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Thermal power prediction of nuclear power plant using neural network and parity space model

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3 Author(s)

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 input preprocessing and a backpropagation network algorithm for network learning are used for the power prediction system. 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. They also show that the error signals resulting from instrumentation problems can be properly treated even when the signals comprising various patterns are noisy or incomplete

Published in:

IEEE Transactions on Nuclear Science  (Volume:38 ,  Issue: 2 )