Methodology for Online Sensor Recalibration

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, with the data used to monitor and verify proper operation of the reactor. 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. With expected longer refueling intervals in many advanced reactor/small modular reactor concepts, fewer opportunities are expected to be available for manual calibration checks and recalibration for many instrument channels.
 Presently, periodic sensor calibration checks are performed manually, with manual recalibration performed if the sensor is found to be out of calibration. 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. Given the large number of sensors (anywhere between 100–2400 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 between $0.5–1M per year per plant. The cost of calibration checks and recalibration are expected to be of greater significance to advanced reactors and small modular reactors, given the industry focus on reducing O&M costs for these reactor concepts as part of improving the economic viability of advanced nuclear power.
 Methods that can compensate for calibration drift by adjusting the calibration automatically and in real-time may be able to further reduce costs associated with recalibration. Such autocalibrations or online automated recalibrations reduce unavailability of instrumentation and improve online maintenance planning flexibility from both resource allocation and online risk evaluation perspectives.
 This paper describes an initial set of algorithms developed for the purpose of detecting drift and correcting for it through an online recalibration method. 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%.