An On-Line Calibration Monitoring Technique Using Support Vector Regression and Principal Component Analysis

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (AASVR) is proposed for the sensor signal validation of the NPP. This paper describes the design of an AASVR-based sensor validation system for a power generation system. Response surface methodology (RSM) is employed to efficiently determine the optimal values of SVR hyperparameters. The proposed PCSVR model was confirmed with actual plant data of Kori Nuclear Power Plant Unit 3 and compared with the AANN model. The results show that the accuracy and sensitivity of the model were very competitive. Hence, this model can be used to monitor sensor performance.