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.
|Title of host publication||11th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies|
|Publisher||American Nuclear Society ANS|
|Number of pages||12|
|Publication status||Published - 2019|
|MoE publication type||A4 Article in a conference publication|
|Event||11th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC&HMIT - Orlando, United States|
Duration: 9 Feb 2019 → 14 Feb 2019
Conference number: 11
|Conference||11th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC&HMIT|
|Period||9/02/19 → 14/02/19|
- data reconciliation
- Monte-Carlo simulations
- thermal power uncertainty determination
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.