JPH03262924A - Power generation plant performance controller - Google Patents

Abstract

PURPOSE:To take a many faceted analysis by gathering plant data when a power generation plant settles and is in a thermally balanced state, inspecting the performance of the plant by using the latest collected data and past data, and outputting the inspection result together with past transition. CONSTITUTION:A plant input means 20 converts respective data on the power generation plant 1 which are inputted through sensors into process data which can be processed in a computer and those data are inputted to a plant settle ment decision means 21 and a plant data gathering means 22. Then the means 21 decides that the plant 1 is in the thermally balanced state and when the decision result of the means 21 indicates the thermally balanced state, the means 22 gathers the process data and stores them in a mass storage device as a data storage means 30. Then a retrieval means 32 retrieves the data 31 stored in the means 30 and the retrieved data are totalized by a calculating means 51 to calculate the plant performance. The transition based upon the current calculation result and past calculation result is outputted from an output means 52.

Description

DETAILED DESCRIPTION OF THE INVENTION [Object of the Invention] (Industrial Field of Application) The present invention relates to a power plant performance management system that detects efficiency decline and aging deterioration in a power plant at an early stage and analyzes the factors thereof.

(Conventional technology) Conventional performance management of power generation plants involves performance calculations every hour on the hour, as typified by operation logs.

When the plant manufacturer hands over the equipment to the electric power company, management is conducted through performance receiving tests to check whether each piece of equipment is performing as per the contract, and the electric power company independently checks whether each piece of plant equipment maintains its initial performance. Management was based on performance evaluation tests conducted several times a year.

Performance calculations for each hour are performed hourly, regardless of operation changes (increases or decreases in generator output), whereas performance receiving tests and performance evaluation tests are performed by keeping the generator output constant. In addition, a heat run is performed for a certain period of time so that the thermal equilibrium state of the entire power generation plant becomes constant, and measurements are performed after the plant has stabilized.

The purpose of performance calculation every hour is to manage the total heat input (fuel, gas, etc.) and heat output (generated power, etc.) to the plant throughout the day.

It does not grasp the performance of each device.

The purpose of performance tests such as performance acceptance tests and performance evaluation tests is to
This is to place the plant in a state of perfect thermal equilibrium and to understand the performance of each device that makes up the plant (boiler, turbine, generator, feed water heater, air heater, condenser, pump, etc.). Generally, this performance test requires the plant to be operated continuously at a constant output for several hours, and normally operated under command from a central power supply station, for the duration of this test. The power plant is separated from the central power supply station and operated independently. In addition, when compiling the test results, the performance of each individual device is calculated, and the generator output is also tested by dividing it into several parts such as 100% output, 75% output, 50% output, and 25% output. For this reason, the test period alone required several days, and the total calculation required more than a month. As a result, performance evaluation tests were only conducted a few times a year.

In addition, recently there is a system that automatically performs performance tests, as shown in Figure 19, which imports data when the power plant reaches a stable state during daily operation and performs it online. It attempts to calculate performance.

In FIG. 31, the data input processing means 20a receives input from the plant 1, the stability criterion determining means 21a determines whether the plant 1 is stable, and if the stability criterion is not satisfied, the stored data is deleted by the stored data erasing means 23a. All are deleted and the process returns to the data input processing means 20a again. When the stability criterion determination means 21a satisfies the stability, the data storage means 24a stores the data. The data amount checking means 25a determines whether the stored data satisfies the required amount, and if it is insufficient, the data is returned to the data input processing means 20a. If the data amount check means 25a satisfies the required amount, the performance calculation means 26a calculates the plant performance.The results of the performance calculation means 26a are outputted in a form by the printout means 27a.

(Problem to be solved by the invention) However, in recent years, due to energy resource problems, power plants have shifted from mainly thermal power to mainly nuclear power, and as a result, fewer new thermal power plants are being built, and aging thermal power plants are being replaced. It is increasing. From an operational point of view, even though thermal power plants were initially designed to serve as a base load for electricity demand, with the increase in nuclear power, they have come to be used for intermediate load operations. In other words, frequent startup and shutdown, increased increases and decreases in generator output, and longer periods of plant inspections called periodic inspections are forcing plants to operate more harshly. Under these circumstances, it has become important to operate the equipment of a power generation plant efficiently and at the same time to detect and deal with abnormalities in the equipment at an early stage, and it has become necessary to review equipment performance management methods.

One such attempt is to determine the performance of equipment using various values recorded in the power generation diary. However, with this method, the power generation log itself is not related to the output, but is data for each hour, so even if you retrieve only the same 50% output data, the contents will be the 50% data when the output is increasing from 25% to 75%. Or, conversely, from 75% to 25%
It may be data that is in the process of being output to. In other words, there is a drawback that there is a large amount of data dispersion. For example, when the load is increasing, the output is slow compared to the amount of fuel input (because the fuel is used to increase the heat retained in the plant), and as a result, efficiency tends to decrease, or the entire plant is not in a state of thermal equilibrium. As a result, the efficiency of some devices was higher, and the efficiency of others was lower, resulting in only products with large errors in performance compared to their original performance.

Therefore, in an attempt to conduct performance tests every day, which had only been done a few times a year, performance calculations were performed using data from the time when the plant had just settled into a thermal equilibrium state while operating according to instructions from the central power supply station. There are also ways to do this. However, although this method allows the measured efficiency to be determined, it is difficult to analyze the cause when the efficiency is decreasing. In other words, in order to analyze the causes of efficiency decline, it is necessary to analyze whether the abnormality occurred suddenly or gradually, taking into account trends from the past, but this is not possible. There's a problem.

In addition, when performing performance calculations, it is good if all plant inputs are computer inputs, but in the case of existing old thermal power plants, it is difficult to put all inputs online. Component analysis results may not be available. In such cases, a system based solely on online processing has problems when applied to an actual plant.

In addition, performance analysis requires processing time due to the processing of large amounts of data, so when performing daily management, the data search, aggregation, calculation, and efficiency reduction analysis processing are performed using one day's worth of data. It was requested that the data be run automatically late at night, and that all the data would be organized and output when people arrive the next morning.

The purpose of the present invention is to automatically save plant data under similar load conditions in a large-capacity storage device when performing a performance test while performing normal load operation in a power generation plant, and to automatically store plant data including the latest data. By performing plant performance calculations and efficiency variation factor analysis using current data and its revised data, and outputting the results, it is possible to accurately grasp the daily operating performance of the power plant and detect equipment abnormalities at an early stage. The object of the present invention is to obtain a power generation plant performance management device that allows the plant to always exhibit maximum efficiency.

[Structure of the Invention] (Means for Solving the Problems) The present invention provides a plant input means for scanning and inputting process data of a power generation plant, and a plant settling means for determining a specific settling state of the plant based on the input process data. a determination means, a plant data collection means for collecting process data when the determination result of the plant settling determination means is a stable state, a data storage means for storing the collected data in a mass storage device, and a data storage means for storing the collected data in a mass storage device; It is equipped with a search means for extracting data, a calculation means for aggregating the search data and calculating plant performance, and an output means for outputting the current calculation result and the transition from the past calculation result.

Furthermore, automatic execution means is provided for giving an execution command to the search means, calculation means, and output means at predetermined intervals. Furthermore, a comparison means for comparing the calculation result of the plant performance with a predetermined reference performance and a comparison with the performance trend from the past.

When it is determined that there is an increase or decrease in performance as a result of this comparison, the analyzer is provided with an analysis means that compares the factors causing the increase or decrease with a reference value and outputs the factors that have the greatest influence. Furthermore, a correction means is provided for displaying and correcting the search data extracted by the search means.

(Effect)- This allows plant data to be collected when the power plant is settled and in a state of thermal equilibrium, and plant performance calculations are performed using the latest collected data and past data.
Output along with past trends.

(Embodiment) FIG. 1 is a block diagram showing a first embodiment of the present invention. The plant input means 20 inputs data indicating the status of each device of the power generation plant 1 (output, rotation speed, temperature, flow rate, pressure, etc.) through a sensor and converts it into a form that can be processed within the computer. The converted process data is input to the plant setting determination means 21 and the plant data collection means 22. The plant settling determination means 21 determines whether the power generation plant 1 is in a state of thermal equilibrium, and the details thereof are shown in FIG. 2.

The process data from the plant input means 20.

Data comparison and determination means 101 of plant settling determination means 21
and is compared with reference data in the reference data storage section 100. That is, the plant state is 100% load, 7
It is determined whether the load is stabilized at 5% load, 50% load, or 25% load. The reference data storage unit 100 stores an upper limit,
The lower limit is stored as one set. This stored data is the same on the auxiliary storage device of the computer, and can be changed as necessary. Then, when the plant condition is stabilized, the timer control means 1
02 and the data collection control means 103.

Upon receiving the command from the data comparison means 101, the timer control means 102 outputs a permission signal to the data collection control means 103 after confirming that the plant has stabilized for a predetermined time period. The data collection control means 103 issues a command for data collection to the plant data collection means 22 when both the plant settling signal from the data comparison means 101 and this permission signal are input.

The plant data collection means 22 is as shown in FIG. The plant data storage section 104 is operated by a signal from the plant setting determination means 21 and reads data from the plant input means 20 . Storage data area section 10
Store data in 5. The storage data area section 105 is
This area can store plant data for 1 hour and 30 minutes. The stored data area section 106 clears the stored data area section 105 when the plant settling determination means 21 determines that the plant is not in a settled state.

FIG. 4 is an explanatory diagram of the data storage means 30, which is composed of an index creation section 107 and a data storage section 108.

The index creation unit 107 creates an index on the data in the storage data area unit 105 and stores it in the data storage unit 108. Further, the data storage section 108 writes the data of the storage data area section 105 and an index into the storage data 31. For the saved data 31, an optical write-once storage device is used as a medium. FIG. 5 shows the format of the saved data 31, which is composed of a management block, an index block, and a collected data block.

FIG. 6 is an explanatory diagram of the search means 32. The search means 32 is composed of an index determining section 109 and a data reading section 110. The index determining section 109 determines whether the index matches the request, and reads out the search condition table and the index data. This operates in response to a search request from the man-machine request means 40. Further, the data succession unit 110 reads data from the stored data 31 and stores the data in the search data 41. FIG. 7 shows the details, and a search request from the operator input section 111 is input to the search means 32 via the man-machine request means 40. Here, the operator input section 111
A keyboard and a mouse are used as the operator confirmation unit 112, and a CRT is used as the operator confirmation unit 112. Further, the search data 41 uses a magnetic storage device as a medium, and consists of a search data management table and a search management table, as shown in FIG.

Next, the calculation means 51 executes the total processing unit 1 as shown in FIG.
13, aggregated data 114, and a performance calculation processing unit 115. The aggregated processing unit 113 aggregates the search data 41 for one hour for each load zone, calculates the average variance maximum deviation, and stores it as aggregated data 114. Performance calculation processing unit 11
The result is passed to 5.

The performance calculation processing unit 115 performs performance calculations by applying a calculation formula based on plant characteristics using one hour's worth of data, and stores the calculation results in the calculation result data 61.

The calculation result data 61 uses a magnetic storage device.

Then, the output means 52 is activated in response to an output request as shown in FIG.
1 to a predetermined format.
6 and its output devices such as CRT, printer, and XY plotter.

Now, suppose that the output (load) of the generator changes as shown in FIG. t0 to t until the load reaches 100%
1, the data comparison/determination section 101 of the plant stabilization determination means 21 continues to check whether or not the plant has been stabilized every second. When the time point t is reached, it is determined that the load has been set, and a timer start signal is output to the timer control unit 102. By the way, 30 minutes have passed since the plant stabilized, and at time t
If the plant becomes unsteady after 60 minutes, a stop signal and a reset signal are output to the timer control unit 102 because the time is shorter than the timer set time of 60 minutes.

Since the timer has received the stop signal and reset signal within the set time, reset the timer.

As a result, the data comparison and determination section 101 outputs a stop signal and a reset signal to the data collection control section 103, and the data collection control section 103
2 of the collection stop and settling deviation.

When the plant data collecting means 22 receives the notification of stopping the collection, it stops the plant data storage section 104 from storing data, and clears the stored data area section 106 to 0 using a clear signal.

On the other hand, the plant has set the timer to 60 from time t□.
If the time is more than 120 minutes, for example, 120 minutes or more, the time t
tz-s, 60 minutes after 1. Then, the timer control section 102 issues a start signal to the data collection control section 103. The data collection control section 103 issues a collection start signal to the plant collection means 22. Upon receiving the collection start signal, the plant data storage unit 104 reads the data from the plant input unit 20 and stores the data in the storage data area 1.
Write data to 05 and update storage time.

After another 60 minutes, at time tx-sa-to, the plant data storage section 104 notifies the data storage means 30 of a start request. In the data storage means 30, in response to the start request, the index creation unit 107 collects 60 minutes of data in the storage data area unit 105 into one, creates an index (save number, collection period, generator output), and stores the data. The data is passed to the storage unit 108, and the data storage unit 108 writes it into the storage data 31. At this time, the data storage unit 108 updates the period of data currently written in the optical storage medium. Note that this data is written at the beginning of the optical storage medium when the optical storage medium becomes full.

Time t11° to tero until time t2. - Repeat the process of 6o. Then, at time t2, one time t2
When time t□ and time t□ are 30 minutes apart, the same process as at time t2 is performed, and at time t, the same process as when time t1 is reached.

When the data is stored in the stored data 31, a search condition group is input to the operator input section ill via the man-machine request means 40, and after confirmation by the operator confirmation section 112, a search request is issued from the operator input section 111.

The search means 32 receives the search request and notifies the search determination unit 109 of the search request. Here, the search request includes the group island of the search condition table.

The search determination unit 109 reads the condition group of the request group from the search condition table, compares it with the index data, and makes a determination. If it matches the search means, a read signal and index are sent to the data read unit 110. The data reading unit 110, which has received the index and the read signal, reads the data from the stored data 31 and stores it in the search data 41. After the operator confirmation section 112 of the man-machine requesting means 40 confirms that the search is complete, the operator input section 111 issues a calculation request. The calculation means 51 receives the calculation request and performs aggregation processing. Calculation processing is performed on all input items such as average, maximum deviation 1 variance, etc. for hourly data from search data 41, and stored in aggregate data 114.
The data is passed to the performance calculation processing unit 115.

The performance calculation processing unit 115 calculates boiler efficiency, turbine efficiency,
Perform calculations similar to performance tests such as heat balance,
The calculation result data 61 is output.

After the operator confirmation unit 112 of the man-machine requesting unit 40 confirms the completion of the calculation, the operator selects an output from the operator input unit 111 and issues a data output request.The output unit 52 receives the output request and performs output conversion processing. The output conversion processing unit 116 converts the search data 41. From calculation result 61.

Data is converted and output according to the format of the output request.Assuming there is enough data stored up to the present of the plant, it is possible to output a transition graph of past performance based on the performance at the delivery test. I can do it. Also,
Improve plant performance by detecting equipment that needs to be replaced before regular inspections, or by finding equipment that is not included in regular inspection items but whose efficiency or performance has significantly decreased and incorporating it into regular inspection items. can be retained.

Next, FIG. 12 shows a second embodiment of the present invention.

This is the same as the first embodiment in which an automatic execution means 71 is added. The automatic execution means 71 is as shown in FIG.

It is composed of an automatic execution table 200, an automatic execution timer part 201, and an automatic execution section 202. The automatic execution table 200 contains automatic execution information from &1 to &100. The automatic execution information includes an execution start date, an execution cycle, a result output light, and a search condition table.

The automatic execution timer part 201 consists of a timer module. Further, the automatic execution unit 202 has a read signal line from the automatic execution table 200 and a read signal line from the automatic execution timer part 201.

Now, the operator input section 11 of the man-machine requesting means 40
When the automatic execution table 1 is input and automatic execution permission is requested, the automatic execution unit 202 of the automatic execution means 71 receives the automatic execution permission and reads the automatic execution table 200.

Further, the time is read from the automatic execution timer part 201 and compared, and if the execution timing has come or if it has passed, a search request is issued (the end of the search request is notified to the automatic execution part).

When the search completion notification is returned, a calculation request is made to the calculation means 51 (the end notification destination is the automatic execution unit).

When the calculation completion notification is returned, output is performed to the output means 52 based on the data output designation of the automatic execution table 200. The end of output is considered as one cycle, and a request for restoration is checked. If a request occurs in the middle of one cycle, it is processed as a primary request. Furthermore, if automatic execution is prohibited during automatic execution, the currently executing process will continue until the end of one cycle and will not terminate midway.

In this second embodiment, when performing routine performance management at a thermal power plant, an operator had to be present at all times, but by providing an automatic execution means, routine work can be performed automatically. If you register it in the table, there will be no mistakes in conditions or periods. Also, if you make a request during the night, you can see the data immediately when you are dispatched, allowing you to respond quickly.

FIG. 14 is a block diagram showing a third embodiment of the present invention. Comparison means 72 and analysis means 73 in the first embodiment
and analysis result data 74 are added. The comparison means 72, as shown in FIG.
, a data comparison section 301 , and a reference data registration section 302 . The reference data section 300 stores reference data for each load band. The data comparison section 301 reads data from the reference data section 300 and also reads data from the calculation result data 61 and compares them. Then, the analysis means 73 is notified of the comparison result. Standard data registration section 302
is for reading data from the calculation result data 61 and writing the data to the reference data section 300.

2. As shown in FIG. 16, the analysis means 73 includes an analysis table 303, an analysis section 304, and an analysis table registration section 30.
It consists of 5. The analysis table 303 includes pumps,
It is created for each equipment such as fans, turbines, and boilers.

The analysis unit 304 reads data from the analysis table 303 and writes it to the analysis result data 74. Also.

The analysis table registration unit 305 writes data to the analysis table 303. The analysis result data 74 uses a magnetic auxiliary storage device as a medium.

Now, the operator input section 11 of the man-machine requesting means 40
When a comparison request is made from 1, the comparison means 72 receives the comparison request and issues a comparison request to the data comparison section 301. The data comparison section 301 takes out the reference data from the reference data section 300 and calculates the calculation result data 62. The result data is extracted from the data, and both are compared to determine the deviation from the reference data, and the result is notified to the analysis means 73. If the reference data is updated due to replacement of one device, etc., the reference data registration section 302 uses the heman machine requesting means 40 to utilize the calculation result 62 and update the reference data 300.

The analysis means 73 reads the deviation notified from the comparison means 72 into the analysis unit 304, reads the data of the corresponding item from the analysis table 303, multiplies the influence weight and the deviation in descending order, and stores it in the analysis data 74. Then, an analysis completion notification is returned to the party that issued the comparison request. If the analysis table 303 is to be changed due to equipment deterioration or the like, the analysis table 303 is updated using the analysis table registration section 305 and the heman machine requesting means 40.

Deviations from standard data are important analytical data for performance management at thermal power plants, but they have the drawback of requiring a large number of items and being time-consuming. However, the comparison means 72 and the analysis means 7
3, you can make faster and more accurate decisions.

Next, a fourth embodiment of the present invention will be explained with reference to FIG. This embodiment is obtained by adding a modification means 42 to the first embodiment. As shown in FIG. 18, the correction means 42 is composed of a data correction section 118 and a correction-related calculation section 117.
The data modification unit 118 modifies the search data 42 and has a modification area.

Further, the correction relation calculation unit 117 corrects the search data 41 and has a relation table.

Now, when the man-machine requesting means 40 makes a modification request to the modification means 42, the data modification section 118 receives the modification request,
The contents of the current search data are returned to the operator confirmation section 112, and the operator inputs the corrected data from the operator input section 111 while confirming the correction.

The search data 41 is read into the internal area of the data correction unit 118, and the correction input is overwritten in the internal area. When the modification is completed, the search data 41 is overwritten with the contents of the internal area, and the modification related calculation unit 117 is notified of the modified location.

The modification-related calculation unit 117 calculates the modification-related portion contained therein and overwrites the search data 41. Then, a modification completion notification is returned.

This allows you to go back and perform performance tests in the past, or when you are currently performing performance tests, and change some parameters and perform performance calculations again, so you don't have to actually operate the plant that way. This also makes it possible to perform simulations, and when replacing new equipment, the effects can be seen before conversion.

In addition, since it is not possible to bring all the inputs online for old power plants, etc., performance calculations are performed by supplementing the missing parameters. This makes it applicable to new and old power plants.

[Effects of the Invention] As described above, according to the present invention, performance such as efficiency of the power generation plant can be calculated based on data collected when the power generation plant is settled and in a state of thermal equilibrium. value is obtained. Furthermore, not only current values but also changes from the past, which are important for efficiency analysis, can be obtained by searching past data, making multifaceted analysis possible.

Performance tests, which previously could only be performed for the same number of years, can now be performed every day. By managing the plant performance of each individual device on a daily basis, it is possible to detect equipment abnormalities and signs of efficiency decline at an early stage, ensuring safe plant operation. , can contribute to high efficiency operation.

In addition, since the automatic execution means can aggregate and calculate the results of yesterday's driving during the night, the next morning, the data can be immediately reflected in today's driving policy, allowing a centrifugal system to be established.

The analysis means outputs efficiency reduction factors.

This makes it easier to consider improving driving methods.

With the correction method, even in existing old plants where all the data necessary for performance management cannot be imported online, data can be added and corrected manually later, and plant performance can be determined based on the results. It is possible to apply the invention to all power plants. In addition, using this correction function, you can perform efficiency simulations such as how much the efficiency will change if you raise the main steam temperature a little more, stop one circulating water pump, and reduce the in-house power by how many MWH. The impact on efficiency can be determined based on actual data.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing a first embodiment of the present invention;
Figure 3 is a block diagram of the plant settling determination means, Figure 3 is a block diagram of the plant data collection means, Figure 4 is a block diagram of the data storage means, Figure 5 is an explanatory diagram of stored data, Figure 6
The figure is a block diagram of the retrieval means, FIG. 7 is an explanatory diagram of the interrelationship between the retrieval means, stored data, and retrieval data, FIG. 8 is an explanatory diagram of the retrieval data, FIG. 9 is an explanatory diagram of the calculation means, and FIG.
The figure is an explanatory diagram of the output means, and FIG. 11 is a characteristic diagram showing load changes. Fig. 12 is a block diagram showing the second embodiment of the present invention, Fig. 13 is an explanatory diagram of automatic execution means, Fig. 14 is a block diagram showing the third embodiment of the invention, and Fig. 15 is a comparison. FIG. 16 is an explanatory diagram of the analysis means, FIG. 17 is a block diagram showing the fourth embodiment of the present invention, FIG. 18 is an explanatory diagram of the correction means, and FIG. 19 is a block diagram of the conventional example. It is a diagram. 1... Power generation plant, 20... Plant input means,
21... Plant setting means, 22... Plant collecting means, 30... Data storage means, 31... Saved data, 32... Searching means, 40... Man-machine requesting means, 41...・Search data. 42... Correction means, 51... Calculation means, 52...
Output means, 61... Calculation result data, 71... Automatic execution means, 72... Comparison means, 73... Analysis means,
74...Analysis results, 100...Reference data storage section,
101...Data comparison and determination section, 102...Timer control section, 103...Data collection control section, 104...
Plant data storage section, 105...Stored data area section, 106...Stored data clear section, 107...
Index creation section, 108...Data storage section, 109...
Index determination unit, 110... Data reading unit, 111.
...Operator input section, 112...Operator confirmation section, 113...Tally processing, 114...Tally data, 1
15... Performance calculation processing, 116... Output conversion processing,
117... Correction related calculation unit, 118... Data correction unit, 200... Automatic execution table, 201... Part of automatic execution timer, 202... Automatic execution unit, 300...
・Reference data section, 301...Data comparison section, 302・
...Reference data registration section, 303... Analysis table. 304... Analysis section, 305... Analysis table registration section.

Claims (4)

[Claims] (1) A plant input means that scans and inputs the process data of the power generation plant, a plant settling determination means that determines a specific settling state of the plant based on the inputted process data, and a determination result of the plant settling determination means that determines whether or not the plant is settled. a plant data collection means for collecting the process data when the process data is in the state; a data storage means for storing the collected data in a mass storage device; a retrieval means for extracting data from the mass storage device; A power generation plant performance management device characterized by comprising a calculation means for totalizing and calculating plant performance, and an output means for outputting current calculation results and changes from past calculation results. (2) The power generation plant performance management device according to claim 1, further comprising automatic execution means for giving an execution command to the search means, the calculation means, and the output means at predetermined intervals. Performance management device. (3) In the power generation plant performance management device according to claim 1, a comparison means for comparing the calculation result of the plant performance with a predetermined reference performance and a performance trend from the past, and the result of this comparison. When it is determined that there is an increase or decrease in performance, analysis means compares the factors causing the increase or decrease with a reference value and outputs the factors having the greatest degree of influence first. (4) The power plant performance management device according to claim 1, further comprising a modification device for displaying and modifying the search data extracted by the search device.