Sudy on the Instance-based Transfer Learning Method Based on the Real-time Prediction Model of the High-voltage Heating Measurement Point Data of Nuclear Power Plant
To address the problems of missing fault data and insufficient observation data in real-time prediction of the high-voltage heating measurement point data of nuclear power plant in engineering practice,an instance-based transfer learning method based on the digital twin simulation data is proposed.Firstly,using digital twin to generate rich features and sufficient samples of nuclear industry-related simulation data to construct the source domain dataset,and using the actual observed data obtained from various monitoring points during the operation of nuclear equipment to construct the target domain dataset.Then,the classification model obtained through cross-validation is used to measure the similarity between the source domain data and the target domain data.Data in the source domain that is similar to the target domain data is given a high weight,making the source domain data more compatible with the target domain data.Finally,a real-time prediction model for nuclear industry data is trained using weighted source domain data.The experimental results show that the instance-based transfer learning method has better performance under small sample and feature missing conditions,and has higher accuracy compared to traditional transfer learning methods.
Nuclear power plantHigh-voltage heatingReal-time predictionDigital twinInstance-based transfer learning
NETL
NSTL
万方数据
