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.

机器学习作为一种有效的数据模式提取方法，在核电工业控制领域得到了广泛的应用，具有很大的应用价值。为了解决分析各种变工况或事故工况的运行数据集严重缺乏的问题，本文采用最大似然估计和最小二乘估计来证明，当核电厂的模拟数据和将不同噪声项的操作数据作为训练数据样本分别训练机器学习算法模型，两种算法在每一时刻的差异仅与噪声分布的均值有关。特别是当噪声分布的均值为 0 时，它们是等价的。结论表明，在训练机器学习算法模型时，仿真数据可以补充运行数据集。当用补充数据集训练算法模型时，补充数据不会改变原算法模型的计算。因此，可以利用仿真数据扩展变工况或事故工况的运行数据集，提高算法模型的计算精度。为验证该结论，本文利用仿真数据和不同噪声项的运行数据，训练具有L2正则项的神经网络算法模型，并利用该算法模型计算主蒸汽管道出口处的蒸汽质量流量。蒸汽发生器的温度和增压器底部的水温。进一步证明，不同噪声项运行数据集训练的算法模型的热工水力暂态运行参数计算仅等效于模拟数据集训练的算法模型计算的平移。并且平移值是噪声分布的均值，证明结论是有效的。