Optimal Tracking Control for a Discrete Time Nonlinear Nuclear Power System

Zhenhua Luan, Mengxuan Wang, Yuzhen Zhang, Qinglai Wei, Tianmin Zhou, Zhiwu Guo, Jun Ling and Junyong Zhai

Mathematical Problems in Engineering, 2022, vol. 2022, 1-9

Abstract: Recently, increasing attention has been paid to nuclear power control with the appeals of clean energy and demands of power regulation to integrate into the power grid. However, a nuclear power system is a discrete-time (DT) nonlinear and complicated system, where the parameters entangle with intrinsic states. Furthermore, the need for huge computational ability due to the high-level order property in the nuclear reactor model causes many difficulties in the power control of nuclear industries. In this study, a new scheme of optimal tracking control for DT nonlinear nuclear power systems is provided to accomplish the power control of a 2500-MW pressurized water reactor (PWR) nuclear power plant. The proposed approach based on the value iteration method is a novel algorithm in the human intelligence community, which has a basic actor-critic structure with neural networks (NNs). The new approach has some modifications, where the cost function is redefined by leveraging the higher-order polynomial to substitute neural networks in the entire actor critic architecture. Simulation results of the 2500-MW PWR nuclear power plant are given to demonstrate the effectiveness of the developed method.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7953358

DOI: 10.1155/2022/7953358

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