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A novel plugged tube detection and identification approach for final super heater in thermal power plant using principal component analysis

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  • Yu, Jungwon
  • Yoo, Jaeyeong
  • Jang, Jaeyel
  • Park, June Ho
  • Kim, Sungshin

Abstract

During startup or normal operations, tube monitoring systems for steam boilers can considerably improve efficiency and reliability in thermal power plants (TPPs). Although several attempts have been made to detect and locate boiler tube leaks, what seems to be lacking is the study for tube plugging, one of the fundamental causes of the leaks and other tube failures. Scale and deposit formations on inner surfaces of tubes cause the tubes to be plugged. Although the formations can be suppressed and removed by chemical treatments for boiler water and steam blowing during startup procedures, it is still difficult to monitor and prevent tube plugging during startup or normal operations. In this paper, a novel plugged tube detection and identification approach is proposed for final super heater (FSH) tube banks. Principal component analysis is applied to tube temperature data for plugging detection and identification. The data are collected from thermocouples installed on the FSH outlet header section. To identify plugged tubes, contribution analysis and the characteristics of plugged tube temperatures are employed. To verify the performance of the proposed method, tube temperature data from an 870 MW supercritical coal-fired TPP are used. The experiment results show that the proposed method can successfully detect and identify plugged tubes. The proposed method can help to decide how many times steam blowing should be performed, whether startup procedures should be delayed or stopped, and which tubes should be maintained. Furthermore, severe tube failures can be prevented by avoiding damage from overheating due to tube plugging.

Suggested Citation

  • Yu, Jungwon & Yoo, Jaeyeong & Jang, Jaeyel & Park, June Ho & Kim, Sungshin, 2017. "A novel plugged tube detection and identification approach for final super heater in thermal power plant using principal component analysis," Energy, Elsevier, vol. 126(C), pages 404-418.
    • Handle: RePEc:eee:energy:v:126:y:2017:i:c:p:404-418
    DOI: 10.1016/j.energy.2017.02.154
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    References listed on IDEAS

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    1. Rostek, Kornel & Morytko, Łukasz & Jankowska, Anna, 2015. "Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks," Energy, Elsevier, vol. 89(C), pages 914-923.
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    Cited by:

    1. Salman Khalid & Hyunho Hwang & Heung Soo Kim, 2021. "Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant," Mathematics, MDPI, vol. 9(21), pages 1-27, November.
    2. Jungwon Yu & Jaeyel Jang & Jaeyeong Yoo & June Ho Park & Sungshin Kim, 2018. "A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant," Energies, MDPI, vol. 11(5), pages 1-19, May.
    3. Truong-Ba, Huy & Cholette, Michael E. & Borghesani, Pietro & Ma, Lin & Kent, Geoff, 2021. "Condition-based inspection policies for boiler heat exchangers," European Journal of Operational Research, Elsevier, vol. 291(1), pages 232-243.
    4. Salman Khalid & Jinwoo Song & Izaz Raouf & Heung Soo Kim, 2023. "Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques," Mathematics, MDPI, vol. 11(8), pages 1-28, April.
    5. Indrawan, Natarianto & Shadle, Lawrence J. & Breault, Ronald W. & Panday, Rupendranath & Chitnis, Umesh K., 2021. "Data analytics for leak detection in a subcritical boiler," Energy, Elsevier, vol. 220(C).

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