JP3881925B2 - Plant operating state prediction method and nuclear plant operating state prediction system - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention is applied to a plant such as a thermal power plant, a steel mill, or a chemical factory, and builds a database by accumulating plant operating conditions and operating state data, and based on knowledge obtained from the database. The present invention relates to a plant operation state prediction method for predicting the future performance of a plant, and a nuclear plant operation state prediction system in which this plant operation state prediction method is applied to a nuclear plant.
[0002]
[Prior art]
For example, in a huge plant such as a nuclear power plant, a thermal power plant, a steel plant, or a chemical factory, a plant performance diagnosis system that determines whether the current operating state is normal or abnormal is applied. ing.
[0003]
In such a plant performance diagnosis system, operation data of the corresponding plant is acquired from the past to the present. Based on the acquired operation data, a database showing the range of operation data in a normal operation state is constructed.
[0004]
Then, the current operation data is compared with this database, and if the current operation data is within the normal range, it is confirmed that the current operation state is normal. On the other hand, if the current operation data deviates from the normal range, it is determined that there is an abnormality in the plant. When it is determined that there is an abnormality in the plant, the operator is informed by issuing an alarm or the like, and appropriate measures are taken to avoid the abnormality.
[0005]
For example, the performance of a pressurized water nuclear power plant (hereinafter referred to as “PWR”) is predicted by the output fluctuation accompanying the change in the performance coefficient of the steam generator and the vacuum degree of the condenser. Among these, the steam generation performance coefficient is predicted in the summertime during the operation cycle, which becomes severe as the initial value of the operation cycle changes. Moreover, the vacuum degree of the condenser is performed using the worst value recorded from the past data. The output under specified conditions such as summertime is evaluated from the output change amount obtained by these two parameters.
[0006]
[Problems to be solved by the invention]
However, although the conventional plant performance diagnosis system can determine the normal / abnormality of the current operation state of the target plant and predict the operation state in the near future as described above, it can predict the operation day. Can't do it.
[0007]
For example, in the case of nuclear power plants, periodic inspections are performed approximately every year. Therefore, if the performance can be predicted on any operation day, it will be possible to grasp the replacement time of the equipment.Therefore, it is possible to replace the equipment without reducing the operating rate using the periodic inspection period. become able to. This also makes it possible to perform subsequent operations stably and safely.
[0008]
However, in the conventional plant performance diagnosis system, as described above, it is possible to predict the operation state in the near future, that is, in the case of a nuclear power plant, only the operation cycle or the next operation cycle can be predicted. It is carried out using the worst condenser vacuum degree and so on. For this reason, since it becomes an excessively severe condition setting, there exists a problem that the usage amount of auxiliary steam, the management operation of water quality, etc. will be burdened a lot.
[0009]
The present invention has been made in view of such circumstances, and a first object of the present invention is to build a database by accumulating plant operating condition data and operating state data, and based on information obtained from this database. Thus, it is an object of the present invention to provide a plant operation state prediction method capable of predicting the future performance of a plant by acquiring a correlation equation between operation conditions and operation state data and using this correlation equation.
[0010]
The second object is to provide a nuclear plant operational state prediction system that applies this plant operational state prediction method to a nuclear plant and facilitates the creation of a nuclear plant performance prediction, nuclear plant operation plan and equipment replacement plan. It is to provide.
[0011]
[Means for Solving the Problems]
In order to achieve the above object, the present invention takes the following measures.
[0012]
That is, in the plant operation state prediction method of the invention of claim 1, in order to achieve the first object, the operation condition data of the plant and the operation result data of the equipment provided in the plant are acquired and acquired. Operation condition data and operation result data When The data obtained by standardizing the acquired operation result data is stored together with the acquired time data, and the operation condition of this plant, the operation result of the equipment and the data obtained by normalizing the operation result data based on each stored data, Each of these correlations is acquired, and based on the acquired correlation, the operation state of the equipment and the secular change are predicted, and the performance prediction of the nuclear power plant and the equipment replacement plan are made.
[0013]
In order to achieve the second object, the nuclear power plant operating state prediction system according to the second aspect of the present invention is provided with operating condition data acquiring means for acquiring operating condition data of the nuclear power plant from the nuclear power plant, and the nuclear power plant. From this steam generator Show characteristic transitions by plant Steam generation performance coefficient data acquisition means for acquiring the steam generation performance coefficient; Means to normalize the acquired steam generation performance coefficient; The operating condition data acquired by the operating condition data acquisition means, and the steam generation performance coefficient acquired by the steam generation performance coefficient data Standardized steam generation performance factor Is stored together with the acquisition time data, and the operation conditions of the nuclear power plant and the steam generation performance coefficient of the steam generator based on the data stored in the data storage means. And normalized steam generation performance coefficient Based on the correlation acquired by the correlation acquisition means, and the correlation acquired by the correlation acquisition means, Acquisition of correlation formula for secular change of steam generation performance coefficient and The steam generation performance of the steam generator From the correlation equation for steam generation performance coefficient obtained from the correlation equation for secular change and the standardized correlation equation for steam generation performance coefficient With performance prediction means to predict , For performance prediction of nuclear power plants and equipment replacement planning The
[0014]
In addition, In order to achieve the second object described above, the nuclear power plant operating state prediction system includes operating condition data acquiring means for acquiring operating condition data of the nuclear power plant from the nuclear power plant, and a condenser provided in the nuclear power plant. The vacuum degree / seawater temperature data acquisition means for acquiring the vacuum degree data and seawater temperature data of the condenser, the operating condition data acquired by the operating condition data acquisition means, and the vacuum degree / seawater temperature data acquisition means A data storage means for storing the vacuum degree data and the seawater temperature data together with the acquisition time data thereof, and the correlation between the operating conditions of the nuclear power plant and the seawater temperature based on each data stored in the data storage means, Correlation acquisition means for acquiring the correlation between the seawater temperature and the vacuum degree of the condenser, and the correlation Based on the correlation obtained by the obtained unit, and a performance prediction means for predicting the performance and aging of the condenser, the performance prediction and design of replacement plans equipment of a nuclear plant You may do .
[0015]
Also, In order to achieve the second object described above, the nuclear power plant operating state prediction system includes operating condition data acquiring means for acquiring operating condition data of the nuclear power plant from the nuclear power plant, and turbine steam for supplying steam to the turbine of the nuclear power plant. Flow rate data acquisition means for acquiring flow rate data of the coolant circulating in the turbine steam loop from a flow meter provided in the loop, operation condition data acquired by the operation condition data acquisition means, and flow rate data acquisition means Based on the data storage means that stores the flow rate data together with the acquisition time data, and the data stored in the data storage means, the correlation between the operating conditions of this nuclear power plant and the coolant flow rate is acquired. Based on the correlation acquired by the correlation acquisition means and the correlation acquisition means Te, and a performance prediction means for predicting the flow rate determined aging of the required flow rate of the coolant, the performance prediction and design of replacement plans equipment of a nuclear plant You may do .
[0016]
Claim 3 In order to achieve the second object, the nuclear plant operating state prediction system according to the invention supplies operating condition data acquisition means for acquiring operating condition data of the nuclear power plant from the nuclear power plant, and supplies steam to the turbine of the nuclear power plant. Steam pressure data acquiring means for acquiring steam pressure data of coolant water circulating in the turbine steam loop from a pressure gauge provided in the turbine steam loop, operating condition data acquired by the operating condition data acquiring means, steam This nuclear power plant is normalized based on the steam pressure data acquired by the pressure data acquisition means and the steam pressure data, and is stored together with the acquisition time data. A correlation acquisition means for acquiring a correlation between the operating conditions of the steam and the steam pressure; Based on the correlation acquired by the relationship acquisition means, it is equipped with a performance prediction means for determining the steam pressure over time and predicting the steam pressure, and performs the performance prediction of the nuclear plant and the planning of equipment replacement. .
[0017]
Claim 4 In order to achieve the second object, the nuclear plant operating state prediction system according to the invention supplies operating condition data acquisition means for acquiring operating condition data of the nuclear power plant from the nuclear power plant, and supplies steam to the turbine of the nuclear power plant. The flow rate data of the coolant circulating in the turbine steam loop from the flow meter provided in the turbine steam loop, the steam pressure data of the coolant circulating in the turbine steam loop from the pressure meter provided in the turbine steam loop, the turbine The temperature data of the coolant circulating in this turbine steam loop from the thermometer provided in the steam loop, the steam generator performance coefficient of the steam generator from the steam generator that evaporates the coolant and supplies steam to the turbine steam loop Operation data acquisition means, operation condition data acquisition means and operation data acquisition Normalization of steam generation performance coefficient acquired by the stage, data storage means for storing each data including the normalized steam generation performance coefficient together with the acquisition time data, and each data stored in the data storage means Based on the correlation acquisition means for acquiring the correlation between the operating conditions of the nuclear power plant and the heat output, and based on the correlation acquired by the correlation acquisition means, the secular change through the operation cycle is obtained and the output is obtained. It is provided with a performance predicting means for predicting, and performs performance prediction of the nuclear power plant and equipment replacement plan.
[0018]
Claim 5 In order to achieve the second object of the present invention, 4 In the nuclear plant operating state prediction system according to the present invention, the performance prediction means further predicts the electrical output, and makes a performance prediction of the nuclear plant and a replacement plan for the equipment.
[0019]
In addition, In order to achieve the second object, the nuclear power plant operating state prediction system is provided for operating condition data acquiring means for acquiring operating condition data of the nuclear power plant from the nuclear power plant, and for monitoring the core of the nuclear power plant. Temperature difference data between the core outlet and the inlet obtained from the core inlet / outlet thermometer, steam pressure data in the turbine obtained from the pressure gauge provided to monitor the turbine of the nuclear power plant, for supplying steam to the turbine The operation data acquisition means for acquiring the temperature data of the coolant circulating in the turbine steam loop using the thermometer provided in the turbine steam loop, and each of the operation data acquired by the operation condition data acquisition means and the operation data acquisition means Data accumulating means for accumulating data together with the acquisition time data, and data accumulating A correlation acquisition means for acquiring a correlation between the operating conditions of the nuclear power plant and the heat output based on each data accumulated in the means, and a turbine steam loop based on the correlation acquired by the correlation acquisition means. Performance prediction means for predicting the temperature of the coolant circulating inside, the temperature difference between the inlet and outlet of the core, and the pressure and aging of the steam in the turbine, and planning the performance prediction of the nuclear plant and equipment replacement plan You may do .
[0020]
DETAILED DESCRIPTION OF THE INVENTION
Embodiments of the present invention will be described below with reference to the drawings.
[0021]
An embodiment of the present invention will be described with reference to FIGS.
[0022]
FIG. 1 is a conceptual diagram of a system configuration of a nuclear power plant operation state prediction system to which a plant operation state prediction method according to an embodiment of the present invention is applied.
[0023]
As shown in FIG. 1, the nuclear power plant 1 includes a nuclear reactor 2, a core R, a steam generator 3, a pressure gauge 4, a turbine 5, a condenser 6, a flow meter 7, a thermometer 8, and a primary. A system loop 9 and a secondary system loop 10 are provided.
[0024]
The core R is provided in the nuclear reactor 2, and the primary system loop 9 is a pipe that continuously supplies coolant to the core R by the primary system pump 25, and in the steam generator 3 provided in the middle Steam is generated from the coolant supplied by the feed water pump 26 of the secondary system loop 10, and the generated steam is supplied to the secondary system loop 10.
[0025]
The primary loop 9 includes a thermometer 23 for monitoring the temperature difference of the core R.
[0026]
In addition to the steam generator 3, the secondary system loop 10 is provided with a pressure gauge 4, a turbine 5, a condenser 6, a flow meter 7, and a thermometer 8. The steam is pressurized by the pressure gauge 4. After being measured, it is supplied to the turbine 5. Then, after being supplied to the rotation of the turbine 5, the water is condensed in the condenser 6, the flow rate is measured by the flow meter 7, and the temperature is measured by the thermometer 8. The pressure of the turbine 5 is monitored by a pressure gauge 22 after the first stage of the turbine.
[0027]
The condenser 6 circulates seawater supplied from a seawater circulation pump 27 provided in the seawater system 24, and removes heat that has not been consumed by the turbine 5. The seawater system 24 includes a thermometer 21 for monitoring the seawater temperature.
[0028]
The nuclear power plant operation state prediction system 11 according to the embodiment of the present invention acquires operation condition data of the nuclear power plant 1 and operation result data of each device provided in the nuclear power plant 1, and acquires the acquired operation condition data, The operation result data is accumulated together with the acquisition time data, and the correlation between the operation condition of the nuclear power plant 1 and the operation result of each device is obtained based on each accumulated data, and based on the obtained correlation. The operation state of the nuclear power plant 1 and each device is predicted.
[0029]
Specifically predicted items are: a) steam generation performance of the steam generator 3; b) pressure of steam circulating in the secondary system loop 10; c) need for coolant circulating in the primary system loop 9. Feed water flow rate, d) performance of condenser 6, e) heat output generated by core R and steam generator 3, f) temperature difference at core inlet / outlet monitoring core R, g) turbine 5 The pressure after the first stage of the turbine h) The surplus or deficiency of the electrical output when the heat output is constant.
[0030]
The nuclear power plant operation state prediction system 11 according to the embodiment of the present invention for predicting each operation state includes an operation condition data acquisition unit 12, a steam generation performance data acquisition unit 13, a temperature data acquisition unit 14, and a steam pressure. Data acquisition unit 15, flow rate data acquisition unit 16, vacuum degree data acquisition unit 17, data storage unit 18, correlation acquisition unit 19, performance prediction unit 20, core temperature difference data acquisition unit 28, turbine A first post-stage pressure data acquisition unit 29, a seawater temperature data acquisition unit 30, and an output unit 31 are provided.
[0031]
The operating condition data acquisition unit 12 acquires operating condition data of the nuclear power plant 1 from the nuclear power plant 1.
[0032]
The steam generation performance data acquisition unit 13 acquires the steam generation performance coefficient of the steam generator 3 from the steam generator 3. The temperature data acquisition unit 14 acquires the temperature data of the coolant circulating in the secondary system loop 10 from the thermometer 8. The steam pressure data acquisition unit 15 acquires the steam pressure data of the coolant circulating in the secondary system loop 10 from the pressure gauge 4. The flow rate data acquisition unit 16 acquires the flow rate data of the coolant circulating in the secondary system loop 10 from the flow meter 7. The vacuum degree data acquisition unit 17 acquires the vacuum degree data of the condenser 6 from the condenser 6. The core temperature difference data acquisition unit 28 acquires temperature difference data at the inlet / outlet of the core R from the thermometer 23. The turbine first-stage post-pressure data acquisition unit 29 acquires the steam pressure in the turbine 5 from the pressure gauge 22. The seawater temperature data acquisition unit 30 acquires seawater temperature data from a thermometer 21 provided in the seawater circulation system 24.
[0033]
The data storage unit 18 is acquired by the operating condition data acquired by the operating condition data acquisition unit 12, the steam generation performance coefficient of the steam generator 3 acquired by the steam generation performance data acquisition unit 13, and the temperature data acquisition unit 14. Coolant temperature data, coolant steam pressure data acquired by the steam pressure data acquisition unit 15, coolant flow rate data acquired by the flow rate data acquisition unit 16, condensate acquired by the vacuum degree data acquisition unit 17 The vacuum degree data of the vessel 6, the temperature difference data acquired by the core temperature difference data acquisition unit 28, the pressure data acquired by the turbine first-stage post-pressure data acquisition unit 29, and the temperature acquired by the seawater temperature data acquisition unit 30 Data is accumulated with each acquisition time data.
[0034]
The correlation acquisition unit 19 is the operation condition data stored in the data storage unit 18, the steam generation performance coefficient, the coolant temperature data, the coolant steam pressure data, the coolant flow rate data, and the vacuum degree of the condenser 6. The following correlation is acquired based on the data, temperature difference data at the inlet / outlet of the core R, turbine first stage post-pressure data, seawater data, and acquisition time data thereof.
[0035]
That is,
1) Acquisition of correlation between the operation conditions of the nuclear power plant 1 and the steam generation performance of the steam generator 3 based on the operation condition data, the steam generation performance coefficient and the acquisition time data thereof.
FIG. 2 is a diagram showing a correlation (prediction) curve between the operation date of the nuclear power plant 1 and the steam generation performance. As shown in FIG. 2, the steam generation performance coefficient shows a characteristic transition by the plant during the operation cycle.
[0036]
2) Acquisition of the correlation between the operating conditions of the nuclear power plant 1 and the steam pressure based on the operating condition data, the steam pressure data, and the acquisition time data thereof.
FIG. 3 is a diagram illustrating an example of a correlation (prediction) curve between the operation date of the nuclear power plant 1 and the steam pressure (steam generator outlet pressure) at the outlet of the steam generator 3 measured by the pressure gauge 4.
[0037]
3) Acquisition of the correlation between the operating conditions of the nuclear power plant 1 based on the operating condition data, the coolant flow rate data and the acquisition time data, and the coolant feed water flow rate.
4 to 6 are diagrams showing correlation curves between the operation day of the nuclear power plant and the feed water flow rate of the coolant circulating in the secondary system loop 10 measured by the flow meter 7, each of which represents a vertical axis. The parameters are different. The nuclear power plant 1 normally includes a plurality of loops including a primary system loop 9 and a secondary system loop 10. The results shown in FIGS. 4 to 6 are for the nuclear power plant 1 having four such loops, and as shown in the legend in the figure, the first loop, the second loop, the third loop, and the fourth loop, respectively. And for each average value of all loops.
[0038]
The vertical axis (ΔT / P1st) in FIG. 4 is the ratio of the inlet / outlet temperature difference (ΔT) of the coolant circulating in the primary system loop 9 to the post-stage pressure (P1st) of the turbine 5. is there. By taking (ΔT / P1st) as the vertical axis, the fluctuation of both parameters due to measurement error or the like is investigated.
[0039]
The vertical axis (Qwc / ΔT) in FIG. 5 indicates the atoms of the coolant that circulates in the primary loop 9 of the heat output (Qwc) obtained from the measured feed water flow rate (no correction or the like is adjusted). It is a ratio to the inlet / outlet temperature difference (ΔT) of the furnace 2. By taking (Qwc / ΔT) as the vertical axis, the apparent increase rate of the feed water flow rate is examined, and the correction amount of the feed water flow rate is obtained.
[0040]
It is the vertical axis | shaft (Qwc / P1st) of FIG. By taking (Qwc / P1st) as the vertical axis, the apparent increase rate of the feed water flow rate is examined, and the correction amount of the feed water flow rate is obtained.
[0041]
4) Acquisition of the correlation between the operating conditions of the nuclear power plant 1 and the vacuum degree of the condenser 6 based on the operating condition data, the vacuum degree data of the condenser 6 and the acquisition time data thereof.
FIG. 7 is a diagram showing a correlation between the seawater temperature in the nuclear power plant 1 and the degree of vacuum of the condenser 6 in the nuclear power plant 1. Note that 1 mmHg corresponds to 0.133 kPa. The symbol A shown in the figure is data acquired in the current operation cycle, the symbol B is the latest data among the data acquired in the current operation cycle, and the line C is a correlation curve and line acquired in the past three operation cycles. D is a correlation curve acquired in the current operation cycle. The condenser vacuum degree tends to decrease as the seawater temperature increases. FIG. 8 is a diagram showing a correlation between the operation date of the nuclear power plant 1 and the seawater temperature. Symbols E to G shown in FIG. 8 correspond to past operation cycles (1) to (3), respectively. Moreover, the symbol H shown in FIG. 8 is the current operation cycle. A line I shown in FIG. 8 is a first correlation curve obtained based on the symbols E to H, and a line J is a second correlation curve.
[0042]
5) Correlation between the operating conditions of the nuclear power plant 1 and the heat output based on the operating condition data and the flow rate data, pressure data, temperature data, temperature difference data, steam generation performance coefficient, and acquisition time data of these data. Get.
FIG. 9 is a diagram showing a correlation curve between the operation date of the nuclear power plant 1 and the heat output of the nuclear power plant 1 obtained as described above. In FIG. 9, the heat output is displayed as a ratio (%) to the rated heat output, but may be displayed as an output value (MW). Thermal output is most likely during summertime during the operating cycle
It tends to be higher.
[0043]
The correlation curves as shown in FIG. 2 to FIG. 9 are, for example, exponential, logarithmic, linear, quadratic, quadratic, quadratic, sixth,... Determine by fitting.
[0044]
The performance predicting unit 20 predicts each plant performance shown below based on the correlation shown in the above 1) to 5) acquired by the correlation acquiring unit 19.
a) Performance prediction of the steam generator 3 based on the correlation between the operating conditions of the nuclear power plant 1 acquired in 1) above and the steam generation performance coefficient of the steam generator 3.
b) Prediction of the steam pressure of the coolant in the secondary system loop 10 based on the correlation between the operating conditions of the nuclear power plant 1 acquired in 2) above and the steam pressure.
c) Predicting the required feed water flow rate circulating in the primary system loop 9 based on the correlation between the operating conditions of the nuclear power plant 1 acquired in 3) and the coolant flow rate.
d) Performance prediction of the condenser 6 based on the correlation between the operating conditions of the nuclear power plant 1 acquired in 4) above and the vacuum degree of the condenser 6.
[0045]
e) Prediction of seawater temperature based on the operating conditions of the nuclear power plant 1 acquired in 4) above, and the correlation between the inlet seawater temperature of the condenser 6 and its acquisition time data.
f) Prediction of the thermal output of the nuclear power plant 1 based on the correlation between the operating conditions of the nuclear power plant 1 acquired in 5) above and the thermal output of the nuclear power plant 1.
g) Prediction of the surplus or deficiency of the electrical output when the heat output is constant based on the correlation between the operating conditions of the nuclear power plant 1 acquired in 5) above and the heat output of the nuclear power plant 1.
[0046]
h) Inlet / outlet temperature difference of the core R, which is an output-related parameter of the nuclear power plant 1, based on the correlation between the operating conditions of the nuclear power plant 1 acquired in 5) and the heat output of the nuclear power plant 1, a secondary system loop 10 temperature, pressure prediction after the first stage of the turbine 5.
[0047]
Next, the operation of the nuclear power plant operation state prediction system to which the plant operation state prediction method according to the embodiment of the present invention configured as described above is applied will be described.
[0048]
First, with reference to the flowchart of FIG. 10, an operation in the case of creating a database of operation data and performing steam generator performance and steam pressure prediction will be described.
[0049]
When the operation data is made into a database and the steam generator performance and the steam pressure are predicted, the operation condition data acquisition unit 12 acquires the operation condition data of the nuclear power plant 1. Further, the steam generation performance data acquisition unit 13 acquires the steam generation performance coefficient from the steam generator 3, and the pressure gauge 4 acquires the pressure of the steam circulating in the secondary system loop 10 (S1). The operating condition data, the steam generation performance coefficient, and the steam pressure acquired in this way are stored in the data storage unit 18 and registered as a database (S2).
[0050]
Then, the steam generation performance coefficient is normalized based on the database registered in step S2 (S3), and the steam generation performance coefficient during the operation cycle is converted into a database based on the normalized steam generation performance coefficient. Is stored in the data storage unit 18 (S4). This normalization is performed by various methods including the method shown in the legend T of FIG. Based on the database created in step S4, the performance prediction unit 20 predicts the operation data during the operation cycle (S5).
[0051]
On the other hand, based on the database registered in step S2, the correlation acquisition unit 19 acquires and normalizes the correlation equation for the secular change of the steam generator performance coefficient (S6). Furthermore, the correlation acquisition unit 19 creates a database of correlation equations for aging (including standardized equations) (S7).
[0052]
Furthermore, based on the database registered in step S2, the steam pressure is standardized in the data storage unit 18 (S8), and the standardized result is converted into a database as steam pressure transition data during the operation cycle. Accumulated in the accumulation unit 18 (S9). Then, based on the accumulated database, the correlation acquisition unit 19 creates a database regarding the secular change of the steam pressure (S10).
[0053]
Based on the databases created in step S4, step S7, and step S10, the performance prediction unit 20 predicts the worst value / most certain value of the steam generator performance coefficient and the steam pressure. In order to predict these values, first, the operation cycle to be predicted and the prediction date are input from the input means (not shown) to the performance prediction unit 20 (S11).
[0054]
In step S12, the worst steam of this operation cycle is determined based on the databases created in step S4, step S7, and step S10 by the performance prediction unit 20 and the operation cycle and the prediction date input in step S11. The most probable value of the generator performance coefficient is predicted (S12).
[0055]
The performance prediction unit 20 predicts the worst steam generation performance coefficient during the operation cycle (S13), and further analyzes the steam pressure based on the prediction result (S14). The analysis of the steam pressure is performed by a program (including a sensitivity function) provided in advance in the performance prediction unit 20.
[0056]
Further, in step S15, the steam pressure of the operation cycle is determined based on each database created in step S4, step S7, and step S10 by the performance prediction unit 20, and the operation cycle and the prediction date input in step S11. The steam pressure as a base for obtaining the value is extracted (S15).
[0057]
In step S16, the worst steam pressure of the operation cycle to be predicted is predicted based on the steam pressure analyzed in step S14 and the base steam pressure extracted in step S15 (S16).
[0058]
On the other hand, in step S17, the performance prediction unit 20 determines the operation cycle to be predicted based on the databases created in step S4, step S7, and step S10, and the operation cycle and the prediction date input in step S11. An average value of the steam generator performance coefficient is predicted (S17).
[0059]
Furthermore, the steam generator performance coefficient during this operation cycle is predicted based on the average value of the steam generator performance coefficient predicted in step S17 (S18), and further, the steam pressure analysis is performed based on the prediction result. (S19). The analysis of the steam pressure is also performed by a program (including a sensitivity function) provided in advance in the performance prediction unit 20 as in step S14.
[0060]
In step S20, the steam pressure of the operation cycle to be predicted is predicted based on the steam pressure extracted in step S15 and the analysis result of the steam pressure made in step S19 (S20).
[0061]
Next, using the flowchart of FIG. 11, the operation in the case of creating a database of operation data and predicting the degree of vacuum of the condenser 6 will be described.
[0062]
When the operation data is made into a database and the degree of vacuum of the condenser 6 is predicted, the operation condition data acquisition unit 12 acquires the operation condition data (including seawater data in this case) of the nuclear power plant 1. . Further, the vacuum degree data acquisition unit 17 acquires the vacuum degree data of the condenser 6. The operating condition data and the vacuum degree data acquired in this way are stored in the data storage unit 18 and registered as a database (S21).
[0063]
Then, based on the database registered in step S21, the correlation acquisition unit 19 obtains seawater temperature yearly variation data for each operation cycle (S22), and further from the operation data for all operation cycles for each plant. An average correlation formula between the date and the seawater temperature is obtained (S23). Furthermore, the correlation formula of the worst seawater temperature of each plant is obtained based on the average correlation formula obtained in step S23 (S24), and the correlation formula of the operation cycle or the seawater temperature for prediction is obtained (S25). .
[0064]
On the other hand, based on the database registered in step S21, the correlation obtaining unit 19 obtains a correlation equation between the vacuum degree of the condenser 6 and the seawater temperature for each operation cycle of each plant (S26). An average correlation formula between the vacuum degree of the condenser 6 of the plant and the seawater temperature is obtained (S27). Then, based on the average correlation equation between the degree of vacuum and the seawater temperature obtained in step S27, the performance prediction unit 20 obtains the secular change of the performance deterioration of the condenser 6 (S28).
[0065]
Based on the correlation equations obtained in step S24, step S25, and step S28, the performance prediction unit 20 predicts the degree of vacuum of the condenser 6. In order to perform these predictions, first, as designation of prediction conditions, a plant name, the number of operation cycles, and a date to be predicted are input from an input unit (not shown) to the performance prediction unit 20 (S29).
[0066]
Then, based on the prediction condition input in step S29, the performance prediction unit 20 obtains the worst-case condenser vacuum degree correlation equation and the condenser vacuum degree at the date of prediction (S30). Further, a correlation equation of the operation cycle or the operation cycle to be predicted is obtained (S31), and further, a correlation equation of the condenser vacuum degree of the operation cycle or the operation cycle to be predicted and a recovery date at the date of prediction. The water vacuum is determined (S32).
[0067]
Next, the operation when the output (electrical output) is predicted will be described using the flowchart of FIG.
[0068]
When the heat output (electric output) is predicted, the operation condition data acquisition unit 12 acquires the operation condition data (base output condition including the electric output) of the nuclear power plant 1 (S41). Based on the operation condition data acquired in this way and the transition information of the predicted steam pressure of the operation cycle or the predicted operation cycle, which is the result obtained by the performance prediction unit 20 in step S20, the performance prediction unit 20 An output conversion (electrical output conversion) of pressure fluctuation is performed (S42).
[0069]
Further, based on the fluctuation of the steam pressure output converted in step S42 and the transition of the predicted condenser vacuum degree of the operation cycle or the predicted operation cycle, which is the result made by the performance prediction unit 20 in step S32, the performance. In the prediction unit 20, the output conversion of the condenser vacuum degree (electrical output conversion) is performed (S43).
[0070]
Next, by operating related equipment, output adjustment (electrical output adjustment) associated with auxiliary steam, water quality management operation, and other plant operations is performed (S44). Then, the performance prediction unit 20 performs worst-case output prediction (electric output prediction) (S45), and the output prediction (electric output prediction) of the operation cycle and the predicted operation cycle (S46).
[0071]
Next, using the flowchart of FIG. 13, the operation in the case of predicting the steam pressure in the operation cycle and the predicted operation cycle will be described. The flowchart shown in FIG. 13A is a case where the worst steam pressure is predicted, and the flowchart shown in FIG. 13B is a case where the steam pressure during nominal operation is predicted.
[0072]
First, as shown in FIG. 13 (a), when the worst steam pressure is predicted, the prediction result of the worst steam pressure based on the steam generator performance coefficient made in step S16 and the relevant result made in step S45. Based on the prediction result of the worst output of the operation cycle and the predicted operation cycle, the output of the steam pressure is corrected in the performance prediction unit 20 (S51), and the worst steam pressure of the operation cycle and the predicted operation cycle is further predicted. (S52).
[0073]
Next, as shown in FIG. 13 (b), when predicting the steam pressure during nominal operation, the prediction result of the steam pressure in the operation cycle or the predicted operation cycle made in step S20, and in step S46, Based on the predicted results of the operation cycle and the predicted operation cycle, the performance prediction unit 20 corrects the output of the steam pressure (S53), and further predicts the steam pressure of the operation cycle and the predicted operation cycle. (S54).
[0074]
Next, the operation in the case of predicting the feed water flow rate of the coolant circulating in the secondary system loop 10 will be described using the flowchart of FIG.
[0075]
In order to predict the feed water flow rate of the coolant circulating in the secondary system loop 10, first, a database of operation data is created. The operation data is made into a database by the operation condition data acquisition unit 12 acquiring the operation condition data of the nuclear power plant 1. Further, the feed water flow rate of the coolant circulating in the secondary system loop 10 is measured by the flow meter 7, and the measurement result is acquired by the flow rate data acquisition unit 16. The operation condition data and the flow rate data acquired in this way are stored in the data storage unit 18 and registered as a database (S61).
[0076]
Next, a plant for predicting the feed water flow rate and the number of operating cycles are input from the input means (not shown) to the performance predicting unit 20 (S62), and further, a feed water flow rate parameter and an output parameter, which are operating conditions at the time of prediction, are input ( S63). Furthermore, a reference value that is an operation condition at the time of prediction is also input (S64).
[0077]
Based on the conditions acquired or input in steps S61 to S64, the performance predicting unit 20 calculates an increase amount of the feed water flow rate parameter and the output parameter (S65). The increase amount of the feed water flow rate parameter and the output parameter is determined in step S88 in consideration of the threshold value input in step S90. Then, based on this increase amount, the feed water flow rate parameter and the output parameter input in step S63 are corrected (S66). Note that the processing after step S65 is all performed by the performance prediction unit 20 unless otherwise specified.
[0078]
On the other hand, in step S67, the feed water flow rate parameter and the output parameter input in step S63 are divided by the weight N and the reference value set in step S64, respectively, thereby obtaining an increase amount of the feed water flow rate parameter and the output parameter. (S67). Increase amounts of the feed water flow rate parameter and the output parameter are determined in step S89 in consideration of the threshold value input in step S90. Then, based on this increase amount, the feed water flow rate parameter and the output parameter input in step S67 are corrected (S68).
[0079]
Of the feed water flow parameter and the output parameter modified in step S66 and the feed water flow parameter and the output parameter modified in step S68, one of the differences (or the selected one) that is less than the reference value is the water supply It is determined as an increase amount of the flow rate parameter (S69).
[0080]
Next, based on the increase amount of the feed water flow parameter determined in step S69, a correlation formula between the increase in the apparent feed water flow rate and the number of operation days is obtained (S70), and the change over time in the apparent feed water flow rate is obtained. Is obtained (S71). Based on the amount of secular change thus obtained, a water supply flow rate considering an apparent increase is predicted (S79). In addition, when the feed water flow rate considering the apparent increase cannot be predicted based on the secular variation obtained in step S71, the following process is performed to predict the feed water flow rate considering the apparent increase. Made.
[0081]
That is, in order to predict the feed water flow rate taking into account the apparent increase, designation of a prediction condition is received from an input means (not shown) (S72). The prediction conditions are the plant name, the number of operation cycles, and the date to be predicted. When the prediction condition is designated in step S72, the following processes in step S45 and step S80 are performed.
[0082]
Step S45 is the same as step S45 shown in FIG. 12, and the worst case output prediction is performed by the performance prediction unit 20 (S45). Based on this, the amount of auxiliary steam used and the amount of water quality management operation are calculated (S73), and the feed water temperature is predicted based on this calculation result (S74). Furthermore, similarly to step S52 of FIG. 13A, the worst steam pressure in the designated operation cycle is predicted (S52). Then, the worst predicted water supply flow rate is predicted by an analysis code provided in advance (S75).
[0083]
On the other hand, in step S76, an initial output is designated (S76), and in step S77, an analysis based on the initial output designated in step S76, the feed water temperature, the steam pressure, etc. is performed, and the feed water flow rate is predicted (S77).
[0084]
In step S78, based on the prediction result made in step S75 and the prediction result made in step S77, (initial water supply flow rate) + (worst predicted water supply flow rate) − (initial water supply flow rate obtained by analysis). The feed water flow rate is calculated according to the equation (S78). Thus, based on the calculation result made at step S78, the water supply flow rate is predicted in consideration of the apparent increase (S79).
[0085]
On the other hand, in step S80, output prediction is performed in the operation cycle and the planned operation cycle (S80). Based on this, the amount of auxiliary steam used and the water quality management operation amount are calculated (S81), and the feed water temperature is predicted based on the calculation result (S82). Furthermore, the steam pressure in the operation cycle or the scheduled operation cycle is predicted (S83). Then, the feed water flow rate in the operation cycle or the planned operation cycle is predicted by the analysis code provided in advance (S84).
[0086]
In step S85, based on the prediction result made in step S84 and the prediction result made in step S77, an equation of (initial water supply flow rate) + (predicted water supply flow rate) − (initial water supply flow rate obtained by analysis) is obtained. Accordingly, the feed water flow rate is calculated (S85). Thus, based on the calculation result made in step S85 and the relational expression between the heat output and the feed water flow rate shown in step S86, the feed water flow rate is predicted in consideration of the apparent increase in the operation cycle or the planned operation cycle. (S87).
[0087]
Next, when predicting other output-related parameters (the temperature difference between the inlet and outlet of the core R, the temperature of the coolant in the secondary system loop 10, the pressure after the first stage of the turbine 5, etc.) using the flowchart of FIG. The operation in will be described.
[0088]
In order to predict other output-related parameters, first, a database of operation data is created. The operation data is made into a database by the operation condition data acquisition unit 12 acquiring the operation condition data of the nuclear power plant 1. Further, the steam generation performance data of the steam generator 3 is measured by the steam generation performance data acquisition unit 13, and the temperature of the coolant circulating in the secondary system loop 10 measured by the thermometer 8 is changed by the temperature data acquisition unit 14. The pressure of the coolant (steam) circulating in the secondary system loop 10 measured by the meter 4 is measured by the steam pressure data acquisition unit 15 of the coolant circulating in the secondary system loop 10 measured by the flow meter 7. The flow rate is acquired by the flow rate data acquisition unit 16, and the vacuum degree data of the condenser 6 is acquired by the vacuum degree data acquisition unit 17. Each output-related parameter acquired in this way is stored in the data storage unit 18 and registered as a database (S91).
[0089]
Next, the plant for predicting the feed water flow rate and the number of operation cycles are designated from the input means (not shown) to the performance prediction unit 20 (S92). Further, each output-related parameter stored in the data storage unit 18 in step S91 is standardized (S93), and stored again in the data storage unit 18 as a database indicating the secular change through the operation cycle (S94).
[0090]
Next, designation of a prediction condition is received from an input unit (not shown) (S95). The prediction condition is an operation cycle and a prediction date to be predicted. When the prediction condition is specified in this way, the performance prediction unit 20 performs the following processing in step S96 and step S99.
[0091]
In step S96, the performance prediction unit 20 performs worst-case output prediction (S96). Based on this, the calculation of the vacuum degree of the condenser 6 under the worst condition of the forecast date and the correction calculation based on the conditions such as the steam pressure are performed (S97). Then, the output-related parameter under the worst condition is predicted based on the calculation result performed in step S97 (S98).
[0092]
On the other hand, in step S99, output prediction in the operation cycle or the scheduled operation cycle is performed (S99). Based on this, the calculation of the vacuum degree of the condenser 6 under the worst condition of the prediction date and the correction calculation based on the conditions such as the steam pressure are performed (S100). And based on the calculation result performed by step S100, the output prediction in the said operation cycle or a scheduled operation cycle is made (S101).
[0093]
As described above, in the nuclear power plant operation state prediction system to which the plant operation state prediction method according to the embodiment of the present invention is applied, the amount of change is obtained by analysis and correlation based on the operation data. Predictions can be made according to plant operating conditions.
[0094]
For example, by changing the performance of the steam generator 3 into a database by formulating, for example, the transition of the performance coefficient, it is possible to predict the operation history during the cycle of heat output or electrical output. It is also possible to formulate the plant-specific seawater temperature by statistically collecting the plant-specific external environment, for example, the seawater temperature. Moreover, long-term performance prediction of the condenser 6 can also be performed by obtaining a mathematical formula indicating the deterioration of the condenser 6 over time.
[0095]
Further, by correcting the steam pressure with the predicted heat output, more accurate prediction can be made. Furthermore, the feed water flow rate can be predicted based on the predicted heat output. Furthermore, it predicts the apparent increase of the feed water flow rate, and predicts other major parameters based on the predicted output that can predict the measured value of the predicted feed water flow rate and the feed water flow rate based on the prediction result. You can also
[0096]
On the other hand, the secular change of the main parameter of the other output can also be predicted based on the correlation and the database. This prediction can be performed not only on the optimal base prediction but also on the worst base, the optimistic base, and the like.
[0097]
As mentioned above, although preferred embodiment of this invention was described referring an accompanying drawing, this invention is not limited to this structure. That is, in the above embodiment, the steam generation performance of the steam generator 3, the vacuum degree of the condenser 6, the flow rate and pressure of the coolant circulating in the secondary system loop 10, The thermal output generated in the core R and the electrical output obtained from the turbine 5 have been described. However, those skilled in the art will be able to use other operating states within the scope of the technical idea of the claimed invention. It is understood that changes and modifications that can be conceived of various changes and modifications also belong to the technical scope of the present invention.
[0098]
【The invention's effect】
As described above, according to the plant operation state prediction method of the present invention, a database is constructed by accumulating plant operation condition data and operation state data, and based on information obtained from this database, the operation condition and By acquiring a correlation formula with the operation state data and using this correlation formula, it becomes possible to predict the future performance of the plant.
[0099]
Further, according to the nuclear plant operation state prediction system of the present invention, the plant operation state prediction method of the present invention can be applied to a nuclear plant to facilitate the prediction of the performance of the nuclear plant and the planning of equipment replacement. It becomes.
[Brief description of the drawings]
FIG. 1 is a conceptual diagram of a system configuration of a nuclear plant operation state prediction system to which a plant operation state prediction method according to an embodiment of the present invention is applied.
FIG. 2 is a diagram showing a correlation (prediction) curve between an operation date of a nuclear power plant and a steam generation performance coefficient.
FIG. 3 is a diagram showing a correlation (prediction) curve between the operating date of the nuclear power plant and the steam pressure at the outlet of the steam generator (SG outlet pressure).
FIG. 4 is a diagram showing a correlation curve between the operating date of the nuclear power plant and the ratio (ΔT / P1st) of the coolant reactor inlet / outlet temperature difference (ΔT) to the turbine first stage post-pressure (P1st).
FIG. 5 is a diagram showing a correlation curve between the operation day of the nuclear power plant and the ratio (Qwc / ΔT) of the thermal output (Qwc) to the coolant reactor inlet / outlet temperature difference (ΔT).
FIG. 6 is a graph showing a correlation curve between the operation date of the nuclear power plant and the ratio (Qwc / P1st) of the heat output (Qwc) to the turbine first stage post-pressure (P1st).
FIG. 7 is a diagram showing the correlation between seawater temperature in a nuclear power plant and the vacuum level of a condenser in the nuclear power plant.
FIG. 8 is a diagram showing the correlation between the operation day of the nuclear power plant and the seawater temperature.
FIG. 9 is a diagram showing a correlation (prediction) curve between the operation date of the nuclear power plant and the heat output of the nuclear power plant.
FIG. 10 is a flowchart showing an operation in the case of creating a database of operation data and predicting a steam generator performance coefficient and a steam pressure.
FIG. 11 is a flowchart showing an operation in the case of creating a database of operation data and predicting the vacuum degree of the condenser.
FIG. 12 is a flowchart showing an operation in a case where output (electric output) is predicted.
FIG. 13 is a flowchart showing the operation when the steam pressure is predicted for the operation cycle and the predicted operation cycle.
FIG. 14 is a flowchart showing the operation in the case of predicting the feed water flow rate of the coolant circulating in the secondary loop.
FIG. 15 is a flowchart showing an operation when predicting other output-related parameters;
[Explanation of symbols]
R ... core
1 ... Nuclear power plant
2 ... Reactor
3. Steam generator
5 ... Turbine
6 ... Condenser
9 ... Primary loop
10 ... Secondary loop
11 ... Nuclear plant operating state prediction system
12 ... Operating condition data acquisition unit
13. Steam generation performance data acquisition unit
14 ... Temperature data acquisition unit
15. Steam pressure data acquisition unit
16 ... Flow data acquisition unit
17 ... Vacuum degree data acquisition unit
18 ... Data storage unit
19 ... correlation acquisition unit
20 ... Performance prediction unit
21 ... Thermometer
22 ... Pressure gauge
23 ... Thermometer
24 ... Seawater system
25 ... Primary pump
26 ... Water supply pump
27 ... Seawater circulation pump
28 ... Core temperature difference data acquisition unit
29 ... Turbine first stage post-pressure data acquisition unit
30 ... Seawater temperature data acquisition unit
31 ... Output section

Claims (5)

Acquire plant operating condition data and operation result data of equipment provided in the plant,
The acquired operating condition data, operating result data, and data obtained by standardizing the acquired operating result data are accumulated together with the acquisition time data, respectively.
Based on the accumulated data, obtain the correlation between the operation conditions of this plant and the data obtained by normalizing the operation results and the operation result data of the equipment,
A plant operation state prediction method that predicts the operation state and secular change of the equipment based on the acquired correlation, and makes a performance prediction of the nuclear plant and a replacement plan of the equipment. Operating condition data acquisition means for acquiring operating condition data of the nuclear power plant from the nuclear power plant,
Steam generation performance coefficient data acquisition means for acquiring a steam generation performance coefficient indicating a characteristic transition by the steam generator plant from the steam generator provided in the nuclear power plant,
Means to normalize the acquired steam generation performance coefficient;
Data storage for storing the operating condition data acquired by the operating condition data acquiring means, the steam generating performance coefficient acquired by the steam generating performance coefficient data, and the normalized steam generating performance coefficient together with the acquisition time data, respectively. Means,
Correlation acquisition that acquires the correlation between the operation conditions of this nuclear power plant, the steam generation performance coefficient of this steam generator and the standardized steam generation performance coefficient based on each data stored in the data storage means Means,
Based on the correlation acquired by the correlation acquisition means, the acquisition of a correlation formula for the secular change of the steam generation performance coefficient and the steam generation performance of the steam generator with the correlation formula for the secular change and the normalized steam generation performance And a performance prediction means for predicting from a correlation equation of steam generation performance coefficient obtained from a correlation equation of coefficients, and a nuclear plant operating state prediction system that performs performance prediction of nuclear plants and equipment replacement plans . Operating condition data acquisition means for acquiring operating condition data of the nuclear power plant from the nuclear power plant,
Steam pressure data acquisition means for acquiring steam pressure data of coolant water circulating in the turbine steam loop from a pressure gauge provided in a turbine steam loop for supplying steam to the turbine of the nuclear power plant;
Data storage means for normalizing the operating condition data acquired by the operating condition data acquiring means, the steam pressure data acquired by the steam pressure data acquiring means and the steam pressure data, and storing each of them together with the acquisition time data; ,
Correlation acquisition means for acquiring a correlation between the operating conditions of the nuclear power plant and the steam pressure based on each data stored in the data storage means;
Based on the correlation acquired by the correlation acquisition means, and a performance prediction means for determining the steam pressure over time and predicting the steam pressure, and making a nuclear plant performance prediction and equipment replacement plan Nuclear plant operating state prediction system. Operating condition data acquisition means for acquiring operating condition data of the nuclear power plant from the nuclear power plant,
Flow rate data of a coolant circulating in the turbine steam loop from a flow meter provided in a turbine steam loop for supplying steam to the turbine of the nuclear power plant, and a pressure gauge provided in the turbine steam loop in the turbine steam loop The steam pressure data of the coolant circulating in the turbine, the temperature data of the coolant circulating in the turbine steam loop from the thermometer provided in the turbine steam loop, the steam is evaporated, and the steam is supplied to the turbine steam loop Operation data acquisition means for acquiring each steam generation performance coefficient of the steam generator from the steam generator;
Normalization of the steam generation performance coefficient acquired by the operation condition data acquisition means and the operation data acquisition means, and data storage means for storing each data including the normalized steam generation performance coefficient together with the acquisition time data thereof When,
Correlation acquisition means for acquiring a correlation between the operating conditions of this nuclear power plant and heat output based on each data stored in the data storage means;
A nuclear power plant that performs performance prediction of the nuclear power plant and drafts a replacement plan of the equipment, including performance prediction means that predicts output by obtaining secular change through the operation cycle based on the correlation acquired by the correlation acquisition means. Plant operating state prediction system. In the nuclear power plant operation state prediction system according to claim 4 ,
The said performance prediction means is a nuclear power plant operation state prediction system which further predicts an electrical output and makes the performance prediction of a nuclear power plant, and the plan of apparatus replacement .

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