Instrument uncertainty evaluation system, and instrument uncertainty evaluation method

Abstract

To provide an instrument uncertainty evaluation system capable of quantitatively evaluating the uncertainty of instruments installed in a plant.SOLUTION: An instrument uncertainty evaluation system 1 includes multiple flow meters for measuring the feedwater flow rate of a plant 3. The instrument uncertainty evaluation system includes: a relative bias component calculation unit 13 that calculates a relative bias component from the time average value of the measurement value by the instrument in a measurement system in which at least one of the flow meters has a calibration record; a water supply fluctuation removal unit 15 that removes the physical time fluctuation component from the measurement value obtained by correcting the time delay between the instruments of the measurement value excluding the relative bias component calculated by the relative bias component calculation unit 13, and calculates the relative random component; and a plant performance evaluation unit 21 that evaluates plant performance measured by the measurement system by using the sum of the bias component calculated from the relative bias component and the random component calculated from the relative random component or using a bias component.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an instrument uncertainty evaluation system and an instrument uncertainty evaluation method for evaluating the uncertainty of instruments that measure the feedwater flow rate, pressure, temperature, etc. of a plant or the like. [Background technology]

[0002] In thermal and nuclear power plants, feedwater supplied to boilers and reactors is heated to generate steam, which drives steam turbines and generates electricity. Therefore, in power plants, it is important to accurately understand the feedwater flow rate in order to control the plant's thermal output. Nuclear plants, in particular, are required to operate within the permitted thermal output range, so in addition to the measured value of the feedwater flow rate, it is also necessary to manage the uncertainty of the feedwater flow meter.

[0003] On the other hand, feedwater flow meters in power plants are used in high-temperature ranges of 200°C or higher. As a result, scale can build up on the surface of the flow nozzle of the feedwater flow meter, which can cause feedwater drift, an increase in the apparent flow rate value during plant operation. Furthermore, feedwater flow meters in power plants are used under high-flow, high-temperature conditions. Therefore, tests to confirm accuracy of feedwater flow meters in power plants under the same flow rate and temperature conditions as the actual equipment are often omitted before installation. In such cases, the flow coefficient, which affects uncertainty, is extrapolated from the value obtained in a low-flow test.

[0004] As mentioned above, the uncertainty of feedwater flow meters is sometimes managed using conservative values ​​that take into account the effects of feedwater drift and deviations due to extrapolation. However, feedwater drift does not always occur, and deviations due to extrapolation also vary depending on the plant. As a result, the uncertainty of feedwater flow meters may be overly conservative.

[0005] In recent years, data reconciliation technology has been proposed to monitor plant performance by finding a plausible solution that satisfies the heat balance of the plant from information on previously designed instruments. This technology corrects the measurement values ​​by using the uncertainty of each instrument as a weight, so it is important to accurately understand the uncertainty.

[0006] For these reasons, it is important to accurately evaluate the uncertainty of instruments, including feedwater flow meters, from plant operation data in terms of thermal power management and plant performance monitoring.

[0007] Patent Document 1 discloses a plant instrumentation control device that determines the most likely estimated true value by comprehensively evaluating data related to the estimation accuracy of the true value estimation means, and calculates the estimated drift amount of each instrument. The true value estimation means uses a linear model, a neural network, data reconciliation, etc., and previously acquired plant operation data is used to adjust and learn the estimation model. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Japanese Patent Application Laid-Open No. 2005-338049 Summary of the Invention [Problem to be solved by the invention]

[0009] In Patent Document 1, the bias component (amount of drift), which is one element of uncertainty, can be predicted from estimated values ​​of a linear model, a neural network, and data reconciliation, but the uncertainty inherent in the estimated values ​​of the linear model, neural network, and data reconciliation themselves cannot be evaluated, and therefore the bias component of each instrument cannot be shown as a quantitative value.Furthermore, in a boiling water reactor, the feedwater flow rate contains temporal fluctuations (hereinafter referred to as feedwater fluctuations), which are mixed with the random component, which is the remaining element of uncertainty, and therefore the method in Patent Document 1 has the problem of not being able to quantitatively evaluate the random component of the uncertainty of the feedwater flow meter.

[0010] Therefore, an object of the present invention is to provide an instrument uncertainty evaluation system and an instrument uncertainty evaluation method that enable quantitative evaluation of the uncertainty of instruments installed in a plant. [Means for solving the problem]

[0011] In order to solve the above-mentioned problems, the meter uncertainty evaluation system of the present invention is a measurement system that has a plurality of meters for the same measurement object, and at least one of the meters has a calibration record. At a relative bias component calculation unit that calculates a relative bias component from a time average value of the measurement value by the meter; a time fluctuation component removal unit that removes physical time fluctuation components from the measurement values ​​obtained by correcting the time delay between the instruments of the measurement values ​​excluding the relative bias component calculated by the relative bias component calculation unit, and calculates a relative random component; and a plant performance evaluation unit that evaluates the performance of the plant measured by the measurement system using the sum of a bias component calculated from the relative bias component and a random component calculated from the relative random component, or using the bias component; The present invention is characterized by having the following.

[0012] The method for evaluating meter uncertainty of the present invention is a measurement system that includes a plurality of meters for the same measurement object, and at least one of the meters has a calibration record. At , The relative bias component calculation unit calculating a relative bias component from a time average value of the measurement value by the meter; a time fluctuation component removing unit removing physical time fluctuation components from measurement values ​​obtained by correcting the time delay between the instruments of the measurement values ​​from which the relative bias components have been removed, and calculating a relative random component; a step of calculating a bias component from the relative bias components; a step of calculating a random component from the relative random components; and a plant performance evaluating unit evaluating the performance of the plant using the sum of the bias component and the random component or using the bias component; of Yes It is characterized by: Other means will be described in the detailed description of the invention. [Effects of the Invention]

[0013] According to the present invention, it is possible to quantitatively evaluate the uncertainty of instruments installed in a plant. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a configuration diagram showing the configuration of an instrument uncertainty evaluation system according to a first embodiment. [Figure 2] FIG. 1 is a configuration diagram showing the configuration of a nuclear power plant. [Figure 3] FIG. 10 is a flowchart showing a flow for determining the time average number of times of the relative bias component calculation unit. [Figure 4] 10 is a graph showing an operation of determining the time average number of times of the relative bias component calculation unit. [Figure 5] 10 is a graph showing an example of input data to a relative bias component calculation unit. [Figure 6] 10 is a graph showing an example of output data from a relative bias component calculation unit. [Figure 7] FIG. 10 is a diagram illustrating an example of output data from a time delay compensation unit. [Figure 8] FIG. 10 is a diagram showing an example of output data from a water supply fluctuation removal unit. [Figure 9] FIG. 10 is a diagram illustrating an example of output data from a normality determining unit. [Figure 10] 10 is a graph showing an example of output data from a normality determining unit. [Figure 11] FIG. 10 is a configuration diagram showing the configuration of an instrument uncertainty evaluation system according to a second embodiment. [Figure 12] FIG. 10 is a configuration diagram showing the configuration of an instrument uncertainty evaluation system according to a third embodiment. [Figure 13] FIG. 10 is a configuration diagram showing the configuration of an instrument uncertainty evaluation system according to a fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0015] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings and equations. First Embodiment In the first embodiment, a condensate flow meter whose uncertainty has been quantitatively evaluated by actual flow verification during a periodic inspection is used as a reference instrument, and a means for quantitatively evaluating the uncertainty of each meter related to the feedwater flow rate is provided by evaluating the deviation from that reference instrument. In addition, the second to fourth embodiments will also describe a turbine performance monitoring means using the uncertainty of each meter obtained by this means, a plant performance monitoring means by data reconciliation, and a means for optimizing meter calibration by meter drift management.

[0016] FIG. 1 is a configuration diagram showing the configuration of a meter uncertainty evaluation system 1 according to the first embodiment. The meter uncertainty evaluation system 1 acquires measurement values ​​from a plant 3. The meter uncertainty evaluation system 1 includes a measurement means 12, a relative bias component calculation unit 13, a time delay compensation unit 14, a water supply fluctuation elimination unit 15, a relative random component calculation unit 16, a normality determination unit 17, a first output means 181, and a second output means 182.

[0017] The measuring means 12 acquires measured values ​​from a plurality of flow meters (meters) that measure the feedwater flow rate of the plant 3. The relative bias component calculation unit 13 calculates the relative bias component from the time average value of the measurement values ​​of the instruments in the measurement system in which at least one of the instruments has a calibration record.

[0018] The time delay compensation unit 14 calculates the time delay between the meters for each measurement value. The water supply fluctuation removal unit 15 functions as a time fluctuation component removal unit that removes the physical time fluctuation component between the meters of each measurement value, thereby making it possible to separate water supply fluctuation from random errors.

[0019] The relative random component calculation unit 16 calculates the relative random component from the component from which the physical time fluctuation has been removed. This relative random component is obtained by removing the relative bias component and the time fluctuation component from the measured value, and is expected to have normality when the instrument is normal.

[0020] The normality determination unit 17 determines whether or not the output data from the water supply fluctuation elimination unit 15 is normal. If the data is normal, the normality determination unit 17 causes the first output means 181 to quantitatively output the uncertainty of each meter, and if the data is not normal, causes the second output means 182 to quantitatively output the uncertainty of each meter. The first output means 181 and the second output means 182 are multiple output means depending on whether or not the relative random component of the measurement value is normal. This enables the meter uncertainty evaluation system 1 to quantitatively evaluate the uncertainty of the meter.

[0021] FIG. 2 is a configuration diagram showing the configuration of the plant 3. The plant 3 includes a condenser 30, a pump 31, a feedwater pump 39, a condensate filtration and demineralization unit 33, an air ejector 35, a condenser 36, a low-pressure feedwater heater 37, a high-pressure feedwater heater 41, a reactor pressure vessel 43, a high-pressure turbine 46, a moisture separator 47, and a low-pressure turbine 48. In the figure, the high-pressure turbine 46 is abbreviated as "high-pressure TB."

[0022] Condensers 30 and 36 condense the steam. Pump 31 and feedwater pump 39 send water to the next component. Condensate filtering and demineralization unit 33 filters and demineralizes the condensed water. Air ejector 35 extracts air mixed in the water. Low-pressure feedwater heater 37 is connected to low-pressure turbine 48 to heat water. High-pressure feedwater heater 41 is connected to high-pressure turbine 46 to heat water. Reactor pressure vessel 43 houses a core loaded with nuclear fuel, and obtains steam when light water is heated and boiled in the core. The steam is led through steam pipes to high-pressure turbine 46, moisture separator 47, and low-pressure turbine 48.

[0023] The dry steam that enters the low-pressure turbine 48 from the main steam pipe drives the low-pressure turbine 48 and is then discharged from the low-pressure turbine 48. The discharged steam is condensed into water in the condenser 30 installed below the low-pressure turbine 48, and this water is then supplied to the reactor pressure vessel 43 again.

[0024] This water is returned to the reactor pressure vessel 43 through the equipment described below. The water discharged from the condenser 30 is pressurized by a pump 31, then passes through a condensate filtration and demineralization unit 33 where it is purified to a quality sufficient for use as reactor feedwater. The purified water passes through an air ejector 35 and a condenser 36, is heated in a low-pressure feedwater heater 37, is pressurized by a feedwater pump 39, and is then heated in a high-pressure feedwater heater 41. The heated water is sent to the reactor pressure vessel 43. The water heated in the reactor pressure vessel 43 is rotated through a high-pressure turbine 46, has unnecessary water removed by a moisture separator 47, and is then led to a low-pressure turbine 48.

[0025] The plant 3 further includes a condensate flow meter 32, flow meters 34 and 38, a feedwater flow meter 42, a flow meter 45, and a water level meter 44 as instruments for measuring the feedwater flow rate.

[0026] Condensate flow meter 32 measures the flow rate of water pumped out by pump 31. Flow meter 34 measures the flow rate of water flowing out from condensate filtration and demineralization unit 33. Flow meter 38 measures the inlet flow rate of feedwater pump 39. Feedwater flow meter 42 measures the flow rate of water fed to reactor pressure vessel 43. Flow meter 45 measures the flow rate of steam sent from reactor pressure vessel 43 to high-pressure turbine 46. And water level gauge 44 measures the water level in reactor pressure vessel 43.

[0027] The meter uncertainty evaluation system 1 first acquires actual measurement values ​​from each meter in the plant 3 via the measurement means 12 . The actual measurement value Xi(t) obtained by the i-th instrument is expressed by the following equation (1) using time t, true value Zi(t), random component of uncertainty Ei(t), bias component of uncertainty Bi, and time delay τi between instruments.

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[0028] When the same object is measured using multiple instruments, the true value will be the same, and can be expressed by the following equation (2).

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[0029] In this embodiment, the uncertainty (Ei+Bi) of each instrument is quantitatively evaluated using equation (2). In this embodiment, the instrument with i=0 is flow meter 34, which is the reference instrument that measures the condensate flow rate. The instrument with i=1 is flow meter 38 that measures the inlet flow rate of feedwater pump 39. The instrument with i=2 is feedwater flow meter 42 that measures the feedwater flow rate. The instrument with i=3 is condensate flow meter 32 that measures the inlet flow rate of condensate filtration demineralization device 33.

[0030] The relative bias component calculation unit 13 determines the tolerance of the residual term ε resulting from finitely discontinuing the time average and the number of time averages n that satisfies the tolerance, and calculates the time average value for each instrument. The tolerance of ε may be set to, for example, 1 / 100 of the uncertainty bias component of the reference instrument.

[0031] The procedure for determining the time average number n will be described with reference to FIGS. The relative bias component calculation unit 13 calculates a reference average value by time-averaging the measurement values ​​of all the instruments over a sufficiently long period (e.g., one hour) (step S10), and calculates the time average value of the target instrument (step S11). The relative bias component calculation unit 13 graphs the deviation from the reference average value against the time average number n (step S12). This graph is shown in Figure 4.

[0032] The vertical axis of Figure 4 is the deviation from the reference average value. The horizontal axis of Figure 4 is the number of time averages n. The solid line in the graph is the time average value of flow meter 34. The thin dashed line in the graph is the time average value of flow meter 38. The coarse dashed line in the graph is the time average value of feedwater flow meter 42. Here, it is shown that the time average values ​​after n times for all meters are below the allowable value of the residual term ε.

[0033] The relative bias component calculation unit 13 adopts n, the stage at which the deviations in all the instruments fall below the allowable value of ε, as the time-average frequency in subsequent evaluations (step S13), and then ends the processing of FIG. The relative bias component calculation unit 13 only needs to perform this procedure once during a driving cycle. The average value of Xi is obtained by averaging the actual measurement values ​​obtained from each instrument n times. The average value of Xi is derived from equation (2) as shown in the following equation (3).

[0034]

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[0035] Transforming equation (3), the relative bias component ΔBi from the bias component B0 of the uncertainty of the reference instrument is obtained. (=Bi-B0) is calculated. The formula for calculating ΔBi is as follows:

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[0036] The relative bias component calculation unit 13 calculates the actual measurement value X'i excluding the relative bias component ΔBi by subtracting the formula (4) from the formula (2). The above is the function of the relative bias component calculation unit 13.

[0037] 5 is a graph showing an example of input data to the relative bias component calculation unit 13. The vertical axis of the graph represents the actual measured value Xi of the feedwater flow rate, and the horizontal axis represents time. X0 is the actual measured value of flow meter 34, which is the reference instrument. X1 is the actual measured value of flow meter 38. X2 is the actual measured value of feedwater flow meter 42. X3 is the actual measured value of condensate flow meter 32.

[0038] Figure 6 is a graph showing an example of output data from the relative bias component calculation unit 13. The vertical axis of the graph represents the feedwater flow rate X'i with the relative bias component removed, and the horizontal axis represents time. X'0 is the value obtained by removing the relative bias component ΔBi from the actual measurement value of flow meter 34, which is the reference instrument. X'1 is the value obtained by removing the relative bias component ΔBi from the actual measurement value of flow meter 38. X'2 is the value obtained by removing the relative bias component ΔBi from the actual measurement value of feedwater flow meter 42. X'3 is the value obtained by removing the relative bias component ΔBi from the actual measurement value of condensate flow meter 32. Compared to the plots in the graph of Figure 5, the difference in the measurement values ​​of each instrument is smaller in the graph of Figure 6.

[0039] Returning to Figure 1, we will continue the explanation. The time delay compensation unit 14 uses the time at the reference instrument as a reference and corrects the time delay τi at each instrument from that time. The main cause of the time delay is the time it takes for pressure waves to propagate through the piping between the instruments. The time delay compensation unit 14 evaluates the time delay τi from the piping length Li between the instruments, the average flow velocity Vi between the instruments, and the average sound speed Ci between the instruments using the following formula.

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[0040] The pipe length Li can be determined from the design drawings. The average flow velocity Vi can be determined by dividing the time-averaged value of the flow rate by the cross-sectional area of ​​the pipe. The average sound speed Ci can be determined from the average pressure and average temperature of the fluid in the pipe via a steam table. The time delay compensation unit 14 corrects the measured value, excluding the relative bias component, using the time delay τi for each instrument calculated using equation (5). To evaluate the time delay more accurately, numerical calculation results based on computational fluid dynamics (CFD) can also be used.

[0041] Figure 7 is a diagram showing an example of output data from the time delay compensation unit 14. The vertical axis of the graph represents the feedwater flow rate X'i compensated for the relative bias component and time delay, and the horizontal axis represents time. X'0 is the value obtained by compensating for the relative bias component and time delay from the actual measurement value of flow meter 34, which is the reference instrument. X'1 is the value obtained by compensating for the relative bias component and time delay from the actual measurement value of flow meter 38. X'2 is the value obtained by compensating for the relative bias component and time delay from the actual measurement value of feedwater flow meter 42. X'3 is the value obtained by compensating for the relative bias component and time delay from the actual measurement value of condensate flow meter 32. Compared to the plots in the graph of Figure 6, the phase difference between the values ​​of each instrument is smaller in the graph of Figure 7.

[0042] Returning to Figure 1, the explanation continues. The water supply fluctuation elimination unit 15 identifies and eliminates the water supply fluctuation component from the actual measurement value from which the relative bias component and time lag have been removed. When the relative bias component ΔBi is removed and the time lag τi is compensated for in equation (2), the water supply flow rate X'i (i = 0 to 3) is calculated as follows:

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[0043] Because water supply fluctuations are a real phenomenon, they are measured by each meter as the time-varying component of the true value Z(t). By subtracting equation (6) from equations (7), (8), and (9), respectively, the time-varying component of the value with water supply fluctuations and bias components removed from the actual measured flow rate value can be obtained.

[0044] Returning to Figure 1, the explanation will continue. The relative random component calculation unit 16 calculates the relative random component using the output data of the water supply fluctuation elimination unit 15. If the relative value from the uncertainty random component E0 of the reference instrument is defined as the relative random component ΔEi (=Ei - E0), the output data of the water supply fluctuation elimination unit 15 is expressed by the following equations (10) to (12).

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[0045] Figure 8 is a diagram showing an example of output data from the feedwater fluctuation elimination unit 15. The vertical axis of Figure 8 represents the time fluctuation component of the actual measurement value excluding feedwater fluctuation, and the horizontal axis represents time. The fine dashed line represents the relative random component ΔE1 of the flow meter 38 relative to the reference meter. The coarse dashed line represents the relative random component ΔE2 of the feedwater flow meter 42 relative to the reference meter. The solid line represents the relative random component ΔE3 of the condensate flow meter 32 relative to the reference meter.

[0046] When the relative random component ΔEi, which is the output data of the water supply fluctuation elimination unit 15, follows a normal distribution, the standard deviation σ of the relative random error ΔEi and the standard deviation σ of each instrument Ei The following statistical relationship holds:

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[0047] The relative random component calculation unit 16 calculates the variance of the relative random components ΔE1, ΔE2, and ΔE3, which are the output data of the water supply fluctuation elimination unit 15, and calculates the square root of the variance. As a result, the relative random component calculation unit 16 calculates the standard deviation σ of the relative random components ΔEi for each instrument. ΔEi Output.

[0048] FIG. 9 is a diagram showing an example of output data of the normality determination unit 17. This data includes an instrument number column, a skewness column, a kurtosis column, and a normality column, and each column stores the determination result of the output data of each instrument. The instrument number column stores a number for identifying the instrument. The skewness column stores an index indicating how asymmetrically skewed the distribution of the measurement data of this instrument is. The kurtosis column stores an index indicating how peaked the distribution of the measurement data of this instrument is compared to a normal distribution. The normality column stores a determination result indicating whether the distribution of the measurement data of this instrument has a predetermined normality. Note that if the distribution has normality, the determination result is "OK," and if the distribution does not have normality, the determination result is "NG."

[0049] The normality determination unit 17 determines whether or not the output data from the water supply fluctuation removal unit 15 is normal, and determines whether or not to use the output data from the relative random component calculation unit 16. The normality determination unit 17 outputs the frequency distribution of the relative random components ΔE1, ΔE2, and ΔE3, which are the input data. The normality determination unit 17 then calculates the skewness and kurtosis of each frequency distribution to quantitatively determine whether or not the data is normal. The ranges for determining whether or not the data is normal are, for example, a skewness of 0.0 to 0.5 and a kurtosis of 2.5 to 3.5, but are not limited to these. It is also possible to provide a means for the user to qualitatively confirm normality by displaying the frequency distribution of the relative random components ΔE1, ΔE2, and ΔE3 so that it can be compared with a fitting curve of a normal distribution.

[0050] FIG. 10 is a graph showing an example of output data from the normality determining unit 17. The vertical axis of this graph shows frequency, and the horizontal axis shows the amount of time variation. The solid line is a fitting curve for the normal distribution.

[0051] When the normality determination unit 17 determines that the actual measurement value from which water supply fluctuations have been removed is normal, the first output means 181 quantitatively calculates and outputs the uncertainty of each instrument. In the relative bias component ΔBi (=Bi-B0) output by the relative bias component calculation unit 13, the bias component B0 of the reference instrument is known from the instrument calibration record during periodic inspection, so the bias component Bi of each instrument is calculated using the following equation (16).

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[0052] From the results of the normality determination unit 17, since the formulas (13) to (15) hold for each instrument, the standard deviation σ of the relative random component output from the relative random component calculation unit 16 is ΔEi The random component of each instrument is quantitatively evaluated using the random component σ of the reference instrument. E0 Since is known from the meter calibration records during periodic inspections, the random component of each meter is calculated using the following equation (17).

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[0053] From the above procedure, the uncertainty of each instrument (Bi + σ Ei ) can be calculated, making it possible to quantitatively grasp the uncertainty of the feedwater flow rate. By inputting these values ​​into a process computer and displaying them, plant operation with optimized thermal output management becomes possible, eliminating excessive conservatism and enabling plant operation with improved power generation within the permitted thermal output range.

[0054] If the normality determination unit 17 determines that the actual measurement value from which water supply fluctuations have been removed is not normal, the second output means 182 quantitatively calculates and outputs the uncertainty of each instrument. In the relative bias component ΔBi (=Bi-B0) output by the relative bias component calculation unit 13, the bias component B0 of the reference instrument is known from the instrument calibration record during periodic inspection. Therefore, the second output means 182 calculates the bias component Bi of each instrument using equation (16). Since the results of the normality determination unit 17 show that equations (13) to (16) do not hold for each instrument, the second output means 182 calculates the standard deviation σ of the random component of each instrument. Ei is replaced with the JIS value or the required accuracy value stated in the instrument specification sheet at the time of delivery to the plant.

[0055] From the above procedure, the uncertainty of each instrument (Bi + σ Ei ) can be calculated, making it possible to quantitatively grasp the uncertainty of the feedwater flow rate. By inputting these values ​​into a process computer and displaying them, plant operation with optimized thermal output management becomes possible, eliminating excessive conservatism and enabling plant operation with improved power generation within the permitted thermal output range.

[0056] The meter uncertainty evaluation system 1 of this embodiment can quantitatively evaluate the uncertainty of feedwater flow meters, which are not subjected to actual flow calibration and are conservatively evaluated taking into account feedwater drift and extrapolation deviation of the flow coefficient, from plant operation data. This makes it possible to optimize the thermal power management of the plant and increase the amount of power generation within the permitted thermal power range.

[0057] Furthermore, highly accurate turbine performance monitoring and plant performance monitoring are possible by using the uncertainty of each instrument based on the operation data obtained by the instrument uncertainty evaluation system 1. From the uncertainty trend of each instrument, it becomes possible to optimize the calibration timing of each instrument and reduce the amount of instrument calibration required.

[0058] Second Embodiment In the second embodiment, a turbine performance monitoring means using the uncertainties of the instruments obtained in the first embodiment will be described.

[0059] 11 is a configuration diagram showing the configuration of a meter uncertainty evaluation system 1A according to the second embodiment. The meter uncertainty evaluation system 1A according to the second embodiment has the same basic configuration as that of the first embodiment, but is configured with a water level fluctuation value correction unit 19 added. The water level fluctuation value correcting unit 19 has a function of correcting the water level fluctuation of the plant 3.

[0060] As shown in Figure 2, a nuclear power plant heats feedwater supplied from the feedwater pipe in the reactor core, generating steam that drives a high-pressure turbine 46 and a low-pressure turbine 48 to generate electricity. In addition to managing the thermal output of the reactor, monitoring the performance of the steam turbine is also important for improving the thermal efficiency of the plant. Monitoring the performance of the steam turbine requires an accurate understanding of the flow rate of the incoming steam. However, because the water level forms within the reactor and fluctuates over time, the instantaneous value of the feedwater flow rate at the reactor inlet and the instantaneous value of the main steam flow rate at the reactor outlet do not match.

[0061] The second embodiment was devised in consideration of the above-mentioned problems. The instrument uncertainty evaluation system 1A of the second embodiment can quantitatively evaluate the uncertainty of the main steam flow rate required for turbine performance monitoring, in addition to the feedwater flow rate, from operational data, enabling efficient plant operation.

[0062] The operation of the meter uncertainty evaluation system 1A of the second embodiment will be described below. First, the measurement means 12 acquires actual measurement values, including the main steam flow rate, from each meter in the plant 3. In this embodiment, the meter with i=0 is the flow meter 34, which is the reference meter. The meter with i=1 is the flow meter 45, which is the main steam flow meter. The meter with i=2 is the water level meter 44, which measures the reactor water level. Using the same variables as in the first embodiment, the actual measurement values ​​acquired by each meter are expressed in equations (18) to (20).

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[0063] Here, Y is the reactor water level, ΔY is the amount of change in the reactor water level over time, and A is the surface area of ​​the reactor water surface.

[0064] As in the first embodiment, the relative bias component calculation unit 13 determines the allowable value of the residual term ε and the time average number n that satisfies the allowable value, and calculates the time average value of each instrument. ΔY is the amount of change in the reactor water level over time, and since the reactor water level is controlled to be constant, the time average value of ΔY is 0. Therefore, the feedwater flow rate X0 is calculated by the following equation (21).

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[0065] The water supply flow rate X1 is calculated by the following formula (22).

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[0066] The relative bias component ΔB1 (=B1-B0) can be obtained by subtracting the equation (21) from the equation (22). Then, the actual measurement value X'1 excluding the relative bias component ΔB1 can be calculated by subtracting the relative bias component ΔB1 from the equation (19), and this can be used as the output data of the relative bias component calculation unit 13.

[0067] The time delay compensator 14 uses the time of the reference instrument as a reference and corrects the time delay τi of each instrument from that time. The correction procedure is the same as in the first embodiment, so a description thereof will be omitted.

[0068] The water level fluctuation value correction unit 19 corrects the deviation of the feedwater flow rate and the main steam flow rate due to the water level fluctuation from the actual measurement data of the reactor water level. The time change amount ΔY of the reactor water level is evaluated by the following equation (23) using the actual measurement data of the reactor water level of equation (20).

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[0069] Here, Δt is the sampling time. The bias component B2 of X2 does not change over time, so it becomes 0 in the process of deriving equation (23). The random component E2 of X2 does not become 0 in the process of deriving equation (23), but since the reactor water level is usually measured by multiple instruments, it is possible to make it nearly 0 by taking the average of each measurement. The water level fluctuation value correction unit 19 outputs the amount of change in water level over time ΔY' calculated by equation (23).

[0070] The water supply fluctuation elimination unit 15 identifies and eliminates the water supply fluctuation component from the actual measurement value from which the relative bias component and time delay have been eliminated. When the relative bias component is eliminated and the time delay is compensated for in equations (18) and (19), the actual measurement value X'i becomes as shown in the following equations (24) and (25).

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[0071] By subtracting equation (24) from equation (25), the relative random component ΔE1 (=E1−E0) can be calculated.

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[0072] The water supply fluctuation elimination unit 15 outputs the relative random component ΔE1 as output data. The relative random component calculation unit 16, normality determination unit 17, first output means 181, and second output means 182 perform the same processing as in the first embodiment, and therefore description thereof will be omitted.

[0073] The second embodiment enables quantitative evaluation of the uncertainty of the main steam flow rate, which is necessary for turbine performance monitoring, in addition to the feedwater flow rate, from operational data. This not only optimizes thermal power management, but also improves the accuracy of turbine performance monitoring, optimizes turbine maintenance timing, and enables plant operation that maximizes turbine efficiency.

[0074] Third Embodiment In the third embodiment, a plant performance monitoring means using data reconciliation will be described.

[0075] 12 is a configuration diagram showing the configuration of a meter uncertainty evaluation system 1B according to the third embodiment. The meter uncertainty evaluation system 1B according to the third embodiment has the same basic configuration as that of the second embodiment, but is configured with a plant performance evaluation unit 21 added.

[0076] In recent years, data reconciliation techniques have been proposed to detect performance degradation of plant equipment, instrument drift, and steam leaks by finding a plausible solution that satisfies the heat balance of the plant from information on existing instruments. Similar plant performance monitoring techniques using machine learning also exist. In these techniques, the uncertainty of each instrument is used as a weight in the correction, so accurate uncertainty evaluation based on operational data is required.

[0077] The third embodiment was devised in consideration of the above-mentioned problems. The meter uncertainty evaluation system 1B of the third embodiment performs data reconciliation that reflects accurate uncertainty based on operational data. This enables highly interpretable and highly accurate thermal power monitoring, turbine performance monitoring, equipment performance monitoring, meter drift monitoring, and steam leak monitoring.

[0078] The measurement means 12, relative bias component calculation unit 13, time delay compensation unit 14, water level fluctuation value correction unit 19, water supply fluctuation removal unit 15, relative random component calculation unit 16, normality judgment unit 17, first output means 181, and second output means 182 are the same as those in the first and second embodiments, so their explanation will be omitted.

[0079] The plant performance evaluation unit 21 evaluates the plant performance using the instrument uncertainty based on the operation data output by the first output means 181 or the second output means 182. As a performance evaluation method, for example, data reconciliation is used. In data reconciliation, the uncertainty of each instrument (Bi + σ Ei ) as weights to calculate a likely solution that satisfies constraints such as heat balance. The evaluation formulas are the following formulas (27) and (28).

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[0080] Here, J is the objective function, xi is the corrected measurement value, and F is a constraint condition configured by heat balance, etc. In this embodiment, the uncertainty (Bi + σ Ei ) is an accurate value based on operational data, enabling more interpretable and highly accurate data reconciliation. The penalty value P, which indicates the magnitude of the correction amount, can be calculated by the following equation (29).

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[0081] By operating the plant using the output value xi from data reconciliation, it is possible to optimize the thermal output management of the plant while operating the plant with improved power generation within the permitted thermal output range.In addition, by monitoring the penalty value, which represents the magnitude of the correction amount shown in equation (29), it is possible to monitor turbine performance, equipment performance, meter drift, and steam leaks.

[0082] Fourth Embodiment In the fourth embodiment, a means for optimizing meter calibration by managing meter drift will be described.

[0083] FIG. 9 is a configuration diagram of an instrument uncertainty evaluation system 1C according to the fourth embodiment. The meter uncertainty evaluation system 1C of the fourth embodiment has the same basic configuration as that of the second embodiment, but is configured with an instrument uncertainty prediction unit 22 and an instrument calibration planning unit 23 added.

[0084] Nuclear power plants have many instruments, and currently, instrument calibration is performed using time-based maintenance. In order to improve the availability of nuclear power plants in the future, it will be necessary to shorten the periodic inspection period, so it is desirable to transition to condition-based maintenance, which determines the calibration period according to the uncertainty state.

[0085] The fourth embodiment has been devised in consideration of the above-mentioned problems. The fourth embodiment makes it possible to accurately calculate uncertainty based on operation data. This makes it possible to grasp the status of the instruments installed in the plant 3, and by performing instrument calibration in condition-based maintenance, it becomes possible to reduce the man-hours required for instrument calibration.

[0086] The measurement means 12, relative bias component calculation unit 13, time delay compensation unit 14, water level fluctuation value correction unit 19, water supply fluctuation removal unit 15, relative random component calculation unit 16, normality judgment unit 17, first output means 181, and second output means 182 are the same as those in the second embodiment, so their explanation will be omitted.

[0087] The instrument uncertainty prediction unit 22 records the instrument uncertainty based on the operation data output by the first output means 181 or the second output means 182, and predicts the time when the uncertainty will reach the allowable uncertainty value based on the change trend of the uncertainty. The allowable uncertainty value is, for example, the required accuracy value listed in the instrument specification table at the time of plant delivery.

[0088] There are various methods for predicting when the uncertainty tolerance will be reached from the uncertainty change trend, but the simplest is linear extrapolation. Depending on the change trend, extrapolation using a quadratic or higher-order function, extrapolation using a polynomial, or prediction methods using machine learning such as neural networks may also be used.

[0089] The meter calibration planning unit 23 formulates a meter calibration plan based on the predicted values ​​of when each meter will reach its uncertainty tolerance, output by the meter uncertainty prediction unit 22. The meter calibrations are planned so that the amount of meter calibration material is evened out over each periodic inspection, so that meter calibrations are not concentrated in a certain periodic inspection.

[0090] The fourth embodiment allows the transition from time-based maintenance to condition-based maintenance, which reduces the amount of meter calibration required and smooths out the amount of meter calibration required for each scheduled inspection, thereby shortening the scheduled inspection period necessary to improve the availability of nuclear power plants.

[0091] (Variation) The present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and are not necessarily limited to those including all of the described configurations. It is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is also possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0092] The above-described configurations, functions, processing units, processing means, etc. may be realized in part or in whole by hardware such as an integrated circuit. The above-described configurations, functions, etc. may be realized by software by a processor interpreting and executing a program that realizes each function. Information such as the programs, tables, and files that realize each function can be stored in a storage device such as a memory, a hard disk, or an SSD (Solid State Drive), or on a storage medium such as a flash memory card or a DVD (Digital Versatile Disk).

[0093] In each embodiment, the control lines and information lines shown are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are interconnected. As modified examples of the present invention, for example, the following (a) to (c) are available. (a) The present invention is not limited to nuclear power plants, but may be applied to other types of power generation facilities such as thermal power plants, and may also be applied to any plants other than power generation facilities. (b) The meter of the present invention is not limited to a flow meter, but may be applied to any meter that measures the same object. (c) The measurement object of the meter of the present invention is not limited to the water supply flow rate, but may be pressure or temperature, or may be other measurement objects.

[0094] The claims of the original application are reproduced below.

[0095] [Claim 1] a relative bias component calculation unit for calculating a relative bias component from a time average value of measurement values ​​of a plurality of instruments in a measurement system having a same measurement object, at least one of the instruments having a calibration record; a time delay compensation unit that calculates a time delay between the measurement values ​​of the meters; a time-varying component removing unit that removes a physical time-varying component of the measurement value; a relative random component calculation unit that calculates a relative random component from the component from which the physical time fluctuation has been removed by the time fluctuation component removal unit; a normality determination unit that determines the normality of the relative random component of the measurement value; An instrument uncertainty evaluation system comprising: [Claim 2] The measurement target of the meter is the plant feedwater flow rate. 2. The instrument uncertainty evaluation system according to claim 1. [Claim 3] a water level fluctuation value correction unit that corrects deviations of the feedwater flow rate and the main steam flow rate of the plant due to water level fluctuations from the water level fluctuation value of the plant, 3. The instrument uncertainty evaluation system according to claim 2. [Claim 4] The measurement target of the instrument is the pressure or temperature of the plant. 2. The instrument uncertainty evaluation system according to claim 1. [Claim 5] the normality determination unit determines whether or not the measurement value is normal based on the skewness and kurtosis of the relative random component. 5. The meter uncertainty evaluation system according to claim 1, wherein: [Claim 6] a plurality of output means according to whether or not the relative random component of the measurement value is normal as determined by the normality determination unit; 2. The meter uncertainty evaluation system according to claim 1, further comprising: [Claim 7] The instrument is installed in a plant, a plant performance evaluation unit that evaluates the performance of the plant using instrument uncertainty based on the operation data output by the plurality of output means; 7. The meter uncertainty evaluation system according to claim 6. [Claim 8] the plant performance evaluation unit evaluates the performance of the plant by calculating a likely solution that satisfies predetermined constraints using the uncertainty of each instrument as a weight; 8. The meter uncertainty evaluation system according to claim 7. [Claim 9] an instrument uncertainty prediction unit having a function of predicting an increasing trend of uncertainty of a measurement value by the instrument; and a meter calibration planning unit that formulates a calibration plan for the meter based on the calibration timing of the meter and the increasing trend of uncertainty predicted by the meter uncertainty prediction unit. 7. The meter uncertainty evaluation system according to claim 6. [Claim 10] The plurality of output means include: a first output means for outputting the sum of the random component of the measurement value and the bias component of the measurement value as the uncertainty of the meter when the relative random component of the measurement value is normal; second output means for outputting a bias component of the measurement value as the uncertainty of the meter when the relative random component of the measurement value is not normal; 10. The meter uncertainty evaluation system according to claim 6, comprising: [Claim 11] the first output means calculates the random component of the measurement value using the standard deviation of the relative random component; 11. The meter uncertainty evaluation system according to claim 10. [Claim 12] A step of calculating a relative bias component from a time average value of measurement values ​​by a plurality of meters in a measurement system having a same measurement object and at least one of the meters having a calibration record; calculating a time delay between the measurements of the meters; removing a physical time-varying component of the measurement value; Calculating a relative random component from the component from which physical time fluctuations have been removed; determining the normality of the relative random component of the measurements; performing a plurality of outputs depending on whether the relative random component of the measurement value is normal or not; A method for evaluating instrument uncertainty, comprising: [Explanation of symbols]

[0096] 1,1A~1C Instrument Uncertainty Evaluation System 3. Plant 12 Measurement methods 13 Relative bias component calculation unit 14 Time delay compensation section 15 Water supply fluctuation removal section (time fluctuation component removal section) 16 Relative random component calculation unit 17 Normality judgment section 181 first output means 182 second output means 19 Water level fluctuation value correction unit 21 Plant Performance Evaluation Department 22 Instrument Uncertainty Estimation Section 23 Instrument Calibration Planning Department 30 Condenser 31 Pump 32 Condensate flow meter 33 Condensate filtration and demineralization equipment 34 Flow meter 35 Air extractor 36 Condenser 37 Low pressure feedwater heater 38 Flow meter 39 Water supply pump 41 High-pressure feedwater heater 42 Water supply flow meter 43 Reactor Pressure Vessel 44 Water level gauge 45 Flow meter 46 High-pressure turbine 47 Moisture separator 48 Low-pressure turbine

Claims

1. In a measurement system having a plurality of meters for the same measurement object, at least one of the meters has a calibration record, a relative bias component calculation unit that calculates a relative bias component from a time average value of the measurement value by the meter; a time fluctuation component removal unit that removes a physical time fluctuation component from the measurement value obtained by correcting the time delay between the instruments of the measurement value obtained by removing the relative bias component calculated by the relative bias component calculation unit, and calculates a relative random component; a plant performance evaluation unit that evaluates the performance of the plant measured by the measurement system using a sum of a bias component calculated from the relative bias component and a random component calculated from the relative random component, or using the bias component; An instrument uncertainty evaluation system comprising:

2. An instrument uncertainty evaluation system according to claim 1, the plant performance evaluation unit evaluates the performance of the plant by calculating a likely solution that satisfies predetermined constraint conditions using the uncertainty of each instrument as a weight; A measuring instrument uncertainty evaluation system characterized by:

3. An instrument uncertainty evaluation system according to claim 2, calculating a penalty value from the uncertainty of each of the instruments and the likely solution; A measuring instrument uncertainty evaluation system characterized by:

4. An instrument uncertainty evaluation system according to claim 1, a normality determination unit that determines the normality of the relative random component of the measurement value; An instrument uncertainty evaluation system comprising:

5. An instrument uncertainty evaluation system according to claim 4, a plurality of output means for outputting the results of the normality determination unit; The plurality of output means include: When the relative random component is normal, a first output means for outputting the sum of the random component and the bias component as the uncertainty of the instrument; In the case where the relative random component is not normal, a second output means for outputting the bias component as the uncertainty of the instrument; A meter uncertainty evaluation system comprising:

6. a step in which a relative bias component calculation unit calculates a relative bias component from a time average value of a measurement value by a plurality of instruments for the same measurement object, at least one of the instruments having a calibration record; a time fluctuation component removing unit removing a physical time fluctuation component from the measurement value obtained by correcting the time delay between the instruments of the measurement value from which the relative bias component has been removed, and calculating a relative random component; calculating a bias component from the relative bias component; calculating a random component from the relative random component; a plant performance evaluation unit evaluating the performance of the plant using the sum of the bias component and the random component or using the bias component; A method for evaluating instrument uncertainty, comprising:

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