Stability Monitoring for BWR based on Singular Value Decomposition Method using Artificial Neural Network
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概要
- 論文の詳細を見る
A new method for evaluating the decay ratios in a boiling water reactor (BWR) using the singular value decomposition (SVD) method had been proposed. In this method, a signal component closely related to the BWR stability can be extracted from independent components of the neutron noise signal decomposed by the SVD method. However, real-time stability monitoring by the SVD method requires an efficient procedure for screening such compoments. For efficient screening, an artificial neural network (ANN) with three layers was adopted. The trained ANN was actually applied to decomposed components of local power range monitor (LPRM) signals that were measured in stability experiments conducted in the Ringhals-1 BWR. In each LPRM signal, multiple candidates were screened from the decomposed components. However, decay ratios could be estimated by introducing appropriate criterions for selecting the most suitable component among the candidates. The estimated decay ratios are almost identical to those evaluated by visual screening in a previous study. The selected components commonly have the largest singular value, the largest decay ratio and the least squared fitting error among the candidates. By virtue of excellent screening performance of the trained ANN, the real-time stability monitoring by the SVD method can be applied in practice.
- 社団法人 日本原子力学会の論文
- 2005-12-25
著者
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Tsuji Masashi
Department Of Nuclear Engineering Graduate School Of Engineering Hokkaido University
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SHIMAZU Yoichiro
Department of Nuclear Engineering, Graduate School of Engineering, Hokkaido University
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MICHISHITA Hiroshi
Makuhari System Laboratory, FUJITSU Ltd.
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Tsuji Masashi
Graduate School of Engineering, Hokkaido University
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Tsuji Masashi
Division Of Energy And Environmental System Graduate School Of Engineering Hokkaido University
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Tsuji Masashi
Graduate School Of Engineering Hokkaido University
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Tsuji Masashi
Department Of Electrical Engineering Graduate School Of Engineering Tokyo University Of Agriculture
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Shimazu Yoichiro
Department Of Energy And Environmental Systems Graduate School Of Engineering Hokkaido University
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Michishita Hiroshi
Makuhari System Laboratory Fujitsu Ltd.
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TSUJI Masashi
Hokkaido University
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