Research on Optimization of Core Power Regulation System of Swimming Pool Reactor Based on PSO-BP Neural Network

Peng Zhiwen; Chen Xiaoliang; Zhu Jiachen; Wang Feng

doi:10.13832/j.jnpe.2024.04.0173

Volume 45 Issue 4

Aug. 2024

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Peng Zhiwen, Chen Xiaoliang, Zhu Jiachen, Wang Feng. Research on Optimization of Core Power Regulation System of Swimming Pool Reactor Based on PSO-BP Neural Network[J]. Nuclear Power Engineering, 2024, 45(4): 173-180. doi: 10.13832/j.jnpe.2024.04.0173

Citation:

Peng Zhiwen, Chen Xiaoliang, Zhu Jiachen, Wang Feng. Research on Optimization of Core Power Regulation System of Swimming Pool Reactor Based on PSO-BP Neural Network[J]. Nuclear Power Engineering, 2024, 45(4): 173-180. doi: 10.13832/j.jnpe.2024.04.0173

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Research on Optimization of Core Power Regulation System of Swimming Pool Reactor Based on PSO-BP Neural Network

doi: 10.13832/j.jnpe.2024.04.0173

Reactor Engineering Technology Research Institute, China Institute of Atomic Energy, Beijing, 102413, China

Received Date: 2023-08-29
Rev Recd Date: 2023-11-21
Publish Date: 2024-08-12

Abstract

Abstract

Based on the MATLAB/Simulink, the simulation model of the power regulation system and the primary heat transfer system of the 49-2 swimming pool reactor was constructed, and the external reactive disturbance simulation test was carried out to verify the accuracy of the model. The proportion integration differentiation (PID) controller combined with particle swarm optimization (PSO) and BP neural network was used as the main controller, and the response of the regulating system under core reactivity and core inlet temperature disturbance was simulated, which was compared with that of the original controller of swimming pool reactor and the traditional BP neural network controller. The results show that the PID controller based on PSO-BP neural network can make the core reach a stable state quickly, with shorter regulating time and smaller overshoot, and has better robustness and stability.
- Particle swarm optimization (PSO),
- BP neural network,
- PID controller,
- Reactor power regulation

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References(11)

References

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