基于重构概念的变负荷工况下核功率预测研究

为适应船用核动力装置变负荷运行工况时功率的调节，提出了一种基于运行数据统计学习的方法计算需求功率，并分别运用支持向量机和BP神经网络两种机器学习方法进行了数值试验。结果表明，在负荷急剧变化过程中，基于数据统计学习的方法计算精度优于物理模型法，特别是基于支持向量机的方法，它可在短时间内经一遍训练即可得到符合精度的训练模型，且可保证其得到的极值解即为全局最优解。此外，该方法还可应对某些输入信号缺失的情况，提高了计算过程的稳定性、可靠性和容错控制能力。

Study of Reactor Power Prediction During Load Change Process Based on Signal Reconstruction

In order to be seasoned with the regulation of reactor power during load change of marine nuclear power plant, a method based on running data learning was studied to compute demand reactor power, and experimentation was processed separately by support vector machine (SVM) and BP neural network. The results show that the method based on running data learning is more accurate than physical model method during the process of load rapid change, especially method based on SVM, which could get a smart predicting model only by one round training in a short time, and further more it can insure that the getting result is most optimal of all. Besides, this method could cope with situation that some input signal default, and therefore it would improve the stability, reliability and fault tolerance of computing.