Localization and Size Estimation for Breaks in Nuclear Power Plants

Ting-Han Lin; Ching Chen; Shun-Chi Wu; Te-Chuan Wang; Yuh-Ming Ferng

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자료유형: 학술저널

저자정보: Ting-Han Lin (National Tsing Hua University) Ching Chen (National Tsing Hua University) Shun-Chi Wu (National Tsing Hua University) Te-Chuan Wang (Institute of Nuclear Energy Research, Atomic Energy Council) Yuh-Ming Ferng (National Tsing Hua University)

저널정보: 한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제54권 제1호

발행연도: 2022.1

수록면: 193 - 206 (14page)

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Several algorithms for?nuclear power plant?(NPP) break event detection, isolation,?localization, and size estimation are proposed. A break event can be promptly detected and isolated after its occurrence by simultaneously monitoring changes in the sensing readings and by employing an interquartile range-based isolation scheme. By considering the multi-sensor data block of a break to be rank-one, it can be located as the position whose lead field vector is most orthogonal to the?noise subspace?of that data block using the Multiple Signal Classification (MUSIC) algorithm. Owing to the flexibility of?deep neural networks?in selecting the best regression model for the available data, we can estimate the break size using multiple-sensor recordings of the break regardless of the sensor types. The efficacy of the proposed algorithms was evaluated using the data generated by Maanshan NPP simulator. The experimental results demonstrated that the MUSIC method could distinguish two near breaks. However, if the two breaks were close and of small sizes, the MUSIC method might wrongly locate them. The break sizes estimated by the proposed deep learning model were close to their actual values, but relative errors of more than 8% were seen while estimating small breaks’ sizes.

#Break Localization #Break Size Estimation #MUltiple SIgnal Classification (MUSIC) #Deep Learning

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