitoring," Applied Energy, Elsevier, vol. 183(C), pages 1374-1388.
    6. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2016. "A dynamic prognosis scheme for flexible operation of gas turbines," Applied Energy, Elsevier, vol. 164(C), pages 686-701.
    7. Luo, Xianglong & Zhang, Bingjian & Chen, Ying & Mo, Songping, 2013. "Operational planning optimization of steam power plants considering equipment failure in petrochemical complex," Applied Energy, Elsevier, vol. 112(C), pages 1247-1264.
    8. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
    9. Chatzimouratidis, Athanasios I. & Pilavachi, Petros A., 2009. "Technological, economic and sustainability evaluation of power plants using the Analytic Hierarchy Process," Energy Policy, Elsevier, vol. 37(3), pages 778-787, March.
    10. Qingcai Yang & Shuying Li & Yunpeng Cao & Fengshou Gu & Ann Smith, 2018. "A Gas Path Fault Contribution Matrix for Marine Gas Turbine Diagnosis Based on a Multiple Model Fault Detection and Isolation Approach," Energies, MDPI, vol. 11(12), pages 1-21, November.

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