AUTOMATED SYNCHRONOUS RESEARCH ON NUCLEAR POWER DIGITAL TWIN MODEL BASED ON REINFORCEMENT LEARNING
The efficient synchronization of the digital twin system and the unit of nuclear power operation is the basis for the optimiza-tion of power plant operation and an important guarantee for nuclear power safety.Taking the digital twin model of nuclear steam system as the research object,this paper proposes an adaptive intelligent optimization algorithm based on the deep deterministic policy gradient of twin delay(TD3PSO).By constructing the action network and action value network,the neural network is used to dynamically gen-erate the hyperparameters required during the operation of the elementary particle swarm optimization algorithm,so as to realize the self-exploration of the hyperparameters,reduce the manual intervention in the algorithm,and solve the problem that the elementary par-ticle swarm optimization algorithm is easy to fall into local optimum.Experiments show that the TD3PSO algorithm is superior to the el-ementary particle swarm optimization algorithm,and compared with the results of manual debugging,the optimization accuracy is im-proved by 93.39%,and the automatic synchronization effect is remarkable.
digital twinsautomated synchronizationadaptivetwin delay deep deterministic policy gradientintelligent optimization
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