Research on fuzzy weighted gain scheduling water level control system of U-tube steam generator

Qiu, Leilei; Huang, Yan; Zhang, Ru; Wang, Haomin; Jiang, Guanfu; Sun, Peiwei; Wei, Xinyu

doi:10.1016/j.anucene.2023.109812

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…Energies 2023, 16,8050 In this formula, cos(SI) represents the cosine value of the angle between CSPGA and CGA. The range of cos(SI) is [−1, 1].…”

Section: Gk-spsa Strategymentioning

confidence: 99%

“…Following that, a tuning formula can be devised using this model. Ultimately, the selection of parameters can be influenced by the model coefficients and the formula [15][16][17][18]. However, accurately determining the relationship between parameters and performance in complex industrial systems can be challenging, making it very difficult to apply this method to the SGLCS.…”

mentioning

confidence: 99%

“…This process may require additional computational resources and time. This drawback can make the parameter-setting process quite tedious, ultimately leading to poorer control performance [16]. However, Geng et al proposed a variety of intelligent optimizations without model methods and applied them in the performance-optimization process of the SGLCS in nuclear power plants.…”

mentioning

confidence: 99%

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

Li,

Yang,

Yang

et al. 2023

Energies

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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.

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“…Energies 2023, 16,8050 In this formula, cos(SI) represents the cosine value of the angle between CSPGA and CGA. The range of cos(SI) is [−1, 1].…”

Section: Gk-spsa Strategymentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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