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Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree

Author

Listed:
  • Yanning Lu
  • Yanzheng Xiang
  • Bo Chen
  • Haiyang Zhu
  • Junfeng Yue
  • Yawei Jin
  • Pengfei He
  • Yibo Zhao
  • Yingjie Zhu
  • Jiasheng Si
  • Deyu Zhou

Abstract

The steam turbine is one of the major pieces of equipment in thermal power plants. It is crucial to predict its output accurately. However, because of its complex coupling relationships with other equipment, it is still a challenging task. Previous methods mainly focus on the operation of the steam turbine individually while ignoring the coupling relationship with the condenser, which we believe is crucial for the prediction. Therefore, in this paper, to explore the coupling relationship between steam turbine and condenser, we propose a novel approach for steam turbine power prediction based on the encode-decoder framework guided by the condenser vacuum degree (CVD-EDF). In specific, the historical information within condenser operation conditions data is encoded using a long-short term memory network. Moreover, a connection module consisting of an attention mechanism and a convolutional neural network is incorporated to capture the local and global information in the encoder. The steam turbine power is predicted based on all the information. In this way, the coupling relationship between the condenser and the steam turbine is fully explored. Abundant experiments are conducted on real data from the power plant. The experimental results show that our proposed CVD-EDF achieves great improvements over several competitive methods. our method improves by 32.2% and 37.0% in terms of RMSE and MAE by comparing the LSTM at one-minute intervals.

Suggested Citation

  • Yanning Lu & Yanzheng Xiang & Bo Chen & Haiyang Zhu & Junfeng Yue & Yawei Jin & Pengfei He & Yibo Zhao & Yingjie Zhu & Jiasheng Si & Deyu Zhou, 2022. "Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-14, October.
    • Handle: RePEc:plo:pone00:0275998
    DOI: 10.1371/journal.pone.0275998
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    References listed on IDEAS

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    1. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    2. Sun, Lei & Liu, Tianyuan & Xie, Yonghui & Zhang, Di & Xia, Xinlei, 2021. "Real-time power prediction approach for turbine using deep learning techniques," Energy, Elsevier, vol. 233(C).
    3. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2020. "Wind turbine power curve modeling for reliable power prediction using monotonic regression," Renewable Energy, Elsevier, vol. 147(P1), pages 214-222.
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    1. Mohammadi, Mobin & Esfahanian, Vahid, 2026. "A comprehensive study to predict steam turbine power of a CCPP using data-driven models based on a holistic approach with a novel set of practical descriptive features," Energy, Elsevier, vol. 354(C).

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