CN103364200B - A kind of gas turbine start-up course state evaluating method - Google Patents
A kind of gas turbine start-up course state evaluating method Download PDFInfo
- Publication number
- CN103364200B CN103364200B CN201310277372.4A CN201310277372A CN103364200B CN 103364200 B CN103364200 B CN 103364200B CN 201310277372 A CN201310277372 A CN 201310277372A CN 103364200 B CN103364200 B CN 103364200B
- Authority
- CN
- China
- Prior art keywords
- gas turbine
- output shaft
- rotating speed
- shaft rotating
- course
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Abstract
The present invention relates to condition monitoring for rotating machinery field, particularly a kind of gas turbine start-up course state evaluating method.The present invention includes following steps: (1) acquisition state information; (2) gas turbine output shaft rotating speed normalized curve band is built; (3) state estimation; (4) normalized curve band is upgraded.The present invention proposes the start-up course appraisal procedure of gas turbine output shaft rotating speed normalized curve band, there is the advantages such as calculated amount is little, computing velocity fast, easy to understand, solve the quantitative state estimation problem of gas turbine start-up course.
Description
Technical field
The present invention relates to condition monitoring for rotating machinery field, particularly a kind of gas turbine start-up course state evaluating method.
Background technology
Gas turbine maneuverability is good, has good peaking performance.So gas turbine starts success or not and directly affects the needs that can gas turbine respond and meet user rapidly.If startup separator, will heavy economic losses be caused, even have influence on the complete of whole electrical network.
The start-up course of gas turbine is a multisystem cross-coupled process.Chinese invention patent 94193471.3 provides a kind of method of the operation conditions for showing turbine in start-up course, its technical characteristic is: other at a datum curve (RV) drawn from the specific parameter of turbine (m.w.b) and operation correlation parameter (kz, kT, kp), also shows the time dependent curve of turbine speed (n) (AV).But the object of this patent carries out suitable display to the operating turbine state in start-up course, and be not used in the state estimation of start-up course.
Compared with Chinese invention patent 94193471.3, the invention provides a kind of gas turbine start-up course state evaluating method, according to repeatedly normally starting the normalized curve band that sample builds gas turbine output shaft rotating speed, contrast actual speed line and normalized curve band, realize the state estimation of start-up course, Timeliness coverage gas turbine starts abnormal conditions, reduces maintenance cost.
Summary of the invention
The object of the present invention is to provide a kind of gas turbine start-up course state evaluating method, realize the state estimation of gas turbine start-up course.
The object of the present invention is achieved like this, the present invention includes following steps:
(1) acquisition state information: gather gas turbine start-up course status information, comprise and start trigger pip and gas turbine output shaft rotating speed;
(2) build gas turbine output shaft rotating speed normalized curve band: accumulation gas turbine normal boot process sample, carry out the statistical study of gas turbine output shaft rotating speed and feature modeling, set up gas turbine output shaft rotating speed normalized curve band;
(3) state estimation: when gas turbine starts triggering, state estimation is carried out to gas turbine start-up course, if when gas turbine output shaft rotating speed is in gas turbine output shaft rotating speed normalized curve band, then start-up course assessment result is normal, and start-up course state estimation terminates; If when gas turbine output shaft rotating speed exceeds normalized curve band, start-up course assessment result is abnormal;
(4) assessment result confirms: to assessment result in step (3) be abnormal start-up course by finally confirming, if confirm that assessment result is correct, then select maintenance measure, start-up course state estimation terminates; If confirm that assessment result is incorrect, be then that abnormal start-up course assessment result changes to normally by assessment result in step (3), these start-up course data be added into the normal boot process sample in step (2) simultaneously;
(5) normalized curve band is upgraded: according to step (2), recalculate coboundary and the lower boundary of gas turbine output shaft rotating speed sample, obtain new gas turbine output shaft rotating speed normalized curve band.
The step setting up gas turbine output shaft rotating speed normalized curve band of step (2) comprising:
(1) accumulate normal boot process sample: when the sample of normal boot process refers to that gas turbine normally starts, gas turbine output shaft rotating speed is triggered to idling rating sampled value during this period of time in startup:
Wherein, small tenon i=1,2,3, m represents sample, j=1,2,3, and, n represents sampling instant;
(2) gas turbine output shaft rotating speed statistical study: find in all samples, maximal value, the minimum value of each sampling instant gas turbine output shaft rotating speed, define i gas turbine output shaft rotating speed sample and be respectively in the maximal value in jth moment, minimum value:
max(X
j)＝1.1*max{x
1,j,x
2,j,…,x
i,j}
min(X
j)＝0.9*min{x
1,j,x
2,j,…,x
i,j}
(3) gas turbine output shaft rotating speed border is calculated: the coboundary and the lower boundary that calculate gas turbine output shaft rotating speed normalized curve band based on the maximal value of gas turbine output shaft rotating speed, minimum value:
X
coboundary=max (X
j), j=1,2 ..., n
X
lower boundary=min (X
j), j=1,2 ..., n
Beneficial effect of the present invention is:
The start-up course appraisal procedure based on gas turbine output shaft rotating speed normalized curve band that the present invention proposes, has the advantages such as calculated amount is little, computing velocity fast, easy to understand, solves the quantitative state estimation problem of gas turbine start-up course.
Accompanying drawing explanation
Fig. 1 is the state estimation process flow diagram of gas turbine start-up course;
Fig. 2 is gas turbine output shaft rotating speed normalized curve band schematic diagram;
Fig. 3 is gas turbine start-up course assessment result example schematic diagram;
Fig. 4 is the gas turbine output shaft rotating speed normalized curve band schematic diagram before and after upgrading.
Embodiment
Below in conjunction with accompanying drawing, the present invention is described further.
Basic step of the present invention comprises the following steps:
First, collection of the present invention can reflect the status information of gas turbine start-up course.
Then, according to the normal boot process sample that history data obtains, adopt statistical law to build gas turbine output shaft rotating speed normalized curve band.
Afterwards, assess the state of this start-up course according to gas turbine output shaft rotating speed normalized curve band, if assessment result is " normally ", then the assessment of this start-up course terminates.
If assessment result is " abnormal ", but through confirming as " normally " start-up course, then the start-up course data this group being determined " abnormal " are added into normal boot process sample, rebuild gas turbine output shaft rotating speed normalized curve band.
Referring to the gas turbine start-up course state estimation process flow diagram shown in Fig. 1, gas turbine start-up course state evaluating method of the present invention is described in detail, comprises the following steps:
Step 101: the information gathering reflection gas turbine start-up course, starts trigger pip and gas turbine output shaft rotating speed.Gas turbine output shaft rotating speed when only collection startup trigger pip is "True".For the information that start-up course is relevant, corresponding sensor can be adopted directly to gather, also can obtain from the database of gas turbine supervisory system.For the collection of parameter, between sample frequency 1Hz to 5Hz.
Step 102: according to the history normal boot process sample of accumulation, adopts statistical law to build gas turbine output shaft rotating speed normalized curve band.
Following is a list the method that statistical law builds gas turbine output shaft rotating speed normalized curve band:
(1) collect normal boot process sample, this is the process of an initial accumulated.For the ease of setting forth specific embodiment of the invention process, assuming that have collected two subnormal startup gas turbine output shaft rotary speed datas, sample frequency 2Hz, 120 seconds sampling times, sampled point 60 at present, namely
j=[0123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960]
X
1=[0000000002.647911.712419.337122.033222.033222.033222.042922.033222.033222.033222.033222.023622.033222.023622.026822.026822.026822.026822.026822.026822.026822.026822.026822.033222.033222.042922.023626.459232.431436.789739.795142.41444.675346.75348.666450.724852.850854.909257.112559.470462.920466.273771.124973.734175.811878.662580.972283.446186.151988.693590.964493.564]
X
2=[0000000002.251610.572118.003522.023622.023622.023622.042922.042922.023622.033222.042922.033222.023622.042922.033222.033222.042922.023622.033222.023622.033222.033222.033222.042922.033222.042922.042926.720132.412137.214941.428344.974947.603450.009752.145354.184456.213858.716762.243966.834271.550174.139976.468979.058881.629384.296586.789789.10991.3893.989296.63799.2656];
(2) find gas turbine output shaft rotating speed in above-mentioned sample and, in maximal value, the minimum value of each sampling instant gas turbine output shaft rotating speed, obtain max(X
j) and min (X
j), sampling instant j=1,2 ..., n.
Such as, the maximal value of sampling instant j=10, the computation process of minimum value
max(X
10)=1.1*max(x
1,10，x
2,10)=1.1*max(2.2516，2.6479)=1.1*2.6479=2.91269
min(X
10)=0.9*min(x
1,10，x
2,10)=0.9*max(2.2516，2.6479)=0.9*2.2516=2.02644
(3) feature modeling: based on maximal value, the minimum value in each moment of gas turbine output shaft rotating speed that step (2) obtains, according to sample sequence, obtains coboundary and the lower boundary of gas turbine output shaft rotating speed normalized curve band.
X
coboundary=[0000000002.9126912.8836421.2708124.2365224.2365224.23652 24.2471924.2471924.2365224.2365224.2471924.2365224.23652 24.2471924.2365224.2365224.2471924.2294824.2365224.22948 24.2365224.2365224.2365224.2471924.2365224.2471924.24719 29.3921135.6745440.9363945.5711349.4723952.3637455.01067 57.3598359.6028461.8351864.5883768.4682973.5176278.70511 81.5538984.1157986.9646889.7922392.7261595.4686798.01991 00.518103.38812106.3007109.19216]
X
lower boundary=[0000000002.026449.5148916.2031519.8212419.8212419.821241 9.8386119.8298819.8212419.8298819.8298819.8212419.821241 9.8212419.8241219.8241219.8241219.8212419.8241219.821241 9.8241219.8241219.8241219.8298819.8298819.8386119.821242 3.8132829.1708933.1107335.8155938.172640.2077742.077743. 7997645.6523247.5657249.4182851.4012553.5233656.6283659. 6463364.0124166.3606968.2306270.7962572.8749875.1014977. 5367179.8241581.8679684.2076]
According to coboundary and the lower boundary of gas turbine output shaft rotating speed, the gas turbine output shaft rotating speed normalized curve band shown in drafting pattern 2.
Step 103: the startup trigger pip of Real-Time Monitoring gas turbine, when monitor start trigger pip be "True" time, start to contrast gas turbine output shaft rotating speed and normalized curve band, judge whether the scope exceeding normalized curve band, if gas turbine output shaft rotating speed is in normalized curve band, then start-up course assessment result is " normally "; If gas turbine output shaft rotating speed exceeds normalized curve band, then start-up course assessment result is " abnormal ".
Such as, the start-up course data that gas turbine is new
X
3=[00000000007.296115.490920.197120.177820.177820.187520.187520.187520.187520.177820.197120.187520.197120.187520.187520.187520.187520.187520.187520.177820.187520.187520.187520.177820.177820.187522.100928.652933.484737.533841.399344.607747.342549.400951.536553.295355.34458.349461.992768.921572.303874.381577.12679.976881.967584.692786.934789.427992.44394.45396.9463];
The normal start up curve band of gas turbine this start-up course curve step 102 calculated is analyzed.Fig. 3 is assessment result, X
3at j=10,11,12,37,38 5 sampling instants exceed gas turbine normal start up curve band lower boundary X
below boundary, therefore start-up course assessment result is " abnormal ".
Step 104: the start-up course assessment result that step 103 obtains is " abnormal ", needs to confirm the correctness of assessment result.Confirm assessment " incorrect ", the start-up course assessment result being " abnormal " by assessment result in step 103 changes to " normally ", simultaneously by this start-up course data X
3be added into the normal boot process sample in step 102.
Step 105: the start-up course that step 103 judges " abnormal ", but the start-up course data confirming as " normally " through step 104 store, be added into normal boot process sample, recalculate according to the normal start up curve band of step 102 pair gas turbine.Concrete step of updating is as follows:
First, according to step 102, calculate new normal boot process sample X
1, X
2and X
3middle gas turbine output shaft rotating speed, in maximal value, the minimum value of each sampling instant gas turbine output shaft rotating speed, obtains max(X
j) and min (X
j), sampling instant j=1,2 ..., n.Such as, maximal value, the minimum value of gas turbine output shaft rotating speed during sampling instant j=10
max(X
10)=1.1*max(x
1,10，x
2,10，x
3,10)=1.1*max(2.2516，2.6479，0)=1.1*2.6479=2.91269
min(X
10)=0.9*min(x
1,10，x
2,10，x
3,10)=0.9*max(2.2516，2.6479，0)=0.9*0=0
Secondly, based on maximal value, minimum value that each moment of gas turbine output shaft rotating speed is new, the new coboundary of gas turbine output shaft rotating speed normal start up curve band and lower boundary is calculated.
X
coboundary=[0000000002.9126912.8836421.2708124.2365224.2365224.23652 24.2471924.2471924.2365224.2365224.2471924.2365224.23652 24.2471924.2365224.2365224.2471924.2294824.2365224.22948 24.2365224.2365224.2365224.2471924.2365224.2471924.24719 29.3921135.6745440.9363945.5711349.4723952.3637455.01067 57.3598359.6028461.8351864.5883768.4682973.5176278.70511 81.5538984.1157986.9646889.7922392.7261595.4686798.01991 00.518103.38812106.3007109.19216]
X
lower boundary=[00000000006.5664913.9418118.1773918.1600218.1600218.1687 518.1687518.1687518.1687518.1600218.1773918.1687518.1773 918.1687518.1687518.1687518.1687518.1687518.1687518.1600 218.1687518.1687518.1687518.1600218.1600218.1687519.8908 125.7876130.1362333.7804237.2593740.1469342.077743.79976 45.6523247.5657249.4182851.4012553.5233656.6283659.64633 64.0124166.3606968.2306270.7962572.8749875.1014977.53671 79.8241581.8679684.2076]
Finally, according to coboundary and the lower boundary of the new gas turbine output shaft rotating speed calculated, obtain the new gas turbine output shaft rotating speed normalized curve band shown in Fig. 4.
Start-up course state estimation terminates.
The present invention analyzes gas turbine start-up course, proposes the concept of gas turbine start-up course normalized curve band.The qualitative assessment of gas turbine start-up course can be realized based on start-up course normalized curve band.
It is optionally that gas turbine output shaft rotating speed normalized curve band upgrades.Only (3) judge the start-up course of " abnormal " in steps, but the start-up course of " normally " is confirmed as through step (4), just these group start-up course data are stored, be added into normal boot process sample, according to obtaining new gas turbine output shaft rotating speed normalized curve band in step (2).
Claims (2)
1. a gas turbine start-up course state evaluating method, is characterized in that, comprises the steps:
(1) acquisition state information: gather gas turbine start-up course status information, comprise and start trigger pip and gas turbine output shaft rotating speed;
(2) build gas turbine output shaft rotating speed normalized curve band: accumulation gas turbine normal boot process sample, carry out the statistical study of gas turbine output shaft rotating speed and feature modeling, set up gas turbine output shaft rotating speed normalized curve band;
(2.1) accumulate normal boot process sample: when the sample of normal boot process refers to that gas turbine normally starts, gas turbine output shaft rotating speed is triggered to idling rating sampled value during this period of time in startup:
Wherein, small tenon i=1,2,3, m represents sample, j=1,2,3, and, n represents sampling instant;
(2.2) gas turbine output shaft rotating speed statistical study: find in all samples, maximal value, the minimum value of each sampling instant gas turbine output shaft rotating speed, define i gas turbine output shaft rotating speed sample and be respectively in the maximal value in jth moment, minimum value:
max(X
j)＝1.1*max{x
1,j,x
2,j,…,x
i,j}
min(X
j)＝0.9*min{x
1,j,x
2,j,…,x
i,j}，
(2.3) gas turbine output shaft rotating speed border is calculated: the coboundary and the lower boundary that calculate gas turbine output shaft rotating speed normalized curve band based on the maximal value of gas turbine output shaft rotating speed, minimum value:
(3) state estimation: when gas turbine starts triggering, state estimation is carried out to gas turbine start-up course, if when gas turbine output shaft rotating speed is in gas turbine output shaft rotating speed normalized curve band, then start-up course assessment result is normal, and start-up course state estimation terminates; If when gas turbine output shaft rotating speed exceeds normalized curve band, start-up course assessment result is abnormal;
(4) assessment result confirms: to assessment result in step (3) be abnormal start-up course by finally confirming, if confirm that assessment result is correct, then select maintenance measure, start-up course state estimation terminates; If confirm that assessment result is incorrect, be then that abnormal start-up course assessment result changes to normally by assessment result in step (3), these start-up course data be added into the normal boot process sample in step (2) simultaneously;
(5) normalized curve band is upgraded: according to step (2), recalculate coboundary and the lower boundary of gas turbine output shaft rotating speed sample, obtain new gas turbine output shaft rotating speed normalized curve band.
2. a kind of gas turbine start-up course state evaluating method according to claim 1, is characterized in that: the sample frequency 2Hz of described structure gas turbine output shaft rotating speed normalized curve band, 120 seconds sampling times, sampled point 60.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310277372.4A CN103364200B (en) | 2013-07-03 | 2013-07-03 | A kind of gas turbine start-up course state evaluating method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310277372.4A CN103364200B (en) | 2013-07-03 | 2013-07-03 | A kind of gas turbine start-up course state evaluating method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103364200A CN103364200A (en) | 2013-10-23 |
| CN103364200B true CN103364200B (en) | 2015-12-02 |
Family
ID=49366053
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310277372.4A Expired - Fee Related CN103364200B (en) | 2013-07-03 | 2013-07-03 | A kind of gas turbine start-up course state evaluating method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103364200B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104460602B (en) * | 2014-11-07 | 2017-12-12 | 浙江大学 | The detection method and its system of industrial stokehold technological process safety |
| CN113374582B (en) * | 2021-07-28 | 2022-09-27 | 哈电发电设备国家工程研究中心有限公司 | Device and method for evaluating running state of gas turbine |
| CN114235423A (en) * | 2021-12-13 | 2022-03-25 | 中国船舶重工集团公司第七0三研究所 | Method for detecting faults of gas turbine lubricating oil supply system |
| CN114235422A (en) * | 2021-12-13 | 2022-03-25 | 中国船舶重工集团公司第七0三研究所 | Method for detecting abnormal starting of gas turbine |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1131450A (en) * | 1993-09-21 | 1996-09-18 | 西门子公司 | Process and device for imaging operational condition of turbine during starting process |
| CN101078373A (en) * | 2007-07-05 | 2007-11-28 | 东北大学 | Combustion controlling device and controlling method for mini combustion turbine |
| CN101487756A (en) * | 2009-01-13 | 2009-07-22 | 东南大学 | Harmonic component rotational speed balancing method in rotating machinery vibration analysis |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6498978B2 (en) * | 2001-05-18 | 2002-12-24 | General Electric Company | System and method for monitoring thermal state to normalize engine trending data |
-
2013
- 2013-07-03 CN CN201310277372.4A patent/CN103364200B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1131450A (en) * | 1993-09-21 | 1996-09-18 | 西门子公司 | Process and device for imaging operational condition of turbine during starting process |
| CN101078373A (en) * | 2007-07-05 | 2007-11-28 | 东北大学 | Combustion controlling device and controlling method for mini combustion turbine |
| CN101487756A (en) * | 2009-01-13 | 2009-07-22 | 东南大学 | Harmonic component rotational speed balancing method in rotating machinery vibration analysis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103364200A (en) | 2013-10-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103364200B (en) | A kind of gas turbine start-up course state evaluating method | |
| CN102282450B (en) | Method and system for monitoring vibratory phenomena occurring in an aircraft's gas turbine engine in operation | |
| RU2664126C2 (en) | Aircraft engine control for anticipating maintenance operations | |
| CN107179503B (en) | Wind turbine generator fault intelligent diagnosis and early warning method based on random forest | |
| CN101762385B (en) | System and method for rotor blade health monitoring | |
| CN101446827B (en) | Process fault analysis device of process industry system and method therefor | |
| US20170074250A1 (en) | Wind turbine condition monitoring method and system | |
| CN102246110B (en) | Standardization of data used for monitoring an aircraft engine | |
| JP5819868B2 (en) | New road detection logic | |
| CN102129078B (en) | Method for uploading positioning data of vehicles | |
| CN101895420B (en) | Rapid detection method for network flow anomaly | |
| CN107013449A (en) | Voice signal based on deep learning recognizes the method and system of compressor fault | |
| CN105003453A (en) | Online monitoring and fault diagnosis system of mine fan | |
| CN109186744A (en) | Turbine blade Evaluation of Cracks system and turbine blade crack warning method | |
| CN102402727A (en) | System and method for predicting residual life of part of engineering machine | |
| CN101808007B (en) | Method for acquiring real-time performance data by network manager | |
| CN107679768A (en) | A kind of Situation Awareness System and its construction method based on real-time data of power grid | |
| CN113776794B (en) | Fault diagnosis method, device and system for embedded edge calculation | |
| CN105300692A (en) | Bearing fault diagnosis and prediction method based on extended Kalman filtering algorithm | |
| CN105843145A (en) | Power generator unit bluetooth data acquisition system and method | |
| CN204113701U (en) | A kind of mine fan on-line monitoring and fault diagnosis system | |
| CN102789676B (en) | Method for designing industrial alarm on basis of alarm evidence fusion | |
| CN111125895B (en) | On-line assessment method for service power characteristics of complete wind driven generator | |
| KR101953558B1 (en) | Apparatus and Method for Fault Management of Smart Devices | |
| CN104182623A (en) | Thermal process data detection method based on equivalent change rate calculation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20151202 Termination date: 20210703 |