Intelligent Techniques for Control and Fault Diagnosis in Pressurized Water Reactor: A Review

Abstract

Nuclear reactors serve approximately 10% of the world’s energy usage, and over 430 Nuclear Power Plants (NPP) are currently built globally. They are safety-critical systems as neutron flux density in the nuclear reactor core has to be critically controlled within limits. The parameters of a reactor core should be monitored and optimally regulated to increase the performance of the system. Also, any fault in an NPP system may potentially compromise plant safety. Thus, implementing early Fault Detection and Diagnosis (FDD) techniques becomes crucial. With considerable advancements in computational speed and electronics becoming costeffective, Artificial Intelligence (AI) has grown implausible in recent times. This review article discusses on few AI techniques to optimally control the neutron flux density and design an effective fault diagnosis algorithm to detect sensor faults in the nuclear reactor core.