Abstract
A significant contributor to nuclear power plant operations and maintenance (O&M) costs is the periodic calibration check of sensors. Periodic calibration checks provide the necessary confidence that the measurements from these sensors are correct, and the data are used to monitor and verify proper reactor operation. The periodicity of calibrations in the nuclear industry can range from once in several weeks for some instrument channels to once every refueling outage (~18 months) for certain safety-significant pressure and level transmitters. Although studies have shown that most (over 90%) sensors are found to stay within calibration specifications over a calibration cycle (~18 months), labor must still be spent to verify that these sensors are within calibration. The longer refueling intervals in many advanced reactor/small modular reactor concepts will result in fewer opportunities for manual calibration checks and recalibration for many instrument channels if needed. Given the high number of sensors in a typical nuclear power plant, the ability to identify sensors that are failing/failed or drifting out of calibration and limit recalibration to those specific sensors has the potential to save $0.5–1M per year per plant. Reducing the number of calibration checks and recalibration of sensors outside of specifications limits can greatly reduce the O&M costs for advanced reactors and small modular reactors. This will directly impact the economic viability of advanced nuclear power. This paper describes an initial set of algorithms developed for the purpose of detecting and correcting for drift through an online recalibration method based on the relationship between the sensor output (current) and the physical quantity of interest (pressure). Initial results on laboratory-scale experimental data indicate the potential of these algorithms to detect calibration drift and update calibrations, with prediction and drift correction accuracy exceeding 95%.
Original language | English |
---|---|
Title of host publication | Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM |
Editors | Chetan Kulkarni, Abhinav Saxena |
Publisher | Prognostics and Health Management Society |
Edition | 1 |
ISBN (Electronic) | 9781936263370 |
DOIs | |
State | Published - Oct 28 2022 |
Event | 2022 Annual Conference of the Prognostics and Health Management Society, PHM 2022 - Nashville, United States Duration: Oct 31 2022 → Nov 4 2022 |
Publication series
Name | Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM |
---|---|
Number | 1 |
Volume | 14 |
ISSN (Print) | 2325-0178 |
Conference
Conference | 2022 Annual Conference of the Prognostics and Health Management Society, PHM 2022 |
---|---|
Country/Territory | United States |
City | Nashville |
Period | 10/31/22 → 11/4/22 |
Funding
This manuscript has been authored by UT-Battelle LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), US Department of Energy, under Award Number DE-AR0001290. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.