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A Stacked Denoising Sparse Autoencoder Based Fault Early Warning Method for Feedwater Heater Performance Degradation

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  • Xingshuo Li

    (Harbin Institute of Technology, Harbin 150001, Heilongjiang, China)

  • Jinfu Liu

    (Harbin Institute of Technology, Harbin 150001, Heilongjiang, China)

  • Jiajia Li

    (Harbin Institute of Technology, Harbin 150001, Heilongjiang, China)

  • Xianling Li

    (Science and Technology on Thermal Energy and Power Laboratory, Wuhan 430205, Hubei, China)

  • Peigang Yan

    (Harbin Institute of Technology, Harbin 150001, Heilongjiang, China)

  • Daren Yu

    (Harbin Institute of Technology, Harbin 150001, Heilongjiang, China)

Abstract

Power grid operation faces severe challenges with the increasing integration of intermittent renewable energies. Hence the steam turbine, which mainly undertakes the task of frequency regulation and peak shaving, always operates under off-design conditions to meet the accommodation demand. This would affect the operation economy and exacerbate the ullage of equipment. The feedwater heater (FWH) plays an important role in unit, whose timely fault early warning is significant in improving the operational reliability of unit. Therefore, this paper proposes a stacked denoising sparse autoencoder (SDSAE) based fault early warning method for FWH. Firstly, the concept of a frequent pattern model is proposed as an indicator of FWH performance evaluation. Then, an SDSAE- back-propagation (BP) based method is introduced to achieve self-adaptive feature reduction and depict nonlinear properties of frequent pattern modeling. By experimenting with actual data, the feasibility and validity of the proposed method are verified. Its detection accuracy reaches 99.58% and 100% for normal and fault data, respectively. Finally, competitive experiments prove the necessity of feature reduction and the superiority of SDSAE based feature reduction compared with traditional methods. This paper puts forward a precise and effective method to serve for FWH fault early warning and refines the key issues to inspire later researchers.

Suggested Citation

  • Xingshuo Li & Jinfu Liu & Jiajia Li & Xianling Li & Peigang Yan & Daren Yu, 2020. "A Stacked Denoising Sparse Autoencoder Based Fault Early Warning Method for Feedwater Heater Performance Degradation," Energies, MDPI, vol. 13(22), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6061-:d:447834
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    References listed on IDEAS

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    1. Zhao, Yongliang & Wang, Chaoyang & Liu, Ming & Chong, Daotong & Yan, Junjie, 2018. "Improving operational flexibility by regulating extraction steam of high-pressure heaters on a 660 MW supercritical coal-fired power plant: A dynamic simulation," Applied Energy, Elsevier, vol. 212(C), pages 1295-1309.
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    Cited by:

    1. Goran S. Kvascev & Zeljko M. Djurovic, 2022. "Water Level Control in the Thermal Power Plant Steam Separator Based on New PID Tuning Method for Integrating Processes," Energies, MDPI, vol. 15(17), pages 1-17, August.
    2. Bruce Stephen, 2022. "Machine Learning Applications in Power System Condition Monitoring," Energies, MDPI, vol. 15(5), pages 1-2, March.

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