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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. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "A data reconciliation based framework for integrated sensor and equipment performance monitoring in power plants," Applied Energy, Elsevier, vol. 134(C), pages 270-282.
    6. Blanco, Jesús M. & Vazquez, L. & Peña, F., 2012. "Investigation on a new methodology for thermal power plant assessment through live diagnosis monitoring of selected process parameters; application to a case study," Energy, Elsevier, vol. 42(1), pages 170-180.
    7. 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.
    8. 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.
    9. Guo, Sisi & Liu, Pei & Li, Zheng, 2016. "Data reconciliation for the overall thermal system of a steam turbine power plant," Applied Energy, Elsevier, vol. 165(C), pages 1037-1051.

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