Monte Carlo Simulations to Evaluate Error Propagation in Computation of Thermal Power

Emil Wingstedt, Olli Saarela

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

    Abstract

    Data reconciliation is a commonly used technique for correcting random errors in measurement data in the process industry. The technique uses models describing the mutual relationships of process variables related to available measurements. These models are based on knowledge of process physics. Measurement readings are adjusted so that especially mass and energy balances described by the model match. The technique has proven effective in reducing measurement uncertainties. The paper presents a Monte Carlo study of error propagation in data reconciliation of the turbine section of a VVER 440 nuclear power plant. Uncertainties in model parameters describing turbine dry efficiencies and the quality of steam exiting the steam generators are considered in addition to measurement noise. The impact of these factors on estimated reactor thermal power is evaluated, both individually and as joint impacts. For both the measurement signals and the plant parameters, the resulting effect on the uncertainty of thermal power is lower than the 2% uncertainty for reasonable levels of added noise. These results support the use of data reconciliation for reducing the uncertainty in thermal power.
    Original languageEnglish
    Title of host publication11th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies
    PublisherAmerican Nuclear Society ANS
    Pages721-732
    Number of pages12
    ISBN (Electronic)978-0-89448-758-3
    ISBN (Print)978-0-8944-8783-5
    Publication statusPublished - 2019
    MoE publication typeA4 Article in a conference publication
    Event11th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC&HMIT - Orlando, United States
    Duration: 9 Feb 201914 Feb 2019
    Conference number: 11

    Conference

    Conference11th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC&HMIT
    Abbreviated titleNPIC&HMIT
    CountryUnited States
    CityOrlando
    Period9/02/1914/02/19

    Fingerprint

    Turbines
    Random errors
    Steam generators
    Energy balance
    Nuclear power plants
    Hot Temperature
    Monte Carlo simulation
    Steam
    Physics
    Uncertainty
    Industry

    Keywords

    • data reconciliation
    • Monte-Carlo simulations
    • thermal power uncertainty determination

    Cite this

    Wingstedt, E., & Saarela, O. (2019). Monte Carlo Simulations to Evaluate Error Propagation in Computation of Thermal Power. In 11th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (pp. 721-732). American Nuclear Society ANS.
    Wingstedt, Emil ; Saarela, Olli. / Monte Carlo Simulations to Evaluate Error Propagation in Computation of Thermal Power. 11th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies. American Nuclear Society ANS, 2019. pp. 721-732
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    abstract = "Data reconciliation is a commonly used technique for correcting random errors in measurement data in the process industry. The technique uses models describing the mutual relationships of process variables related to available measurements. These models are based on knowledge of process physics. Measurement readings are adjusted so that especially mass and energy balances described by the model match. The technique has proven effective in reducing measurement uncertainties. The paper presents a Monte Carlo study of error propagation in data reconciliation of the turbine section of a VVER 440 nuclear power plant. Uncertainties in model parameters describing turbine dry efficiencies and the quality of steam exiting the steam generators are considered in addition to measurement noise. The impact of these factors on estimated reactor thermal power is evaluated, both individually and as joint impacts. For both the measurement signals and the plant parameters, the resulting effect on the uncertainty of thermal power is lower than the 2{\%} uncertainty for reasonable levels of added noise. These results support the use of data reconciliation for reducing the uncertainty in thermal power.",
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    Wingstedt, E & Saarela, O 2019, Monte Carlo Simulations to Evaluate Error Propagation in Computation of Thermal Power. in 11th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies. American Nuclear Society ANS, pp. 721-732, 11th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC&HMIT , Orlando, United States, 9/02/19.

    Monte Carlo Simulations to Evaluate Error Propagation in Computation of Thermal Power. / Wingstedt, Emil; Saarela, Olli.

    11th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies. American Nuclear Society ANS, 2019. p. 721-732.

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

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    AU - Saarela, Olli

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    N2 - Data reconciliation is a commonly used technique for correcting random errors in measurement data in the process industry. The technique uses models describing the mutual relationships of process variables related to available measurements. These models are based on knowledge of process physics. Measurement readings are adjusted so that especially mass and energy balances described by the model match. The technique has proven effective in reducing measurement uncertainties. The paper presents a Monte Carlo study of error propagation in data reconciliation of the turbine section of a VVER 440 nuclear power plant. Uncertainties in model parameters describing turbine dry efficiencies and the quality of steam exiting the steam generators are considered in addition to measurement noise. The impact of these factors on estimated reactor thermal power is evaluated, both individually and as joint impacts. For both the measurement signals and the plant parameters, the resulting effect on the uncertainty of thermal power is lower than the 2% uncertainty for reasonable levels of added noise. These results support the use of data reconciliation for reducing the uncertainty in thermal power.

    AB - Data reconciliation is a commonly used technique for correcting random errors in measurement data in the process industry. The technique uses models describing the mutual relationships of process variables related to available measurements. These models are based on knowledge of process physics. Measurement readings are adjusted so that especially mass and energy balances described by the model match. The technique has proven effective in reducing measurement uncertainties. The paper presents a Monte Carlo study of error propagation in data reconciliation of the turbine section of a VVER 440 nuclear power plant. Uncertainties in model parameters describing turbine dry efficiencies and the quality of steam exiting the steam generators are considered in addition to measurement noise. The impact of these factors on estimated reactor thermal power is evaluated, both individually and as joint impacts. For both the measurement signals and the plant parameters, the resulting effect on the uncertainty of thermal power is lower than the 2% uncertainty for reasonable levels of added noise. These results support the use of data reconciliation for reducing the uncertainty in thermal power.

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    Wingstedt E, Saarela O. Monte Carlo Simulations to Evaluate Error Propagation in Computation of Thermal Power. In 11th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies. American Nuclear Society ANS. 2019. p. 721-732