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An Artificial Neural Network Compensated Output Feedback Power-Level Control for Modular High Temperature Gas-Cooled Reactors

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  • Zhe Dong

    (Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
    Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education of China, Beijing 100084, China)

Abstract

Small modular reactors (SMRs) could be beneficial in providing electricity power safely and also be viable for applications such as seawater desalination and heat production. Due to its inherent safety features, the modular high temperature gas-cooled reactor (MHTGR) has been seen as one of the best candidates for building SMR-based nuclear power plants. Since the MHTGR dynamics display high nonlinearity and parameter uncertainty, it is necessary to develop a nonlinear adaptive power-level control law which is not only beneficial to the safe, stable, efficient and autonomous operation of the MHTGR, but also easy to implement practically. In this paper, based on the concept of shifted-ectropy and the physically-based control design approach, it is proved theoretically that the simple proportional-differential (PD) output-feedback power-level control can provide asymptotic closed-loop stability. Then, based on the strong approximation capability of the multi-layer perceptron (MLP) artificial neural network (ANN), a compensator is established to suppress the negative influence caused by system parameter uncertainty. It is also proved that the MLP-compensated PD power-level control law constituted by an experientially-tuned PD regulator and this MLP-based compensator can guarantee bounded closed-loop stability. Numerical simulation results not only verify the theoretical results, but also illustrate the high performance of this MLP-compensated PD power-level controller in suppressing the oscillation of process variables caused by system parameter uncertainty.

Suggested Citation

  • Zhe Dong, 2014. "An Artificial Neural Network Compensated Output Feedback Power-Level Control for Modular High Temperature Gas-Cooled Reactors," Energies, MDPI, vol. 7(3), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:3:p:1149-1170:d:33441
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    References listed on IDEAS

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    1. Vujić, Jasmina & Bergmann, Ryan M. & Škoda, Radek & Miletić, Marija, 2012. "Small modular reactors: Simpler, safer, cheaper?," Energy, Elsevier, vol. 45(1), pages 288-295.
    2. Zhe Dong, 2012. "Dynamic Output Feedback Power-Level Control for the MHTGR Based On Iterative Damping Assignment," Energies, MDPI, vol. 5(6), pages 1-34, June.
    3. Zhe Dong, 2013. "A Neural-Network-Based Nonlinear Adaptive State-Observer for Pressurized Water Reactors," Energies, MDPI, vol. 6(10), pages 1-20, October.
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    Citations

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    Cited by:

    1. Dong, Zhe & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2018. "Multi-layer perception based model predictive control for the thermal power of nuclear superheated-steam supply systems," Energy, Elsevier, vol. 151(C), pages 116-125.
    2. Liben Gao & Yujie Dong & Huiping Guo, 2022. "Selection of Planning Options of Electricity and Freshwater Cogeneration Method Based on High-Temperature Gas-Cooled Reactor," Energies, MDPI, vol. 15(12), pages 1-14, June.
    3. Young Jin Kim & Byung Jin Lee & Kunwoo Yi & Yoon Jae Choe & Min Chul Lee, 2020. "Numerical Study on the Effects of Relative Diameters on the Performance of Small Modular Reactors Driven by Natural Circulation," Energies, MDPI, vol. 13(22), pages 1-17, November.
    4. Zhe Dong, 2014. "Saturated Adaptive Output-Feedback Power-Level Control for Modular High Temperature Gas-Cooled Reactors," Energies, MDPI, vol. 7(11), pages 1-20, November.
    5. Jiang, Di & Dong, Zhe, 2020. "Dynamic matrix control for thermal power of multi-modular high temperature gas-cooled reactor plants," Energy, Elsevier, vol. 198(C).

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