An integrated approach to performance monitoring and fault diagnosis of nuclear power systems.

摘要

An integrated approach to performance monitoring and fault diagnosis was developed in this dissertation for nuclear power plants using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for fault diagnosis. In the applications to nuclear power plants, on the one hand, routine operation data may not be able to characterize the relationships among process variables because operating setpoints may change and thermal fluid components may experience degradation. On the other hand, physical models always have uncertainty and are often too complicated in terms of model structure to design residual generators for fault diagnosis. Therefore, a realistic fault diagnosis method needs to combine the strength of physical models in modeling a wide range of anticipated operation conditions and the strength of statistical data driven modeling in feature extraction. In the developed robust data driven model-based approach, the changes in operation conditions are simulated using physical models and model uncertainty is extracted from plant operation data such that the fault effects on process variables can be decoupled from model uncertainty and normal operation changes. It was found that the developed method could eliminate false alarms due to model uncertainty and deal with operating condition changes of nuclear power plants.; The developed algorithms were demonstrated using the International Reactor Innovative and Secure (IRIS) Helical Coil Steam Generator (HCSG) systems. A thermal hydraulic model was developed for this system. It was revealed through steady state simulation that the primary coolant temperature profile could be used to indicate the water inventory inside the HCSG tubes. The performance monitoring and fault diagnosis module was developed to monitor sensor faults, flow distribution abnormality, and heat performance degradation for both steady state and dynamic operating conditions.; This dissertation will bridge the gap between the theoretical research on computational intelligence and the engineering design in performance monitoring and fault diagnosis for nuclear power plants. The new algorithms have the potential of being integrated into the Generation III and Generation IV nuclear reactor I&C design after they are tested on current nuclear power plants or Generation IV prototype reactors.