Fuzzy logic based fault diagnosis of a PWR nuclear power plant

Abstract

Proper and timely fault diagnosis is of premier importance to guarantee the safe and reliable operation of Nuclear Power Plants (NPPs). If faults occur in NPPs, it is very difficult for a human operator to perform routine tasks, such as distinguishing normal from abnormal conditions and predicting future states, etc. In this paper, a fuzzy inference system is adopted for the diagnosis of abrupt faults in a nonlinear model of a typical Pressurised Water Reactor (PWR). The fuzzy system is tested with different shapes of Membership Functions (MFs). The if-then rules, representing the underlying processes, are inferred from the available fault-symptom relations. The symptoms are generated using plant model measurements.