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Application of nonlinear principal component analysis technique to nuclear power plants
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2019 (English)In: International Conference on Nuclear Engineering, Proceedings, ICONE, ASME Press, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Traditionally, manual calibration of sensors is required and performed during each refueling outage. If the traditional time-directed calibration is replaced by an online monitoring technique, the maintenance cost will be significantly reduced since only the abnormal sensors identified in on-line monitoring need to be re-calibrated or replaced off-line. The Nonlinear Principal Component Analysis (NLPCA), such as Auto-Associative Neural Network (AANN) and Auto-Associative Kernel Principal Component Analysis (AAKPCA), can describe the nonlinear correlation between sensors such as power, temperature, pressure and flowrate. In this paper, AANN and AAKPCA model are tested by simulated redundant data and Tennessee-Eastman process data. The results show that both of them have a high ability of prediction and a low sensitivity. Therefore, they are can be used in on-line monitoring.

Place, publisher, year, edition, pages
ASME Press, 2019.
Keywords [en]
Auto-associative kernel principal component analysis, Auto-associative neural network, Feature extraction, Nonlinear correlation, Online monitoring
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Identifiers
URN: urn:nbn:se:kth:diva-258166Scopus ID: 2-s2.0-85071385804ISBN: 9784888982566 (print)OAI: oai:DiVA.org:kth-258166DiVA, id: diva2:1356812
Conference
27th International Conference on Nuclear Engineering: Nuclear Power Saves the World!, ICONE 2019; Tsukuba International Congress Center,Tsukuba, Ibaraki; Japan; 19 May 2019 through 24 May 2019
Note

QC 20191002

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved

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Ma, Weimin

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