Investigations on the performance of intelligent techniques for control and fault diagnosis in pressurized water reactor

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. If neutron flux becomes uncontrolled due to an actuator failure, it leads to explosion and radiation hazards as witnessed in Chernobyl and Fukushima events. It is desirable that the parameters of a reactor core are 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 cost-effective, Artificial Intelligence (AI) has made implausible growth in recent times. This research utilizes a 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. newline