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Intelligent steam turbine start-up control based on deep reinforcement learning

Author

Listed:
  • Zhu, Guangya
  • Guo, Ding
  • Li, JinXing
  • Xie, Yonghui
  • Zhang, Di

Abstract

The requirement for frequent start-ups and shutdowns is prevalent in turbo-generator units to accommodate fluctuating loads during flexible operations. These cause drastic changes in temperature and stress, leading to instantaneous structural deformations. Hence, research on intelligent start-up control is essential for ensuring safety. In this work, a rotor stress field reconstruction model based on a deep convolutional neural network was first designed. The accuracy of predicting the stress distribution in the critical area reaches 99.7 %. The time cost of the trained neural network model is 0.11s in a single case, shortened by 99.8 % with comparison to finite element analysis. Then, a Twin Delayed Deep Deterministic Policy Gradient-based Main Steam Temperature Controller for the Rotor Start-up was proposed and developed.

Suggested Citation

  • Zhu, Guangya & Guo, Ding & Li, JinXing & Xie, Yonghui & Zhang, Di, 2025. "Intelligent steam turbine start-up control based on deep reinforcement learning," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225009776
    DOI: 10.1016/j.energy.2025.135335
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    References listed on IDEAS

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    1. Kosman, Gerard & Rusin, Andrzej, 2001. "The influence of the start-ups and cyclic loads of steam turbines conducted according to European standards on the component's life," Energy, Elsevier, vol. 26(12), pages 1083-1099.
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    3. Nowak, Grzegorz & Rusin, Andrzej & Łukowicz, Henryk & Tomala, Martyna, 2020. "Improving the power unit operation flexibility by the turbine start-up optimization," Energy, Elsevier, vol. 198(C).
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    Cited by:

    1. Tang, Zhenhao & Xu, Mengen & Li, Jiyuan & Cao, Shengxian & Zhang, Wenjian, 2025. "Enhancing rotor temperature field prediction for steam turbines using EC-SSC-DNN: A structural-symmetry-constrained deep learning framework," Energy, Elsevier, vol. 340(C).

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