Performance Optimization Method of Steam Generator Liquid Level Control Based on Hybrid Iterative Model Reconstruction

• Published in: Engineering proceedings Vol. 37; no. 1; p. 111
• Main Authors: Xiaoyu Li, Xiangsong Kong, Changqing Shi, Jinguang Shi, Zean Yang
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.05.2023
• Subjects: hybrid model; performance optimization; small-sample modeling; steam generator level control
• ISSN: 2673-4591
• DOI: 10.3390/ECP2023-14628

The steam generator is an important piece of energy exchange equipment for nuclear power plants, and the level control of the steam generator plays a key role in the stable operation of the plants. To improve the level control performance of the steam generator, it is necessary to adjust the parameters of the level control system during the commissioning process of nuclear power plants. However, the parameter setting process relies heavily on the experience of engineers, requires a lot of historical data, and is difficult to ensure the best. To address these issues, this paper proposes a hybrid iterative model reconstruction-based steam generator level control performance optimization method based on the idea of data-driven optimization. The method proposes a fusion idea and implementation mechanism in which process data and the hybrid model are jointly driven under the data-driven framework to maximize the advantages of different modeling mechanisms to achieve the performance optimization of a steam generator level control system. The method first constructs the initial dataset with a small-sample Latin-square experiment design and then builds two different fitting models, SVM and Kriging, based on the initial dataset respectively, under the hybrid model fusion idea. After that, the particle swarm optimization algorithm is used to calculate the optimal point of the current valid model, and the optimization process is controlled by establishing the iteration termination judgment based on the historical iteration data. Then, the current iteration point is used to dynamically reconstruct the two types of models. Finally, the two types of models are dynamically reconstructed using the current iteration points. The above process is iterated until the optimal iterative process of the system is satisfied. From the experimental results, it can be seen that compared with two types of single-model optimization methods, this method can reduce the iteration final value by 13.57% and 16.27%, respectively, with a slightly increased number of iterations. These results indicate that this method can significantly improve the efficiency of optimizing the control performance of the steam generator liquid level.