JP7780403B2 - System performance monitoring device and system performance monitoring method - Google Patents

Description

The present invention relates to a power system performance monitoring device and a power system performance monitoring method.

At thermal and nuclear power plants, there is a growing need for quantitative monitoring of system functions in order to streamline plant maintenance.

Patent document 1 describes an invention that uses true value estimation models to estimate true values based on the actual measured values of each detector signal output from multiple detectors installed in a plant.

Japanese Patent Application Laid-Open No. 2005-338049

In a plant's seawater cooling system, for example, the condition changes due to fluctuations in seawater temperature. Even when conditions change in this way, it is important to distinguish between instrument error and actual fluctuations in order to monitor system performance with high accuracy.

Data Validation and Reconciliation (DVR) technology is another measurement error reduction technique that has been put into practical use. DVR technology is a measurement error reduction method that uses information from redundant existing sensors and constraints to calibrate instrument errors and obtain reliable values.

DVR technology requires redundancy of measurement values and constraints, but due to the difficulty of setting these up, there have been no examples of DVR technology being applied to system monitoring. Specifically, monitoring system flow rate, pump performance, and heat exchanger performance using DVR technology requires a combination of remote measurement data from a process computer and on-site instrument data measured at designated locations in the system during patrol inspections.

However, DVR technology is based on the premise of heat balance at a given time, but since the time at which field instrument data is acquired is not the same, DVR technology cannot be applied to plants as is. Therefore, it was not possible to apply DVR technology to plants and evaluate system performance by separating actual fluctuations from instrument errors.
Therefore, an object of the present invention is to evaluate system performance with high accuracy by separating actual fluctuations from instrument errors.

In order to solve the above-mentioned problems, the system performance monitoring device of the present invention is characterized by having an input means for inputting measurement values of a plurality of remote instruments installed in the system and measuring remotely , and measurement values of field instruments installed at predetermined locations and acquired by workers , an estimation means for calculating an estimated value of the field instruments at a predetermined time based on constraint conditions that model the characteristics of the system and time series data of the measurement values of the remote instruments, and a measurement error reduction means for calculating a correction value that reduces the error in the measurement value of the instrument based on the measurement values of the remote instruments, the constraint conditions, and the estimated value of the field instruments calculated by the estimation means.

The system performance monitoring method of the present invention is characterized by comprising the steps of: receiving, via an input means, measurement values from a plurality of remote instruments installed in the system and measuring remotely, and measurement values from field instruments installed at predetermined locations in the system and acquired by workers; calculating an estimated value of the field instrument at a predetermined time from constraint conditions that model the characteristics of the system and time series data of the measurement values of the remote instruments input from the input means; and calculating , via a measurement error reduction means, a correction value that reduces the error in the measurement value of the instrument based on the measurement values of the remote instruments, the constraint conditions, and the estimated values of the field instruments received by the input means.
Other means will be described in the detailed description of the invention.

This invention makes it possible to distinguish between actual fluctuations and instrument errors and evaluate system performance with high accuracy.

1 is a block diagram of a system performance monitoring device according to an embodiment of the present invention. FIG. 10 is a diagram showing the relationship between the measured values and errors of each instrument. 10 is a flowchart of a system performance monitoring process. 10 is a flowchart of a process for reducing an error in a measurement value. 10 is a graph showing estimated values of a physical model. 10 is a graph showing deviation correction. 1 is a graph showing DVR ratings. FIG. 10 shows measurement data before applying DVR evaluation. FIG. 10 shows estimates of measurement data after applying DVR evaluation. FIG. 1 is a diagram showing a system including a pump and a pressure sensor. 1 is a graph showing the relationship between flow rate and head. 10 is a graph showing a method for detecting pump deterioration. FIG. 1 is a diagram showing a system including a heat exchanger. FIG. 10 is a diagram showing a system relating to pressure loss characteristics. 10 is a graph showing the relationship between flow rate and pressure loss. FIG. 2 is a diagram showing a system related to a turbine. 1 is a graph showing the relationship between flow rate and stage pressure . FIG. 2 is a diagram showing a system relating to a valve. 10 is a graph showing the relationship between flow rate and valve opening degree.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
FIG. 1 is a block diagram of a system performance monitoring device 1 according to this embodiment.
A system performance monitoring device 1 is connected to a pipe 2 so as to be able to remotely receive data from a plurality of flow meters 3a to 3d installed on the pipe 2. The pipe 2 also has a field flow meter 4a that is temporarily installed at a predetermined location by an operator during patrol. The system performance monitoring device 1 includes an input unit 11, a measurement error reduction unit 12, a field meter measurement error setting unit 13, an estimation unit 14, a degradation detection unit 15, and a storage unit 16. The storage unit 16 stores a physical model 161 in which constraints of the system including the pipe 2 are modeled.

The input unit 11 inputs the measured values of multiple flow meters 3a-3d installed on the pipe 2. In this case, the pipe 2 is the system, and the multiple flow meters 3a-3d are instruments. The on-site flow meter 4a is an on-site instrument that measures on-site instrument data at a specified location. The on-site flow meter 4a is used by workers to obtain the indicated value at a specified location when performing patrol inspections at the site. The measured values of the multiple flow meters 3a-3d have a specified error from the true value.

The measurement error reduction unit 12 calculates an estimated value of the flow rate in the pipe 2 by reducing the errors in the measurement values of the multiple flow meters 3a-3d through DVR evaluation, based on the measurement values of the multiple flow meters 3a-3d and constraint conditions that model the characteristics of the system. The measurement error reduction unit 12 inputs the field instrument data of the field flow meter 4a, along with the signal data and constraint conditions of the flow meters 3a-3d, and calculates an estimated value by reducing the errors in the measurement values of the multiple flow meters 3a-3d. Note that the error in the measurement value here refers to the deviation between the measurement value and the true value.

After inspecting the system, the worker acquires field meter data multiple times using the field flow meter 4 a. The field meter measurement error setting unit 13 calculates the average measurement value and measurement error of the field flow meter 4 a based on the multiple acquired data.
The estimation unit 14 estimates the measurement data value at the evaluation time at the location of the on-site flow meter 4 a from the constraint conditions and the time-series data of the signals of each meter. Here, the on-site flow meter 4 a is a on-site meter that measures a predetermined location during patrol inspection by an operator.

The deterioration detection unit 15 detects deterioration of a system based on the system's measurement values. The deterioration detection unit 15 issues an alarm when it detects deterioration of a system. Note that when the deterioration detection unit 15 detects deterioration of a system, it may switch from that system to another system. This allows the plant to continue operating by switching from the deteriorated system to another healthy system.

The memory unit 16 is a large-capacity storage device such as a hard disk, and stores the physical model 161 of this system.

This invention focuses on the fact that at least one data item in a system is continuous measurement data received remotely, and that field instrument data is associated with remote measurement data via a physical model or the like. The challenges of this embodiment are addressed in three phases: (1) physical model construction, (2) deviation correction, and (3) DVR performance evaluation.

(1) Physical Model Construction In the physical model construction phase, the plant manager constructs a physical model 161, which is a constraint condition for DVR evaluation for linking measurement data with field instrument data, such as heat balance, pump QH characteristics, heat balance equations and heat transfer equations for heat exchangers, route pressure loss-flow rate relationship equations, etc. The plant manager stores this physical model 161 in advance in the storage unit 16 of the system performance monitoring device 1.

(2) Deviation Correction In the deviation correction phase, an operator acquires field instrument data immediately after an equipment inspection when instrument and equipment abnormalities can be ignored, and inputs the data to the system performance monitoring device 1. The system performance monitoring device 1 then combines the field instrument data and the measurement data to correct the deviation of the physical model 161, and matches the characteristics of the physical model 161 to the sound state of the actual equipment. The operator also acquires this field instrument data multiple times from each field instrument and inputs it to the system performance monitoring device 1. The system performance monitoring device 1 calculates the standard deviation from the variance in the field instrument data, and sets the error of the field instrument.

(3) DVR performance evaluation In the DVR performance evaluation phase, the system performance monitoring device 1 interpolates the values at the same time of each field instrument data acquired separately during patrol inspections via a time series curve created from the remotely measured data and the physical model 161. The system performance monitoring device 1 further performs DVR evaluation from the field instrument data and remotely measured data, the time of which has been interpolated with an error set , via the constraints of the physical model 161. The system performance monitoring device 1 calculates the system flow rate from the deviation between the DVR evaluation value and an estimated value of the flow rate, etc., obtained using the physical model characteristics tailored to the actual equipment, and monitors equipment deterioration, etc.

This invention makes it possible to optimally monitor system functions without installing additional instruments. Furthermore, this invention makes it possible to distinguish between actual fluctuations and instrument errors and evaluate system performance with high accuracy. Furthermore, by quantitatively monitoring pump performance degradation and heat exchanger performance degradation, which are important for monitoring system functions, based on actual equipment operating data, it becomes possible to streamline inspection intervals.

FIG. 2 is a diagram showing the relationship between the measured values of each instrument and the error.
The graphs, from left to right, show the flow meters 3a, 3b, 3c, and 3d. The dashed lines and hatching in the graphs indicate the frequency distribution of the measured values and errors of the flow meters 3a, 3b, 3c, and 3d. The solid line in the left graph shows the distribution of the estimated values after DVR correction using the measured values of only the flow meter 3a. The solid line in the second graph from the left shows the distribution of the estimated values after DVR correction using the measured values of the flow meters 3a and 3b. The solid line in the third graph from the left shows the distribution of the estimated values after DVR correction using the measured values of the flow meters 3a, 3b, and 3c. The solid line in the fourth graph from the left shows the distribution of the estimated values after DVR correction using the measured values of the flow meters 3a, 3b, 3c, and 3d. The vertical axis of the graph shows the measured values or the estimated values after DVR correction. The horizontal axis of the graph shows the frequency at which the values are measured.

The origin, where the thick dashed horizontal axis and the vertical axis intersect, is the true value. As shown in the legend, the dashed lines and hatching indicate the distribution of measurement values for each instrument. The solid line indicates the distribution of estimated values after DVR correction.

The difference between the peak of the distribution of measured values and the true value is the bias error e1. The spread of the distribution of estimated values is the random error e2. The sum of the bias error e1 and the random error e2 is the maximum error of the measured value.

As shown in Figure 2, from the graph for flow meter 3a on the left to the graph for flow meter 3d on the right, the bias error and random error of each meter are dispersed. In contrast, the bias error and random error of the estimated value after DVR correction both become smaller as the redundancy of the measurement values increases.

FIG. 3 is a flowchart of the system performance monitoring process.
First, the input unit 11 inputs the measured values of a plurality of meters installed in the piping 2, which is a system (step S10). Then, the input unit 11 determines whether or not there is on-site meter data measured by the on-site flow meter 4a (step S11).
If there is field instrument data (Yes), the input unit 11 accepts the input of the field instrument data (step S12) and proceeds to step S13. In step S12, the field instrument measurement error setting unit 13 sets an estimated value and error of the field instrument data from the field instrument data acquired multiple times. If there is no field instrument data (No), the input unit 11 proceeds to step S15.

The estimation unit 14 determines whether the acquired field instrument data is at the evaluation time (step S13). If the acquisition time of the field instrument data is not at the evaluation time (No), the estimation unit 14 estimates the field instrument data at the evaluation time measured by the field flow meter 4a from the time series data of the measurement values of multiple meters installed in the system (step S14), and proceeds to step S15. If the acquisition time is at the evaluation time (Yes), the estimation unit 14 proceeds to step S15.

In step S15, the measurement error reduction unit 12 calculates estimated values that reduce the errors in the measurement values of multiple instruments based on constraint conditions that model the system characteristics. Here, the measurement error reduction unit 12 calculates estimated values in which the errors in the measurement values have been calibrated using DVR evaluation.

In step S16, the degradation detection unit 15 determines whether the system has deteriorated. If the degradation detection unit 15 determines that the system has deteriorated (Yes), it issues an alarm indicating that the system has deteriorated (step S17) and ends the processing of Figure 3. If the degradation detection unit 15 determines that the system has not deteriorated (No), it ends the processing of Figure 3.

In this embodiment, the estimation unit 14 uses field instrument data with errors set through multiple measurements to calculate an estimate from the set physical model and remotely measured data. Furthermore, the estimation unit 14 interpolates the estimated value of the field instrument data at the DVR evaluation time from the time-series curve. Using the interpolated estimated value of the field instrument data, the remotely measured data, and the physical model, the measurement error reduction means performs an error reduction evaluation, thereby estimating the system flow rate with high accuracy and monitoring the equipment performance.

FIG. 4 is a flowchart of the process for reducing errors in the measurement values.
First, the input unit 11 inputs the measured values of a plurality of meters installed in the system (step S20). Here, the input unit 11 inputs the measured values of the flow meters 3a to 3d shown in FIG. 1. Then, an operator measures the field meter data multiple times using the field flow meter 4a (step S21) and inputs the acquired field meter data to the system performance monitoring device 1. The input unit 11 accepts the input of the field meter data (step S22).

The field meter measurement error setting unit 13 calculates errors in the field meter data from the field meter data acquired multiple times after the system inspection. After calculating estimated values of parameters such as system flow rate (step S23) based on the estimated values of the field meter data calculated by the estimating unit 14 , the errors calculated by the field meter measurement error setting unit 13 , the measured values of the flow meters 3a to 3d, which are multiple meters that perform measurements remotely, and constraint conditions that model the characteristics of the system, the processing in FIG. 4 ends.

<<Data Validation Reconciliation (DVR) Explanation>>
In continuous process plants such as oil refineries, petrochemical plants, general chemical plants, and power plants, flow rates, temperatures, pressures, and liquid levels are measured in real time to understand the operating state of the process. These measurements are inevitably observed as values containing errors. Data validation reconciliation (DVR) is a technique for obtaining better estimates of the operating state of a process by applying a physical model 161 that describes material and energy balances to these raw measurements. In other words, it utilizes the redundancy in the number of measurement points for the physical model 161 to obtain a set of estimates that satisfy all the relationship equations of the physical model 161 and minimize the amount of correction to the original measurements. Here, an example is described in which field instruments Y and Z, which measure at the system site, are used to reduce instrument errors.

5 is a graph showing estimated values of a physical model of a certain system. The vertical axis of the graph represents system flow rate, and the horizontal axis of the graph represents time.
The solid line in the graph indicates the estimated value of the physical model. The dashed line indicates the true value. The estimated value of the physical model has a certain deviation from the true value due to various error factors.

6 is a graph showing deviation correction. The vertical axis of the graph represents the system flow rate. The horizontal axis of the graph represents time. Time tc is the timing when the equipment was inspected.
The dashed-dotted line in the graph indicates an estimated value estimated using a physical model set from remotely measured values. The solid line in the graph indicates an estimated value estimated from deviation-corrected values estimated from the physical model. The dashed line indicates the true value. Area 51 represents multiple pieces of on-site measurement data measured by a worker using a field instrument at a certain patrol time. Area 52 represents multiple pieces of on-site measurement data measured by a worker using a field instrument at a different patrol time. By using this multiple pieces of on-site measurement data and applying deviation correction to the estimated values from the physical model, it is possible to estimate values close to the true value.

Figure 7 is a graph showing DVR evaluation. The vertical axis of the graph represents system flow rate. The horizontal axis of the graph represents time. Time tc is the time when the worker inspected the equipment. Time te is the time when the DVR evaluation was performed.

The solid line on the graph represents the true value. The circular icons represent field measurement data measured by a worker using field instrument Y. The fine dashed line on the top is a data estimation curve based on the field measurement data measured by a worker using field instrument Y. This data estimation curve allows the field instrument data at field instrument Y at time te, which is the DVR evaluation timing, to be estimated.

The triangle icon indicates the field measurement data measured by field instrument Z. The rough dashed line below the solid line is a data estimation curve based on the field measurement data measured by field instrument Z. This data estimation curve allows the field measurement data at the location of field instrument Z at the time of DVR evaluation to be estimated.

This embodiment makes it possible to monitor the performance of systems outside the thermal cycle, such as auxiliary cooling water systems and seawater systems, which was previously difficult, without installing additional instruments. Furthermore, by quantitatively monitoring the deterioration of pump and heat exchanger performance, it becomes possible to streamline inspection intervals.

<Example of measuring pipe flow rate using a flow meter>
FIG. 8 is a diagram showing measurement data before applying DVR evaluation.
There is a simple system as shown in Figure 8, and flow meters 31a to 31c are installed in the piping of each system to measure the mass flow rate. The measured value A of flow meter 31a is 50 t/h. The measured value B of flow meter 31b is 25 t/h. The measured value C of flow meter 31c is 20 t/h.

The error of flow meter 31a is ±10%. The error of flow meter 31b is ±5%. The error of flow meter 31c is ±3%.

Pipe A branches into pipe B and pipe C. In this case, the equation A = B + C holds true as the equation that represents the material balance (physical model). Therefore, since there are three variables and one equation that defines their relationship, if there are 3 - 1 = 2 measurement points, the flow rate at the other measurement points can theoretically be calculated. In other words, the degrees of freedom of this process are 2. If all three variables are measured, there is one more measurement value than the degrees of freedom of the process, so the measurement redundancy is 1.

On the other hand, actual flow rate measurements, due to their uncertainty, do not satisfy the process material balance equation. This situation is common in system measurements. DVR evaluation takes advantage of this measurement redundancy to reduce the uncertainty of individual measurements while obtaining best estimates that satisfy the physical model. These best estimates can also be used to calculate process performance indicators and estimate unmeasured variables.

DVR evaluation involves finding the best estimates of process variables using the least squares method as follows when there are a greater number of measurement points than the degrees of freedom of the process (redundant measurement points exist), as in the above example: Since a physical model generally becomes a nonlinear simultaneous equation, a nonlinear least squares problem is solved.
Equation (1) is a basic equation relating to the degree of consistency.

Equation (2) is an equation related to constraints. The constraints are configured from a physical model of the system. Here, the physical model is a turbine model or the like.

FIG. 9 shows the estimated values of the measurement data after applying the DVR evaluation.
The estimated value of the measurement data after applying DVR evaluation for flow meter 31a is 46.27 t/h. The estimated value of the measurement data after applying DVR evaluation for flow meter 31b is 25.93 t/h. The estimated value of the measurement data after applying DVR evaluation for flow meter 31c is 20.34 t/h.

The error of the flow meter 31a is ±5.04 % , the error of the flow meter 31b is ±4.51%, and the error of the flow meter 31c is ±2.90%.

In the examples of Figures 8 and 9, if the "uncertainty" of each measurement value can be estimated, it becomes possible to perform the DVR evaluation described above. The DVR evaluation allows for the acquisition of estimated values at each measurement point that satisfy the material balance and have less uncertainty than the original measurement value.

In this way, when there are more measurement points than the process model and its degrees of freedom, DVR evaluation makes it possible to obtain more reliable measurement point estimates that satisfy the process model by providing the uncertainty of the measurement values at each measurement point. It also makes it possible to calculate process performance indicators and unmeasured variables, along with their uncertainties, which can be calculated as a function of the obtained estimates.

In the examples of Figures 8 and 9, even if one of the flow measurement points becomes unmeasurable due to a meter failure or other reason, measurement redundancy remains, so it is possible to perform DVR evaluation. In other words, the flow rate at the unmeasurable measurement point can be estimated as an unmeasured variable. However, in this case, the uncertainty of each estimated value will be greater overall than if there were no meter failure.

Below, we will explain an example of calculating flow rate from other measured values via a physical model. This makes it possible to calculate flow rate at locations where no flow meter is installed.

<<Example of measuring flow rate from pump performance curve>>
In this embodiment, the system is a pipe including a pump. The deterioration detection unit 15 can detect deterioration of the pump from the deviation between the discharge flow rate calculated from the pump performance curve and the system flow rate calculated by the measurement error reduction unit 12.

FIG. 10 is a diagram showing a system including a pump and a pressure sensor.
A pump 22 is installed in the pipe 2 , and a pressure sensor 32 a is installed downstream of the pump 22 , and a pressure sensor 32 b is installed downstream of the pump 22 .

FIG. 11 is a graph showing the relationship between the flow rate and the head.
The vertical axis of the graph represents the head. The horizontal axis of the graph represents the flow rate. Here, the head refers to the differential pressure between the inlet and outlet of the pump 22. The differential pressure can be calculated from the difference between the measurement value of the pressure sensor 32a and the measurement value of the pressure sensor 32b. The lower the differential pressure, the greater the flow rate, and here the relationship is expressed by equation (3).

When the differential pressure is ΔP ₁ , the flow rate Q ₁ can be obtained by substituting the differential pressure ΔP ₁ into equation (3).

FIG. 12 is a graph showing a method for detecting pump deterioration.
The solid line indicates the QH characteristics of the pump 22 in a healthy state, and the dashed line indicates the QH characteristics of the pump 22 in a deteriorated state.

If there is a deviation between the system flow rate evaluation value _Q2 obtained by DVR evaluation using various physical models and the flow rate _Q1 discharged by a pump 22 in a healthy state, which is estimated from the differential pressure at the pump inlet and outlet using a physical model of the performance curve of the pump 22, it is determined that the Q-H characteristic curve of the pump 22 has dropped, and pump deterioration is detected.

<<Example of determining flow rate from heat transfer characteristics of a heat exchanger>>
In this embodiment, the measurement error reducing unit 12 calculates the flow rate of the cooling water using the definition equation of the heat exchange amount of the heat exchanger as a constraint condition, and the deterioration detecting unit 15 detects deterioration of the heat exchanger.

FIG. 13 is a diagram showing a system including the heat exchanger 23.
The heat exchanger 23 exchanges heat between the cooling water flowing through the pipe 2a installed horizontally in the figure and the cooling water flowing through the pipe 2b installed vertically in the figure. The flow rate of the pipe 2a is _G1 .

A thermometer 33a is installed at the inlet of the pipe 2a, and a thermometer 33b is installed at the outlet. The measured value of the thermometer 33a is _T1 . The measured value of the thermometer 33b is _T2 .
A thermometer 33c is installed at the inlet of the pipe 2b, and a thermometer 33d is installed at the outlet. The measured value of the thermometer 33c is _T3 . The measured value of the thermometer 33d is _T4 .

Equation (4) is the first heat transfer equation for the heat exchanger 23.

From equation (4), since the overall heat transfer coefficient k and the heat transfer area A are known, the logarithmic mean temperature difference θm can be found from the measured values T ₁ , T ₂ , T ₃ , and T ₄ , and the heat exchange amount Q can be found.
Equation (5) is the second heat transfer equation for the heat exchanger 23.

The flow rate _G1 can be calculated from the heat exchange amount Q calculated by equation (4) and the inlet/outlet temperature difference ΔT using equation (5). In other words, the measurement error reduction unit 12 calculates the flow rate based on the performance curve of the heat exchanger 23.
The performance of the heat exchanger 23 is expressed by the overall heat transmission coefficient k in equation (4). In the heat transfer characteristic equation (4), if the flow rate _G1 is known from other model characteristics and DVR evaluation, the heat exchange amount Q can be found using only equation (5).
In equation (4), if Q, A, and θm are known, k can be found, and if the value of k decreases continuously, a decrease in the performance of the heat exchanger 23 (heat transfer tube fouling) can be detected.

The reason why on-site instrument data must be entered is that the inlet and outlet temperatures of the heat exchanger 23 can only be measured using on-site instruments, and there may be cases where an instrument capable of measuring remotely is not installed.

<<Example of calculating flow rate from pressure loss characteristics>>
FIG. 14 is a diagram showing a system relating to pressure loss characteristics.
A pressure gauge 34a and a pressure gauge 34b are installed in the pipe 24. In this system, a pressure loss occurring region is included between the installation location of the pressure gauge 34a and the installation location of the pressure gauge 34b.
The measured value of the pressure gauge 34a is _P3 . The measured value of the pressure gauge 34b is _P4 . The pressure difference between the two is _ΔP3 . The equation for calculating the pressure loss is shown in equation (6).

FIG. 15 is a graph showing the relationship between the flow rate and the pressure loss.
The vertical axis of the graph represents pressure loss, and the horizontal axis of the graph represents flow rate. In this case, the larger the pressure loss, the larger the flow rate.
The solid line in the graph can be expressed by the following equation (7).

The flow rate _Q3 at that time can be calculated by calculating _ΔP3 from the measured values of the pressure gauges 34a and 34b and substituting this into equation (7).
The reason why the input of field instrument data is necessary is that the pressure gauges 34a and 34b are field instruments, and there are cases where instruments capable of measuring remotely are not installed.

<<Example of measuring flow rate from turbine characteristics>>
FIG. 16 is a diagram showing a system related to the turbine 25.
In the turbine 25, a predetermined stage flow rate _Q5 flows in each stage , and a predetermined stage pressure _P5 is generated.

FIG. 17 is a graph showing the relationship between flow rate and stage pressure .
The vertical axis of the graph represents the stage pressure of the turbine 25. The horizontal axis of the graph represents the flow rate. The stage flow rate _Q5 of the turbine 25 is approximately proportional to the pressure and can be expressed by the following equation (8).

Therefore, the stage flow rate _Q5 is calculated from the stage pressure _P5 . That is, the measurement error reducing unit 12 calculates the flow rate based on the stage pressure of the turbine 25.

<<Example of measuring flow rate from valve opening>>
FIG. 18 is a diagram showing a system relating to the valve 26.
A valve 26 is provided in the piping. A valve opening detector 36a is provided in the valve 26. The valve opening detector 36a detects the valve opening _OCV . A flow meter 36b is provided on the outlet side of the valve 26. The flow meter 36b detects the flow rate _Q7 .

FIG. 19 is a graph showing the relationship between the flow rate and the valve opening.
The vertical axis of the graph represents the valve opening, and the horizontal axis of the graph represents the flow rate. The valve opening and the flow rate satisfy the relational expression (9).

Therefore, the measurement error reducing unit 12 can obtain the flow rate _Q7 from the valve opening _OCV .

(Modification)
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.

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

In each embodiment, the control lines and information lines shown are those considered necessary for explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be assumed that almost all components are interconnected.

1 System performance monitoring device 11 Input unit (input means)
12 Measurement error reduction unit (measurement error reduction means)
13 Field instrument measurement error setting unit (field instrument measurement error setting means)
14 Estimation section (estimation means)
15 Deterioration detection unit (deterioration detection means)
16 Memory unit 161 Physical model 2 Pipes 3a to 3d Flow meter (instrument)
4a Field flow meter (field meter)

Claims (14)

an input means for inputting measurement values of a plurality of remote meters installed in the system and measuring remotely , and measurement values of field meters installed at predetermined locations and acquired by an operator ;
an estimation means for calculating an estimated value of the field meter at a predetermined time based on constraints that model the characteristics of the system and time-series data of the measurement values of the remote meter;
a measurement error reduction means for calculating a correction value that reduces an error in the measurement value of the meter based on the measurement value of the remote meter, the constraint conditions, and the estimated value of the field meter calculated by the estimation means ;
A system performance monitoring device comprising: a field instrument measurement error setting means for acquiring measurement values of the field instrument multiple times after the inspection of the system and calculating an error in the measurement values of the field instrument;
2. The system performance monitoring device according to claim 1 , further comprising : a deterioration detection means for detecting deterioration of the system based on a measurement value of the system;
2. The system performance monitoring device according to claim 1. the system includes a pump;
the deterioration detection means detects deterioration of the pump from a deviation between a discharge flow rate calculated from a performance curve of the pump and a system flow rate calculated from the measurement error reduction means;
4. The system performance monitoring device according to claim 3 . the system includes a heat exchanger;
In the measurement error reduction means, a definition equation of the heat exchange amount of the heat exchanger is set as the constraint condition,
The deterioration detection means detects deterioration of the heat exchanger.
4. The system performance monitoring device according to claim 3 . The measurement value of the remote meter is any one of the flow rate, temperature, and pressure of the liquid or gas flowing in the system.
2. The system performance monitoring device according to claim 1. the system includes a pump;
the measurement error reducing means calculates the flow rate based on a performance curve of the pump.
7. The system performance monitoring device according to claim 6 . the system includes a heat exchanger;
the measurement error reducing means calculates the flow rate based on a performance curve of the heat exchanger.
7. The system performance monitoring device according to claim 6 . The system includes a pressure loss generation region,
the measurement error reducing means calculates the flow rate based on the pressure loss in the pressure loss occurring region.
7. The system performance monitoring device according to claim 6 . the system includes a turbine;
the measurement error reducing means calculates the flow rate based on the stage pressure of the turbine.
7. The system performance monitoring device according to claim 6 . the system includes a valve;
the measurement error reduction means calculates the flow rate from the valve opening degree of the valve.
7. The system performance monitoring device according to claim 6 . The deterioration detection means issues an alarm when it detects deterioration of the system.
4. The system performance monitoring device according to claim 3. the deterioration detection means switches from the system to another system when it detects deterioration of the system;
4. The system performance monitoring device according to claim 3. a step of receiving, via an input means, measurement values of a plurality of remote meters that are installed in the system and perform remote measurements, and measurement values of field meters that are installed at predetermined locations in the system and acquired by an operator ;
calculating an estimated value of the field meter at a predetermined time from constraints that model the characteristics of the system and time-series data of the measurement values of the remote meter input from the input means;
a step in which a measurement error reduction means calculates a correction value that reduces an error in the measurement value of the meter based on the measurement value of the remote meter, the constraint condition, and the estimated value of the field meter received by the input means;
A system performance monitoring method comprising: