Power Prediction in Nuclear Power Plants Using a Back-Propagation Learning Neural Network

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Abstract
An artificial neural network—a data processing system with a number of simple highly interconnected processing elements in an architecture inspired by the structure of the human brain—is proposed for the prediction of thermal power in nuclear power plants (NPPs). The back-propagation network (BPN) algorithm is applied to develop models of signal processing. A number of case studies are performed with emphasis on the applicability of the network in a steady-state high power level. The studies reveal that the BPN algorithm can precisely predict the thermal power of an NPP. It also shows that the defected signals resulting from instrumentation problems, even when the signals comprising various patterns are noisy or incomplete, can be properly handled.

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