EP2105887A1 - Procédé de diagnostic d'une turbine à gaz - Google Patents

Procédé de diagnostic d'une turbine à gaz Download PDF

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Publication number
EP2105887A1
EP2105887A1 EP08005950A EP08005950A EP2105887A1 EP 2105887 A1 EP2105887 A1 EP 2105887A1 EP 08005950 A EP08005950 A EP 08005950A EP 08005950 A EP08005950 A EP 08005950A EP 2105887 A1 EP2105887 A1 EP 2105887A1
Authority
EP
European Patent Office
Prior art keywords
gas turbine
compressor
determined
mass flow
turbine
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.)
Withdrawn
Application number
EP08005950A
Other languages
German (de)
English (en)
Inventor
Rolf Grosse-Laxzen
Klaus Dr. Werner
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens AG
Original Assignee
Siemens AG
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Siemens AG filed Critical Siemens AG
Priority to EP08005950A priority Critical patent/EP2105887A1/fr
Priority to JP2011501199A priority patent/JP4906977B2/ja
Priority to MX2010010608A priority patent/MX2010010608A/es
Priority to US12/934,358 priority patent/US9466152B2/en
Priority to PCT/EP2009/053440 priority patent/WO2009118311A1/fr
Priority to EP09725928.7A priority patent/EP2257933B1/fr
Priority to CN200980111286.8A priority patent/CN102099835B/zh
Priority to RU2010144075/08A priority patent/RU2517416C2/ru
Publication of EP2105887A1 publication Critical patent/EP2105887A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C3/00—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01D—NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES
    • F01D25/00—Component parts, details, or accessories, not provided for in, or of interest apart from, other groups
    • F01D25/002—Cleaning of turbomachines
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2270/00—Control
    • F05D2270/40—Type of control system
    • F05D2270/44—Type of control system active, predictive, or anticipative
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2270/00—Control
    • F05D2270/70—Type of control algorithm
    • F05D2270/708—Type of control algorithm with comparison tables
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2270/00—Control
    • F05D2270/70—Type of control algorithm
    • F05D2270/71—Type of control algorithm synthesized, i.e. parameter computed by a mathematical model

Definitions

  • the invention relates to a method for the diagnosis of a gas turbine comprising several components, in which the additional power by which the operating power of the gas turbine would increase in the event of a cleaning of one of the components is automatically predicted.
  • the pollution of the gas turbine is caused by the adhesion of particles to the surfaces. Oil and water mist help dust and aerosols settle on the blades. The most common contaminants and deposits are mixtures of water wetting, water-soluble and water-insoluble materials. In the gas turbine, soiling can occur due to ash deposits and unburned, solid cleaning preparations. Such air pollutants adhere to the components of the flow path of the gas turbine and react with them as scales. It also causes erosion by particle impact and abrasion, commonly referred to as erosion.
  • pieces of ice that form at the inlet of the gas turbine can dissolve and strike the components in the flow path of the gas turbine.
  • a so-called anti-icing system is used. Here is prevented by air preheating, the temperature of the Air entering the gas turbine does not sink below freezing and thus does not freeze the water.
  • An offline wash causes a greater performance recovery than an online wash. With the help of an offline laundry, performance gains of several percent can be achieved. Online laundry results in lower performance recovery. The most effective bucket cleaning can be achieved with a combination of online and offline washes. A regular online laundry extends the time intervals between the required offline washes.
  • the optimal time for off-line laundry is often determined by the operator for purely economic operational considerations, e.g. B. in low load periods. This means that the decision on the timing of elimination of contamination of one of the components of the turbine system, for. B. by a wash of the compressor based only on empirical values under economic aspects or under preliminary studies with fixed boundary conditions.
  • the determination of the timing of the offline wash may be made based on an up-to-date prediction of the gas turbine power gain expected by the off-line wash.
  • a prognosis is usually created based on the development of the compressor efficiency of the gas turbine, which serves as a parameter for the strength of the contamination of the compressor.
  • the measurement data used to determine the compressor efficiency may be provided with relatively high data uncertainty, allowing accurate prediction of the expected performance gain from off-line laundry and thus makes it difficult to determine the cost-optimal time for the operation of the gas turbine for such offline washing.
  • the invention is therefore based on the object to provide a method of the type mentioned above, which allows a particularly reliable prognosis of the expected performance gain in a cleaning.
  • This object is achieved according to the invention by using the intake mass flow of the gas turbine as a parameter when forecasting the additional power.
  • the invention is based on the consideration that the selection of the time of a required to obtain a high operating performance of the gas turbine off-line laundry can be achieved at very low cost by the most accurate forecast of the performance gain by such offline washing the gas turbine.
  • the selection of the time of a required to obtain a high operating performance of the gas turbine off-line laundry can be achieved at very low cost by the most accurate forecast of the performance gain by such offline washing the gas turbine.
  • the statistical uncertainties should be minimized. This can be done, for example, by improving the measuring equipment or by increasing the number of measurements. However, such an increase only leads to a reduction in the statistical error, but systematic errors in the forecast of the additional service should also be minimized to a large extent. This can be achieved by additionally using additional parameters for forecasting the additional service.
  • One such quantity, which is characteristic of the performance of the gas turbine, is the intake mass flow of the gas turbine.
  • the compressor In a gas turbine, the compressor is on the flow medium side all other components such. B. upstream of the combustion chamber. Accordingly, the compressor is the environmental influences such as incoming dust and dirt particles most exposed component.
  • a cleaning of the compressor is therefore carried out in particular, since this has the highest degree of contamination and thus a corresponding cleaning has a particularly positive influence on the recovery of operating performance of the gas turbine.
  • the Ansaugmassenstrom as a parameter for the operating performance of the gas turbine is usually not measured directly because of the high cost, the large uncertainty and the risk of damage, but indirectly determined by balancing. Very expensive instruments would have to be used for a direct measurement, because first of all there are very high temperatures, and secondly it is absolutely necessary to avoid the sensors breaking off because of the probably high consequential damage to the turbine blading.
  • the turbine inlet pressure with the help of the mass pressure equation according to Stodola in a value for the Transfer suction mass flow while in each case resistance coefficients can be determined from the combustion chamber pressure loss or the pressure loss between environment and compressor inlet, which can be used to determine the Ansaugmassenstroms.
  • Such determination of the Ansaugmassenstroms without resolution of an energy balance is associated with much lower statistical errors and therefore allows an even more accurate forecast of the additional power by which the operating performance of the gas turbine would increase in the case of cleaning one of the components.
  • a preliminary value for the Ansaugmassenstrom is determined for determining the Ansaugmassenstroms from a number of input characteristics a preliminary value for the Ansaugmassenstrom, wherein for each provisional value by cross-comparison with the respective other provisional values in each case a validated value is determined.
  • a cross-match can be done, for example, based on the VDI2048.
  • This is essentially based on the balancing principle according to Gauss, whose basic idea is not only to use the minimum quantity of measured quantities required for a solution, but also to record all achievable measured variables together with the associated variances and covariances. For the present method, this means that all achievable input parameters are used to each determine a preliminary value for the Ansaugmassenstrom.
  • the true values of the input characteristics should be such that all resulting provisional values are the same.
  • the Gauss method gives consistent values for the actual values of the measured quantities and validated values for the intake mass flow.
  • the validated values for the intake mass flow generated in this way are then averaged and thus form one with a particularly low statistical Error provided characteristic for determining the operating performance of the gas turbine.
  • the intake mass flow should not be provided as the sole parameter for determining the operating performance of the gas turbine.
  • the compressor efficiency of the gas turbine is additionally used as a parameter.
  • thermodynamic parameters of the gas turbine are dependent on the respective ambient conditions such as air pressure and outside temperature. Nevertheless, in order to be able to compare measured values at different times, the respective parameters should be normalized to reference conditions.
  • the norm is the ISO conditions (temperature 15 ° C, pressure 1.013 bar, humidity 60%).
  • a reference value for the operating performance of a just cleaned gas turbine is required.
  • the operating performance of the gas turbine apart from its contamination state, is also dependent on the pollution-independent erosion, and therefore essentially on the operating age of the gas turbine.
  • parameters of identical and / or construction-type gas turbines are advantageously used as comparison variables in the prognosis of the additional power.
  • a prognosis of the temporal development of the respective parameter is created. Such a prognosis is possible by several evaluations of the input parameters or measured values at different times.
  • a particularly cost-optimal operation of the gas turbine is possible if a determination of the timing of an offline gas turbine laundry is taken not only under purely economic aspects, such as in low load periods, but based on an accurate forecast of the operating performance of the gas turbine in the future. For this purpose, it is advantageously determined depending on the value of the determined additional power in balance with the overall economic effort, whether the gas turbine is temporarily stopped to eliminate the pollution and optionally determines an optimal time for the temporary shutdown.
  • a determination of a time for such offline washing can be made on the basis of a much more precise analysis in which the costs and benefits of off-line laundry can be accurately weighed against each other.
  • the method finds application in a gas turbine plant with a gas turbine comprising several components and with a control system which is connected on the data input side to a number of sensors arranged to determine input parameters in the gas turbine, the control system comprising a prognosis module.
  • data of a database with comparison variables of structurally identical and / or building-like gas turbines can be read into the prognosis module.
  • the forecasting module should have a correspondingly open architecture, which makes such reading possible.
  • This can be, for example by means of a mobile data carrier or via a permanent data connection to the database, ie the database can be stored on a writable memory within the control system or stored on an external server which is connected via a data transmission line to the control system of the gas turbine.
  • the data obtained in the gas turbine can also be used to expand the database by making it available to the database and stored there.
  • a prognosis module for use in a gas turbine plant is suitable for carrying out the method.
  • the advantages achieved by the invention are in particular that a comparatively precise analysis of the degree of contamination of the gas turbine, in particular its compressor is possible by the additional consideration of Ansaugmassenstroms the gas turbine.
  • Ansaugmassenstroms the gas turbine.
  • the method described here also allows the determination of the Ansaugmassenstromes without any knowledge of the fuel data and without solving an associated with high uncertainties energy balance.
  • the gas turbine 1 has a compressor 2 for combustion air, a combustion chamber 4 and a turbine 6 for driving the compressor 2 and a generator not shown in detail or a working machine.
  • the turbine 6 and the compressor 2 are arranged on a common, also referred to as a turbine rotor turbine shaft 8, with which the generator or the working machine is connected and which is rotatably mounted about its central axis 9.
  • the combustor assembly 4 includes a number of individual burners 10 around the turbine shaft 8 for combustion of a liquid or gaseous fuel.
  • the turbine 6 has a number of rotatable blades 12 connected to the turbine shaft 8.
  • the blades 12 are arranged in a ring on the turbine shaft 8 and thus form a number of blade rows.
  • the turbine 6 comprises a number of fixed vanes 14, which are also fixed in a ring shape with the formation of rows of vanes inside the turbine 6.
  • the blades 12 serve to drive the turbine shaft 8 by momentum transfer from the turbine 6 flowing through Working medium M.
  • the compressor 2 is the component of the gas turbine 1 which is closest to the air inlet 16. Accordingly, it is most exposed to dirt deposits and the resulting contamination of the gas turbine 1. To prevent a reduction in the operating performance of the gas turbine 1, therefore, the compressor 2 must be cleaned regularly. In this case, so-called online washes can be carried out relatively frequently, for example once a day, for which no standstill of the gas turbine 1 is required. At longer intervals, the turbine should be shut down to remove stubborn dirt for offline washing.
  • the gas turbine 1 comprises a control system 18 which is connected via a data line 20 to various sensors 22 arranged inside the gas turbine 1.
  • the control system 18 comprises a prognosis module 24, which processes the input parameters detected by the sensors 22 and determines the degree of contamination of the gas turbine and the expected gain in operating performance for an offline washing performed on the basis of this data.
  • comparative data of structurally identical or construction-type gas turbines can be read into the forecasting module.
  • the control system is connected via a further data line 20 to a database 26 containing such comparison data.
  • the database 26 may be located on an external database server, not shown in greater detail. Alternatively, the comparison data can also be read without permanent data connection to the database 26 via a mobile data carrier.
  • FIG. 2 1 shows a graph of the operating power of a typical gas turbine 1.
  • the line L1 shows the operating power of the gas turbine 1 at the time of commissioning 30.
  • the line L2 shows the theoretical maximum power of the gas turbine over its running time, its drop only by aging and irreversible Pollution is generated.
  • the line L3 shows the additional influence of the reversible pollution on the operating performance of the gas turbine.
  • Section I shows the influence of regular online laundry on the operating performance of the gas turbine. This is carried out at regular intervals 32, for example once a day. This results in a comparatively low increase in performance, but cumulatively contributes to the maintenance of the gas turbine 1 in a not inconsiderable manner via the frequent online washes.
  • the time points 34 should be chosen in a forward-looking manner, which can be done on the one hand on the basis of economic criteria such as electricity price or fuel price, on the other hand based on the operational variables of the gas turbine.
  • the anticipated performance gain from off-line laundry should be known for optimal determination of the time 34 of offline laundry.
  • FIG. 3 schematically shows the flow of the method for determining the additional power by which the operating performance of the gas turbine 1 would increase in the case of cleaning the compressor.
  • the turbine inlet pressure 40a the combustion-chamber pressure loss 40b
  • the turbine inlet pressure 40a the combustion-chamber pressure loss 40b
  • a preliminary value for the intake mass flow 42a is determined based on the mass pressure equation according to Stodola.
  • the pressure loss in the combustion chamber 40b and the pressure loss between the ambient and the compressor inlet 40c are transferred via a constant drag coefficient approach to provisional values for the intake mass flow 42b and 42c, respectively.
  • the different approaches initially provide different preliminary values for the intake mass flow 42a, 42b and 42c. With the secondary condition that all intake mass flows should be the same, data validation is then carried out based on VDI2048. This corrects the readings based on the specified uncertainties so that the preliminary values for the suction mass flow are practically the same. On the one hand, validated values for the intake mass flow 44 are produced from the input characteristics corrected in this way; on the other hand, the validated input parameters can be used as a basis for calculating the compressor efficiency 46.
  • the two results 62 are then converted to a gas turbine power with the aid of gas turbine type-specific indices 64.
  • the thus obtained forecast of the additional power in the case of a cleaning of the compressor is finally supplied to the output 68.
  • the gas turbine is taken into account, to determine the Ansaugmassenstroms no energy balance is resolved and no information about the gas turbine power and the fuel is required, in particular no indication of its calorific value and its mass flow ,
  • the turbine operator can determine the time 34 for an offline laundry based on company-specific data.
  • overall a cheaper operation of the gas turbine is possible.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Structures Of Non-Positive Displacement Pumps (AREA)
  • Engine Equipment That Uses Special Cycles (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Control Of Positive-Displacement Pumps (AREA)
EP08005950A 2008-03-28 2008-03-28 Procédé de diagnostic d'une turbine à gaz Withdrawn EP2105887A1 (fr)

Priority Applications (8)

Application Number Priority Date Filing Date Title
EP08005950A EP2105887A1 (fr) 2008-03-28 2008-03-28 Procédé de diagnostic d'une turbine à gaz
JP2011501199A JP4906977B2 (ja) 2008-03-28 2009-03-24 ガスタービンの吸入質量流量の決定方法
MX2010010608A MX2010010608A (es) 2008-03-28 2009-03-24 Procedimiento para la determinacion del caudal masico de succion de una turbina de gas.
US12/934,358 US9466152B2 (en) 2008-03-28 2009-03-24 Method for determining the suction mass flow of a gas turbine
PCT/EP2009/053440 WO2009118311A1 (fr) 2008-03-28 2009-03-24 Procédé de détermination du flux massique d'aspiration d'une turbine à gaz
EP09725928.7A EP2257933B1 (fr) 2008-03-28 2009-03-24 Procédé de diagnostic d'une turbine à gaz
CN200980111286.8A CN102099835B (zh) 2008-03-28 2009-03-24 用于确定燃气轮机的抽吸质量流的方法
RU2010144075/08A RU2517416C2 (ru) 2008-03-28 2009-03-24 Способ определения массового расхода всасывания газовой турбины

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP08005950A EP2105887A1 (fr) 2008-03-28 2008-03-28 Procédé de diagnostic d'une turbine à gaz

Publications (1)

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EP2105887A1 true EP2105887A1 (fr) 2009-09-30

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EP08005950A Withdrawn EP2105887A1 (fr) 2008-03-28 2008-03-28 Procédé de diagnostic d'une turbine à gaz
EP09725928.7A Not-in-force EP2257933B1 (fr) 2008-03-28 2009-03-24 Procédé de diagnostic d'une turbine à gaz

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EP09725928.7A Not-in-force EP2257933B1 (fr) 2008-03-28 2009-03-24 Procédé de diagnostic d'une turbine à gaz

Country Status (7)

Country Link
US (1) US9466152B2 (fr)
EP (2) EP2105887A1 (fr)
JP (1) JP4906977B2 (fr)
CN (1) CN102099835B (fr)
MX (1) MX2010010608A (fr)
RU (1) RU2517416C2 (fr)
WO (1) WO2009118311A1 (fr)

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US9354618B2 (en) 2009-05-08 2016-05-31 Gas Turbine Efficiency Sweden Ab Automated tuning of multiple fuel gas turbine combustion systems
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Publication number Publication date
WO2009118311A1 (fr) 2009-10-01
EP2257933B1 (fr) 2016-07-27
JP4906977B2 (ja) 2012-03-28
EP2257933A1 (fr) 2010-12-08
JP2011515620A (ja) 2011-05-19
CN102099835A (zh) 2011-06-15
US20110247406A1 (en) 2011-10-13
US9466152B2 (en) 2016-10-11
CN102099835B (zh) 2014-12-17
RU2517416C2 (ru) 2014-05-27
RU2010144075A (ru) 2012-05-10
MX2010010608A (es) 2010-11-09

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