DEVELOPMENT OF AN OPTIMAL SENSOR PLACEMENT STRATEGY FOR NUCLEAR POWER SYSTEMS

摘要

Fault diagnosis is an important area in the nuclear industry for effective and continuous operation of power plants. All the approaches for fault diagnosis depend critically on the sensors that measure important process variables. The locations of these sensors determine the effectiveness of the various diagnostic methods. However, the emphasis of most fault diagnosis approaches is primarily on procedures to perform fault detection and isolation (FDI) given a set of sensors. Little attention has been given to the actual allocation of sensors for achieving the efficient FDI performance. This paper presents a graph-based approach that serves as a solution for the optimization of sensor locations to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data and to fit a hyper-plane to the data. The fault direction for each of the fault scenarios is obtained on the prediction errors, and fault isolation is then accomplished from projections on the fault directions. Simulation results of the helical coil steam generator (HCSG) units of the International Reactor Innovative & Secure (IRIS) nuclear reactor demonstrate the implementation of the proposed FDI approach with optimized sensor locations for large industrial systems.