Reduction of uncertainty of thermal performance calculations in nuclear power plants

Olli Saarela, Emil Wingstedt

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Abstract

    The OECD Halden Reactor Project (HRP) has taken an active role in facilitating implementation of technology advances and in particular application of condition monitoring techniques for operation support. TEMPO [1] is a system based on physical models for thermal performance monitoring and optimization developed at the HRP. The system aims at satisfying information needs associated with condition monitoring, on-line calibration monitoring of plant measurements, process fault detection and diagnosis. Data reconciliation [2] combines a first principles model and process measurement data to calculate the most likely process state. The use of data reconciliation requires an analytically redundant set of measurements, i.e. that information about measured entities can be deducted from other measurements through the model, e.g. from mass and heat balances. Uncertainties of the computed results depend on both the uncertainties of the measurement data and the inaccuracy and uncertainty of the data reconciliation model. The TEMPO system has been in daily use in the analysis of thermal performance of the secondary side of the Loviisa NPP turbine cycle for several years [3]. This paper presents an approach where distributions of measurement errors are estimated from observed time series data. Principal Component Analysis (PCA) is used to identify process variability modes observable in several measurements, which are then subtracted from observed data to estimate the distributions of measurement errors. Uncertainty estimates for the reconciled values are then computed using Monte Carlo simulation. The approach described above is applied to a turbine section of a nuclear power plant. The estimated distributions of measurement errors are utilized in both reactor power monitoring and sensor drift detection. The results indicate that the measurement uncertainty in the process examined is not too significant a source of uncertainty affecting the results computed with data reconciliation.
    Original languageEnglish
    Title of host publication9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies
    PublisherCurran Associates Inc.
    Pages2130-2140
    ISBN (Print)978-1-5108-0809-6
    Publication statusPublished - 2015
    MoE publication typeA4 Article in a conference publication
    Event9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC & HMIT  - Charlotte, United States
    Duration: 22 Feb 201526 Feb 2015

    Conference

    Conference9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC & HMIT 
    Abbreviated titleNPIC & HMIT
    CountryUnited States
    CityCharlotte
    Period22/02/1526/02/15

    Fingerprint

    Nuclear power plants
    Measurement errors
    Condition monitoring
    Monitoring
    Turbines
    Hot Temperature
    Uncertainty
    Fault detection
    Principal component analysis
    Failure analysis
    Time series
    Calibration
    Sensors

    Keywords

    • thermal performance
    • data reconciliation
    • uncertainty

    Cite this

    Saarela, O., & Wingstedt, E. (2015). Reduction of uncertainty of thermal performance calculations in nuclear power plants. In 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies (pp. 2130-2140). Curran Associates Inc..
    Saarela, Olli ; Wingstedt, Emil. / Reduction of uncertainty of thermal performance calculations in nuclear power plants. 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies. Curran Associates Inc., 2015. pp. 2130-2140
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    title = "Reduction of uncertainty of thermal performance calculations in nuclear power plants",
    abstract = "The OECD Halden Reactor Project (HRP) has taken an active role in facilitating implementation of technology advances and in particular application of condition monitoring techniques for operation support. TEMPO [1] is a system based on physical models for thermal performance monitoring and optimization developed at the HRP. The system aims at satisfying information needs associated with condition monitoring, on-line calibration monitoring of plant measurements, process fault detection and diagnosis. Data reconciliation [2] combines a first principles model and process measurement data to calculate the most likely process state. The use of data reconciliation requires an analytically redundant set of measurements, i.e. that information about measured entities can be deducted from other measurements through the model, e.g. from mass and heat balances. Uncertainties of the computed results depend on both the uncertainties of the measurement data and the inaccuracy and uncertainty of the data reconciliation model. The TEMPO system has been in daily use in the analysis of thermal performance of the secondary side of the Loviisa NPP turbine cycle for several years [3]. This paper presents an approach where distributions of measurement errors are estimated from observed time series data. Principal Component Analysis (PCA) is used to identify process variability modes observable in several measurements, which are then subtracted from observed data to estimate the distributions of measurement errors. Uncertainty estimates for the reconciled values are then computed using Monte Carlo simulation. The approach described above is applied to a turbine section of a nuclear power plant. The estimated distributions of measurement errors are utilized in both reactor power monitoring and sensor drift detection. The results indicate that the measurement uncertainty in the process examined is not too significant a source of uncertainty affecting the results computed with data reconciliation.",
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    Saarela, O & Wingstedt, E 2015, Reduction of uncertainty of thermal performance calculations in nuclear power plants. in 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies. Curran Associates Inc., pp. 2130-2140, 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC & HMIT , Charlotte, United States, 22/02/15.

    Reduction of uncertainty of thermal performance calculations in nuclear power plants. / Saarela, Olli; Wingstedt, Emil.

    9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies. Curran Associates Inc., 2015. p. 2130-2140.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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    T1 - Reduction of uncertainty of thermal performance calculations in nuclear power plants

    AU - Saarela, Olli

    AU - Wingstedt, Emil

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    N2 - The OECD Halden Reactor Project (HRP) has taken an active role in facilitating implementation of technology advances and in particular application of condition monitoring techniques for operation support. TEMPO [1] is a system based on physical models for thermal performance monitoring and optimization developed at the HRP. The system aims at satisfying information needs associated with condition monitoring, on-line calibration monitoring of plant measurements, process fault detection and diagnosis. Data reconciliation [2] combines a first principles model and process measurement data to calculate the most likely process state. The use of data reconciliation requires an analytically redundant set of measurements, i.e. that information about measured entities can be deducted from other measurements through the model, e.g. from mass and heat balances. Uncertainties of the computed results depend on both the uncertainties of the measurement data and the inaccuracy and uncertainty of the data reconciliation model. The TEMPO system has been in daily use in the analysis of thermal performance of the secondary side of the Loviisa NPP turbine cycle for several years [3]. This paper presents an approach where distributions of measurement errors are estimated from observed time series data. Principal Component Analysis (PCA) is used to identify process variability modes observable in several measurements, which are then subtracted from observed data to estimate the distributions of measurement errors. Uncertainty estimates for the reconciled values are then computed using Monte Carlo simulation. The approach described above is applied to a turbine section of a nuclear power plant. The estimated distributions of measurement errors are utilized in both reactor power monitoring and sensor drift detection. The results indicate that the measurement uncertainty in the process examined is not too significant a source of uncertainty affecting the results computed with data reconciliation.

    AB - The OECD Halden Reactor Project (HRP) has taken an active role in facilitating implementation of technology advances and in particular application of condition monitoring techniques for operation support. TEMPO [1] is a system based on physical models for thermal performance monitoring and optimization developed at the HRP. The system aims at satisfying information needs associated with condition monitoring, on-line calibration monitoring of plant measurements, process fault detection and diagnosis. Data reconciliation [2] combines a first principles model and process measurement data to calculate the most likely process state. The use of data reconciliation requires an analytically redundant set of measurements, i.e. that information about measured entities can be deducted from other measurements through the model, e.g. from mass and heat balances. Uncertainties of the computed results depend on both the uncertainties of the measurement data and the inaccuracy and uncertainty of the data reconciliation model. The TEMPO system has been in daily use in the analysis of thermal performance of the secondary side of the Loviisa NPP turbine cycle for several years [3]. This paper presents an approach where distributions of measurement errors are estimated from observed time series data. Principal Component Analysis (PCA) is used to identify process variability modes observable in several measurements, which are then subtracted from observed data to estimate the distributions of measurement errors. Uncertainty estimates for the reconciled values are then computed using Monte Carlo simulation. The approach described above is applied to a turbine section of a nuclear power plant. The estimated distributions of measurement errors are utilized in both reactor power monitoring and sensor drift detection. The results indicate that the measurement uncertainty in the process examined is not too significant a source of uncertainty affecting the results computed with data reconciliation.

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    KW - data reconciliation

    KW - uncertainty

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    EP - 2140

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    PB - Curran Associates Inc.

    ER -

    Saarela O, Wingstedt E. Reduction of uncertainty of thermal performance calculations in nuclear power plants. In 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies. Curran Associates Inc. 2015. p. 2130-2140