JPS6354122B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to performance monitoring techniques for steam-powered power plants.

First, an outline of a conventional performance monitoring device for a steam turbine plant will be explained.

In FIG. 1, the cycle configuration of the plant will first be explained. Steam generated in the boiler 7 passes through the main steam pipe and enters the high pressure turbine 1.

The steam that has completed its work in the high pressure turbine 1 is
It passes through the low-temperature reheat pipe and returns to the boiler 7 again. This steam is reheated in the boiler and enters the low pressure turbine 2 through a high temperature reheat pipe.

The work performed by the steam in the high-pressure and low-pressure turbines is converted into electrical energy in the generator 3. Exhaust steam from the low pressure turbine is transferred to condenser 4
The water is reduced to water by the water supply pump 5, and the boiler 7
is supplied with water. In order to improve plant efficiency, a feed water heater 6 is generally installed in this water supply system, and the feed water is heated by extraction air from the high pressure turbine 1.

Drain from the feed water heater 6 is collected into the condenser 4. The above is an example of a plant cycle configuration.

As shown in FIG. 1, the conventional performance monitoring device inputs each data signal from the pressure detection device 9, temperature detection device 10, output detection device 11, and flow rate detection device 12 to the heat rate calculation device 8. ,
This allows you to calculate the heat rate.

FIG. 2 shows a flowchart of a conventional performance monitoring device.

As shown in FIG. 2, in the data detection process 14, data is detected at fixed time intervals, and the detected data is sequentially processed in the data averaging and integration calculation process 15, and the calculation results are stored in a storage area 16 in the computer. be done. Also, in the detection duration check step 17, the detection duration time is compared with the bogey value (set value), and if the detection duration time is less than the bogey value, the process returns to the initial data detection step 14 and the above-mentioned process is performed. Execute again.

If the detection duration satisfies the bogey value, the process proceeds to the next performance calculation step 18.

In a performance calculation step 18, performance calculation is performed based on the data in the storage area 16, and in a display step 19, the calculation results and detected data are displayed by printing out or the like.

After completing the display process 19, return to the detection process 14,
The steps from the detection process 14 to the display process 19 are repeatedly executed.

The above is an example of the configuration of a conventional performance monitoring device. Conventional performance monitoring devices input detected operating status values (pressure, temperature, flow rate, electrical output, etc.) into a computer, perform performance calculations, and generate electricity. Daily reports, etc., are printed out at 30 minute or 1 hour intervals, and upon operator request, the simple average performance within that time period.

In this case, if the detection device etc. operates normally and accurate operating status values are input into the computer,
Accurate performance calculations can be expected, but the validity of the detected operating status values, i.e., input data, is not constantly checked, and abnormal values may be mixed into the input data due to instrument failure, disconnection, or aging of the detection device. In this case, the performance calculation results printed out have the disadvantage of being meaningless.

In addition, since the input data is immediately integrated or averaged in the computer, it is not easy to determine whether abnormal values have been mixed in afterward, which reduces confidence in all data and reduces the functionality of the performance monitoring device. There is a risk that it will not be sufficient.

The purpose of the present invention is to detect operating state values (pressure,
An object of the present invention is to provide a method for monitoring the performance of a steam turbine plant, which performs highly reliable performance monitoring by providing a function for checking the validity of temperature, flow rate, electric output, etc.).

That is, the feature of the present invention is that the operating state value detected from each part of the steam turbine plant is compared with its set value (bogie value), and the rate of change (deviation value) of the operating state value with respect to the bogie value is determined. ,
The present invention provides a highly reliable performance monitoring method in which the detected operating state value is determined to be effective for performance monitoring when the value is within a specified value, and the performance of a steam turbine plant is calculated.

Next, a method for monitoring the performance of a steam turbine plant, which is an embodiment of the present invention, will be described with reference to the drawings. In Fig. 3, the cycle configuration of the plant is the same as that shown in Fig. 1, so the explanation will be omitted, and the equipment for performance monitoring will be explained as follows.
Main steam pressure P ₁ ,
High temperature reheat steam pressure P ₂ , low temperature reheat steam pressure P ₃ , feed water heater outlet pressure P ₄ , feed water heater inlet pressure P ₅ ,
The bleed pressure P ₆ and the feed water heater drain pressure P ₇ are detected and input to the input data validity determination device 13, respectively. In addition, the plurality of temperature detection devices 10 detect main steam temperatures T ₁ , T ₁ ′, high temperature reheat steam temperatures T ₂ , T ₂ ′,
Low temperature reheat steam temperature T ₃ , T ₃ ′, bleed air temperature T ₆ , feed water heater inlet temperature T ₅ , feed water heater outlet temperature T ₄ , T ₄ ′,
The feed water heater drain temperature T ₇ is detected and input to the input data validity determination device 13 .

In addition, the output detection device 11 detects the generator output L.
is detected, and the flow rate of water supplied to the boiler 7 (main water supply flow rate F ₀ ) is detected by the flow rate detection device 12.

These detection data are input to the heat rate calculation device 8 via the input data validity determination device 13.

These processes will be explained with reference to FIG.

In FIG. 4, data detected by each of the detection devices 9 to 12 is passed to an input data validity determination device 13. In FIG. Details of the determination device 13 will be described later.

Each data from the determination device 13 is averaged by average value calculation devices 21a to 21d in order to eliminate data variations within the measurement time. The average value calculating device 21a outputs the average values of main steam pressure P ₁ , high temperature reheat steam pressure P ₂ , low temperature reheat steam pressure P ₃ , feed water heater outlet pressure P ₄ , and feed water heater inlet pressure P ₅ , bleed pressure P ₆ and feed water heater drain pressure P ₇ are calculated and input to the enthalpy calculation device 22. Similarly, the average value calculation device 21b outputs the average main steam temperatures T ₁ , T ₁ ′, and the high temperature reheat steam temperature.
T ₂ , T ₂ ′, low temperature reheat steam temperature T ₃ , T ₃ ′, feed water heater outlet temperature T ₄ , T ₄ ′, feed water heater inlet temperature T ₅ , bleed air temperature T ₆ , feed water heater drain temperature T ₇ is calculated and input to the enthalpy calculation device 22.

In addition, the average value calculation device 21c calculates the average main water supply flow rate F ₀ and inputs it to the flow rate calculation device 23.
The average value calculation device 21d calculates the average value of the plant output L, and inputs the calculated data from the enthalpy calculation device 22 and the flow rate calculation device 23 to the heat rate calculation device 8, where the plant heat rate calculation is performed. It's summery.

In this enthalpy calculation device 22, as shown in FIG. 6, pressure and temperature average value calculation devices 21a, 2
Pressure temperature data from 1b T ₁ to T ₇ , T ₁ ′ to T ₄ ′,
Using P ₁ to _{P 7} , intersection points are found on a diagram (Mollier diagram) in which the vertical axis is enthalpy and the horizontal axis is entropy, and enthalpies H ₁ to _{H 7} corresponding to the intersection points are calculated. In other words, the enthalpy of main steam H ₁ is obtained from T ₁ and P ₁ , the enthalpy H ₂ of high temperature reheated steam is obtained from T ₂ and P ₂ , the enthalpy H ₃ of low temperature reheated steam is obtained from T ₃ and P ₃ , and T ₄ , the enthalpy H ₄ at the outlet of the feed water heater from P ₄ , the enthalpy H ₅ at the inlet of the feed water heater from T ₅ and P ₅ , and the enthalpy of bleed air from T ₆ and P ₆ .
The enthalpy H ₇ of the feed water heater drain is calculated from H ₆ and T ₇ and P ₇ , respectively.

This data 46 of enthalpies H ₁ to _{H 7} is input to the flow rate calculation device 23 and the heat rate calculation device 8 .

On the other hand, the flow rate calculation device 23 is formed of a main steam flow rate calculation device 47, a low temperature reheated steam flow rate calculation unit 48, and a high temperature reheated steam flow rate calculation unit 49, as shown in FIG.

When the plant cycle has the configuration shown in FIG.
Assuming that F ₀ and the main steam flow rate F ₁ are equal, the main steam flow rate F ₁ is calculated from F ₁ =F ₀ .

In the low temperature reheat steam flow rate calculator 48, the low temperature reheat steam flow rate _F3 is determined from the relationship between the main steam flow rate _F1 and the extracted steam amount _F4 shown below.

In other words, the extraction steam flow rate F ₄ is F ₄ =F ₀ −(H ₄ −H ₅ )/H ₆ −H ₇ based on the heat balance around the feed water heater.

Therefore, the low temperature reheat steam flow rate F ₃ is F ₃ =F ₁ −F ₄ =F ₁ −F ₀ (H ₄ −H ₅ )/H ₆ −H ₇ ∴F ₃ =F ₁ −F ₁ (H ₄ − Calculated from H ₅ )/H ₆ − _{H 7} .

In the high temperature reheat steam flow rate calculator 49, the high temperature reheat steam flow rate _F2 and the low temperature reheat steam flow rate _F3 are equal.

Therefore, the high temperature reheat steam flow rate F ₂ is calculated from F ₂ =F ₃ .

As described above, the flow rate F ₁₃ determined by the flow rate calculation device 23 and the main water supply flow rate F ₀ which is the detection data are input to the heat rate calculation device 8 .

The data input to the heat rate calculation device 8 are flow rate data F ₀₃ , enthalpy data H ₁₄ , and output data L.

As the output data L, the average value calculated by the average value calculation device 21d is input.

The heat rate (heat consumption rate) HR is determined by the following formula.

HR=F ₁ ·H ₁ -F ₀ ·H ₄ +F ₂ ·H ₂ -F ₃ ·H ₃ /L The heat rate HR determined by the heat rate calculation device 8 is displayed on a printout or the like.

The above is the explanation of the performance monitoring method.

Next, the input data validity determination device 13 will be explained.

As shown in FIG. 5, the pressures _P1 to _P7 , temperatures _T1 to _T7 , T1 _' to
The data of T ₄ ′, flow rate F ₀ , and output L are input to the determination device 13 .

The pressures P ₁ to P ₇ , temperatures T ₁ to T ₇ , T ₁ ′ to _{T 4} ′, and temperature data F ₀ go to the classifier 24, and the output data L is
The process proceeds to the arithmetic units 25 and 43.

First, the output data L will be explained.

Arithmetic units 25 and 43 calculate bogey values that serve as a reference for checking the validity of detected data.

That is, as shown in Fig. 8, the relationship between the bogey value of pressure, temperature, and flow rate data and the plant load is determined and stored in advance, and the intersection with this graph is determined by inputting the output data, and from this intersection Calculate the bogey value and use the next setter 26, 44
are set as reference bogey values A ₀ and B ₀ respectively.

On the other hand, in the classifier 24, the pressure, temperature, and flow rate data are classified according to the number of input points. For example, in the case of Fig. 3, when there are two input points, the main steam temperature T ₁ , T ₁ ', the low temperature reheat steam temperature
T ₃ , T ₃ ′, high-temperature reheating steam temperature T ₂ , T ₂ ′, feed water heater outlet feed water temperature T ₄ , T ₄ ′, and other detected data have one input point.

The detection data of two input points is sent to the arithmetic unit 27.

In the calculator 27, the detected data and the setting device 26
Calculate the deviation from the standard bogey value set in .

In other words, the detection data of two input points is
To explain this by displaying A ₁ , A ₂ and the reference bogey value as A ₀ , the calculator 27 calculates the values of |1-A ₁ /A ₀ | and |1-A ₂ /A ₀ | It is.

The calculation result then proceeds to the next comparator 28, where it is compared with the deviation bogey value _x1 stored in the storage unit 30, and then proceeds to the determiner 29.

In the determiner 29, 2 calculated by the calculator 27
Only when both of these values are smaller than the deviation bogey value x ₁ , that is, when it is determined that both detection data A ₁ and A ₂ are valid detection data, the process proceeds to the next arithmetic unit 33. In all other cases, it is determined that either or both of the detected data A ₁ and A ₂ is abnormal data, and the process proceeds to the comparator 32 .

The calculator 33 calculates the average value of the detection data A ₁ and A ₂ , and the calculation result, that is, the value of A ₁ +A ₂ /2, is used as the detection data 2 in the next setting device 34.
It is set as the representative value above.

Comparator 32, like comparator 28,
A comparison is made with the deviation bogey value _x1 stored in the internal memory.

After the comparison is completed, the process proceeds to the determiner 35.

In the judger 35, if either one of the calculated deviation values |1-A ₁ /A ₀ |, |1-A ₂ /A ₀ | is smaller than the deviation bogey value x ₁ , two detected data points A ₁ ,
Assuming that either one of A ₂ is valid detection data, the process proceeds to the setting device 37 and sets the calculated deviation value |1-
If both A ₁ /A ₀ | and |1−A ₂ /A ₀ | are larger than the deviation bogey value x ₁ , it is assumed that both of the two detection data points A ₁ and A ₂ are abnormal detection data. Setting device 3
Proceed to step 6.

The setting device 37 detects the detection data A 1 or _{A 2} of the calculated deviation value |1-A ₁ /A ₀ |, |1-A ₂ /A ₀ |, whichever satisfies the deviation bogey value x ₁ . 2 data points A ₁ ,
However, since both detection data A ₁ and A ₂ were determined to be abnormal data by the setter 36, the reference bogey value set by the setter 26 was set as the representative value of A _2.
A ₀ is set as the representative value of the detection data A ₁ and A ₂ .

Next, a case where the number of input points is one will be explained.

In other words, the detection data of one input point is
B ₁ , and the standard bogey value set by the setter 44 is expressed as B ₀ . The arithmetic unit 38 calculates the deviation |1-B ₁ /B ₀ |.

The deviation value |1-B ₁ /B ₀ | is the deviation bogey value stored in the storage unit 45 in the next comparator 39.
Proceed to the determiner 40 where it is compared with _x2 .

In the determiner 40, if the deviation value |1-B ₁ /B ₀ | is smaller than the deviation bogey value x ₂ , the detected data
B ₁ is determined to be valid data and proceeds to the setter 41, and if it is larger than the deviation bogey value x ₂ , the detected data B ₁ proceeds to the setter 42 as abnormal data.

The setting device 41 sets the detection data B ₁ as valid data, and the setting device 42 sets the detection data B ₁ as
Since this is abnormal data, the reference bogey value B ₀ set by the setting device 44 is set as detected data.

Then, the detection data of the two input points set by these setting devices 34, 36, and 37, that is, T ₁ ~
T ₄ and T ₁ ′ to _{T 4} ′ and detection data of one input point set by setting devices 41 and 42, that is, T ₅ to _{T 7} ,
P ₁ to P ₇ and F ₀ are input to the heat rate calculating device 8, where the heat rate of the plant is calculated.

The above is an explanation of the block diagram of the input data validity determining device 13 shown in FIG.

Next, the details of the above-mentioned steam turbine plant performance monitoring method will be explained using a flowchart.

In FIG. 9, first, data detected in a data detection process 14 (this process 14 corresponds to the detection devices 9 to 14 shown in FIG. 4) is transferred to a data input process 5.
3, the data is input to a data classification process 54 according to the number of input points (this process 54 corresponds to the classifier 24 in FIG. 5).

In the step 54, the detected data is classified according to the number of input points, and when the number of input points is 2 A ₁ and A ₂ , such as T ₁ T _{1 ′} to _{T 4} T ₄ ′, the detection data goes to the deviation calculation step 55 and is The number of input points is 1 like ₅ ~ T ₇ , P ₁ ~ P ₇ , F ₀
For _B1 , proceed to the deviation calculation process 56. In addition, the output data L in the detection data is the bogey value calculation process 6.
Proceed to step 7.

Bogey value calculation process 67 (this process 67 is the fifth
(corresponding to the calculators 25, 43 and setting devices 26, 44 in the figure) calculates the intersection point from the output data in the detected data on the graph of the relationship between the plant load and the bogie value shown in FIG. 8, which has been set in advance. The reference bogey values A ₀ and B ₀ are set using the bogey value at the intersection point. And the reference bogey value A ₀ ,
B ₀ is determined for each detected data, and this value is input to the deviation calculation processes 55 and 56.

In the deviation calculation process 55 (this process 55 corresponds to the arithmetic unit 27 in FIG. 5), the detected data A ₁ ,
Due to A ₂ and the reference bogey value A ₀ from process 67 |
1-A ₁ /A ₀ | and |1-A ₂ /A ₀ |, that is, the deviation value is calculated.

Similar to the process 55, the deviation calculation process 56 (this process corresponds to the arithmetic unit 38 in FIG. 5) calculates the detected data.
A deviation value, |1-B ₁ /B ₀ |, is calculated from B ₁ and the reference bogey value B ₀ .

After the calculation is completed, these deviation values are input to detected data validity checking steps 57 and 58, respectively.

The deviation value |1-A ₁ /A ₀ |, |1-A ₂ /A ₀ | when the number of input points is 2 is the process 57 (this process 57 is performed by the comparator 28, determiner 29, and (corresponding to the container 30), compare it with the deviation bogey value x ₁ ,
When both of the deviation values are smaller than the deviation bogey value, that is, | 1-A ₁ /A ₀ | < x ₁ and | 1-A ₂ /A ₀ | < x ₁ , the detected data A ₁ and A ₂ are Assuming that both data are valid, the process proceeds to the average value calculation process 60.

Furthermore, if at least one of the two deviation values is larger than the deviation bogey value x ₁ , the detection data A ₁ ,
It is determined that at least one of _A2 is abnormal data, and the process proceeds to the second detected data validity check step 59.

Process 60 (This process 60 is performed by the arithmetic unit 3 in FIG.
3) is to calculate the average value of the detection data A ₁ and A ₂ , and the average value, A ₁ +A ₂ /2, is calculated in the next detection data setting process 61 (this process 61 is shown in FIG. (corresponding to the setting device 34), the detection data A ₁ ,
A is set as the representative value A of ₂ .

That is, A=A ₁ +A ₂ /2.

Step 59 (This step 59 is performed by comparator 3 in FIG.
2 and the determiner 35) compares the deviation value and the deviation bogey value in the same manner as in step 57.

If one of the two deviation values is smaller than the deviation bogey value, that is, |1-A ₁ /A ₀ |<x ₁ or |1-A ₂ /A ₀ |<x ₁ , it is detected. Either data A ₁ or A ₂ is
Only valid data proceeds to the next detected data setting step 62, and data determined to be abnormal is cleared.

Also, if the two deviation values are both larger than the deviation bogey value, that is, |1-A ₁ /A ₀ |≧x ₁ and |1-A ₂ /A ₀ |≧x ₂ , detection is not possible. Since both data A ₁ and A ₂ are abnormal data, the process proceeds to the next bogey value setting step 63 and the abnormal data is cleared.

Detection data setting process 62 (this process 62 is the fifth
The setter 37 in the figure) sets the detected data determined to be valid data as a representative value, which means A=A ₁ or A ₂ .

The bogey value setting process 63 will be described later.

Also, the deviation value |1-B ₁ /B ₀ | in the case of one detection point is the deviation value |1-B 1 /B 0 | A comparison is made with the bogey value x ₂ .

When the deviation value |1-B ₁ /B ₀ | is smaller than the deviation bogey value x ₂ , that is, |1-B ₁ /B ₀ | < x ₂ , the detected data B ₁ is set as valid data. Proceed to step 64 and if it is larger, i.e. |1−
When B ₁ /B ₀ |≧x ₂ , the detected data B ₁ is treated as abnormal data and proceeds to the bogey value setting step 63.

The detected data setting process 64 (this process 64 corresponds to the setter 41 in FIG. 5) is a process of setting the detected data _B1 as valid data. In other words, B= _B1 .

Next, a bogey value setting step 63 proceeding from steps 59 and 64 (this step 63 is performed by the setter 3 in FIG.
6, 42) will be explained.

Detection data A ₁ , A ₂ , input into this process 63
B ₁ is determined to be abnormal data through the process described above, and using this data for performance calculations will affect the reliability of the performance calculation results, that is, the heat consumption rate. lead to downfall.

Therefore, the abnormal data A ₁ , A ₂ , and B ₁ are cleared, and instead, in the case of two detection points A ₁ and A ₂ , the reference bogey value A ₀ from step 67 is set as the detection data, If the number of detection points is 1 point B ₁ ,
The reference bogey value B ₀ from step 67 is set as detection data. That is, A=A ₀ and B=B ₀ .

At this time, the message output step 65 notifies the operator by printing out or the like that the reference bogey values A ₀ and B ₀ have been set in place of the detected data.

The above is the explanation of the process 63.

The data set in each setting process 61, 62, 63, 64 then proceeds to the next average value calculation process 15, where an average value is calculated for each data. The calculation result (average value) is stored in the storage device in the data storage step 16 and simultaneously proceeds to the next full data validity check step 66.

In step 66, the above-mentioned steps are executed for all the input data, and it is confirmed whether the validity of the data has been checked. If all the data has been completed, the process proceeds to the next detection duration check step 17. If it does not finish, run the above process again,
Check the validity of all data.

The detection duration check step 17 is a step in which the time elapsed since the start of detection is compared with the bogey value. That is, the time for performing performance calculation is set in advance, and only when that time (0.5Hr to 1Hr) has elapsed, the heat rate is calculated in the performance calculation process 18 based on the data in the storage process 16. . When the detection duration time is less than the bogey value, the steps after data input step 53 are repeatedly executed until the bogey value is satisfied.

The heat rate calculated in step 18 is:
In the display step 19, the information is displayed by printing out or the like.

After displaying, the process returns to the data detection process 14 and again.
Execute the above process.

As described above, if the deviation of the operating status value input to the computer is within the specified value, the operating status value is judged to be valid and effective for performance monitoring. This is a steam power plant performance monitoring method that makes it possible to obtain high performance calculation results.

Incidentally, in the detected data validity check processes 57, 58, and 59 shown in FIG. The detected data is always stored in the storage device as the bogey values A ₀ and B ₀ used, and the stored data is used for reference comparison, that is, the validity of the data is verified by comparing with past data. You may also check.

This method will be specifically explained below.

FIG. 10 shows the plant operation status.

In Figure 10, first, plant operation 6
During the test run period 70 up to 9, each detection data corresponding to the load is collected, and the reference bogey values A ₀ and B ₀ are determined from the collected data. Based on this reference bogey value, detection during the period from plant operation 69 to several months or the first periodic inspection 70a (i.e., period when there is little past data to be compared with detection data: first performance monitoring period) Check the validity of the data.

During this period, sequential detection data is stored in the storage device.

Therefore, at the end of the first performance monitoring period, a reference bogey value is determined based on the detection data collected during that period, and the reference bogey value used during the first performance monitoring period is changed.

During the second performance monitoring period, the validity of the data is checked using this changed bogey value.

Repeat this operation in sequence.

As described above, changing the standard bogey values A ₀ and B ₀ based on past data makes it possible to set the standard bogey value that corresponds to deterioration over time of plant equipment and detection equipment, and performance calculation This has a significant effect on improving the reliability of the data used for the process and performance calculation results (heat rate).

In addition to this, it is also possible to use the relationship between the bleed air pressure and the water supply temperature as a method of checking the validity of specific data in the detected data.

As is clear from the above description, according to the present invention, it is possible to realize a steam turbine plant performance monitoring method that enables extremely reliable performance monitoring because it is possible to check the validity of detected operating status values. There is an effect called.

[Brief explanation of drawings]

Fig. 1 is a schematic diagram showing a conventional performance monitoring device for a steam turbine plant, Fig. 2 is a flow diagram showing the operation of the performance monitoring device shown in Fig. 1, and Fig. 3 is a steam turbine according to an embodiment of the present invention. A schematic diagram showing a plant performance monitoring device, FIG. 4 is a control block diagram of the performance monitoring device in FIG. 3, FIG. 5 is a detailed block diagram of the determination device in FIG. 4, and FIG. Fig. 7 is an explanatory diagram of the flow rate calculation device and heat rate calculation device in Fig. 4, Fig. 8 is a diagram of the relationship between the plant load and bogie value, and Fig. 9 is the monitoring shown in Fig. 4. FIG. 10 is a flowchart showing an outline of the performance monitoring method in the device, and is an explanatory diagram of the plant operation state. 1...High pressure turbine, 2...Low pressure turbine, 3
... Generator, 4 ... Condenser, 5 ... Water pump, 6
... Feed water heater, 7 ... Boiler, 8 ... Heat rate calculation device, 9 ... Pressure detection device, 10 ... Temperature detection device, 11 ... Output detection device, 12 ... Flow rate detection device, 13 ... Input data validity determination device, 14...Data detection process, 15...Data average, integration calculation process, 16...Data storage process, 1
7...Detection duration check process, 18...Performance calculation process, 19...Display process, 20...Measurement duration check function, 21a to 21d...Average value calculation device, 22...Enthalpy calculation device, 23... …
Flow rate calculation device, 24...Classifier, 25, 27, 3
3, 38, 43... Arithmetic unit, 26, 34, 36,
37, 41, 42, 44...Setter, 28, 3
2, 39... Comparator, 29, 35, 40... Judgment device, 30, 45... Container, 46... Enthalpy data, 47... Main steam flow rate calculator, 48... Low temperature reheat steam flow rate calculation 49...High temperature reheat steam flow rate calculator, 50...Flow rate data, 51...Heat rate, 52...Input data validity check function, 53...Data input process, 54...Data classification by number of input points Process, 55... Deviation calculation process, 56
... Deviation calculation process, 57, 58, 59... Detected data validity check process, 60... Average value calculation process, 61, 62, 64... Detected data setting process,
63...Bogie value setting process, 65...Message output process, 66...All data validity check confirmation process, 67...Bogey value calculation process, 68...Trial operation period, 69...Start of operation, 70a to 70e... Periodic inspection.

Claims (1)

[Scope of Claims] 1. A steam turbine plant performance monitoring method that detects operating state values and loads of each part of a steam turbine plant and calculates the performance of the steam turbine plant based on these detected data. Setting a reference value for each driving state value, determining the validity of each driving state value based on a comparison value between the driving state value and the reference value, and determining the driving state value determined to be valid. The performance of the steam turbine plant is calculated from the reference value set based on the load, and if it is determined to be unjustified, the performance of the steam turbine plant is calculated from the standard value set based on the load and replaced with the operating state value, and the performance of the steam turbine plant is monitored. A method for monitoring the performance of a steam turbine plant, characterized in that: 2. In a steam turbine plant performance monitoring method that detects the operating status values and loads of each part of the steam turbine plant and calculates the performance of the steam turbine plant based on these detected data, the steam turbine plant is Record the operating status values of each part of the steam turbine plant when the steam turbine plant is operated at the load, calculate the reference value of the operating status value when the steam turbine plant is currently operated at the predetermined load, and calculate the operating status value and its standard. The validity of each operating state value is determined based on the comparison value with the value, and the performance of the steam turbine plant is calculated from each operating state value determined to be valid. 1. A method for monitoring the performance of a steam turbine plant, characterized in that, in the case of a load, the performance of the steam turbine plant is calculated and monitored from a reference value set based on the load in place of an operating state value.