Application of genetic algorithms to fault diagnosis in nuclear power plants

摘要

A nuclear power plant (NPP) is a complex and highly reliable special system. Without expert knowledge, fault confirmation in the NPP can be prevented by illusive and real-time signals. A new method of fault diagnosis, based on genetic algorithms (GAs) has been developed to resolve this problem. This NPP fault diagnosis method combines GAs and classical probability with an expert knowledge base. By assessing the state of the NPP, the GA colony undergoes a transformation that produces an individual adapted to the NPP's condition. Experiments performed on the 950 MW full size simulator at the Bejing NPP simulation training center show that this method has comparative adaptability to diagnose signals and faults changed with time, imperfect expert knowledge, illusive signals and other phenomena.