Power prediction of nuclear power plant using backpropagation learning neural network

A neural network paradigms, which is a data processing system with a number of simple highly interconnected processing elements in an architecture inspired by the structure of the brain, is proposed for the application to the prediction of thermal power in Nuclear Power Plant (NPP). The Back Propagation Network (BPN) algorithm is applied to develop the models of signal processing. A number of case studies were performed with emphasis on the applicability of network in a steady state high power level. It is revealed that the BPN algorithm can precisely predict the thermal power of NPP. It is also shown that the defected signals resulting from instrumentation problem, even when the signals comprising various patterns are noisy or incomplete, can be also properly handled in the case study