Prediction of unmeasurable parameters of NPPs using different model-free methods based on cross-correlation detection of measurable/unmeasurable parameters: A comparative study

Abstract

In this paper cross-correlation of measurable/unmeasurable parameters of nuclear power plants (NPPs) are detected. Correlation techniques including Pearson’s, Spearman’s, and Kendall-tau give appropriate input parameters for training/prediction of the target unmeasurable parameters. Fuel and clad maximum temperatures of uncontrolled withdrawal of control rods (UWCR) transient of Bushehr nuclear power plant (BNPP) are used as the case study target parameters. Different model-free methods including decision tree (DT), feed-forward back propagation neural network (FFBPNN) accompany with different learning algorithms (i.e. gradient descent with momentum (GDM), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), and Bayesian regularization (BR)), and support vector machine (SVM) with different kernel functions (i.e. linear and Gaussian functions) are employed to predict the target parameters. Comparison of the results indicates that BR learning algorithm of FFBPNN gives more precise results. Moreover, DT results is comparable with LM learning algorithm of FFBPNN. In addition, SVM results with Gaussian kernel function is better than SVM results with linear one. Results show that cross-correlation detection among the parameters has decisive effect on performance of learning algorithms. This claim is verified by prediction of the target parameters using either fewer number of correlated input parameters or uncorrelated input parameters. The input parameters without strong correlation have greater errors in forecasting. In addition, more parameters with strong correlation give better results. Prediction of unmeasurable parameters of NPPs can be used as a support system for the NPPs operators to perform more appropriate actions in confrontation with transients.