Applying Neurofuzzy Computing for Safety Improvement of Nuclear Power Reactor

Citation:
Metwally, M. A., A. Aboshosha, D. K. Ibrahim, and E. E. L. - D. A. EL-Zahab, "Applying Neurofuzzy Computing for Safety Improvement of Nuclear Power Reactor", 14th International Middle East Power Systems Conference (MEPCON’10), Cairo University, Egypt, 2010.

Abstract:

Nuclear Power Reactors (NPRs) are large in scale and complex, so the information from local fields is excessive, and therefore plant operators cannot properly process it. When a plant malfunction occurs, a great data influx is occurred, so the cause of the malfunction cannot be easily or promptly identified.
A typical NPR may have around 2,000 alarms in the Main Control Room (MCR) in addition to the display of analog data. During plant transients, hundreds of alarms may be activated in a short time. Hence, to increase the plant safety, this paper proposes a support system based on neurofuzzy that assists alarming and diagnosis systems. Throughout this framework the neurofuzzy fault diagnosis system is employed to fault diagnosis of nuclear reactors. To overcome weak points of both linguistic and neuro learning based approaches, integration between the neural networks and fuzzy logic has been applied by which the
integrated system will inherit the strengths of both approaches.

Notes:

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