ANALYSIS METHOD FOR A GAS TURBINE
20210040899 ยท 2021-02-11
Assignee
Inventors
Cpc classification
F05D2260/80
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F05D2240/35
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F01D17/085
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F05D2220/32
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F23N2225/16
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F23N5/102
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F02C9/28
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F05D2270/303
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F23N2241/20
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F23N2225/21
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F01D21/003
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
International classification
F02C9/28
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F01D21/00
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
Abstract
The gas turbine with a plurality of combustors for igniting gas and method includes receiving first temperature measurements for a first plurality of probing points, each associated with one of the plurality of combustors. The method includes receiving second temperature measurements for a second plurality of probing points, each located downstream of the plurality of combustors. The method includes determining an association between the first plurality of probing points and the second plurality of probing points. The determining includes using the first and second temperature measurements and position information for the first and second plurality of probing points to determine swirl characteristics for the gas turbine. The swirl characteristics representing the angular shift between the ignited gas at the plurality of combustors and the ignited gas at the second plurality of probing points.
Claims
1. An analysis method for a gas turbine, the gas turbine comprising a plurality of combustors for igniting gas, the analysis method comprising: receiving first temperature measurements for a first plurality of probing points, each of the first plurality of probing points being associated with one of the plurality of combustors; receiving second temperature measurements for a second plurality of probing points, each of the second plurality of probing points being located downstream of the plurality of combustors; and determining an association between the first plurality of probing points and the second plurality of probing points, the determining comprising using the first and second temperature measurements and position information for the first and the second plurality of probing points to determine swirl characteristics for the gas turbine, the swirl characteristics representing an angular shift between the ignited gas at the plurality of combustors and the ignited gas at the second plurality of probing points.
2. The method as claimed in claim 1, further comprising: outputting the swirl characteristics.
3. The method as claimed in claim 1, wherein using the first and second temperature measurements and the position information to determine the swirl characteristics comprise solving an optimisation problem using the first and second temperature measurements and position information as inputs, and the swirl characteristics as an unknown parameter to be determined.
4. The method as claimed in claim 3, wherein solving the optimisation problem comprises solving an equation dgt()=A+Bcgt(.sub.1), where dgt() is the second temperature measurement for the second probing point at position , where cgt(.sub.1) is the first temperature measurement for the first probing point at position (.sub.1), where .sub.1 is the swirl characteristics, where A and B are optional unknown parameters.
5. The method as claimed in claim 4, wherein A comprises a baseline temperature value C.sub.1, and wherein solving the optimisation problem further comprises determining the baseline temperature value C.sub.1.
6. The method as claimed in claim 5, wherein B comprises a dilation factors C.sub.2, and wherein solving the optimisation problem further comprises determining the dilation factor C.sub.2.
7. The method as claimed in claim 6, wherein A comprises a hot spot correction value, the hot spot correction value being for taking into account a presence of hot spots and cold spots within the gas turbine, and wherein solving the optimisation problem further comprises determining the hot spot correction value.
8. The method as claimed in claim 7, wherein the hot spot correction value is represented by an equation C.sub.3 cos(N(.sub.2)), where C.sub.3 is the maximum temperature difference between a hot spot and a cold spot, N is a predetermined value, and .sub.2 is position information representing a difference between a position of a hot spot from a selected one of the second probing points.
9. The method as claimed in claim 3, wherein solving the optimisation problem comprises solving a global optimisation problem to identify a global optimal range for the unknown parameter(s), the global optimisation problem is optionally solved using a genetic algorithm.
10. The method as claimed in claim 9, wherein solving the optimisation problem further comprises solving a local optimisation problem to determine a local optimum solution from the global optimal range for the unknown parameter(s), the local optimisation problem is optionally solved using a quasi-Newton algorithm.
11. The method as claimed in claim 1, wherein the gas turbine comprises an interduct located downstream of the plurality of combustors, and wherein the second plurality of probing points are located within the interduct.
12. The method as claimed in claim 11, wherein the second plurality of probing points are located around a circumference of the interduct.
13. The method as claimed in claim 1, wherein the gas turbine comprises an exhaust located downstream of the plurality of combustors, and wherein the second plurality of probing points are located within the exhaust.
14. A computer readable medium having instructions recorded thereon which, when executed by a processing device, cause the processing device to perform the method as claimed in claim 1.
15. A gas turbine comprising: a plurality of combustors for igniting gas; a controller operable to receive first temperature measurements for a first plurality of probing points, each of the first plurality of probing points being associated with one of the plurality of combustors; receive second temperature measurements for a second plurality of probing points, each of the second plurality of probing points being located downstream of the plurality of combustors; and determine an association between the first plurality of probing points and the second plurality of probing points, the determining comprising using the first and second temperature measurements and position information for the first and the second plurality of probing points to determine swirl characteristics for the gas turbine, the swirl characteristics representing an angular shift between the ignited gas at the plurality of combustors and the ignited gas at the second plurality of probing points.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0052] Examples of the present disclosure will now be described with reference to the accompanying drawings, in which:
[0053]
[0054]
[0055]
[0056]
[0057]
[0058]
[0059]
[0060]
[0061]
[0062]
[0063]
[0064]
DETAILED DESCRIPTION
[0065] With reference to
[0066]
[0067] In more detail, at the left end of the gas turbine 10 according to
[0068] The gas turbine 10 of
[0069] The gas turbine 10 of
[0070] The gas turbine 10 of
[0071]
[0072] At the left end of the engine 10 according to
[0073] The combustors 24 each comprise a burner 36 for introducing fuel into the inside of the corresponding combustor 24 and igniting the fuel/air mixture. A burner 36 comprises a pilot burner 37. Such a pilot burner 37 is shown in detail in
[0074] The combusted propulsion gas 18 flows through the power turbine 16 expanding thereby and driving the rotor shaft 12. The expanded propulsion gas 18 then enters an exhaust duct 26. At an exit 28 of the power turbine 16 into the exhaust duct 26 several second temperature sensors 30a in the form of so called power turbine exit thermocouples are positioned at different probing points 32a. By placing the second temperature sensors 30a at the power turbine exit 28 the probing points 32a are located downstream from the combustors 24.
[0075] The gas turbine 10 of
[0076] The temperatures measured by the first temperature sensors 42 and the second temperature sensors 30a are received by a controller 44.
[0077]
[0078] The high-pressure turbine 50 is attached to the first rotor shaft 46 as is the compressor 14. The low-pressure turbine 52 is mounted on the second rotor shaft 48. The gas duct 34 contains an interduct 54 for guiding the propulsion gas 18 from the high-pressure turbine 50 to the low-pressure turbine 52. Instead of an arrangement of the second temperature sensors 30a at the power turbine exit 28 according to
[0079] While the above example gas turbines 10 are described as measuring temperature using thermocouples, it will be appreciated that other approaches of measuring temperature are within the scope of the present invention. For example, the temperature sensors could be resistance based temperature sensors. Further, the temperature sensors could measure the temperature indirectly. For example, the temperature may be inferred from another measurement of a property of the gas turbine 10.
[0080] The controllers 44 for the gas turbines 10 described above may be remote from their respective gas turbines 10 and may be operated to receive data from and/or transmit data to the gas turbine 10 other a wired or wireless network. In some implementations, the controllers 44 may also be an integral part of the gas turbine 10.
[0081] In the above example gas turbines 10, the controller 44 receives first temperature measurements for the first plurality of probing points 40 and second temperature measurements for the second plurality of probing points 32a, 32b. The controller 44 further operates to determine an association between the first plurality of probing points 40 and the second plurality of probing points 32a, 32b. This determining comprises using the first and second temperature measurements and the position information for the first and second plurality of probing points 40, 32a, 32b to determine swirl characteristics for the gas turbine 10.
[0082] In more detail, the swirl characteristics may be considered as representing the angular shift between the ignited gas at the combustor outlets for the plurality of combustors and the ignited gas at the second plurality of probing points 32a, 32b. The swirl characteristics are due to the ignited gas travelling through the turbine 10 in a complex, spiralling trajectory, rather than a straight trajectory. Ignited gas from each combustor 24 will follow an individual spiralling trajectory, a spiralling cluster, that will generally not mix with the trajectories of gas flowing from the other combustors 24. The effect of this is that, at the second plurality of probing points, 32a, 32b, the ignited gas can be considered to have gone through an angular shift relative to the combustor outlet.
[0083] Significantly, the controller 44 uses the first and second temperature measurements and position information for the first and second plurality of probing points 40, 32a, 32b to determine the swirl characteristics for the gas turbine 10. Simple temperature measurements along with the position information are thus advantageously used to determine the swirl characteristics. The realisation that the temperature measurements and position information may be used in this way is perhaps counterintuitive, but the implementation is beneficial in terms of its simplicity over the existing more complicated approaches.
[0084] In one example implementation, a model is defined to represent the relationship between the second temperature measurements and the first temperature measurements. The model represents the effect of the swirl characteristics on the gas profile. Solving the model involves determining the relationship between the first and second temperature measurements, and thus results in the determination of the swirl characteristics. The swirl characteristics may then be output, and may be applied to subsequently generated temperature measurement data to determine the relationship between the first and second temperature measurements. In this way, it is possible to determine which combustor 24 is responsible for which second temperature measurement.
[0085] In this example, determining the swirl characteristics comprises solving an optimisation problem defined by the model. The first and second temperature measurements and position information are used as inputs for the model, and the swirl characteristics as an unknown parameter to be determined.
[0086] The model may be represented by the equation:
dgt()=A+Bcgt(.sub.1) (1)
[0087] Thus, the controller operates to solve the optimisation problem represented by equation (1).
[0088] In this example, dgt() is the second temperature measurement for the second probing point at position . The second temperature measurement may be in degrees centigrade ( C.), but other units of measuring temperature are within the scope of the present invention. The position may be an angular position given in degrees (), but other units of measuring angle are within the scope of the present invention.
[0089] In this example, cgt(.sub.1) is the first temperature measurement for the first probing point at position (.sub.1). The first temperature measurement may be in degrees centigrade ( C.), but other units of measuring temperature are within the scope of the present invention.
[0090] In this example, .sub.1is the unknown swirl characteristic, that are determined by solving the optimisation problem. The swirl characteristic may be a swirl angle given in degrees (), but other units of measuring angle are within the scope of the present invention.
[0091] In this example, A and B are unknown parameters. A may be given in degrees centigrade ( C.), but other units of measuring temperature are within the scope of the present invention. B may be a dimensionless parameter.
[0092] In operation, the controller 44 uses the known values of dgt(), cgt(), and to find the unknown values A, B, and .sub.1. In this way, by solving the equation (1) above, the controller is able to determine the swirl characteristics .sub.1.
[0093] The controller 44 may use optimisation techniques to determine the unknown values. In particular, the controller 44 may solve an optimisation problem using known optimisation techniques. For example, sequential quadratic programming (SQP) techniques may be used.
[0094] In advantageous implementations, SQP techniques are not used. This is because, SQP is a constrained optimisation, and is thus has found to be only efficient for local searches. As such, for SQP techniques to be effective, the algorithm requires accurate constrained ranges, and a near-optimal starting potion in order to arrive at an optimal solution.
[0095] Instead, advantageous implementations of the present invention solve the optimisation problem by solving a global optimisation problem to identify a global optimal range for the unknown parameter(s). The global optimisation problem is optionally solved using a genetic algorithm (GA). It has been found that global optimisation techniques, and particular Gas, are well suited for problems where there is limited prior knowledge of the characteristics of the objective function. For example, where there is limited knowledge of the parameter range, continuity, differentiability, and linearity or non-linearity of the problem. This helps to reduce the possibility of the algorithm being trapped into an unsatisfactory local extrema.
[0096] The use of global optimisation techniques such as GAs can successfully identify a range for the global optima. They may, however, not be able to identify the exact solution in the identified local range, unless a large number of generations and/or large population size are considered. Consequently and beneficially, the controller 44 may apply a global-local optimisation scheme. In particular, solving the optimisation problem may further comprise the controller 44 solving a local optimisation problem to determine a local optimum solution from the global optimal range for the unknown parameter(s). This means that after searching optimized parameters in a broader range by using the global optimisation method, the obtained parameter ranges can be fed into a local unconstrained minimization method as a starting point, to accurately locate the optimal estimates for the model parameters. The local optimisation problem is optionally solved using a Newton algorithm, advantageously a Quasi-Newton algorithm. For local unconstrained minimization, the Quasi-Newton is a advantageous example. Quasi-Newton methods use curvature information at each iteration to formulate a quadratic model problem. This helps avoid a large amount of calculation, comparing to the conventional Newton-type methods.
[0097] The present invention is not limited to any particular form of parameters A and B. Moreover, the parameters A and B may in turn comprise multiple unknown parameters. It will be appreciated that the skilled person given the teaching of the present invention will be able to select appropriate parameters A and B given, for example, factors such as the type of gas turbine.
[0098] In one example implementation, the unknown parameter A may comprises a baseline temperature value C.sub.1. The baseline temperature value C.sub.1.may be a baseline temperature value for the region of the gas turbine 10 where the second plurality of probing points 32a, 32b are located. That is, the baseline temperature value may be a baseline temperature value for the interduct 54 or exhaust 26 of the gas turbine 10. Solving the optimisation problem may thus further comprise determining the baseline temperature value C.sub.1. In this way, the equation solved by the optimisation problem may be expressed as: dgt()=C.sub.1+Bcgt(.sub.1).
[0099] In one example implementation, A may separately or additionally comprise a hot spot correction value. The hot spot correction value may be for taking into account the presence of hot spots and/or cold spots within the gas turbine. Solving the optimisation problem further comprises determining the hot spot correction value.
[0100] The hot spot correction value may be represented by the equation C.sub.3 cos(.sub.2)). C.sub.3 may be the maximum temperature difference between a hot spot and a cold spot. This may be considered as the hot-cold sport amplitude. N may be the number of hot spots, and may be determined based on the number of combustion chambers. .sub.2may be position information representing the difference between a position of a hot spot from a selected one of the second probing points. For example, .sub.2 may be the angular separation between the hot spot and a selected one of the second probing points. .sub.2 may be considered as the hot spot rotational angle. That is, the difference may be in the form of an angle. In this way, the equation solved by the optimisation problem may be expressed as:
dgt()=C.sub.1+Bcgt(.sub.1)+C.sub.3 cos(N(.sub.2)).
[0101] In one example implementation may be an optional unknown scaling factor parameter. B may comprises a dilation factor C.sub.2. The dilation factor may be a dilation factor of the first temperature measurements at the combustors. The dilation factor may be a dimensionless ratio parameter. Solving the optimisation problem may thus further comprise determining the dilation factor C.sub.2. In this way, the equation solved by the optimisation problem may be expressed as: dgt()=A+C.sub.2cgt(.sub.1).
[0102] In one example implementation, the equation solved by the optimisation problem may thus be expressed as:
dgt()=C.sub.1+C.sub.2cgt(.sub.1)+C.sub.3 cos(N(.sub.2)) (2)
[0103] It will be appreciated that solving the equation does not necessarily mean finding a perfect mathematical solution. Instead, solving may simply mean finding an apparent optimal solution based on conditions such as computational resources and the desired execution time. The solution may be considered as the result once a convergence or exit criterion is reached during the running of the algorithm.
[0104] An example implementation of the present invention will now be described in relation to the gas turbine 10 of
[0105]
[0106] In one example implementation, the relationship between the BTT profile and the IDT profile may be expressed by the equation (2) as defined above. The controller is operable to solve the equation defined above to determine values for the five unknown parameters.
[0107] Solutions to equation (2) using example optimisation techniques will be known be described. In these examples, the ranges of the parameters are initialised to have broad values. That is, the following values for the parameters are
[0108] initialised C.sub.1:[0,1000]; C.sub.2:[0,2]; C.sub.3:[0,200]; .sub.1:[0,360]; .sub.2:[0,60]. This means that temperature value C.sub.1 has a maximum value of 100 degrees centigrade, the dilation factor C.sub.2 has a maximum ratio value of 2, the hot-cold spot temperature difference C.sub.3 has a maximum value of 200 degrees centigrade, the swirl angle has a maximum value of 360 degrees, and the difference between a position of a hot spot from a selected one of the second probing points .sub.2has a maximum value of 60 degrees.
[0109] The results from different optimisation algorithms within the scope of the present invention are shown in the below Table 1.
TABLE-US-00001 TABLE 1 Fitted parameters C.sub.1 C.sub.2 C.sub.3 .sub.1 .sub.2 RMSE Method ( C.) (/) ( C.) () () ( C.) GA.sup.a 528.92 0.403 38.60 55.38 29.43 7.52 GA.sup.b 574.89 0.335 38.34 55.38 29.41 7.28 SQP.sup.c 732.50 0.098 0 0 59.53 28.87 SQP.sup.d 801.59 0 36.43 151.72 29.35 11.65 SQP.sup.e 577.22 0.332 38.22 56.58 29.42 7.29 GAQN 572.49 0.338 38.26 55.38 29.42 7.27 Here, GA.sup.a is a genetic algorithm (GA) executed once; GA.sup.b is a genetic algorithm executed 20 times, with the result having the lowest root-mean-square-error (RMSE) selected; SQP.sup.c is a SQP algorithm executed with the starting points [0, 0, 0, 0, 0]; SQP.sup.d is a SQP algorithm executed with the starting points [500, 1, 100, 180, 30]; SQP.sup.e is a SQP algorithm executed with the starting points [600, 0.3, 40, 50, 30]; and GAQN is the advantageous GA-Quasi-Newton approach.
[0110] The results of Table 1 show that one performance of a GA can identify a global solution of the parameters. By executing GA more times, the solutions can be more accurate, however, it is more expensive computationally. On the other hand, SQP will give more accurate solutions, if the starting points of the parameters are closer to the optimal solutions. However, when little is known about the exact parameter ranges and starting points, this may be difficult to achieve in practice. Table 1 thus shows that while all of the algorithm approaches within the scope of the present invention are capable of solving the optimisation problem, the global-local optimisation scheme as embodied by the GA-QN method is advantageous for its robustness and effectiveness. GA-QN can perform better than GA alone, in terms of accuracy and time cost, and it can overcome the difficulties occurred in the SQP or other similar optimisation methods, which demand more exact parameter ranges and starting points in order to get accurate solutions.
[0111]
[0112] The original BTT profile in
[0113]
[0114]
[0115]
[0116]
[0117]
[0118]
[0119]
[0120]
[0121] The features of the present invention may also be applied in conjunction with other combustion monitoring approaches, which use only the downstream gas temperature profiles, to link the features of the downstream gas temperature profiles to source the problematic combustion chambers, which will make the diagnostics of the gas turbine combustion systems more efficiently and with higher certainty.
[0122]
[0123] Step S0 comprises receiving first temperature measurements for a first plurality of probing points, each of the first plurality of probing points being associated with one of the plurality of combustors.
[0124] Step S1 comprises receiving second temperature measurements for a second plurality of probing points, each of the second plurality of probing points being located downstream of the plurality of combustors.
[0125] Step S2 comprises determining an association between the first plurality of probing points and the second plurality of probing points. The determining comprising using the first and second temperature measurements and position information for the first and second plurality of probing points to determine swirl characteristics for the gas turbine. The swirl characteristics representing the angular shift between the ignited gas at the plurality of combustors and the ignited gas at the second plurality of probing points.
[0126] At least some of the example embodiments described herein may be constructed, partially or wholly, using dedicated special-purpose hardware. Terms such as component, module or unit used herein may include, but are not limited to, a hardware device, such as circuitry in the form of discrete or integrated components, a Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuit (ASIC), which performs certain tasks or provides the associated functionality. In some embodiments, the described elements may be configured to reside on a tangible, persistent, addressable storage medium and may be configured to execute on one or more processors. These functional elements may in some embodiments include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Although the example embodiments have been described with reference to the components, modules and units discussed herein, such functional elements may be combined into fewer elements or separated into additional elements. Various combinations of optional features have been described herein, and it will be appreciated that described features may be combined in any suitable combination. In particular, the features of any one example embodiment may be combined with features of any other embodiment, as appropriate, except where such combinations are mutually exclusive. Throughout this specification, the term comprising or comprises means including the component(s) specified but not to the exclusion of the presence of others.
[0127] Although a few advantageous embodiments have been shown and described, it will be appreciated by those skilled in the art that various changes and modifications might be made without departing from the scope of the invention, as defined in the appended claims.
[0128] Attention is directed to all papers and documents which are filed concurrently with or previous to this specification in connection with this application and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference.
[0129] All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and/or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and/or steps are mutually exclusive.
[0130] Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features.
[0131] The invention is not restricted to the details of the foregoing embodiment(s). The invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.