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Monitoring strategies for a combined cycle electric power generator

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  • Finn, Joshua
  • Wagner, John
  • Bassily, Hany

Abstract

Electric power generation systems require continuous monitoring to ensure safe and reliable operation. The data available from plant sensors supplied to the control systems may also be analyzed to verify proper operation and predict future behavior. In this paper, a combined cycle electric power plant has been monitored using limit and trend checking, reconstructed phase planes, and regression curves for transient and steady-state power generation. Representative experimental results are presented and discussed to illustrate the strengths of the proposed analysis strategies on a 510Â MW combined-cycle system and a 180Â MW steam turbine. The phase space analysis provides a means of visual inspection of operational anomalies and also offers a context for numerical analysis of the anomalous behavior. The statistical prognostic method provided regression errors below 2.0% for two of the three proposed plant signal combinations. However, all signal combinations offered the opportunity for system monitoring and diagnosis in terms of threshold violations which varied from 2.7% to 5.4% for these two signal sets. Overall, the monitoring strategies exhibited great promise for power generation system applications and merit further study.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:8:p:2621-2627
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    References listed on IDEAS

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    Cited by:

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    5. Syed, Mohammed S. & Dooley, Kerry M. & Madron, Frantisek & Knopf, F. Carl, 2016. "Enhanced turbine monitoring using emissions measurements and data reconciliation," Applied Energy, Elsevier, vol. 173(C), pages 355-365.
    6. Blanco, J.M. & Vazquez, L. & Peña, F. & Diaz, D., 2013. "New investigation on diagnosing steam production systems from multivariate time series applied to thermal power plants," Applied Energy, Elsevier, vol. 101(C), pages 589-599.
    7. Guo, Sisi & Liu, Pei & Li, Zheng, 2018. "Enhancement of performance monitoring of a coal-fired power plant via dynamic data reconciliation," Energy, Elsevier, vol. 151(C), pages 203-210.
    8. Ganesan, P. & Rajakarunakaran, S. & Thirugnanasambandam, M. & Devaraj, D., 2015. "Artificial neural network model to predict the diesel electric generator performance and exhaust emissions," Energy, Elsevier, vol. 83(C), pages 115-124.
    9. 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.

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