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In nuclear power plants, the Core Thermal Power (CTP) is maintained at a constant level during plant operation, so it is important to accurately evaluate the CTP from measurement data to ensure plant safety and improve economic efficiency. The Data Validation & Reconciliation (DVR) which is one of statistical data analysis methods can greatly reduce measurement errors by correcting measurement data with constraint conditions. Hitachi is developing the plant performance monitoring system called “HAPPS” based on the DVR algorithm. The most important input of the DVR is the measurement uncertainty of each instrument. In this study, we developed the algorithm to estimate the measurement uncertainty from measurement data to increase the efficiency of the DVR. The developed algorithm was validated using a benchmark problem, and good agreements were obtained between the evaluation value and true value. We incorporated this algorithm into the HAPPS and performed the HAPPS analysis in condensate-feedwater line with simulated data. The HAPPS based on the developed algorithm showed a further 40% reduction in measurement uncertainty compared to the conventional DVR.

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Development of the Hitachi Advanced Plant Performance Diagnosis System for Nuclear Power Plant Performance Monitoring: Instrument Error Evaluation for Core Thermal Power Management

  • 会议论文

  • 发表日期：

    2026年

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  • Akinori Tamura,
  • Nobuyuki Shinohara,
  • Kenji Sasaki,
  • Norikazu Hamaura,
  • Seiji Nemoto

摘要

In nuclear power plants, the Core Thermal Power (CTP) is maintained at a constant level during plant operation, so it is important to accurately evaluate the CTP from measurement data to ensure plant safety and improve economic efficiency. The Data Validation & Reconciliation (DVR) which is one of statistical data analysis methods can greatly reduce measurement errors by correcting measurement data with constraint conditions. Hitachi is developing the plant performance monitoring system called “HAPPS” based on the DVR algorithm. The most important input of the DVR is the measurement uncertainty of each instrument. In this study, we developed the algorithm to estimate the measurement uncertainty from measurement data to increase the efficiency of the DVR. The developed algorithm was validated using a benchmark problem, and good agreements were obtained between the evaluation value and true value. We incorporated this algorithm into the HAPPS and performed the HAPPS analysis in condensate-feedwater line with simulated data. The HAPPS based on the developed algorithm showed a further 40% reduction in measurement uncertainty compared to the conventional DVR.