Equivalence analysis of simulation data and operation data of nuclear power plant based on machine learning

Abstract As an effective data pattern extraction method, machine learning is widely used in the field of nuclear power industry control, and has a great application. In order to solve the problem that there is a serious lack of operation data set for analyzing various variable conditions or accident conditions, this paper uses the maximum likelihood estimation and the least square estimation to prove that, when the simulation data of nuclear power plant and the operation data with different noise terms are used as the training data samples to train the machine learning algorithm models respectively, the difference between the two algorithms at each moment is only related to the mean of noise distribution. Especially when the mean of noise distribution is 0, they are equivalent. The conclusion shows that when the machine learning algorithm model is trained, the simulation data can supplement the operation data set. When the algorithm models are trained with the supplementary data set, the supplementary data will not change the calculations of the original algorithm model. Therefore, the simulation data can be used to expand the operation data set of variable conditions or accident conditions to improve the calculation accuracy of the algorithm models. In order to verify the conclusion, this paper uses simulation data and operation data with different noise terms to train the neural network algorithm models with L2 regularization term, and uses the algorithm models to calculate the steam mass flow rate at the outlet of main steam pipe of steam generator and the water temperature at the bottom of pressurizer. It is further proved that the calculations of thermal hydraulic transient operation parameters of the algorithm models trained by the operation data set with different noise terms are only equivalent to the translation of the calculations of the algorithm models trained by the simulation data set. And the value of translation is the mean of the noise distribution, which proves that the conclusion is effective.