Thermal power estimation by fouling phenomena compensation using wavelet and principal component analysis

A small percentage of reactor thermal power can be overestimated because of fouling phenomena in a secondary feedwater flowmeter. This study proposes a signal processing technique for the compensation of a degraded flowmeter such a secondary feedwater flowmeter in nuclear power plants. The technique proposed is mainly focused on noise classification and step-by-step noise reduction. The noises focused are classified into the rapid distortion caus ed by environmental interference, the flow fluctuation according to plant s tate transition and the degradation by fouling phenomena qualitatively. The multi-step de-noising technique reduces each noise by three techniques ste p-by-step. The wavelet analysis as a low frequency pass filter to remove th e rapid distortion, the linear principal component analysis (PCA) to pl edi ct a steady-state value from the fluctuation, and the non-linear PCA implem ented as an autoassociative neural network (AANN) to predict an original va lue from the signal including fouling phenomena are developed. The main pur pose of this approach is to make an AANN concentrate on compensating the de gradation by fouling phenomena itself. For the demonstration the signals fr om a simulator and signal modeling were used so that the role and the perfo rmance of each noise removal step was represented. In addition a thermal po wer deviation estimator is proposed to recognize the degradation effect of each operating parameter for reactor thermal power calculation. (C) 1000 Pu blished by Elsevier Science S.A. All rights reserved.