How data validation and reconciliation improves nuclear power plant performance
December 17, 2021
Data validation and reconciliation is a software modeling method that uses station process measurements, fundamental equations, and statistical analysis to produce a set of corrected measurements. This analysis determines the most likely process values, which can be used to optimize performance. By Graig Pattison
The majority of US nuclear reactors have reached their initial licensed lifespan of 40 years and have renewed their licenses for an additional 20 years. Some stations are going ahead with more license renewals to operate for 80 years. But age-induced failures in instrumentation and control systems can reduce the reliability of plant systems and increase the potential for plant trips.
Data validation and reconciliation (DVR) can provide insight as instrumentation errors increase – often observable precursors of failure. This can help the utility to develop better and more cost effective mitigation strategies for aging instrumentation.
The purpose of an instrument is to provide the actual value of a measured parameter. Unfortunately, all instrumentation has a certain amount of error associated with it, so their measured values have an uncertainty or confidence interval. Sources of instrumentation errors can include:
- The intrinsic precision of the instrument itself.
- Improper installation of the instrument or an unsuitable location chosen for installation (for example, a flowmeter installed immediately after a bend in a pipe).
- The “drift” error that can occur between instrument calibrations, or if the instrument is never calibrated.
- The compensation that may be required to determine a desired resulting parameter depending on the measurement system. The errors associated with the compensation are then part of the final value of the desired parameter.
- Errors introduced due to the nature and precision of the acquisition system.
Many instrument locations do not have redundant measurements, so it is even more important that a measurement be as accurate as possible. Many secondary instruments are overlooked and not regularly maintained, whether due to parts, costs, labor, low priority over other work, etc. These problems can exacerbate the error and lead to instruments providing inaccurate results.
There are many systems available that can be used to help monitor a station while in operation, but a weakness of most in-line monitoring systems is that they depend on plant instrumentation to provide accurate measurements. continuously. Most stations also use thermodynamic modeling systems, but while these are suitable for certain scenarios, their weakness is that they often rely on very few input measurements to produce the output. Assuming that the plant’s instrumentation provides accurate continuous measurements, or relying on a small number of measurements, it may mean that there are errors in decisions, calculations, modifications, etc., with unintended consequences. The DVR helps to solve these problems.
What is Data Validation and Reconciliation (DVR)?
For a nuclear power plant, a DVR model will look at 200 to 300 measured variables and their associated uncertainties. This includes temperature, pressure, flow and power.
The DVR establishes the functional relationship between these measurements to produce a redundant measurement system and uses the mathematical calculations and methodologies described in German technical standard VDI-2048. These methodologies include the use of empirical covariance matrices and the principle of Gaussian correction.
Empirical covariance matrices relate errors in measurement uncertainties to fundamental equations, such as mass and energy balance and thermodynamic properties. With matrices and the Gaussian correction principle, the DVR eliminates systematic errors, minimizes random errors, and tailors measurements to measurement accuracies with the smallest amount of correction. The measurement corrections are a function of the uncertainty placed on the measurement and the redundancies associated with this measurement. The end result is a set of statistically analyzed most probable values (closest to the true value) for all of the station’s measurements (reconciled measurements). This process also calculates the most likely values for locations throughout the cycle where no instrumentation is present.
In addition to providing the most likely values, the DVR calculates the uncertainty associated with each reconciled measurement, the penalty associated with each reconciled measurement, and an overall quality score for the data set.
The calculated uncertainty is determined for each of the closely spaced measurements, providing a reduced uncertainty value and a more accurate reflection of the closely spaced values. The calculated penalty is based on the difference between the reconciled and measured values (error) from the original measurement uncertainty. As the error increases, the penalty for the measurement increases. Once the penalty exceeds the 95% confidence interval associated with this measurement, the label is flagged by the software and considered suspicious. The quality score validates all the reconciled results and is calculated on the basis of the ratio between the weighted sum of all the penalties and the value of the Chi-square test. The Chi-square test value is calculated based on the number of redundancies in the model and the statistical certainty of the test (95% confidence). The quality must remain below 1 to be considered valid and acceptable. If the quality exceeds 1, it is likely that there are significant errors associated with the model or with the measured values.
What are the benefits of data validation and reconciliation (DVR)?
There are many advantages to implementing DVR:
- Online validation of all values measured by the station that are included in the DVR model.
- Pseudo-measurements for places in the cycle where no instrument is installed. Using current station measurements and mass and energy balance equations, pressures, temperatures, flows, etc. can be viewed for any part of the cycle, not just where the instruments are installed. This provides additional visibility for other parts of the cycle and saves money by delivering measurements to locations without additional instrumentation.
- Operation as close as possible to specifications, eliminating unnecessary margins.
- Less preventive maintenance performed on instrumentation, as it can be deferred. The measurement penalty can be used to help identify instruments that are faulty, drifting, or need to be calibrated. The result is reduced maintenance and better management of the aging of the plant’s instrumentation and control systems.
- Early detection of instrumentation degradation. This helps stations make repairs or corrections before problems arise and avoids the consequences of inaccurate measurements.
- Decreases dependence on measurements from a single instrument when no physical redundancy exists.
- Increased raw power as the station can implement the DVR result correction factors for feedwater flow errors due to nozzle clogging.
- Recovering the uncertainty of the margin, which would allow the DVR to replace expensive flow meters. This is currently being evaluated in the United States.
- Identify plant issues that cause production loss, such as cycle isolation leaks.
- Provide a more accurate understanding of station operating conditions and parameters by reducing the uncertainty associated with station measurements.
These advantages have led many stations to implement the DVR in recent years. GSE TrueNorth has successfully contributed to the implementation of Belsim’s DVR software in over 30 nuclear units in the United States. These stations all use the DVR to provide better online monitoring of their station’s performance, improve problem identification, and help resolve lost generation issues.
Three of the stations implemented energy harvesting using the DVR to determine feedwater flow correction. The energy recovery effort was successful with a combined gain of 20 MWe. ??
Data validation and reconciliation (DVR) can provide insight when instrumentation errors increase
Figure 1: Mass flow measurements before DVR
Figure 2: Mass flow reconciliation results after DVR