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IK-SPSA-Based Performance Optimization Strategy for Steam Generator Level Control System of Nuclear Power Plant

Author

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  • Pengcheng Geng

    (School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361021, China
    State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China)

  • Xiangsong Kong

    (School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361021, China
    State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China)

  • Changqing Shi

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China)

  • Hang Liu

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China)

  • Jiabin Liu

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China)

Abstract

The steam generator (SG) is a critical component of the steam supply system in the nuclear power plant (NPP). Hence, it is necessary to control the SG level well to ensure the stable operation of the NPPs. However, its dynamic level response process has significant nonlinearity (such as the ‘swell and shrinks’ effect) and time-varying properties. As most of the SG level control systems (SGLCS) are constructed based on the Proportional-Integral-Derivative (PID) controllers with fixed parameters, the controller parameters should be optimized to improve the performance of the SGLCS. However, traditional parameters tuning methods are generally experience-based, cumbersome, and time-consuming, and it is difficult to obtain the optimal parameters. To address the challenge, this study adopts a knowledge-informed simultaneous perturbation stochastic approximation (IK-SPSA) based on adjacent iteration points information to improve the performance of the SGLCS. Rather than the traditional controller parameter tuning method, the IK-SPSA method optimizes the control system directly by using measurements of control performance. The method’s efficiency lies in the following aspects. Firstly, with the help of historical information during the optimization process, the IK-SPSA can dynamically sense the current status of the optimization process. Secondly, it can accomplish the iteration step size tuning adaptively according to the optimization process’s current status, reducing the optimization cost. Thirdly, it has the stochastic characteristic of simultaneous perturbation, which gives it high optimization efficiency to optimize high dimensional controller parameters. Fourthly, it incorporates an intelligent termination control mechanism to accomplish optimization progress control. This mechanism could terminate the optimization process intelligently through historical iterative process information, avoiding unnecessary iterations. The optimization method can improve the stability, safety, and economy of SGLCS. The simulation results demonstrated the effectiveness and efficiency of the method.

Suggested Citation

  • Pengcheng Geng & Xiangsong Kong & Changqing Shi & Hang Liu & Jiabin Liu, 2022. "IK-SPSA-Based Performance Optimization Strategy for Steam Generator Level Control System of Nuclear Power Plant," Energies, MDPI, vol. 15(19), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7387-:d:936425
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    References listed on IDEAS

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    1. Keivan Rahimi-Adli & Egidio Leo & Benedikt Beisheim & Sebastian Engell, 2021. "Optimisation of the Operation of an Industrial Power Plant under Steam Demand Uncertainty," Energies, MDPI, vol. 14(21), pages 1-28, November.
    2. Erickson Diogo Pereira Puchta & Priscilla Bassetto & Lucas Henrique Biuk & Marco Antônio Itaborahy Filho & Attilio Converti & Mauricio dos Santos Kaster & Hugo Valadares Siqueira, 2021. "Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller," Energies, MDPI, vol. 14(12), pages 1-20, June.
    3. Deliang Zeng & Yanqiu Zheng & Wei Luo & Yong Hu & Qingru Cui & Qing Li & Chen Peng, 2019. "Research on Improved Auto-Tuning of a PID Controller Based on Phase Angle Margin," Energies, MDPI, vol. 12(9), pages 1-16, May.
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