Safety improvement of nuclear power reactor using soft computing techniques

Nowadays, Nuclear Reactors (NR) are large in scale and complex, they are expected to be operated with high levels of reliability and safety. Hence to increase plant safety, to achieve, maintain system stability and assure satisfactory in order to meet the increasing demands for automated system and to detect and diagnose system failures and malfunctions. When a plant malfunction occurs, a great data influx is occurred. This paper proposes a support system based on neuro fuzzy approach conjunction with Genetic Algorithm that assist alarming and diagnosis system. Throughout this framework Neurofuzzy fault diagnosis system is employed to diagnosis the fault of nuclear reactors. Hence to overcome weak points of both neuro learning and linguistic based approaches by which the integrated system will inherit the strength of both approaches and to optimize the Neurofuzzy outcomes using Genetic Algorithm resulting to show the efficiency is obtained by GA is greater and the inaccurate information of the alarming system also compared with Neurofuzzy diagnosis system.