Fault diagnosis of generation IV nuclear HTGR components – Part I: The error enthalpy–entropy graph approach

Highlights: ► The h–s graph can be successfully applied for fault diagnosis of HTGR components. ► Sensitivity analyses are utilized to quantify probable HTGR fault parameters. ► Dynamic fault conditions are described using single static fault signatures. - Abstract: Fault diagnosis (FD) is an important element in modern nuclear power plant (NPP) diagnostic systems. In this respect, FD of generation II and III water-cooled nuclear energy systems has become an active research topic to continually improve levels of reliability, safety, and operation. However, evolutionary advances in reactor and component technology together with different energy conversion methodologies support the investigation of alternative approaches to FD. Within this context, the basic aim of this two part series is to adopt the application of the enthalpy–entropy (h–s) graph approach for FD of generation IV nuclear high temperature gas-cooled reactor (HTGR) components. In Part I, the error method is utilized to derive fault patterns from the h–s graph in order to classify malfunctions via the fault classification index (FCI) in the nuclear reactor, turbo-machinery (gas turbine and compressors), heat exchangers (pre-cooler, intercooler, and recuperator) and the primary transporting medium of the working fluid. The study is conducted on a 165 MW model of the main power system (MPS) of the Pebble Bed Modular Reactor (PBMR) that is based on a single-shaft, closed-loop, direct Brayton thermodynamic cycle. Illustrative signatures that correspond to 24 single fault transients, categorized in three fault classes by means of a sensitivity analysis of a simplified HTGR, are presented. FD is demonstrated for steady state operation as well as load following of the MPS during normal power operation of the plant. In Part II of the series, a second classifier named the area error method is devised for NPP supervision to ultimately address the FD problem using a multiple classifier system. The application of the proposed h–s graph approach (both methods) is specifically illustrated for classification of an emulated fault transient in data from the real prototype Pebble Bed Micro Model (PBMM) plant.