A neural-network based variable universe fuzzy control method for power and axial power distribution control of large pressurized water reactors. (15th September 2022)

• Record Type: Journal Article
• Authors: Wan, Jiashuang; Jiang, Qingfeng; Liao, Longtao; Wu, Shifa; Wang, Pengfei
• Is Part Of: Annals of nuclear energy. Volume 175(2022)
• Publication Date: 2022-09-15
• Subjects: Reactor power -- Axial offset -- Variable universe fuzzy control -- Neural network; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2022.109241
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: A neural network based variable universe fuzzy control method is proposed for large PWRs. A scaling factor is used to adjust input fuzzy universe adaptively to maximize fuzzy rule utilization. A neural network is designed to optimize the proportionality coefficient of the scaling factor online. The method achieves good control of both core power and axial power distribution for large PWRs. Abstract: This paper proposes a neural network-based variable universe fuzzy controller (NN-VUFC) for power and axial power distribution control of large pressurized water reactors (PWRs). First, a two-node reactor kinetics model is introduced, and the fuzzy inference rules are formulated for the reactor power and axial power distribution control according to their deviations from corresponding setpoint values. Then, based on the idea of variable universe, the scaling factor of the input fuzzy universe is designed to maximize the utilization of fuzzy rules. Finally, a neural network is designed to optimize the proportionality coefficient of the scaling factor online to further improve the fuzzy control performance. Simulation results of the CPR1000 reactor under typical transient conditions show that the NN-VUFC can realize good control of both the reactor power and axial power distribution, and its control performance is effectively improved compared to traditional fuzzy controllers.