JP2002256888A - Auction system for gas turbine hot part - Google Patents

Abstract

PROBLEM TO BE SOLVED: To promote the distribution of gas turbine hot parts by adding the information about the remaining value thereof to them to auction. SOLUTION: There is provided an auction system for gas turbine hot parts in which the list of gas turbine hot parts and the information about the remaining value thereof are disclosed on a server 21 connecting to the Internet to auction and entertain bids. The server 21 is provided with a prediction means 221 for predicting the life expectancy of the gas turbine hot parts, and the remaining value is calculated on the basis of the life expectancy predicted by the means 221.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an auction system for gas turbine hot parts, and more particularly to an auction of gas turbine hot parts in which the transparency of the auction is enhanced by adding an operation history or a life expectancy diagnosis result to a used hot part. About the system.

[0002]

2. Description of the Related Art Gas turbine components, particularly components used in a high-temperature environment such as a combustor, a combustion nozzle, a turbine vane, and a turbine rotor blade (hereinafter referred to as high-temperature components) are made of expensive materials having high heat resistance. It is used. In addition, these high-temperature parts require high quality control in manufacturing, and the manufacturing cost is high for other parts.

[0003] The life time of these high-temperature components is greatly affected by operating conditions such as the operating time and the number of startups, but is generally 20,000 to 50,000 hours, and is usually discarded after the life time expires. . In addition, even before the expiration of the life time, depending on the operating conditions, there may be a case where the material must be disposed of due to a defect such as a defect or breakage due to fatigue, creep, or the like.

[0004]

Incidentally, the life time is calculated based on a small number of data such as the accumulated operation time or the number of startups, and is therefore not always accurate. Further, assuming that the lifetime has expired,
Alternatively, even a high-temperature component that is discarded as having a defect in the material can be used more continuously by repairing or relaxing operating conditions.

SUMMARY OF THE INVENTION The present invention has been made in view of the above-mentioned problems, and a gas turbine high-temperature component is added to a used gas turbine high-temperature component by adding information on the residual value of the high-temperature component to auction. Promote the distribution of parts.

[0006]

The present invention employs the following means in order to solve the above-mentioned problems.

[0007] A gas turbine high-temperature parts auction system for publishing a list of high-temperature parts for a gas turbine and residual value information of the high-temperature parts on a server connected to the Internet, auctioning the high-temperature parts and receiving a bid, The server includes a life expectancy predicting unit that predicts a life expectancy of the gas turbine high-temperature component, and calculates the remaining value based on the life expectancy predicted by the life expectancy predicting unit. Further, the life expectancy predicting means can estimate the life expectancy based on the operation time, the number of starts, and the load history of the gas turbine. The life expectancy predicting means can estimate the life expectancy based on thermal fatigue damage and creep damage of the gas turbine.

[0008]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to FIGS. FIG. 1 is a diagram illustrating a gas turbine high-temperature component auction system according to the present embodiment. In the figure, reference numerals 1a, 1b, and 1c denote users who operate a gas turbine such as a gas turbine generator. Reference numeral 11 denotes gas turbine maintenance and inspection data;
Denotes a gas turbine, 13 denotes a gas turbine operation control panel, 1
4a is a server on the user side. The server 14a acquires the maintenance information 11 accumulated by the user, the sensor information obtained during the operation of the gas turbine, and the operation information such as the operation control information obtained from the operation control panel 13, and transmits the obtained information to the communication line 4 such as the Internet. Is transmitted to the server 21 on the maker side via.

Reference numeral 2 denotes a gas turbine manufacturer, and reference numeral 3 denotes a database owned by the manufacturer 2, which stores the operation information of high-temperature components of the gas turbine as history information. Also, it stores part management information such as a used part or used equipment list managed by the manufacturer. Reference numeral 4 denotes a communication line such as the Internet, for example, and the maker 2 can obtain management information such as the operation information of high-temperature parts or a list of generated used parts via the line. Reference numeral 22 denotes a residual value determining unit that determines the residual value of the high-temperature component, and the residual value determining unit 22 stores a residual value information in a memory 22.
0, life expectancy diagnosis analysis program 221, breakage probability prediction program 222, demand power prediction program 223 for predicting demand power (sales power), operation period revenue prediction program 224 for predicting power sale revenue during gas turbine operation period, suspension period It consists of a loss prediction program 225 and a parts replacement cost prediction program 226.

Reference numeral 5 denotes an auction (auction) site established on a communication line such as the Internet, which discloses the management information on used parts or used equipment managed by the manufacturer via, for example, a server 21. At this time, the manufacturer 2 can present, in addition to the component management information, operation history information for each high-temperature component generated from the operation information and residual value information for each high-temperature component generated from the information. The residual value information can be generated based on the life expectancy generated for each high-temperature component using the life expectancy analysis program from the part management information and the history information, the predicted life expectancy under changed operating conditions, and the like.

FIG. 2 is a diagram showing an example of a disclosure screen to be disclosed on the auction site. An auction participant, eg, user 1b, accesses the auction site via the Internet. At this time, the user password can be set to restrict the participants. The accessed participant first operates the "GT type selection" button 21 to select a gas turbine type including a high-temperature part desired to be purchased (since the specifications of the high-temperature part differ depending on the gas turbine type). Next, the "high-temperature component selection" button 22 is operated to select a desired high-temperature component. The figure shows an example in which the transition piece 221 is selected. At this time, a photograph of the selected component or a drawing from which the general specifications can be determined can be displayed in the center of the screen. Further, a button for opening information necessary for purchasing the price, delivery date, life expectancy, usage record, weight, warranty period, and the like of the selected component can be set in the lower row.

The "inquiry" button 23 at the lower right is used when acquiring information that cannot be obtained by this system or when there is a question. When this button is operated, a screen for inputting an inquiry is displayed, and the input information is transferred to the maker or the auction owner via e-mail or the like. When the "order" button 24 at the lower right is operated, the desired selling price of the manufacturer and the desired purchasing price of the purchaser are displayed. In this state, the purchase applicant can enter the purchase price and execute the order.

FIG. 3 is a diagram showing processing of the auction system according to the present embodiment. First, in step 1, the server 14a on the user 1a side fetches the sensor information, operation control information, and maintenance and inspection information of the gas turbine. In step 2, the acquired data is transmitted to the server 21 on the manufacturer side via a communication line such as the Internet. In step 3, the server 21 on the manufacturer side
Receives these data and stores the data received in step 4 in memory. In step 5, using the life expectancy diagnosis analysis program, the residual value information is generated for each high-temperature component. In step 6, the gas turbine owned by the user 1a can be purchased as a used product based on the generated residual value information. After performing inspection and repair on the purchased used parts in step 7, the specifications of the used parts and the desired sales price table are set in step 8, and an auction is prepared.

In step 9, the auction seller (maker) accesses the auction site,
Based on the specifications of the used parts and the desired sales price list, the specifications for auction are input to an auction site. In step 10, the desired retail price is input, and in step 11, the information is disclosed to an auction site.

[0015] In step 12, the successful bidder (user) of the auction accesses the auction site and checks the specifications of the used parts, and then checks the selling price in step 13. If it is determined in step 14 that the user wants to purchase the product at the selling price or more, the process proceeds to step 15; In step 15, the desired purchase price is input, and the process proceeds to step 16. In step 16, the desired selling price is compared with the desired purchasing price. If the desired selling price is lower than the desired selling price, the process returns to step 13; if the desired selling price is higher than the desired selling price, the process returns to step 110. . If they match, the processing is terminated assuming that the trade has been established.

As described above, operation information such as maintenance and inspection information, sensor information, operation control information, etc. of a gas turbine owned by a user is obtained from a remote location via the Internet or the like, and the obtained information is analyzed and analyzed by a server. The diagnosis can be used to determine the residual value of the gas turbine hot components. In addition, since the management information, the operation history information, and the information on the residual value are added to the parts and released to the auction site, highly transparent auctions can be performed, and resources can be effectively used. it can.

FIG. 4 to FIG. 7 are diagrams showing examples of generating residual value information for each component based on the component management information and the history information. FIG. 4 is a diagram illustrating a system for diagnosing a residual value of a gas turbine high-temperature component. In the figure,
The gas turbine power generation equipment 101 includes the gas turbine compressor 10
2, a combustor 103 and a turbine 104, and a generator 10 driven by the gas turbine
5 is comprised. When the power generation equipment is a combined power generation plant mainly using a gas turbine, a waste heat recovery boiler 106, a steam turbine 107, a generator 108 driven by the steam turbine, and a condenser 109 are added. Various sensors are attached to these main devices to monitor their status. These sensors are connected to the operation monitoring device 11 by a cable 110, respectively.
The sensor information is processed by the operation monitoring device 11 or stored as history information.

The operation monitoring device 111 sends the sensor information and the operation control information to the device management device 115 through the operation monitoring device side communication device 112, the communication line 113, and the device management device 115 side communication device 114. The operation monitoring device 111
The operation monitoring device side communication device 112 can be the same computer. Further, the device-management-device-side communication connector 114 may be an internal device of the device management device 115. As the communication line 113, a dedicated line such as a public telephone line, an Internet line, a satellite line, or the like can be used. When an Internet line is used as the communication line 113, a firewall may be connected to the operation monitoring device side communication device 112 and the device management device side communication device 114 in consideration of data security.

The sensor attached to the gas turbine power generation equipment 101 provides information on the power generation equipment, for example, temperature, pressure,
Measure information such as vibration. Information obtained by the measurement is sent to the operation monitoring device 111. Operation monitoring device 111
Always determines whether or not the operating gas turbine power generation equipment 101 is in a normal state. The normal and abnormal determination of the operating gas turbine is performed by a normal method used for determining the abnormality of the operating gas turbine, for example, by using an abnormality determination method of comparing the measured value of the exhaust gas temperature and vibration with its allowable value. good. If the gas turbine power generation equipment 101 is in a normal state, information and operation information selected by the sensor are stored in a data storage device connected to the operation monitoring device 111, and a part of the data is stored once / one day. Device management device 11
Send to 5. Further, information that largely relates to damage to important equipment in the gas turbine equipment, for example, information on temperature and pressure, is transmitted from the operation monitoring device 111 to the equipment management device 115 at a frequency of, for example, about once per second. When the gas turbine power generation equipment is in some abnormal state, all data obtained by the sensor through the communication device 112 on the power generation equipment side, the communication line 113, and the communication device 114 on the device management device side are transmitted to the device management device 115, for example, by one. It is transmitted at a frequency of about once per second.

The device management device 115 can be configured as a client server system using a plurality of computers. For example, the device management device 115 is connected to the analysis server 116, W
It can be composed of a WWW server 119, WWW browsers 120 and 121, a LAN 22, and the like. The analysis server 116 has a database 117 on damage and a knowledge base 118 on damage.

Regardless of whether the gas turbine power generation equipment 101 is normal or abnormal, various sensor information and operation control information transmitted by the operation monitoring device 111 are sent to the analysis server 116 in the device management device 115, In a database 117 located at The measurement information of the sensor, the operation control information, and analysis in the analysis server 116,
The processed information is viewed on a computer in the device management device 115,
Can be searched and processed. For example, the operator can evaluate the diagnosis result of the device based on the data analyzed by the analysis server 116. Further, the maintenance / maintenance (planning) department can formulate a maintenance / maintenance plan for the target device based on the data analyzed in the analysis server 116.
Further, the design department can use the data analyzed in the analysis server 116 for design and development support of the device.

When the analysis server 116 receives the information from the operation monitoring device 111, the arithmetic processing unit in the analysis server 116 accesses the database and calls the operation information of the device up to the present. In addition, the analysis server 116 uses the operation control information and the sensor information to
01 can be diagnosed for the damage and the life of the equipment constituting it.

FIG. 5 is a diagram for explaining the life expectancy diagnosis process based on the damage diagnosis. First, in step 1, sensor information of the power generation facility is obtained. In step 2, the temperature, stress, and strain of each high-temperature component are calculated using a relational expression (described later) created in advance. In step 3,
A change in the damage rate of the material is obtained based on the information calculated in the previous step. In step 4, operation control information of the gas turbine is obtained. In step 5, creep damage and thermal fatigue damage of the material are calculated based on the change in the damage rate obtained in step 3 and the operation control information obtained in step 4. In step 6, the equivalent operation time is calculated. In step 7, damage information is provided to the operator of the gas turbine. In step 8, the driver inputs driving plan information, and in step 9, the remaining life is calculated based on the driving plan. In step 10, the calculated life expectancy information is provided to the gas turbine operator.

FIG. 6 is a flowchart of a life expectancy diagnosis process based on damage diagnosis. In executing the life expectancy diagnosis process, preparations shown in steps 101 to 105 are made in advance. First, in step 101, a master curve showing the relationship between the material temperature change ΔT and the damage rate D is created. This curve is created for thermal fatigue damage and creep damage for each material or site of the hot component of interest. In step 102, an expression, "Expression 1", representing the relationship between the sensor information and the thermal boundary condition of the target high-temperature component is created.

[0025]

(Equation 1) In analyzing the temperature T _m , stress σ _m , and strain ε _m of the target high-temperature component by the finite element method, a thermal boundary condition of the target high-temperature component is estimated based on sensor information (temperature) attached to the components of the gas turbine. . Thermal boundary conditions is the cooling gas temperature T _b around the gas temperature T _a and the target high-temperature parts of the target high-temperature parts. A relational expression between these temperatures and the sensor information (temperatures T _α , T _β ) is created in advance as Expression 1. In the relational expression shown here, the thermal boundary condition of the target high-temperature component is expressed as a linear function of the sensor information. However, other functional forms may be used, and the sensor information to be used may be increased.

In step 103, a thermal boundary condition used for the finite element analysis of the target high-temperature component is calculated. In step 104, a thermal / structural analysis is performed by the finite element method. The thermal boundary condition at this time is, for example, the surrounding gas condition is ± 10 ° C. based on the design condition, and the cooling gas condition is ± 40 ° C. based on the design condition. The operating conditions of the gas turbine include a thermal boundary condition when the gas turbine is tripped, a typical thermal boundary condition when the load of the gas turbine fluctuates, and a stress σ due to centrifugal force applied to the rotating blade when there is no thermal stress. _m , strain ε _m, etc. are determined in advance. In step 105, based on the results up to the previous step, an equation representing the relationship between the thermal boundary condition and the temperature T _m , stress σ _m , strain ε _m ,
"Expression 2" is created.

[0027]

(Equation 2) In the relational expression shown here, the temperature T _m , the stress σ _m , and the strain ε _m are:
Although it is expressed as a quadratic function of the thermal boundary condition of the target high-temperature component, other functional forms may be used, and the thermal boundary condition to be used can be further increased.

Steps 101 to 105
And sensor information and temperature T_m, Stress σ _m, Strain ε_mRelational expression
After that, without performing finite element analysis
Creep damage D_cAnd thermal fatigue damage D_fCalculating
Can be. These equations also provide information on damage.
And store it in the knowledge base 118.

Next, a life expectancy diagnosis process based on actual damage diagnosis is performed. First, in step 107, a component to be diagnosed is selected. In step 109, the operator determines and inputs the operation plan PL of the gas turbine power generation equipment. The input data is sent to the analysis server 16. In step 110, the coefficient analysis server 16 uses the knowledge base to online damage diagnosis A, B, C, and the data of the reference damage rate _{D c0, D f 0} is read. In step 111, the analysis server 116 transmits the sensor information and the operation information from the gas turbine power generation facility 101, for example, the number of operations i, the number of starts j, the number of load changes k, the number of trips 1, the actual operation time H, and the temperature T _nm . Read.

Since the creep damage depends on the time spent at a certain temperature, the creep damage can be evaluated by the time actually applied to an arbitrary temperature during the operation of the gas turbine. On the other hand, since the thermal fatigue damage _Df-OL depends on the temperature fluctuation, a relatively large temperature fluctuation related to the start and stop,
The number of times is measured for each temperature change caused by a load change during operation, a temperature change caused by a gas turbine trip, and the like.

In step 113, the calculated temperature T
Analysis of creep damage and thermal fatigue damage is performed based on _{m 1} , stress σ _m , and strain ε _m . "Equation 3" is used to calculate the creep damage.

[0032]

(Equation 3) In Equation 3, part D _c-OL governed by the creep damage of the online equivalent operating time L _OL to be described later, the operation time H _i, creep damage rate D _co upon design _criteria, and creep damage rate D by a temperature change _It is represented by the rate of change of _c .

In addition, "Equation 4" is used for calculating the thermal fatigue damage.

[0034]

(Equation 4)In equation 4, the online equivalent operation time L_OLThermal fatigue loss
Part D governed by wounds _f-OLIs the thermal fatigue at the design standard
Damage rate D_f0Fatigue damage rate D due to temperature and temperature change_fof
Expressed by the rate of change. Each of the three terms in the equation is activated
Thermal fatigue related to shutdown, macro load change during operation
Thermal fatigue when the trip stops.

Next, using this information, the online equivalent operation time _LOL is calculated by "Equation 5".

[0036]

(Equation 5) In Equation 5, the equivalent operation time L _OL of the component device is represented by a linear sum of a part D _c-OL governed by creep damage and a part D _f-OL governed by thermal fatigue damage, as represented by Equation 3. Shall be. Note that these expressions can be read from the knowledge base 118.

In step 114, it is determined whether or not the current equivalent operation time has already passed the replacement life of the target part. If the replacement life has not expired, that is, if the calculated online equivalent operation time _LOL is smaller than the replacement reference time of each device, the remaining life (remaining life) is calculated in step 27. If the replacement life has expired,
That is, if the calculated online equivalent operation time _LOL is longer than the replacement reference time of each device, step 1 is executed.
Proceed to 16. For the calculation of the remaining life, 1, a method based on regression analysis of the data so far, a method based on an operation pattern of a design standard, a method based on a rate of change (differential value) at an evaluation time, and the like are applied. it can.

In step 116, the above 1 to 3
Maximum expected lifetime _{RL m ax,} and calculates a minimum expected life _{RL min,} and displays the result in step 117 using the method.

In step 118, the conversion coefficient is written in the knowledge base 118. In step 119, the operator looks at the online equivalent operation time L _OL , maximum expected life RL _max , and minimum expected life RL _min ,
It is determined whether to continue the operation of the device. Step 12
At 0, the operation plan R is input if the operation is to be continued, and the process proceeds to step 109; otherwise, the operation is stopped at step 121.

FIG. 7 is a diagram showing a life expectancy result by damage diagnosis. The horizontal axis in the figure indicates the actual operation time of the device, and the vertical axis indicates the online equivalent operation time. As shown in the figure, the current damage diagnosis result is shown in real time, and further, as shown by the hatched portion in the figure, the predicted value and the prediction range of the life expectancy are quantitatively shown.

[0041]

As described above, according to the present invention, the distribution of gas turbine high-temperature parts is performed by adding information on the residual value of the high-temperature parts to the gas turbine high-temperature parts for auction. Can be promoted.

[Brief description of the drawings]

FIG. 1 is a diagram illustrating a gas turbine auction system according to an embodiment of the present invention.

FIG. 2 is a diagram illustrating an example of a disclosure screen to be disclosed on an auction site.

FIG. 3 is a diagram showing processing of an auction system.

FIG. 4 illustrates a system for diagnosing the residual value of a gas turbine hot component.

FIG. 5 is a diagram illustrating life expectancy diagnosis processing based on damage diagnosis.

FIG. 6 is a flowchart of a life expectancy diagnosis process based on a damage diagnosis.

FIG. 7 is a view showing a life expectancy prediction result by damage diagnosis.

[Explanation of symbols]

 1a, 1b, 1c User 2 Manufacturer 3 Database 4 Communication line 5 Auction site 11 Maintenance and inspection data 12 Gas turbine 13 Operation control panel 14a, 14b, 14c, 21 Server

 ──────────────────────────────────────────────────続 き Continuing on the front page (72) Inventor Takeshi Ishida 5-2-1, Omika-cho, Hitachi City, Ibaraki Prefecture Within the Information Control Systems Division, Hitachi, Ltd. (72) Inventor Naoyuki Nagabuchi Omika-cho, Hitachi City, Ibaraki Prefecture 7-2-1, Hitachi, Ltd. Electric Power and Electricity Development Laboratory (72) Inventor Hiroshi Haruyama 7-1-1, Omika-cho, Hitachi City, Ibaraki Prefecture In-house Hitachi, Ltd. Hitachi Research Laboratory

Claims (7)

[Claims] 1. A gas turbine high-temperature parts auction system for publishing a list of gas turbine high-temperature parts and residual value information of the high-temperature parts on a server connected to the Internet, auctioning the high-temperature parts, and receiving a bid. The server comprises a life expectancy predicting means for estimating the life expectancy of the gas turbine high-temperature component, and calculates the remaining value based on the life expectancy predicted by the life expectancy predicting means, wherein the auction system for the gas turbine high-temperature part. 2. The auction system for gas turbine hot parts according to claim 1, wherein the life expectancy predicting means estimates the life expectancy based on the operation time, the number of starts, and the load history of the gas turbine. 3. The auction system for gas turbine hot parts according to claim 1, wherein said life expectancy predicting means estimates the life expectancy based on thermal fatigue damage and creep damage of the gas turbine. 4. The gas turbine according to claim 3, wherein the thermal fatigue damage is calculated from thermal fatigue related to starting and stopping, thermal fatigue related to macroscopic load fluctuation, and thermal fatigue related to trip stopping. Auction system for hot parts. 5. The auction system for gas turbine hot parts according to claim 3, wherein the creep damage is calculated from a creep damage rate based on a design standard and a change rate of the creep damage rate due to a temperature change. 6. The method according to claim 1, wherein the life expectancy predicting means includes means for estimating a life expectancy under an operating condition including at least an operating temperature change. An auction system for gas turbine high-temperature parts. 7. The gas turbine high-temperature component according to claim 1, wherein the life expectancy predicting means estimates the life expectancy based on the operation time, the number of starts, the load history and the fuel usage history of the gas turbine. Auction system.