Exploring signal interactions in PWR nuclear power plants through noise-based signal processing

Abstract

Nuclear power plants operate as complex, interconnected systems with tightly interlinked variables. Monitoring systems within such plants gather data from various process parameters. This paper systematically analyzes key signals in a typical PWR power plant. We develop a dynamic model of the PWR plant encompassing mathematical models for the reactor core, steam generator, pressurizer, turbine, and condenser. This model is rigorously validated against reference data. We use this model to analyze signal perturbations in hypothetical scenarios, employing both time-domain and frequency-domain analyses. Our results demonstrate that noise in one signal noticeably impacts others. Furthermore, the extent of this impact varies depending on the nature of their connection. Direct connections exhibit a higher susceptibility to noise-induced interference, whereas intermediate connections typically show a diminished impact. Through signal analysis, we identify correlated signal pairs, offering insights into processes during abnormal conditions. These findings have implications for enhancing the safety and reliability of nuclear power plant operations.