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Hybrid modelling and digital twin development of a steam turbine control stage for online performance monitoring

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  • Yu, Jianxi
  • Liu, Pei
  • Li, Zheng

Abstract

Increasingly tight integration of renewable power sources with thermal power sources leads to frequent operation at off-design modes of thermal power plants. It brings challenges including reduced efficiency, increased costs and safety issues in power plants. At a component level, load changes of thermal power plants are mainly carried out through actions of control stage systems of steam turbines. Accurate performance monitoring of control stage systems is key to enable faster and more flexible operation of thermal power plants thus provides opportunities to accommodate more renewable power with limited penalty to efficiency and safety. In this paper, a hybrid modelling method based on operation data and first-principle mechanism is proposed for performance monitoring of control stage systems. The hybrid modelling method features a) flow rate calculation of high-pressure control valves with limited measurement data, and b) flow and efficiency characteristics calculation over full working ranges of control stages. The proposed hybrid modelling method is validated via two case studies of a 330 MW subcritical steam turbine and a 1000 MW ultra-supercritical steam turbine, where digital twins of control stages are developed. Results show that average relative errors between simulated and measured values of exit pressure and exit temperature of control stages are within a range of one percent, providing great potential for plant-wide digital twin development and online performance monitoring.

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  • Yu, Jianxi & Liu, Pei & Li, Zheng, 2020. "Hybrid modelling and digital twin development of a steam turbine control stage for online performance monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:rensus:v:133:y:2020:i:c:s1364032120303683
    DOI: 10.1016/j.rser.2020.110077
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    References listed on IDEAS

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    Cited by:

    1. Rong Xie & Muyan Chen & Weihuang Liu & Hongfei Jian & Yanjun Shi, 2021. "Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    2. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    3. Chen, Zhen & Zhou, Di & Zio, Enrico & Xia, Tangbin & Pan, Ershun, 2023. "Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Zhao, Guanjia & Ma, Suxia, 2023. "Data-driven modeling-based digital twin of supercritical coal-fired boiler for metal temperature anomaly detection," Energy, Elsevier, vol. 278(PA).
    5. Mezzour Ghita & Benhadou Siham & Medromi Hicham & Mounaam Amine, 2022. "HT-TPP: A Hybrid Twin Architecture for Thermal Power Plant Collaborative Condition Monitoring," Energies, MDPI, vol. 15(15), pages 1-38, July.
    6. Yu, Wei & Patros, Panos & Young, Brent & Klinac, Elsa & Walmsley, Timothy Gordon, 2022. "Energy digital twin technology for industrial energy management: Classification, challenges and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    7. Lei Zhang & Zongliang Qiao & Bingsen Hei & Youfei Tang & Shasha Liu, 2022. "Optimization of Steam Distribution Mode for Turbine Units Based on Governing Valve Characteristic Modeling," Energies, MDPI, vol. 15(23), pages 1-15, December.
    8. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).

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