THERMAL POWER PREDICTION OF NUCLEAR-POWER-PLANT USING NEURAL NETWORK AND PARITY SPACE MODEL

Abstract

A power prediction system was developed using an artificial neural network paradigm that was combined with a parity space signal validation technique. The parity space signal validation algorithm for the input preprocessing and the backpropagation network algorithm for the network learning are used for the power prediction system. A number of case studies were performed with emphasis on the applicability of the network in a steady-state high power level. The studies reveal that these algorithms can precisely predict the thermal power in a nuclear power plant. It also shows that the error signals resulting from instrumentation problems, even when the signals comprising various patterns are noisy or incomplete, can be properly treated.