Nuclear Power Plant Instrumentation and Control Cable Prognostics Using Indenter Modulus Measurements

As the fleet of nuclear power plants (NPPs) approach their1 original qualified life (typically 40 years) and operators seek license extensions, regulators require assurance that they can continue to operate safely in the decades to come. Some of the most important, yet often overlooked components, are the cables that provide the signal paths for instrumentation and control (I&C) systems used to ensure safe and efficient operation of NPPs. In response to this, the authors explore the use of expanding indenter modulus (IM), an industry-accepted technique for cable condition monitoring, into a prognostic tool for predicting the remaining useful life (RUL) of I&C cables. Not only is this technique non-destructive, but it can be performed while NPP cables are in service, thus making it practical for adoption into existing cable condition monitoring programs. In this paper, the authors describe an accelerated aging cable test bed used to acquire several types of measurement parameters as cables age. Additionally, practical techniques are described in which simple IM measurements can be leveraged for condition monitoring and RUL estimation. Error analysis indicates the proposed method is superior to conventional RUL estimation techniques, such as simple trending and curve fitting. The authors demonstrate that using IM can potentially provide a non-destructive, in-situ estimation of RUL for I&C cables. As described in this paper, the IM data clearly shows trends as a function of cable age, and shows promising performance for RUL estimation especially compared with conventional techniques

Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants

This report describes the status of ongoing research towards the development of advanced algorithms for online calibration monitoring. The objective of this research is to develop the next generation of online monitoring technologies for sensor calibration interval extension and signal validation in operating and new reactors. These advances are expected to improve the safety and reliability of current and planned nuclear power systems as a result of higher accuracies and increased reliability of sensors used to monitor key parameters. The focus of this report is on documenting the outcomes of the first phase of R&D under this project, which addressed approaches to uncertainty quantification (UQ) in online monitoring that are data-driven, and can therefore adjust estimates of uncertainty as measurement conditions change. Such data-driven approaches to UQ are necessary to address changing plant conditions, for example, as nuclear power plants experience transients, or as next-generation small modular reactors (SMR) operate in load-following conditions

A Review of Sensor Calibration Monitoring for Calibration Interval Extension in Nuclear Power Plants

Currently in the United States, periodic sensor recalibration is required for all safety-related sensors, typically occurring at every refueling outage, and it has emerged as a critical path item for shortening outage duration in some plants. Online monitoring can be employed to identify those sensors that require calibration, allowing for calibration of only those sensors that need it. International application of calibration monitoring, such as at the Sizewell B plant in United Kingdom, has shown that sensors may operate for eight years, or longer, within calibration tolerances. This issue is expected to also be important as the United States looks to the next generation of reactor designs (such as small modular reactors and advanced concepts), given the anticipated longer refueling cycles, proposed advanced sensors, and digital instrumentation and control systems. The U.S. Nuclear Regulatory Commission (NRC) accepted the general concept of online monitoring for sensor calibration monitoring in 2000, but no U.S. plants have been granted the necessary license amendment to apply it. This report presents a state-of-the-art assessment of online calibration monitoring in the nuclear power industry, including sensors, calibration practice, and online monitoring algorithms. This assessment identifies key research needs and gaps that prohibit integration of the NRC-approved online calibration monitoring system in the U.S. nuclear industry. Several needs are identified, including the quantification of uncertainty in online calibration assessment; accurate determination of calibration acceptance criteria and quantification of the effect of acceptance criteria variability on system performance; and assessment of the feasibility of using virtual sensor estimates to replace identified faulty sensors in order to extend operation to the next convenient maintenance opportunity. Understanding the degradation of sensors and the impact of this degradation on signals is key to developing technical basis to support acceptance criteria and set point decisions, particularly for advanced sensors which do not yet have a cumulative history of operating performance

A Review of Sensor Calibration Monitoring for Calibration Interval Extension in Nuclear Power Plants

Currently in the United States, periodic sensor recalibration is required for all safety-related sensors, typically occurring at every refueling outage, and it has emerged as a critical path item for shortening outage duration in some plants. Online monitoring can be employed to identify those sensors that require calibration, allowing for calibration of only those sensors that need it. International application of calibration monitoring, such as at the Sizewell B plant in United Kingdom, has shown that sensors may operate for eight years, or longer, within calibration tolerances. This issue is expected to also be important as the United States looks to the next generation of reactor designs (such as small modular reactors and advanced concepts), given the anticipated longer refueling cycles, proposed advanced sensors, and digital instrumentation and control systems. The U.S. Nuclear Regulatory Commission (NRC) accepted the general concept of online monitoring for sensor calibration monitoring in 2000, but no U.S. plants have been granted the necessary license amendment to apply it. This report presents a state-of-the-art assessment of online calibration monitoring in the nuclear power industry, including sensors, calibration practice, and online monitoring algorithms. This assessment identifies key research needs and gaps that prohibit integration of the NRC-approved online calibration monitoring system in the U.S. nuclear industry. Several needs are identified, including the quantification of uncertainty in online calibration assessment; accurate determination of calibration acceptance criteria and quantification of the effect of acceptance criteria variability on system performance; and assessment of the feasibility of using virtual sensor estimates to replace identified faulty sensors in order to extend operation to the next convenient maintenance opportunity. Understanding the degradation of sensors and the impact of this degradation on signals is key to developing technical basis to support acceptance criteria and set point decisions, particularly for advanced sensors which do not yet have a cumulative history of operating performance

Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants

This report describes the status of ongoing research towards the development of advanced algorithms for online calibration monitoring. The objective of this research is to develop the next generation of online monitoring technologies for sensor calibration interval extension and signal validation in operating and new reactors. These advances are expected to improve the safety and reliability of current and planned nuclear power systems as a result of higher accuracies and increased reliability of sensors used to monitor key parameters. The focus of this report is on documenting the outcomes of the first phase of R&D under this project, which addressed approaches to uncertainty quantification (UQ) in online monitoring that are data-driven, and can therefore adjust estimates of uncertainty as measurement conditions change. Such data-driven approaches to UQ are necessary to address changing plant conditions, for example, as nuclear power plants experience transients, or as next-generation small modular reactors (SMR) operate in load-following conditions