WO2005090764A1 - Procede et dispositif pour etablir des diagnostics dans un systeme de turbine - Google Patents

Procede et dispositif pour etablir des diagnostics dans un systeme de turbine Download PDF

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Publication number
WO2005090764A1
WO2005090764A1 PCT/EP2004/009060 EP2004009060W WO2005090764A1 WO 2005090764 A1 WO2005090764 A1 WO 2005090764A1 EP 2004009060 W EP2004009060 W EP 2004009060W WO 2005090764 A1 WO2005090764 A1 WO 2005090764A1
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Prior art keywords
gas turbine
determined
compressor
contamination
offline
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PCT/EP2004/009060
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German (de)
English (en)
Inventor
Tobias JOCKENHÖVEL
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Siemens Aktiengesellschaft
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Publication of WO2005090764A1 publication Critical patent/WO2005090764A1/fr

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02C—GAS-TURBINE PLANTS; AIR INTAKES FOR JET-PROPULSION PLANTS; CONTROLLING FUEL SUPPLY IN AIR-BREATHING JET-PROPULSION PLANTS
    • F02C9/00—Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants
    • 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
    • F05D2260/00—Function
    • F05D2260/80—Diagnostics

Definitions

  • the invention relates to a method and a device for diagnosing a turbine system with a gas turbine comprising several components, the gas turbine having at least one compressor and one filter.
  • Contamination of the compressor is caused by particles adhering to the surfaces. Oil and water mist help dust and aerosols to stick to the blades. The most frequently occurring soiling and deposits are mixtures of water wetting, water-soluble and water-insoluble materials. Contamination from ash deposits and unburned, solid cleaning products can occur in the gas turbine. Such air pollutants adhere to the components of the flow path of the gas turbine like scales and react with them. The loss of material due to chemical reactions of metals with pollutants is called hot corrosion. Beat larger, hard particles that are larger than 20 ⁇ m, on the surfaces of the flow components, material abrasion occurs.
  • Particle impact and abrasion are commonly referred to as erosion.
  • an anti-icing system is used.
  • air preheating prevents the temperature of the air from dropping below freezing when entering the gas turbine and thus preventing the water from freezing.
  • An anti-icing (English w anti-icing ") of the gas turbine is usually carried out from a temperature of 278.15 K.
  • the contamination and erosion causes an increased surface roughness of the blades. This leads to large friction losses in the gas turbine.
  • the laminar boundary layer flow can change into a turbulent flow and there is an increase in losses due to the increasing flow resistance.
  • the radial gap increases due to abrasion and corrosion. The gap flow increases and the performance of the system decreases.
  • the aging of the compressor has a negative impact on the gas turbine efficiency ⁇ GT , the gas turbine power P GT and the gas turbine outlet mass flow m ⁇ l.
  • Positive effects for the circuit in a turbine plant e.g. B. in a gas and steam power plant, only result from a slightly higher outlet temperature and better steam turbine efficiency.
  • the predominant factor here is the significant reduction in the outlet mass flow, which has a direct influence on the achievable performance.
  • compaction Buckets can be washed in online and offline mode. In online mode, the turbine system continues to operate during cleaning, whereas in offline mode it is shut down completely and rotated for about six hours with a K-cell rotating device to cool it down.
  • Offline washing results in greater performance recovery than online washing. With the help of offline washing, performance recoveries of up to 3% can be achieved. Online washing results in an average recovery of approx. 1_% of power. The most effective window cleaning can be achieved with a combination of online and offline washes. Regular online washing extends the time intervals between the required offline washes.
  • the system must be shut down for offline washing. To avoid thermal stresses, it is cooled for six hours using a shaft rotating device. If offline washing represents a significant impairment of operation, the blades are washed online. The gas turbine load is only slightly reduced. Online washes are mainly used to avoid the build-up of the dirt layer. Online washing is usually carried out once a day with demineralized water and washing with detergents every third day. Offline washing should take place once a month or after a so-called "trip". If the turbine system has not been cleaned for more than 350 hours, offline washing must be carried out, since the online cleaning method can no longer remove dirt.
  • Table 1 Categorization of aging and pollution: Category Example Countermeasures permanent old surface roughness, none, as a rule component changes too expensive, non-rain-increasing gap-blade change, replaceable old luster, regenerable soiling, ' drain online and / or
  • FIG. 1 for the prior art shows a basic course of the aging of a gas turbine based on a new and clean system condition.
  • the history shows how the performance parameters decrease over the course of the operating time.
  • Permanent losses are caused by increased surface roughness and changes in components. The replacement of the affected components would not be worthwhile because the costs incurred outweigh the benefits achieved.
  • the permanent signs of aging are shown in FIG. 1, section a. Phenomena that cannot be regenerated, such as increasing gap losses, increasing cooling air consumption and lower turbine efficiency, can be reduced by changing the turbine blades; see FIG. 1, section b.
  • the third phenomenon of aging is considered regenerable.
  • the natural aging process can be postponed to a certain extent by changing the filter and washing offline; see FIG. 1, section c.
  • Curve 1 of FIG. 1 shows the aging that could be achieved by hand washing, changing the filter and thermodynamically optimizing the gas turbine.
  • the gas turbine also includes an upstream air filter.
  • a pressure loss takes place via this filter, which affects the thermal performance of the gas turbine and the overall system. This pressure loss increases in the course of operation. In addition, the pressure drop is dependent on the current volume flow, air humidity, temperature and pressure.
  • This pressure loss is, for example, in a control and / or regulation program for the turbine system, for. B. in the so-called program KREISPR, used as an input variable.
  • program KREISPR used as an input variable.
  • the corresponding equation is:
  • the influences and results are too noisy.
  • the optimal time for online laundry is currently through determines the operator based on purely economic aspects, e.g. B. in low load times. That is, the previous decisions about the time of removal of contamination of one of the components of the turbine system, e.g. B. by washing the compressor, e.g. B. an online or offline laundry, or by replacing the filter, are based only on empirical values under economic aspects or under preliminary studies with fixed boundary conditions.
  • the invention is therefore based on the object of specifying a method and a device for diagnosing a turbine system having a gas turbine comprising a plurality of components, the operation of the turbine system, which is as cost-effective as possible, being made possible at the same time.
  • the object with regard to the method is achieved by the features of claim 1.
  • the object is achieved according to the invention with the features of claim 24.
  • the invention is based on the consideration that for a turbine system that is both low-wear and high-performance, it should be monitored and continuously diagnosed. For this purpose, in particular an additional power to be achieved by removing contamination of the turbine system is determined and continuously determined.
  • the present diagnostic concept determines the current power loss due to a degree of contamination on which the turbine system is based, eg. B. by a dirty filter or a dirty compressor.
  • the method for diagnosing the turbine system with a plurality of components, for. B. a compressor and a filter, comprehensive gas turbine automatically predicts the additional power by which an operating power of the gas turbine is increased if one of the components is removed from contamination.
  • the additional power is determined on the basis of at least one model calculation of the gas turbine and at least one operating variable representing the gas turbine.
  • a static and / or a dynamic company variable is or is determined as the company variable.
  • Time-dependent measured values in particular ambient pressure, compressor outlet pressure, pressure loss, operating hours, time of last removal of contamination, compressor mass flow, compressor inlet temperature, compressor outlet temperature, instantaneous turbine output, are determined as the dynamic operating variable.
  • the dynamic operating variables are therefore current or instantaneous measured values recorded on the gas turbine, for example by means of sensors.
  • Geometric dimensions of the gas turbine, duration of removal of the contamination, electricity generation costs and / or total output of the turbines are used as the static operating variable.
  • the static operating variables are characteristic output or operating variables that describe the gas turbine and its components in more detail.
  • the company size is validated on the basis of at least one plausibility check and / or a stationary check, in particular on current environmental conditions.
  • the validated farm size is then processed using the model calculation.
  • a stationarity check of the dynamic operating variables or measured values is understood in particular to mean the determination of the deviation of the measured values, taking into account a tolerance range for the deviation of a current measured value from a previous measured value.
  • the measured values can be combined before processing, which brings about a reduction in the number of measured values to be processed by means of the model calculation.
  • the measured values are averaged over a period of 15 minutes.
  • Non-plausible measured values identified on the basis of the plausibility check are not included in the time averaging.
  • Measured values from faulty sensors are also not taken into account in the averaging.
  • the averaged measured values are also checked for plausibility of their stationarity.
  • the recorded measured values can also be used instead of validated measured values. This can make it a big one
  • network measured values can measure several measurements of the same measured variable, e.g. B. distributed several temperature measurements over a pipe circumference, spatially averaged very close. Non-plausible network measured values are discarded before spatial averaging or, alternatively, their confidence intervals are expanded.
  • a current degree of pollution, a current grade and / or a current state of aging of one or more components of the gas turbine and / or an isentropic compressor efficiency for an unpurified and / or a contaminated gas turbine are determined by means of the model calculation.
  • a first additional service which can be achieved by eliminating contamination by offline washing of the gas turbine, in particular a compressor
  • a second additional service which is achieved by removing contamination of the Gas turbine
  • a third additional service which can be achieved by removing a contamination of the gas turbine by changing a filter
  • the operating parameters are preferably standardized.
  • the operating parameters are standardized to reference conditions, in particular ISO conditions, and then processed using the model calculation.
  • the standardized operating variables are used to determine a reference degree of pollution, a reference quality level and / or a reference aging state of one or more components of the gas turbine and / or an isentropic reference compressor efficiency for an unpurified and / or one contaminated gas turbine determined.
  • a loss of power and / or additional power validated to current environmental conditions and / or additional power in relation to reference environmental conditions can additionally or alternatively be determined on the basis of the standardized operating variable and the current validated operating variable using the model calculation.
  • the diagnostic variables determined on the basis of the model calculation are reduced. For example, a grade with a value greater than 1 and / or a negative degree of pollution are rejected.
  • a grade with a value greater than 1 and / or a negative degree of pollution are rejected.
  • the diagnosis is only carried out when the gas turbine is operated in the stationary full-load state and not with anti-icing.
  • a predicted and / or determined additional service can also be rejected if the associated value is negative.
  • a diagnostic variable ascertained on the basis of the model calculation in particular an additional power, a power loss, a degree of contamination, is output.
  • the diagnostic size determined is displayed visually.
  • a regression function determined on the basis of the diagnostic size for the last removal of a contamination, in particular through an offline wash or an online wash can also be output.
  • the regression function is preferably output as a function of a user-defined threshold value with a color change. In this way, contamination of the gas turbine or one of its components that goes beyond the usual level can be identified particularly quickly and easily by a user, so that appropriate measures, such as carrying out washing or filter replacement, can be taken.
  • the regression function with a prognosis about the future course of the diagnostic variable for example from a predetermined and thus elapsed period of time since the last removal of contamination, in particular through an offline wash, can be carried out independently of the determined diagnostic variable.
  • B. with a color marking when determining one of the diagnosis-large loss costs of a shutdown, washing costs, an additional profit after an offline wash and / or an amortization time of an offline wash can be taken into account.
  • a forecast module for determining the additional power, by which the operating power of the gas turbine is increased in the event that contamination of the gas turbine is removed.
  • the forecast module can be designed as a software module or as an electronic circuit.
  • the forecast module is expediently implemented in an automation system for controlling and / or regulating the turbine system.
  • an operator of a turbine system uses the determined diagnostic size, in particular an additional service that can be achieved depending on the elimination of contamination, for one of the components of the turbine system to have a current and predicted information status about the state of the Turbine system and its components, in particular about the current degree of pollution and a related power loss, is available.
  • a current and predicted information status about the state of the Turbine system and its components, in particular about the current degree of pollution and a related power loss
  • thermodynamic operating variables of the turbine system determined online or offline are taken into account.
  • Economic data such as cost data, are only considered statically.
  • cost data In the case of an offline diagnosis, the customer or user is offered a user interface in which cost data can be updated and predictions can be made about the optimal washing time (point of worthwhile online or offline washing).
  • FIG. 2 shows schematically a display for outputting diagnostic variables of a turbine system
  • FIG. 3 schematically shows an example of the course of an operating variable, a measured value or a diagnostic variable
  • 5 shows a schematic diagram of the course of the power loss of the turbine system as a function of the degree of contamination of the compressor
  • 6 schematically shows a diagram of the course of the total costs of removing pollution as a function of time
  • FIG. 2 schematically shows a turbine system 6 with a gas turbine 8, a compressor 10 upstream of the gas turbine 8, which in turn is preceded by a filter 12.
  • cleaned fresh air FL is compressed by means of the filter 12 and supplied as compressed air vL to a combustion chamber 14.
  • Natural gas is supplied to the combustion chamber 14 as fuel B.
  • the compressed air vL is heated in the combustion chamber 14 and fed to the gas turbine 8 for relaxation as heated air aL and discharged as combustion air VL.
  • the gas turbine 8 is coupled via a shaft 16 to the compressor 10 and a generator 18 for generating electrical energy.
  • FIG. 2 shows an show with the diagram for the turbine system 6 shown, with the individual components - gas turbine 8, compressor 10, filter 12, combustion chamber 14, generator 18 - associated operating variables BG, such as. B.
  • FIG. 3 shows one of the recorded operating variables BG, e.g. B. the filter pressure, shown as a function of time.
  • FIG. 2 shows the turbine system 6 with the ascertained and validated values for the individual operating variables BG as described above as an indication which is made available to a user of a control system and / or control system for the turbine system.
  • further values of determined operating variables BG e.g. B. dissipation factor and power losses are displayed.
  • the display is not limited to the BG sizes shown.
  • the course over the time of a single or several operating variables can be displayed on a further display, as shown for example in FIG. 3.
  • FIGS. 4.1 to 4.3 Using the flow chart shown in FIGS. 4.1 to 4.3 in tabular form for carrying out the method for diagnosing the turbine system 6, which comprises a gas turbine 8 with a compressor 10 and a filter 12, the diagnostic method is described in more detail below.
  • the Diagnoseve_trfahren is based on the consideration of the relative influence of removing contamination of the turbine system 6, e.g. B. by changing the filter and / or washing the compressor 10, for. B. to determine an online or offline Verdiciter wash.
  • the diag- nose with sufficient accuracy and as simple as possible.
  • the diagnosis should be easy to integrate into an existing diagnosis program, whereby existing modules should be used.
  • the diagnosis can be carried out both offline and online. Online diagnosis has more simplifying assumptions, such as B. standard density, standard gas composition etc. In the offline diagnosis, the user can enter this data manually.
  • An online diagnosis is divided into five categories: preprocessing (see FIG. 4.1), gas turbine diagnosis (see FIG. 4.2), post-processing, in particular calculation of compressor parameters, calculation of a power loss or additional power (see FIG. 4.3) and output of the diagnostic variable, z. B. by visualization (see FIG 4.4).
  • the process is fully automated in the online diagnosis. Except for preprocessing, these categories are started manually in the offline diagnosis.
  • the specified steps relate to a gas turbine 8.
  • a diagnosis for another gas turbine is carried out analogously (in the same load cases).
  • step 1 there is a plausibility check of the incoming measured values MW, which are recorded, for example, by a control and / or regulating system and are stored in a database for the diagnosis.
  • the measured values MW are dynamic operating variables BG, e.g. B. filter pressure, ambient pressure, compressor discharge pressure, compressor mass flow, compressor inlet pressure etc.
  • step 2 a stationarity check of the input data or measured values MW is carried out every 30 s. A corresponding flag is set and the values are written to the database.
  • step 3 the measured values MW are averaged over a period of 15 minutes. Implausible measured values MW and faulty sensors are not included in the averaging.
  • step 4 the calculated mean values are checked again for plausibility of the stationarity.
  • step 5 a spatial averaging of the network measured values according to VDI 2640 is carried out with a further plausibility test.
  • the input data can optionally be checked for plausibility, obviously incorrect values can be accepted with standard values, and the confidence intervals can be extended if necessary.
  • step 6 the overall circuit diagram of the turbine system 6 is validated in accordance with VDI 2048. Mass and energy conservation balances are used for the validation. The validation changes the measured values MW in order to maintain a consistent state with the system model.
  • Validated measurement values MW V (with index V for validated, Komp for compressors, m for mass flow, GT for gas turbine, AMB for ambient conditions, T for temperature, P for power) are now available for the further calculation steps: • v m Comp nV.AMB r GT
  • the determined values are written into the database with the ending W .V ".
  • the calculation [5] - [6] can also be carried out in gas turbine solo mode.
  • step 8 a forecast or model calculation (also "expected” calculation) of the overall circuit diagram, ie the turbine system 6, is carried out with the current ambient conditions with regard to the gas turbine 8 using a control and / or regulating program (the so-called KREISPR program). and a calibrated type file that shows the clean condition.
  • the compressor or compressor efficiency h, ' ⁇ ⁇ mp k e i - ambient conditions result from the prognosis.
  • the values from the forecast or expected value calculation are written into the database with the ending ".E".
  • step 9 a forecast calculation with reference conditions (ISO) is carried out.
  • the reference calculation results in a reference compressor efficiency ⁇ 'TM under reference conditions. This value is written to the database with the ending ".Rl".
  • step 10 a check is carried out for the full load state of the turbine system 6.
  • a binary signal from the control and / or regulation system e.g. B. the so-called OTC controller used.
  • Additional measuring points can also be used for a plausibility test.
  • Active unregulated anti-icing operation leads to the exclusion of the invoice. Preheating the air causes losses in performance and efficiency. These are generally not corrected because the temperature increase in the air and the air mass flow (in the uncontrolled case) are unknown. If necessary, however, they can be taken into account.
  • step 11 the current (dirty) compressor efficiency is calculated under ISO conditions.
  • the same relationships are assumed here: "R SO n V, lSO _ j - .VV..AAMMBB '' ⁇ lhh, .KKoommpp -" - lh, Komp 'I hh..KKoommpp "E E., ⁇ AM ⁇ ffiB •• ö ⁇ "'fh. Komp
  • the factor f nMtemng takes into account non-regenerable and permanent aging. In principle, it is a function of the equivalent operating hours: J ⁇ , aging ⁇ J ⁇ , aging VEOH) [10]
  • the aging condition or factor refers to the type file used. If a new type file is calibrated and used, the aging factor must be reset to zero.
  • a pollution factor for the compressor 10 is determined as follows:
  • a quality grade for the compressor 10 can also be determined, according to:
  • the upper limit can be checked with a data series.
  • a plausibility check is carried out on the basis of the range of the pollution factor for pollution according to equation [1]. If values ⁇ 0 for the pollution factor JV ersc mutzung or values> are determined 1 for grade G h omp the compaction ⁇ ters 10 (eg just after an off-laundering in the noise band.), They are not for a prognosis or diagnosis considered in the form of a trend line.
  • a power loss is determined in the postprocessing on the basis of the difference between the current turbine state and the clean state.
  • a reference calculation (ISO conditions) of the gas turbine circuit diagram with the current pressure loss ⁇ p pilter is determined via the filter 12 under ISO conditions and stored in the database as ".M".
  • this conversion can also be carried out manually.
  • the current measured pressure drop Ap Fj [ter through the filter 12, converted to ISO conditions as ⁇ p ⁇ °, is used as an input variable in the gas turbine forecast calculation.
  • a first additional power £ ⁇ P TM temchange is determined under reference conditions, which results from a filter change, according to: ⁇ pFilter change _ pR SO _ pAp £ ° r -i ⁇ -I i ⁇ i Qj. - r GT GT! • ⁇ *
  • step 13 the validated gas turbine power with dirty filter 12 and compressor 10 is determined from the values of the reference calculation and the forecast calculation (also called “expected” calculation) based on ISO conditions, in accordance with:
  • a second additional power AP ⁇ F '" can be determined from equations [14] and [15] under reference conditions that result from offline washing:
  • the constant factor f P ⁇ offline takes into account that the complete power loss cannot be recovered by an offline wash.
  • the factor f P ⁇ A ⁇ , augmentation is first assumed to be constant, for it may optionally include a trend line as a function of the equivalent operating hours are assumed in accordance with: JP.alternation ⁇ J P.alternation EOH) [17]
  • the determined values are written into the database with the extension ".E".
  • individual measured values MW and / or values derived or validated from them, e.g. B. the power loss can be written into the database.
  • steps 16, 17, a regression curve can additionally be displayed for an output, in particular display of the trend line or forecast line for a diagnostic variable and / or operating variable, for example according to FIG. 3.
  • This regression curve is expediently reset at the time of a last, previous offline wash.
  • the time of the last offline wash can be determined by a binary signal or alternatively by the wash speed, which must run for at least three hours.
  • a linear regression curve A linear regression curve is formed from the number n of values for the current power loss that has been determined since the last offline compressor wash signal. The confidence interval is not displayed. Trend lines with a positive slope are discarded.
  • an exponential regression curve An exponential regression curve is formed from the number n of values for the power loss determined since the last offline compressor wash signal. The confidence interval is not displayed. A strictly monotonically falling curve of the compressor efficiency is assumed. If this is not the case, the system switches to linear regression.
  • the regression function for. B. the linear regression
  • the display can optionally be done with a color change (with user-defined threshold). Alternatively, a color change can also be displayed based on operating experience values, for example after four weeks without offline washing.
  • the stored values for the operating variables BG are taken into account in the database. As already described in more detail above under FIGS. 4.1 to 4.2 for the online diagnosis, these values are preprocessed, that is to say recorded and possibly validated.
  • the user can start the turbine diagnosis, determination of the compressor parameters and the power losses manually.
  • the user can optionally carry out all steps, in particular determine all load cases and the associated values, or only parts, ie only values for individual components, eg. B. only the compressor 10 or the filter 12 or for both of them. Is z. If, for example, only a forecast or expected calculation is carried out, the necessary already validated values "" .V "are automatically read from the database.
  • the resulting OffZLine results are not written into the online database, they are expediently written in an associated database for offline sizes.
  • the diagnosis or forecast module includes an additional function.
  • the online database with the currently recorded measured values MW and the resulting validated values is taken into account.
  • the user can, for example, interactively enter current values for static and / or dynamic operating variables BG, e.g. B. current Kos enish and electricity prices, is requested.
  • the determination of an optimal washing time for the compressor 10 is described below using the forecast module.
  • the forecast module is used to identify and determine a long-term trend of pollution based on a diagnostic model.
  • the aim is to provide the operator with a To provide decision support for the time of offline laundry.
  • the operating parameters BG such as compressor parameters and power losses, are taken into account in the online diagnosis:
  • Compressor parameters • Validated isentropic compressor efficiency under ISO conditions ⁇ ° mp ; Compressor quality grade G ⁇ jj?
  • point clouds When the values are displayed, there are several point clouds. As described above, these point clouds are preferably used to lay regression functions as regression curves or compensation curves. Extrapolation of the regression curves means that pollution will be predicted in the future.
  • FIG. 5 shows an example of such a time profile for the power loss P G ⁇ as a function of the degree of contamination of the compressor 10.
  • the parameters a2, al and aO are determined using a non-linear calculation method.
  • the power losses of the gas turbine 8 due to a contaminated compressor 10 amount to a maximum value of approximately 90%.
  • the power loss P G ⁇ as shown in FIG. 5 can be determined currently and thus online. Alternatively or additionally, the power loss P G ⁇ can also be displayed for the past from stored measured values MW. This means that both a diagnosis for the past and a forecast for the future and / or a trend can be determined from previous and currently recorded measured values MW.
  • Offline washing should preferably be in a period of low electricity prices with a subsequent (longer) period of higher electricity prices.
  • a lot of assumptions would have to be made and these would always be updated by the user.
  • constant revenues and costs for a generated _Megawatt-hour of electrical energy are assumed.
  • the determination of the optimum washing time is described below using an exemplary turbine system 6, in particular a gas and Steam turbine power plant with. with a capacity of 200 MW.
  • the following diagnosis calculation relates to the optimal time for an offline wash for the compressor 10.
  • the process steps apply analogously to the filter change and / or the soot blowing as a further possibility for removing the contamination in the turbine system 6.
  • the specific observation period is:
  • loss costs per day result in the amount of:
  • the values e el , k el and t are parameterizable constants.
  • the current loss costs can be summarized, e.g. B, convert arithmetically and specifically per past day: ⁇ ez ⁇ ) [ 21 ]
  • the cost of a wash is made up of the reduced profit during the wash (6 hours) and the costs.
  • staff and detergents ⁇ rwaesche _ r, I ⁇ _ ⁇ ptot f , ⁇ rWaschstoff r 99.
  • the factor k last takes into account the load for the turbine system 6 which is possible at the time of washing and which can be implemented on the market. The larger the factor k last , the lower the costs of a see.
  • washing costs are also converted specifically for one day: ', v-washing washing ⁇ r - J 1 K special ⁇ washing * * ⁇ "
  • J wash represents the periodicity of washing. If the operator (in extreme cases) were to wash every day, he would have specific costs of EUR 74,000 per day. If he washes every 30 days, however, he would have specific costs of only approx. EUR 2,500 per day.
  • FIG 7 also clarifies that the optimum is very flat in all cases. The operator is thus shown a possible, in particular optimal, time window in which offline washing is recommended. A sharp or precise recommendation is avoided.
  • the amortization time can be determined from this:
  • the forecast module PM which is designed as a software module, is composed of several method steps which can be combined into several software modules, at least in two modules B1, B2, as shown in more detail in FIG :
  • the two modules B1 and B2 serve the following functions: •
  • the first module B1 (referred to as "TDYgt_thermo" by way of example) is used to carry out a thermodynamic analysis of the turbine system 6. The method steps are specified in more detail in the description of FIGS. 4.1 to 4.2.
  • the first module B1 is implemented, for example, in C ++ and can be called up from a higher-level control and / or regulation module for the turbine system 6 or its diagnosis. Given the full load condition and validation, etc., the forecast module PM is started. All required constants, calculation values etc. are stored in the database (referred to as "TDY database” in the example).
  • TDY database the database
  • the second component B2 (in the exemplary embodiment referred to as "TDYgt_finance") takes into account the effort for removing the contamination of the turbine system 6, as specified with reference to FIG 4.3. In addition to a time expenditure, a financial expenditure, as already described above, is taken into account and output. To take the effort into account, the diagnosis of the turbine system 6 can be carried out in offline or online mode.
  • the second component is expediently implemented as a JAVA applet.
  • FIG. 8 shows an example of a forecast module PM formed from a number of building blocks B1 to Bn.
  • the forecast module PM includes the first component B1 for thermodynamic diagnosis of the turbine system 6 and the second component B2 for taking into account the effort for removing contamination of the turbine system 6, in particular according to the processing and diagnosis described in FIG. 4.3.
  • the first and second building blocks B1 and B2 have a third building block B3 for preprocessing operating sizes BG and their measured values MW according to FIG. 4.1 and a fourth module B4 for validating the operating sizes BG and their measured values MW according to FIG 4.2 upstream. All of the blocks B1 to B4 of the forecast module PM access the data stored in a first database DB1, such as current and / or previous measured values MW and / or operating variables BG.
  • a further database contains DB2 diagnostic models for existing turbine systems or standard turbine systems, to which the first module B1 and the fourth module B4 have access, for example for referencing standard conditions or for comparing the current diagnosis with a stored model.
  • the forecast module PM can also be implemented in a higher-level diagnostic system for the turbine system 6 of a control and / or regulating system.
  • the PM forecast module or its implementation is subjected to a review process and is subject to version control.
  • the first and second additional service e.g. B.
  • the degree of pollution, the level of quality these are stored in the database DBl.
  • the actual output e.g. B. via a display, as shown for example in FIG. 9, therefore does not carry out its own calculations, eg. B. Formation of the regression curves.
  • the regression curves are, for example, in the first block B1, ie in the thermodynamic diagnostic part, determined. The regression curves must always be started at the time of the last offline wash.
  • the second component B2 then outputs, in particular displays, the determined measured values MW, the operating variables BG and the diagnostic variables, such as the first and second additional power, the degree of pollution, the state of aging, the compressor efficiency and / or the quality, whereby In addition to FIG. 5, trend lines of the calculated thermodynamic process or operating variables BG or diagnostic variables are also displayed.
  • curves shown in FIGS. 6 and 7 can be determined online and continuously updated in the display.
  • FIGS. 10 to 14 schematically show in table form the measured values, operating variables, diagnostic variables, etc. detailed.

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

L'invention concerne un procédé et un dispositif permettant d'établir des diagnostics dans un système de turbine (6), qui comprend une turbine à gaz (8) comportant plusieurs composants (10, 12). Une puissance additionnelle est pronostiquée de manière automatique, puissance additionnelle de laquelle une puissance d'exploitation de la turbine à gaz (8) est augmentée en cas d'élimination d'une salissure au niveau d'un des composants (10, 12).
PCT/EP2004/009060 2004-02-23 2004-08-12 Procede et dispositif pour etablir des diagnostics dans un systeme de turbine WO2005090764A1 (fr)

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EP04004081 2004-02-23

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EP1983158A1 (fr) * 2007-04-20 2008-10-22 Siemens Aktiengesellschaft Procédé pour la détermination de l'entretien périodique d'une turbomachine et système de réglage
EP2105887A1 (fr) * 2008-03-28 2009-09-30 Siemens Aktiengesellschaft Procédé de diagnostic d'une turbine à gaz
WO2010054132A2 (fr) * 2008-11-06 2010-05-14 General Electric Company Système et procédé de lavage de moteur
EP2267650A1 (fr) * 2009-06-25 2010-12-29 Hitachi Power Europe GmbH Entretien orienté sur l'état
GB2502078A (en) * 2012-05-15 2013-11-20 Optimized Systems And Solutions Ltd A method of optimising engine wash event scheduling
CN107201951A (zh) * 2016-03-16 2017-09-26 通用电气公司 涡轮发动机清洁系统和方法
EP3293367A1 (fr) * 2016-09-12 2018-03-14 General Electric Company Système et procédé de surveillance basée sur l'état de filtres de turbine
EP3293384A1 (fr) * 2016-09-12 2018-03-14 General Electric Company Système et procédé de surveillance basée sur l'état d'un compresseur

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Cited By (21)

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Publication number Priority date Publication date Assignee Title
WO2008128894A1 (fr) * 2007-04-20 2008-10-30 Siemens Aktiengesellschaft Procédé d'évaluation de dates de maintenance planifiées d'une turbomachine ainsi que système de commande pour une turbomachine
EP1983158A1 (fr) * 2007-04-20 2008-10-22 Siemens Aktiengesellschaft Procédé pour la détermination de l'entretien périodique d'une turbomachine et système de réglage
CN102099835B (zh) * 2008-03-28 2014-12-17 西门子公司 用于确定燃气轮机的抽吸质量流的方法
EP2105887A1 (fr) * 2008-03-28 2009-09-30 Siemens Aktiengesellschaft Procédé de diagnostic d'une turbine à gaz
WO2009118311A1 (fr) * 2008-03-28 2009-10-01 Siemens Aktiengesellschaft Procédé de détermination du flux massique d'aspiration d'une turbine à gaz
JP2011515620A (ja) * 2008-03-28 2011-05-19 シーメンス アクチエンゲゼルシヤフト ガスタービンの吸入質量流量の決定方法
CN102099835A (zh) * 2008-03-28 2011-06-15 西门子公司 用于确定燃气轮机的抽吸质量流的方法
US9466152B2 (en) 2008-03-28 2016-10-11 Siemens Aktiengesellschaft Method for determining the suction mass flow of a gas turbine
WO2010054132A2 (fr) * 2008-11-06 2010-05-14 General Electric Company Système et procédé de lavage de moteur
WO2010054132A3 (fr) * 2008-11-06 2011-10-20 General Electric Company Système et procédé de lavage de moteur
EP2267650A1 (fr) * 2009-06-25 2010-12-29 Hitachi Power Europe GmbH Entretien orienté sur l'état
GB2502078B (en) * 2012-05-15 2015-10-14 Rolls Royce Controls & Data Services Ltd Engine wash optimisation
GB2502078A (en) * 2012-05-15 2013-11-20 Optimized Systems And Solutions Ltd A method of optimising engine wash event scheduling
CN107201951A (zh) * 2016-03-16 2017-09-26 通用电气公司 涡轮发动机清洁系统和方法
EP3236019A1 (fr) * 2016-03-16 2017-10-25 General Electric Company Système et procédé de nettoyage de moteur à turbine à gaz
US10385723B2 (en) 2016-03-16 2019-08-20 General Electric Company Turbine engine cleaning systems and methods
EP3293367A1 (fr) * 2016-09-12 2018-03-14 General Electric Company Système et procédé de surveillance basée sur l'état de filtres de turbine
EP3293384A1 (fr) * 2016-09-12 2018-03-14 General Electric Company Système et procédé de surveillance basée sur l'état d'un compresseur
US20180073389A1 (en) * 2016-09-12 2018-03-15 General Electric Company System and method for condition-based monitoring of a compressor
JP2018044546A (ja) * 2016-09-12 2018-03-22 ゼネラル・エレクトリック・カンパニイ 圧縮機の状態に基づく監視をするためのシステムおよび方法
US10724398B2 (en) 2016-09-12 2020-07-28 General Electric Company System and method for condition-based monitoring of a compressor

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