GK-SPSA-Based Model-Free Method for Performance Optimization of Steam Generator Level Control Systems

Xiaoyu Li, Zean Yang, Yongkuan Yang, Xiangsong Kong (), Changqing Shi and Jinguang Shi
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Xiaoyu Li: School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361021, China
Zean Yang: School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361021, China
Yongkuan Yang: School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361021, China
Xiangsong Kong: School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361021, China
Changqing Shi: State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China
Jinguang Shi: School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361021, China

Energies, 2023, vol. 16, issue 24, 1-14

Abstract: The Steam Generator (SG) is a crucial component of a Nuclear Power Plant (NPP), generating steam to transfer heat from the primary loop to the secondary loop. The control performance of the Steam Generator Level Control System (SGLCS) plays a crucial role in the normal operation of the SG. To improve the system’s performance, the parameters of the control system should be optimized. However, the steam generator and its corresponding control system are highly complex, exhibiting nonlinearity and time-varying properties. Conventional parameter-setting methods mainly rely on engineers’ experience, and are laborious and time-intensive. To tackle the aforementioned challenges, a Model-Free Optimization (MFO) method based on Knowledge-informed Historical Gradient-based Simultaneous Perturbation Stochastic Approximation (GK-SPSA) is applied to the performance optimization of the steam generator level control system. The GK-SPSA algorithm is a variant of the traditional SPSA algorithm. The fundamental idea of this revised algorithm is to maximize the utilization of historical gradient information generated during the optimization process of the SPSA algorithm, with the aim of enhancing overall algorithm performance in a model-free optimization context. Based on the effective utilization of historical gradient information, the GK-SPSA algorithm exhibits two improvements over the SPSA algorithm. The first improvement is related to the recognition of the online optimization progress, utilizing the state of the optimization progress to dynamically adjust the optimization step size. The second improvement is related to gradient estimation compensation, employing compensation rules to enhance the accuracy of gradient estimation, thus improving the optimization efficiency. Through simulation experiments, it can be observed that there is not much difference in the final iteration values among the GK-SPSA, IK-SPSA, and SPSA methods. However, the iteration count of GK-SPSA is reduced by about 20% compared to SPSA and by 11.11% compared to Knowledge-informed SPSA (IK-SPSA). The results indicate that this method can significantly improve the efficiency of parameter tuning for the liquid level control system of a steam generator.

Keywords: performance optimization; SG level control; knowledge-informed SPSA; historical iteration information (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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