Study on Missing Data Filling Algorithm of Nuclear Power Plant Operation Parameters

Abstract

By analyzing the recorded operation data of a nuclear power plant (NPP), its results can serve the fault detection or operation experience feedback. Data missing exists in the recorded operation data. It may lower the data quality and affect the accuracy of the analysis results. In order to improve the data quality, two parts of researches are carried on. Firstly, to locate the missing data accurately the detecting algorithm for missing data of the NPP operation parameters based on wavelet analysis. Different judging basis is proposed for discrete and continuous missing respectively. Then, the filling method based on the hot deck algorithm are studied. As the dynamic properties of the parameters are closely related to the operating state of NPP, the similarity of the operation parameter vectors are formed to express the similarity of the operating states, so as to fulfill the requirements of the hot deck algorithm. To improve the accuracy of the measuring results, taken the differences between the characteristics of the analog parameters and the switch parameters into consideration, the similarity measurements using Mahalanobis distance for the analog parameter vectors and the matching measure for the switch parameter vectors are studied respectively. Finally, the operation data is taken to build the experiment data set for the algorithm verification. The results shows that the designed algorithm performs much better than the mean interpolation method and LSTM.