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 used to measure the flow rate, pressure, temperature, etc., of feedwater in plants and the like. [Background technology]

[0002] In power plants such as thermal and nuclear power plants, feedwater supplied to boilers and reactors is heated to generate steam, which then drives steam turbines to produce electricity. Therefore, accurately understanding the feedwater flow rate is crucial for controlling the plant's thermal output. Nuclear power plants, in particular, are required to operate within the limits of their licensed thermal output, and in addition to measuring the feedwater flow rate, it is 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. Therefore, scale can accumulate on the flow nozzle surface of the feedwater flow meter, potentially causing feedwater drift where the apparent flow rate increases during plant operation. Furthermore, feedwater flow meters in power plants are used under high flow rate and high-temperature conditions. For this reason, accuracy testing under the same flow rate and temperature conditions as the actual equipment is often omitted before installation. In this case, the flow coefficient, which affects uncertainty, is extrapolated from the value obtained in low-flow rate tests.

[0004] As described above, the uncertainty of feedwater flow meters is sometimes managed with conservative values ​​that take into account the effects of feedwater drift and deviations due to extrapolation. However, feedwater drift does not always occur, and the deviations due to extrapolation vary from plant to plant. Therefore, the uncertainty of feedwater flow meters may be excessively conservative.

[0005] In recent years, data reconciliation techniques have been proposed to monitor plant performance by finding the most plausible solution that satisfies the plant's heat balance from information on existing design equipment. In this technique, the uncertainty of each instrument is used as a weight to correct the measured values, so accurate understanding of the uncertainty is crucial.

[0006] For the reasons stated above, accurately evaluating the uncertainty of instruments, including feedwater flow meters, from plant operating data is important for thermal output management and plant performance monitoring.

[0007] Patent Document 1 discloses a plant instrumentation control device that calculates the estimated drift amount for each instrument by determining the most likely estimated true value through a comprehensive evaluation of data related to the estimation accuracy of the true value estimation device, and a true value estimation device that estimates true values ​​based on plant operating data, and a true value estimation device that calculates the estimated drift amount for each instrument. Linear models, neural networks, data reconciliation, etc., are used in the true value estimation device, and plant operating data acquired in advance is used for adjusting and training the estimation model. [Prior art documents] [Patent Documents]

[0008] [Patent Document 1] Japanese Patent Publication No. 2005-338049 [Overview of the project] [Problems that the invention aims to solve]

[0009] In Patent Document 1, the bias component (drift), which is one element of uncertainty, can be predicted from estimates of linear models, neural networks, and data reconciliation. However, the uncertainty inherent in the estimates of linear models, neural networks, and data reconciliation themselves cannot be evaluated, so it was not possible to show the bias component of each instrument as a quantitative value. Furthermore, in boiling water reactors, the feedwater flow rate includes temporal fluctuations (hereinafter referred to as feedwater fluctuations), which are mixed with the random component, which is the remaining element of uncertainty. Therefore, there was a problem in that the method of Patent Document 1 could not quantitatively evaluate the random component of the uncertainty of the feedwater flow meter.

[0010] Therefore, the 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] To solve the aforementioned problems, the instrument uncertainty evaluation system of the present invention is a measurement system equipped with multiple instruments for the same measurement target, and at least one of the instruments has a calibration record, comprising: a relative bias component calculation unit that calculates a relative bias component from the time average value of the measured values ​​by the instruments; and a time variation component removal unit that removes a physical time variation component from the measured value obtained by correcting the time delay between the instruments of the measured value excluding the relative bias component calculated by the relative bias component calculation unit, and calculates a relative random component. A normality determination unit that determines the normality of the relative random component of the measured value, and based on the determination of the normality determination unit, The plant performance evaluation unit is characterized by having an evaluation unit that evaluates the performance of the plant measured by the measurement system 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 the bias component.

[0012] The instrument uncertainty evaluation method of the present invention is applicable to a measurement system equipped with multiple instruments for the same measurement object, where at least one of the instruments has calibration records. The relative bias component calculation unit calculates the relative bias component from the time-average value of the measurement values obtained by the instruments. The time-variation component removal unit removes the physical time-variation component from the measurement values after correcting the time lag between the instruments of the measurement values excluding the relative bias component, and calculates the relative random component. The normality determination unit performs the step of determining the normality of the relative random component of the measured value, The steps of calculating the bias component from the relative bias component and the steps of calculating the random component from the relative random component, and the plant performance evaluation unit Based on the determination of the normality determination unit, using the sum of the bias component and the random component, or using the bias component, to evaluate the performance of the plant.

Advantages of the Invention

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

Brief Description of the Drawings

[0014] [Figure 1] It is a block diagram showing the configuration of the instrument uncertainty evaluation system according to the first embodiment. [Figure 2] It is a block diagram showing the configuration of the nuclear power plant. [Figure 3] It is a flowchart showing the flow of determining the number of time-average times of the relative bias component calculation unit. [Figure 4] It is a graph showing the operation of determining the number of time-average times of the relative bias component calculation unit. [Figure 5] It is a graph showing an example of the input data of the relative bias component calculation unit. [Figure 6] It is a graph showing an example of the output data of the relative bias component calculation unit. [Figure 7] It is a diagram showing an example of the output data of the time-lag compensation unit. [Figure 8] It is a diagram showing an example of the output data of the feedwater fluctuation removal unit. [Figure 9] It is a diagram showing an example of the output data of the normality judgment unit. [Figure 10] It is a graph showing an example of the output data of the normality judgment unit. [Figure 11] It is a block diagram showing the configuration of the instrument uncertainty evaluation system according to the second embodiment. [Figure 12] It is a block diagram showing the configuration of the instrument uncertainty evaluation system according to the third embodiment. [Figure 13] It is a block diagram showing the configuration of the instrument uncertainty evaluation system according to the fourth embodiment.

Mode for Carrying Out the Invention

[0015] Hereinafter, the mode for carrying out the present invention will be described in detail with reference to each drawing and mathematical formula. 《First Embodiment》 In the first embodiment, a condensate flow meter in which uncertainty is quantitatively evaluated by an in-service calibration during a periodic inspection is used as a reference instrument, and by evaluating the deviation from it, means for quantitatively evaluating the uncertainty of each instrument related to the feed water flow rate is provided. Further, the turbine performance monitoring means using the uncertainty of each instrument obtained by this means, the plant performance monitoring means by data reconciliation, and the means for optimizing instrument calibration by instrument drift management will also be described in the second to fourth embodiments.

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

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

[0018] The time delay compensation unit 14 calculates the time delay between instruments for each measured value. The water supply fluctuation removal unit 15 functions as a time variation component removal unit that removes the physical time variation component between instruments for each measured value. This makes it possible to separate water supply fluctuations from random errors.

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

[0020] The normality determination unit 17 determines whether or not the data is normal from the output data of the water supply fluctuation removal unit 15. If the data is normal, the normality determination unit 17 outputs the uncertainty of each instrument quantitatively from the first output means 181; if the data is not normal, it outputs the uncertainty of each instrument quantitatively from the second output means 182. The first output means 181 and the second output means 182 are multiple output means corresponding to the presence or absence of normality in the relative random component of the measured value. This allows the instrument uncertainty evaluation system 1 to quantitatively evaluate the uncertainty of the instruments.

[0021] Figure 2 is a diagram showing the configuration of Plant 3. Plant 3 consists of a condenser 30, a pump 31, a feedwater pump 39, a condensate filtration and demineralization unit 33, an air extractor 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 diagram, the high-pressure turbine 46 is abbreviated as "high-pressure TB".

[0022] Condensers 30 and 36 condense steam. Pumps 31 and feedwater pump 39 send water to the following components. The condensate filtration and desalination unit 33 filters and desalinates the condensed water. The air extractor 35 extracts air mixed with the water. The low-pressure feedwater heater 37 is connected to the low-pressure turbine 48 to heat the water. The high-pressure feedwater heater 41 is connected to the high-pressure turbine 46 to heat the water. The reactor pressure vessel 43 contains the reactor core loaded with nuclear fuel and obtains steam by heating light water in the core and bringing it to a boil. This steam is led through steam piping to the high-pressure turbine 46, the moisture separator 47, and the low-pressure turbine 48.

[0023] Dry steam entering the low-pressure turbine 48 from the main steam piping drives the low-pressure turbine 48, and is then discharged from the low-pressure turbine 48. The discharged steam condenses into water in the condenser 30 located at the bottom of the low-pressure turbine 48, and this water is then used again as feedwater to the reactor pressure vessel 43.

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

[0025] Plant 3 further comprises instruments for measuring the feedwater flow rate, including 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.

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

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

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[0028] When the same object is measured with 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 i=0 is defined as the flow meter 34, which is a reference instrument for measuring the condensate flow rate. The instrument i=1 is defined as the flow meter 38 for measuring the inlet flow rate of the feedwater pump 39. The instrument i=2 is defined as the feedwater flow meter 42 for measuring the feedwater flow rate. The instrument i=3 is defined as the condensate flow meter 32 for measuring the inlet flow rate of the condensate filtration and desalination device 33.

[0030] The relative bias component calculation unit 13 determines the permissible value of the residual term ε and the number of time averages n that satisfy the permissible value by truncating the time average at a finite point, and calculates the time average value of each instrument. The permissible value of ε may be, for example, 1 / 100 of the uncertainty bias component of the reference instrument.

[0031] The procedure for determining the time-averaged count n will be explained with reference to Figures 3 and 4. The relative bias component calculation unit 13 calculates a reference average value by time averaging the measured values ​​of all instruments over a sufficiently long period (e.g., 1 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 number of time averagings n (step S12). This graph is shown in Figure 4.

[0032] The vertical axis in Figure 4 represents the deviation from the reference mean. The horizontal axis in Figure 4 represents the number of time-averaged measurements n. The solid line in the graph represents the time-averaged value of flow meter 34. The thin dashed line in the graph represents the time-averaged value of flow meter 38. The coarse dashed line in the graph represents the time-averaged value of water supply flow meter 42. This shows that the time-averaged values ​​after n measurements for all instruments are less than or equal to the allowable residual term ε.

[0033] The relative bias component calculation unit 13 terminates the process shown in Figure 3 when it adopts the n at the stage in which the deviation falls below the allowable value of ε for all instruments as the number of time averages in subsequent evaluations (step S13). The relative bias component calculation unit 13 only needs to perform this procedure once during the operating cycle. The average value of Xi is obtained by averaging the measured values ​​obtained from each instrument n times. The average value of Xi is derived from equation (2) as shown in equation (3) below.

[0034]

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[0035] By rearranging equation (3), we obtain the relative bias component ΔBi from the bias component B0 of the uncertainty of the reference instrument. We will find (=Bi-B0). The formula for calculating ΔBi is as follows:

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[0036] The relative bias component calculation unit 13 calculates the measured value X'i excluding the relative bias component ΔBi by subtracting equation (4) from equation (2). This completes the function of the relative bias component calculation unit 13.

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

[0038] Figure 6 is a graph showing an example of the 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 measured value of the reference instrument, the flow meter 34. X'1 is the value obtained by removing the relative bias component ΔBi from the measured value of the flow meter 38. X'2 is the value obtained by removing the relative bias component ΔBi from the measured value of the feedwater flow meter 42. X'3 is the value obtained by removing the relative bias component ΔBi from the measured value of the condensate flow meter 32. Compared to the plot in the graph of Figure 5, the difference between the measured values ​​of each instrument is smaller in the graph of Figure 6.

[0039] Let's return to Figure 1 and 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 there. The main cause of the time delay is the time it takes for the pressure wave to propagate through the piping between the instruments. The time delay compensation unit 14 evaluates the time delay τi from the length of the piping between the instruments Li, the average flow velocity between the instruments Vi, and the average sound velocity between the instruments Ci using the following formula.

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[0040] The pipe length Li is determined from the design drawings. The average flow velocity Vi is determined from the time-averaged flow rate divided by the pipe cross-sectional area. The average sound velocity Ci is determined from the average pressure and average temperature of the fluid inside the pipe via a vapor table. The time delay compensation unit 14 corrects the measured values, excluding the relative bias component, using the time delay τi at each instrument obtained by equation (5). To evaluate the time delay more accurately, numerical calculation results based on computational fluid dynamics (CFD) may be used.

[0041] Figure 7 shows an example of the output data of the time delay compensation unit 14. The vertical axis of the graph shows the feedwater flow rate X'i with relative bias component and time delay compensated, and the horizontal axis shows time. X'0 is the value with relative bias component and time delay compensated from the measured value of the reference instrument, the flow meter 34. X'1 is the value with relative bias component and time delay compensated from the measured value of the flow meter 38. X'2 is the value with relative bias component and time delay compensated from the measured value of the feedwater flow meter 42. X'3 is the value with relative bias component and time delay compensated from the measured value of the condensate flow meter 32. Compared to the plot of the graph in Figure 6, the phase difference between the values ​​of each instrument is smaller in the graph in Figure 7.

[0042] Returning to Figure 1, the explanation continues. The water supply fluctuation removal unit 15 identifies and removes the water supply fluctuation component from the measured value after the relative bias component and time delay have been removed. When the relative bias component ΔBi is removed and the time delay τi is compensated in equation (2), the water supply flow rate X'i (from i=0 to 3) is as follows.

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[0043] Since feedwater fluctuations are a real phenomenon, they are measured by each instrument as a 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 measured flow rate, with feedwater fluctuations and bias components removed, can be obtained.

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

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[0045] Figure 8 shows an example of output data from the feedwater fluctuation removal unit 15. The vertical axis in Figure 8 shows the time variation component of the measured value excluding feedwater fluctuations, and the horizontal axis shows time. The fine dashed line is the relative random component ΔE1 of the flow meter 38 relative to the reference instrument. The coarse dashed line is the relative random component ΔE2 of the feedwater flow meter 42 relative to the reference instrument. The solid line is the relative random component ΔE3 of the condensate flow meter 32 relative to the reference instrument.

[0046] If the relative random component ΔEi, which is the output data of the water supply fluctuation removal unit 15, follows a normal distribution, then the standard deviation of the relative random error is σ 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 output data from the water supply fluctuation removal unit 15, and calculates its square root. This allows the relative random component calculation unit 16 to determine the standard deviation σ of the relative random component ΔEi for each instrument. ΔEi Outputs.

[0048] Figure 9 shows an example of the output data from the normality determination unit 17. This data includes an instrument number column, a skewness column, a kurtosis column, and a normality column, with each column storing the determination result for 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 peaky the distribution of the measurement data of this instrument is compared to a normal distribution. The normality column stores a determination result indicating whether or not the distribution of the measurement data of this instrument has a predetermined normality. The determination result is "OK" when it has normality, and "NG" when it does not have normality.

[0049] The normality determination unit 17 determines whether the data is normal from the output data of the water supply fluctuation removal unit 15 and decides whether to accept the output data of the relative random component calculation unit 16. The normality determination unit 17 outputs the frequency distributions of the input data, which are the relative random components ΔE1, ΔE2, and ΔE3. The normality determination unit 17 then calculates the skewness and kurtosis of each frequency distribution and quantitatively determines whether the data is normal. The range in which normality is determined is, for example, a skewness of 0.0 or more and 0.5 or less, and a kurtosis of 2.5 or more and 3.5 or less, but is not limited to this. Furthermore, a means may be provided for users to qualitatively confirm normality by displaying the frequency distributions of the relative random components ΔE1, ΔE2, and ΔE3 in a way that allows them to be compared with a fitting curve of a normal distribution.

[0050] Figure 10 is a graph showing an example of the output data from the normality determination unit 17. In this graph, the vertical axis represents frequency, and the horizontal axis represents the time variation. The solid line is a fitted curve for a normal distribution.

[0051] If the normality determination unit 17 determines that the measured values ​​after removing water supply fluctuations are normal, the first output means 181 quantitatively calculates and outputs the uncertainty of each instrument. In the relative bias component ΔBi (=Bi-B0) output of 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, equations (13) to (15) hold true for each instrument, so the standard deviation σ of the relative random component, which is the output of the relative random component calculation unit 16, is ΔEi The random component of each instrument is quantitatively evaluated using the following: Random component σ of the reference instrument E0 Since this is known from the instrument calibration records during periodic inspections, the random component of each instrument is calculated using the following formula (17).

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[0053] From the above procedure, the uncertainty of each instrument (Bi + σ) Ei This allows for the determination of the uncertainty in the feedwater flow rate, making it possible to quantitatively understand the uncertainty. By inputting and displaying these values ​​in a process computer, it becomes possible to operate the plant with optimized thermal output management, thereby eliminating excessive maintainability and enabling plant operation that increases power generation within the range of the licensed thermal output.

[0054] If the measured values ​​after removing water supply fluctuations by the normality determination unit 17 are determined to be abnormal, the second output means 182 quantitatively calculates and outputs the uncertainty of each instrument. In the relative bias component ΔBi (=Bi-B0), which is the output of the relative bias component calculation unit 13, the bias component B0 of the reference instrument is known from the instrument calibration record during periodic inspections. Therefore, the second output means 182 calculates the bias component Bi of each instrument using equation (16). From the result of the normality determination unit 17, equation (13) to equation (16) does not hold for each instrument, so the second output means 182 calculates the standard deviation σ of the random component of each instrument. Ei This will be replaced by the required accuracy values ​​listed in the JIS values ​​or the instrument specifications sheet provided at the time of plant delivery.

[0055] From the above procedure, the uncertainty of each instrument (Bi + σ) Ei This allows for the determination of the uncertainty in the feedwater flow rate, making it possible to quantitatively understand the uncertainty. By inputting and displaying these values ​​in a process computer, it becomes possible to operate the plant with optimized thermal output management, thereby eliminating excessive maintainability and enabling plant operation that increases power generation within the range of the licensed thermal output.

[0056] According to the instrument uncertainty evaluation system 1 of this embodiment, the uncertainty of the feedwater flow meter, which is conservatively evaluated considering feedwater drift and extrapolation deviation of the flow coefficient without actual flow calibration, can be quantitatively evaluated from plant operating data. This makes it possible to optimize the thermal output management of the plant and improve the amount of power generated within the range of the approved thermal output.

[0057] Furthermore, by using the uncertainty of each instrument based on the operating data obtained from the instrument uncertainty evaluation system 1, highly accurate turbine performance monitoring and plant performance monitoring become possible. The uncertainty trends of each instrument allow for optimization of the calibration timing of each instrument, thereby reducing the amount of instrument calibration required.

[0058] 《Second Embodiment》 In the second embodiment, a turbine performance monitoring means using the uncertainty of each instrument obtained in the first embodiment will be described.

[0059] Figure 11 is a configuration diagram showing the configuration of the instrument uncertainty evaluation system 1A of the second embodiment. The instrument uncertainty evaluation system 1A of the second embodiment has the same basic configuration as the first embodiment, but a water level fluctuation value correction unit 19 has been added. The water level fluctuation correction unit 19 has the function of correcting the water level fluctuations of plant 3.

[0060] As shown in Figure 2, a nuclear power plant generates electricity by heating feedwater supplied from feedwater pipes in the reactor core, generating steam, and driving high-pressure turbines 46 and low-pressure turbines 48. In addition to managing the thermal output of the reactor, monitoring the performance of the steam turbines is also important for improving the thermal efficiency of the plant. Monitoring the performance of the steam turbines requires accurately understanding the inflowing steam flow rate. However, because the water level in the reactor is formed and fluctuates over time, the instantaneous value of the feedwater flow rate at the reactor inlet does not coincide with the instantaneous value of the main steam flow rate at the reactor outlet.

[0061] The second embodiment was devised in view of the above-mentioned problems. According to the instrument uncertainty evaluation system 1A of the second embodiment, in addition to the feedwater flow rate, the uncertainty of the main steam flow rate, which is necessary for monitoring turbine performance, can also be quantitatively evaluated from the operating data, enabling efficient operation of the plant.

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

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

[0064] The relative bias component calculation unit 13, similar to the first embodiment, determines the allowable value of the residual term ε and the number of time-averaging cycles n that satisfy the allowable value, and calculates the time-averaged value of each instrument. ΔY is the time change in reactor water level, and since the reactor water level is controlled to remain constant, the time-averaged 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 using the following formula (22).

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

[0067] 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 there. The correction procedure is the same as in the first embodiment, so the explanation is omitted.

[0068] The water level fluctuation correction unit 19 corrects the deviations associated with water level fluctuations in the feedwater flow rate and main steam flow rate from the measured reactor water level data. The time change ΔY of the reactor water level is evaluated using the measured reactor water level data in equation (20) and the following equation (23).

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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 approximately 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) as the output data.

[0070] The water supply fluctuation removal unit 15 identifies and removes the water supply fluctuation component from the measured value from which the relative bias component and time delay have been removed. In equations (18) and (19), when the relative bias component is removed and the time delay is compensated for, the measured value X'i becomes as shown in equations (24) and (25) below.

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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 removal unit 15 outputs the relative random component ΔE1 as data. The relative random component calculation unit 16, the normality determination unit 17, the first output means 181, and the second output means 182 perform the same processing as in the first embodiment, so their explanation is omitted.

[0073] In the second embodiment, in addition to the feedwater flow rate, the uncertainty of the main steam flow rate, which is necessary for monitoring turbine performance, can also be quantitatively evaluated from the operating data. Therefore, in addition to optimizing thermal output management, it becomes possible to improve the accuracy of turbine performance monitoring, optimize turbine maintenance timing, and operate the plant in a way that maximizes turbine efficiency.

[0074] 《Third Embodiment》 The third embodiment describes a plant performance monitoring method using data reconciliation.

[0075] Figure 12 is a configuration diagram showing the configuration of the instrument uncertainty evaluation system 1B of the third embodiment. The instrument uncertainty evaluation system 1B of the third embodiment has the same basic configuration as the second embodiment, but with the addition of a plant performance evaluation unit 21.

[0076] In recent years, data reconciliation techniques have been proposed to detect performance degradation of plant equipment, instrument drift, and steam leaks by seeking the most plausible solution that satisfies the plant's heat balance from information on existing equipment designs. 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, thus requiring accurate uncertainty evaluation based on operating data.

[0077] The third embodiment was devised in view of the above-mentioned problems. The instrument uncertainty evaluation system 1B of the third embodiment performs data reconciliation that reflects accurate uncertainty based on operating data. This enables highly explainable and accurate thermal output monitoring, turbine performance monitoring, equipment performance monitoring, instrument 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 determination unit 17, first output means 181, and second output means 182 are the same as in the first and second embodiments, so their description is 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, a plausible solution that satisfies the constraint conditions such as heat balance is calculated with the uncertainty (Bi+σ Ei ) of each instrument as a weight. The evaluation expressions are the following expressions (27) and (28).

Number

Number

[0080] Here, J is the objective function, xi is the measured value after correction, and F is the constraint condition composed of heat balance and the like. In the present embodiment, since the uncertainty (Bi+σ Ei ) serving as the correction weight becomes an accurate value based on the operation data, more explanatory and highly accurate data reconciliation becomes possible. The penalty value P representing the magnitude of the correction amount can be calculated by the following expression (29).

Number

[0081] By operating the plant using xi which is the output value of data reconciliation, it becomes possible to optimize the heat output management of the plant and improve the power generation amount within the range of the allowable heat output. Also, by monitoring the penalty value representing the magnitude of the correction amount shown in expression (29), it becomes possible to monitor turbine performance, equipment performance, instrument drift, and steam leak.

[0082] 《Fourth Embodiment》 The fourth embodiment will explain the means for optimizing instrument calibration by instrument drift management.

[0083] Figure 9 is a diagram showing the configuration of the instrument uncertainty evaluation system 1C in the fourth embodiment. The instrument uncertainty evaluation system 1C of the fourth embodiment has the same basic configuration as the second embodiment, but with the addition of an instrument uncertainty prediction unit 22 and an instrument calibration planning unit 23.

[0084] Nuclear power plants have numerous instruments, and currently, instrument calibration is performed using time-based maintenance. In order to improve the operating rate of nuclear power plants in the future, it is necessary to shorten the periodic inspection period, so it is desirable to shift instrument calibration to condition-based maintenance, where calibration timing is determined according to the uncertainty level.

[0085] The fourth embodiment was devised in view of the above-mentioned problems. The fourth embodiment makes it possible to calculate accurate uncertainty based on operating data. This makes it possible to understand the status of the instruments installed in Plant 3, and reduces the man-hours required for instrument calibration in condition-based maintenance.

[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 determination unit 17, first output means 181, and second output means 182 are the same as in the second embodiment, so their description is omitted.

[0087] The instrument uncertainty prediction unit 22 records the instrument uncertainty based on the operating data output by the first output means 181 or the second output means 182, and predicts when the uncertainty tolerance will be reached based on its trend of change. The uncertainty tolerance is, for example, the required accuracy value stated in the instrument specification sheet at the time of plant delivery.

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

[0089] The instrument calibration planning unit 23 receives the predicted time for each instrument to reach its uncertainty tolerance, output by the instrument uncertainty prediction unit 22, and formulates an instrument calibration plan. The calibration is planned so that instrument calibration is not concentrated in any particular periodic inspection, and the amount of instrument calibration work is smoothed out across each periodic inspection.

[0090] The fourth embodiment allows for a shift from time-based maintenance to condition-based maintenance for instrument calibration, which was previously performed using time-based maintenance. This reduces the amount of instrument calibration required and smooths out the amount of instrument calibration required for each periodic inspection, thereby shortening the periodic inspection period necessary to improve the operating rate of nuclear power plants.

[0091] (modified version) The present invention is not limited to the embodiments described above, and includes various modifications. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. It is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0092] Each of the above configurations, functions, processing units, and processing means may be implemented in part or in whole by hardware, such as an integrated circuit. Each of the above configurations and functions may also be implemented in software by a processor interpreting and executing a program that implements each function. Information such as programs, tables, and files that implement each function can be stored in a recording device such as memory, a hard disk, or an SSD (Solid State Drive), or on a recording 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 deemed necessary for explanation and do not necessarily represent all control lines and information lines in the actual product. In practice, it can be assumed that almost all components are interconnected. Examples of modified versions of the present invention include the following (a) to (c). (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 plant other than power generation facilities. (b) The instrument of the present invention is not limited to a flow meter and may be applied to any instrument that measures the same object. (c) The instrument of the present invention is not limited to measuring water flow rate, but may also measure pressure, temperature, or other things.

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

[0095] [Claim 1] In a measurement system equipped with multiple instruments for the same measurement target, wherein at least one of the instruments has a calibration record, a relative bias component calculation unit calculates a relative bias component from the time average value of the measured values ​​by the instrument. A time delay compensation unit calculates the time delay between the measured values ​​of the instruments. A time variation component removal unit removes the physical time variation component of the measured value. The time-varying component removal unit calculates a relative random component from the component from which the physical time variation has been removed. A normality determination unit that determines the normality of the relative random component of the measured value, An instrument uncertainty evaluation system characterized by having [a certain feature]. [Claim 2] The instrument measures the feedwater flow rate of the plant. The instrument uncertainty evaluation system according to feature 1. [Claim 3] The plant is equipped with a water level fluctuation correction unit that corrects the deviations in the feedwater flow rate and main steam flow rate of the plant due to water level fluctuations, based on the water level fluctuation values ​​of the plant. The instrument uncertainty evaluation system according to feature 2. [Claim 4] The instrument measures either the pressure or temperature of the plant. The instrument uncertainty evaluation system according to feature 1. [Claim 5] The normality determination unit determines whether or not the measured value is normal based on the skewness and kurtosis of the relative random component. The instrument uncertainty evaluation system according to any one of claims 1 to 4. [Claim 6] A plurality of output means corresponding to whether or not the relative random component of the measured value determined by the normality determination unit is normal, The instrument uncertainty evaluation system according to claim 1, characterized by having the following: [Claim 7] The aforementioned instrument is installed in the plant. The plant performance evaluation unit further includes a unit that evaluates the performance of the plant using the instrument uncertainty based on the operating data output by the plurality of output means. The instrument uncertainty evaluation system according to feature 6. [Claim 8] The plant performance evaluation unit evaluates the performance of the plant by calculating a plausible solution that satisfies predetermined constraints, using the uncertainty of each instrument as a weight. The instrument uncertainty evaluation system according to feature 7. [Claim 9] An instrument uncertainty prediction unit having a function to predict the increasing trend of uncertainty in the measured value by the aforementioned instrument, The system further includes an instrument calibration planning unit that formulates a calibration plan for the instrument based on the calibration timing of the instrument and the increasing uncertainty predicted by the instrument uncertainty prediction unit. The instrument uncertainty evaluation system according to feature 6. [Claim 10] The aforementioned plurality of output means are A first output means outputs the sum of the random component of the measured value and the bias component of the measured value as the uncertainty of the instrument, provided that the relative random component of the measured value is normal. A second output means that outputs the bias component of the measured value as the uncertainty of the instrument when the relative random component of the measured value is not normal, An instrument uncertainty evaluation system according to any one of claims 6 to 9, characterized by comprising the above. [Claim 11] The first output means calculates the random component of the measured value using the standard deviation of the relative random component. The instrument uncertainty evaluation system according to claim 10. [Claim 12] In a measurement system equipped with multiple instruments for the same object to be measured, and at least one of the instruments having a calibration record, the step of calculating the relative bias component from the time average value of the measured values ​​by the instrument, A step of calculating the time delay between the measured values ​​of the instruments, A step of removing the physical time-varying component of the measured value, A step of calculating a relative random component from a component from which physical time variations have been removed. A step of determining the normality of the relative random component of the measured value, A step of performing multiple outputs depending on whether or not the relative random component of the measured value is normal, A method for evaluating instrument uncertainty, characterized by performing the following. [Explanation of Symbols]

[0096] 1.1A~1C Instrument Uncertainty Evaluation System 3 Plants 12. Measuring means 13. Relative bias component calculation unit 14-hour delay compensation department 15. Water supply fluctuation removal section (time-varying 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 Prediction Unit 23 Instrument Calibration Planning Department 30 Condenser 31 pumps 32 Condensate flow meter 33. Condensate filtration and desalination system 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 equipped with multiple instruments for the same measurement target, where at least one of the instruments has a calibration record, A relative bias component calculation unit that calculates the relative bias component from the time-averaged value of the measured value by the aforementioned instrument, A time variation component removal unit removes the physical time variation component from the measured value obtained by correcting the time delay between instruments of the measured value after removing the relative bias component calculated by the relative bias component calculation unit, and calculates the relative random component. A normality determination unit that determines the normality of the relative random component of the measured value, A plant performance evaluation unit evaluates the performance of the plant measured by the measurement system, based on the judgment of the normality determination unit, 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 the bias component. An instrument uncertainty evaluation system characterized by having [a certain feature].

2. An instrument uncertainty evaluation system according to claim 1, The plant performance evaluation unit evaluates the performance of the plant by calculating a plausible solution that satisfies predetermined constraints, using the uncertainty of each instrument as a weight. An instrument uncertainty evaluation system characterized by the following:

3. The instrument uncertainty evaluation system according to claim 2, A penalty value is calculated from the uncertainty of each instrument and the most likely solution. An instrument uncertainty evaluation system characterized by the following:

4. An instrument uncertainty evaluation system according to claim 1, If the relative random component is normal, A first output means that outputs the sum of the random component and the bias component as the uncertainty of the instrument, If the aforementioned relative random component is not normal, A second output means that outputs the bias component as the uncertainty of the instrument, An instrument uncertainty evaluation system characterized by comprising the following:

5. In a measurement system equipped with multiple instruments for the same measurement target, and in which at least one of the instruments has a calibration record, the relative bias component calculation unit calculates the relative bias component from the time average value of the measured values ​​by the instrument, The time variation component removal unit performs the following steps: remove the physical time variation component from the measured value obtained by correcting the time delay between the instruments of the measured value after removing the relative bias component, and calculate the relative random component; The normality determination unit performs the step of determining the normality of the relative random component of the measured value, A step of calculating a bias component from the aforementioned relative bias component, A step of calculating a random component from the aforementioned relative random component, The plant performance evaluation unit evaluates the plant performance using the sum of the bias component and the random component, or using the bias component, based on the judgment of the normality determination unit. A method for evaluating instrument uncertainty, characterized by having [a certain feature].

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