CN114970364A - Method and device for determining component characteristics of gas turbine and electronic equipment - Google Patents

Method and device for determining component characteristics of gas turbine and electronic equipment Download PDF

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CN114970364A
CN114970364A CN202210647512.1A CN202210647512A CN114970364A CN 114970364 A CN114970364 A CN 114970364A CN 202210647512 A CN202210647512 A CN 202210647512A CN 114970364 A CN114970364 A CN 114970364A
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gas turbine
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李俊昆
章旋
白涛
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China United Heavy Gas Turbine Technology Co Ltd
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Abstract

The disclosure provides a method and a device for determining component characteristics of a gas turbine, electronic equipment and a storage medium, and relates to the technical field of gas turbines, wherein the method comprises the following steps: obtaining an initial migration coefficient matrix based on the existing component characteristic curve; establishing a simulation model of the gas turbine component to be estimated based on the migration coefficient matrix and the existing component characteristic curve, and determining a target function; determining a simulation loss value based on all-condition operation data of the gas turbine to be estimated, a component simulation model and a constraint optimization algorithm, and acquiring a migration coefficient matrix; when the simulated loss value is less than the loss threshold, component characteristic data is determined based on the data. The method has the advantages that whether the part simulation model is matched with the operation data of the gas turbine to be estimated or not is determined by establishing the gas turbine part simulation model to be estimated, so that the part characteristic data of the gas turbine to be estimated is determined based on the part characteristics of the existing gas turbine and the operation data of the gas turbine to be estimated, and the method is high in operability, efficiency and accuracy.

Description

Method and device for determining component characteristics of gas turbine and electronic equipment
Technical Field
The present disclosure relates to the field of gas turbine technology, and in particular, to a method and an apparatus for determining characteristics of a component of a gas turbine, an electronic device, and a storage medium.
Background
The characteristics of the components of the gas turbine, namely a compressor flow characteristic line, a compressor efficiency characteristic line, a turbine flow characteristic line and a turbine efficiency characteristic line, are important parameters for analyzing the performance of the gas turbine and establishing a mechanism model method of the gas turbine.
In the prior art, the main way of obtaining the characteristics of the gas turbine components is to calculate an initial gas turbine component characteristic line step by step through design parameters provided by a gas turbine manufacturer, and then correct the component characteristic line by performing a test, which is costly and the gas turbine manufacturer is reluctant to provide specific design parameters of the gas turbine components.
Another method for obtaining the characteristics of the gas turbine components is a method for obtaining the characteristic line of the gas turbine components based on an elliptic equation. The method assumes that the characteristics of the components of the gas turbine are in an elliptical shape, firstly uses an initial elliptical equation to indicate the characteristics of the components of the gas turbine, then defines the rotation, expansion and translation transformation coefficients of the elliptical equation, and finally corrects the transformation coefficients through steady-state data. The characteristics of the components of the gas turbine cannot be all represented by elliptic curves, even if the ellipse is subjected to expansion, translation and rotation transformation, the transformed ellipse is still an elliptic curve, the characteristics of the components in the whole range of the gas turbine are represented by the elliptic curves with large errors, and the method cannot be applied to the conditions of different IGV opening degrees.
BRIEF SUMMARY OF THE PRESENT DISCLOSURE
The present disclosure is directed to solving, at least to some extent, one of the technical problems in the related art.
To this end, it is an object of the present disclosure to propose a method of determining characteristics of a component of a gas turbine.
A second object of the present disclosure is to provide a component characteristic determination apparatus of a gas turbine.
A third object of the present disclosure is to provide an electronic device.
A fourth object of the present disclosure is to propose a non-transitory computer readable storage medium.
A fifth object of the present disclosure is to propose a computer program product.
To achieve the above object, an embodiment of a first aspect of the present disclosure provides a method for determining a characteristic of a component of a gas turbine, including: acquiring a transformation strategy set, and selecting a target transformation strategy from the transformation strategy set; based on a target transformation strategy, carrying out migration transformation on a component characteristic curve of the existing gas turbine so as to obtain an initial migration coefficient matrix of the component characteristic of the gas turbine to be estimated; establishing a simulation model of the gas turbine component to be estimated based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine, and determining a target function of the simulation model of the gas turbine component to be estimated; acquiring all-condition data of the gas turbine to be estimated, determining a simulation loss value of a target function based on the all-condition data, a simulation model of the gas turbine to be estimated and a constraint optimization algorithm, and acquiring a target migration coefficient matrix corresponding to the simulation loss value; and determining the component characteristic data of the gas turbine to be estimated based on the target transformation strategy, the target migration coefficient matrix and the component characteristic data of the existing gas turbine in response to the simulation loss value being smaller than the loss threshold value.
According to one embodiment of the present disclosure, the method for determining the characteristic of the component of the gas turbine further includes: establishing at least one component model based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine; and combining the component models to generate a simulation model of the gas turbine component to be estimated.
According to one embodiment of the present disclosure, a migration transformation is performed on a component characteristic curve of an existing gas turbine to obtain an initial migration coefficient matrix of a component characteristic of the gas turbine to be estimated, including: and carrying out migration transformation on a compressor flow characteristic line, a turbine flow characteristic line, a compressor efficiency characteristic line and a turbine efficiency characteristic line of the existing gas turbine based on quadratic polynomial migration to generate an initial migration coefficient matrix of the characteristic lines of the gas turbine to be estimated under the specific rotating speed and specific inlet guide vane opening data.
According to one embodiment of the disclosure, the full operating condition data includes obtaining IGV opening data of a first inlet guide vane of the existing gas turbine, and the method further includes: acquiring second IGV opening data in the component characteristic curve; and determining third IGV opening degree data based on the first IGV opening degree data and the second IGV opening degree data, and replacing the third IGV opening degree data with the first IGV opening degree data in the full-operating-condition data.
According to one embodiment of the present disclosure, the third IGV opening degree data is also determined using the following equation:
Figure BDA0003686595030000031
wherein, IGV o Second IGV opening degree data that is a characteristic curve of the component,
Figure BDA0003686595030000032
is the minimum value of the second IGV opening degree data,
Figure BDA0003686595030000033
is the maximum value of the second IGV opening degree data,
Figure BDA0003686595030000034
is the minimum value of the first IGV opening degree data,
Figure BDA0003686595030000035
is the maximum value of the first IGV opening degree data, IGV t Is the third IGV.
According to one embodiment of the disclosure, determining an objective function of a simulation model of a gas turbine component to be estimated includes: respectively determining the gas compressor outlet temperature, the turbine outlet temperature, the output power and the loss function of the gas turbine flow balance of a simulation model of a gas turbine component to be estimated; determining a target function based on a loss function of the outlet temperature of the gas compressor, a loss function of the outlet temperature of the turbine, a loss function of the output power, a loss function of the flow balance of the gas turbine and a preset weight; wherein, the objective function is determined by adopting the following formula:
Figure BDA0003686595030000036
wherein
Figure BDA0003686595030000037
For the compressor outlet temperature calculated by the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000038
for the ith input the actual measured compressor outlet temperature,
Figure BDA0003686595030000039
is the maximum measure of the compressor outlet temperature,
Figure BDA00036865950300000310
for the turbine outlet temperature calculated for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA00036865950300000311
for the ith input the actual measured turbine outlet temperature,
Figure BDA00036865950300000312
is the maximum measure of the turbine outlet temperature,
Figure BDA0003686595030000041
for the output power calculated for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000042
actual measured output power for the ith input, P Max Is the maximum measure of the output power,
Figure BDA0003686595030000043
for the compressor outlet flow calculated by the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000044
for the amount of fuel of the ith input,
Figure BDA0003686595030000045
for the turbine inlet flow calculated by a turbine flow characteristic line for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000046
for the maximum estimated turbine inlet flow, α, β, γ, λ are weighting coefficients.
According to an embodiment of the disclosure, the method further comprises: in response to the simulated loss value being greater than or equal to the loss threshold; adjusting a constraint optimization algorithm, recalculating a simulation loss value, and acquiring a new target migration coefficient matrix; and determining the component characteristics of the gas turbine to be estimated based on the target transformation strategy, the adjusted target migration coefficient matrix, the component characteristics of the existing gas turbine and the adjusted constraint optimization algorithm in response to the adjusted simulation loss value being smaller than the loss threshold value.
According to an embodiment of the disclosure, the method further comprises: in response to the recalculated simulated loss value being greater than or equal to the loss threshold; and re-determining a target transformation strategy from the transformation strategies which are not adopted in the transformation strategy set, and re-establishing the simulation model of the gas turbine component to be estimated based on the re-determined target transformation strategy.
To achieve the above object, an embodiment of a second aspect of the present disclosure provides a component characteristic determining apparatus for a gas turbine, including: the first acquisition module is used for acquiring a transformation strategy set and selecting a target transformation strategy from the transformation strategy set; the second acquisition module is used for carrying out migration transformation on the component characteristic curve of the existing gas turbine based on a target transformation strategy so as to acquire an initial migration coefficient matrix of the component characteristic of the gas turbine to be estimated; the determining module is used for establishing a simulation model of the gas turbine component to be estimated based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine, and determining an objective function of the simulation model of the gas turbine component to be estimated; the third acquisition module is used for acquiring all-working-condition data of the gas turbine to be estimated, determining a simulation loss value of the target function based on the all-working-condition data, a simulation model of the gas turbine component to be estimated and a constraint optimization algorithm, and acquiring a target migration coefficient matrix corresponding to the simulation loss value; and the calculation module is used for responding to the simulation loss value smaller than the loss threshold value and determining the component characteristic data of the gas turbine to be estimated based on the target transformation strategy, the target migration coefficient matrix and the component characteristic data of the existing gas turbine. .
According to one embodiment of the disclosure, the determining module is further configured to: establishing at least one component model based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine; and combining the component models to generate a simulation model of the gas turbine component to be estimated.
According to an embodiment of the disclosure, the second obtaining module is further configured to: and carrying out migration transformation on a compressor flow characteristic line, a turbine flow characteristic line, a compressor efficiency characteristic line and a turbine efficiency characteristic line of the existing gas turbine based on quadratic polynomial migration to generate an initial migration coefficient matrix of the characteristic lines of the gas turbine to be estimated under the specific rotating speed and specific inlet guide vane opening data.
According to an embodiment of the disclosure, the third obtaining module is further configured to: acquiring second IGV opening data in the component characteristic curve; and determining third IGV opening degree data based on the first IGV opening degree data and the second IGV opening degree data, and replacing the third IGV opening degree data with the first IGV opening degree data in the full-operating-condition data.
According to one embodiment of the disclosure, the determining module is further configured to: respectively determining the outlet temperature of a gas compressor, the outlet temperature of a turbine, the output power and the loss function of the flow balance of the gas turbine of a simulation model of a gas turbine component to be estimated; determining a target function based on a loss function of the outlet temperature of the gas compressor, a loss function of the outlet temperature of the turbine, a loss function of the output power, a loss function of the flow balance of the gas turbine and a preset weight; wherein, the objective function is determined by adopting the following formula:
Figure BDA0003686595030000051
wherein
Figure BDA0003686595030000052
For the compressor outlet temperature calculated by the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000053
for the ith input the actual measured compressor outlet temperature,
Figure BDA0003686595030000054
is the maximum measure of the compressor outlet temperature,
Figure BDA0003686595030000055
for the turbine outlet temperature calculated for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000061
for the ith input the actual measured turbine outlet temperature,
Figure BDA0003686595030000062
is the maximum measure of the turbine outlet temperature,
Figure BDA0003686595030000063
for the output power calculated for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000064
actual measured output power for the ith input, P Max Is the maximum measure of the output power,
Figure BDA0003686595030000065
for the compressor outlet flow calculated by the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000066
the amount of fuel that is the ith input,
Figure BDA0003686595030000067
for the turbine inlet flow calculated by a turbine flow characteristic line for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000068
for the maximum estimated turbine inlet flow, α, β, γ, λ are weighting coefficients.
According to one embodiment of the disclosure, the computing module is further configured to: in response to the simulated loss value being greater than or equal to the loss threshold; adjusting a constraint optimization algorithm, recalculating a simulation loss value, and acquiring a new target migration coefficient matrix; and determining the component characteristics of the gas turbine to be estimated based on the target transformation strategy, the adjusted target migration coefficient matrix, the component characteristics of the existing gas turbine and the adjusted constraint optimization algorithm in response to the adjusted simulation loss value being smaller than the loss threshold value.
According to one embodiment of the disclosure, the computing module is further configured to: in response to the recalculated simulated loss value being greater than or equal to the loss threshold; and re-determining a target transformation strategy from the transformation strategies which are not adopted in the transformation strategy set, and re-establishing the simulation model of the gas turbine component to be estimated based on the re-determined target transformation strategy.
To achieve the above object, an embodiment of a third aspect of the present disclosure provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to perform a method for determining characteristics of a component of a gas turbine engine as embodied in the first aspect of the present disclosure.
To achieve the above object, a fourth aspect of the present disclosure provides a non-transitory computer readable storage medium storing computer instructions for implementing a method for determining component characteristics of a gas turbine according to an embodiment of the first aspect of the present disclosure.
To achieve the above object, a fifth aspect of the present disclosure provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program is used to implement the method for determining the component characteristics of the gas turbine according to the first aspect of the present disclosure.
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FIG. 1 is a schematic illustration of a method of determining a characteristic of a component of a gas turbine according to an embodiment of the present disclosure;
FIG. 2 is a schematic illustration of another method of determining a characteristic of a component of a gas turbine according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart diagram illustrating an overall method for determining characteristics of components of another gas turbine engine according to one embodiment of the present disclosure;
FIG. 4 is a schematic illustration of a gas turbine component characteristic determining apparatus according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and intended to be illustrative of the present disclosure, and should not be construed as limiting the present disclosure.
Fig. 1 is a schematic view of an exemplary embodiment of a method for determining characteristics of a component of a gas turbine according to the present disclosure, as shown in fig. 1, the method for determining characteristics of a component of a gas turbine includes the steps of:
s101, obtaining a transformation strategy set, and selecting a target transformation strategy from the transformation strategy set.
The gas turbine has the advantages of high comprehensive energy utilization efficiency, no smoke and dust, low emission, high equipment reliability and high safety factor, and uses fuel as clean energy. Meanwhile, the gas turbine is flexible in operation, can adapt to various power generation requirements, and has robust regulation and control capabilities including the capabilities of quick start, quick loading, quick load variation, deep peak regulation and the like on the premise of meeting the robustness.
The characteristics of the gas turbine components include a compressor flow characteristic line, a compressor efficiency characteristic line, a turbine flow characteristic line, a turbine efficiency characteristic line, and the like. The gas turbine flow characteristic line, the gas compressor efficiency characteristic line, the turbine flow characteristic line and the turbine efficiency characteristic line of different types of gas turbines can be different, and correspondingly, the conversion algorithm of the gas turbine component characteristic line of each type of gas turbine can also be different. For example, the transformation algorithm may be a binomial migration algorithm, a trinomial migration algorithm, or the like.
By way of example with the formula, the flow characteristic of the compressor can be expressed with the following formula:
Figure BDA0003686595030000081
Figure BDA0003686595030000082
wherein p is 1 For the inlet pressure, pi, of the compressor c For the pressure ratio of the compressor, N for the rotational speed of the compressor, T 1 The inlet temperature of the compressor, the IGV the opening degree of the inlet guide vane of the compressor, and the w c For the compressor flow, eta c Compressor efficiency.
The flow characteristics of the turbine can be expressed by the following equation:
Figure BDA0003686595030000083
Figure BDA0003686595030000084
wherein p is 3 For turbine inlet pressure, pi t Is the turbine expansion ratio, N is the compressor rotation speed, T 3 For turbine inlet temperature, w t For turbine flow, eta t Is the turbine efficiency.
In the embodiment of the present disclosure, the set of transformation strategies may be a set of different migration algorithm combinations performed on each gas turbine component, and these algorithm combinations may include the same algorithm or may be different algorithms, which are not limited herein, and the specific requirements are set according to actual requirements.
In the embodiment of the present disclosure, the target transformation policy selected from the transformation policy set may be selected randomly or according to a certain rule.
Alternatively, the transformation strategy may be selected based on the model of the gas turbine. The same conversion strategy can be selected for the same model of gas turbine or for similar models of gas turbines.
Alternatively, the transformation strategy may also be selected based on the main function of the gas turbine, i.e. gas turbines of different functions may have a corresponding transformation strategy.
S102, performing migration transformation on the component characteristic curve of the existing gas turbine based on a target transformation strategy to obtain an initial migration coefficient matrix of the component characteristic of the gas turbine to be estimated.
After the target transformation strategy is obtained, migration transformation can be carried out on the component characteristic curve of the existing gas turbine based on the target transformation strategy so as to obtain an initial migration coefficient matrix of the component characteristic of the gas turbine to be estimated. It should be noted that the gas turbine to be estimated and the existing gas turbine are the same type of gas turbine or the similar type of gas turbine, and alternatively, the gas turbine to be estimated and the existing gas turbine may also be gas turbines capable of achieving the similar function or the same function.
By means of migration transformation, the component characteristic curve of the existing gas turbine is transformed into an initial migration coefficient matrix which is close to the component characteristic of the gas turbine to be estimated, and therefore the purpose of predicting the characteristic of the gas turbine to be estimated can be achieved.
In the disclosed embodiment, the existing gas turbine component characteristic line is generally represented by a data table, and characteristic data outside the data table can be obtained by interpolating the data table when the gas turbine component characteristic line is reused. For the characteristics of the components of the air compressor, the corresponding relations of the pressure ratio, the reduced flow and the efficiency under different reduced rotating speeds and different IGV opening combinations are given; for the characteristics of the turbine parts, corresponding relations of expansion ratio and reduced flow and expansion ratio and efficiency under different reduced rotating speeds are given. When the characteristic lines of the gas turbine components are subjected to transfer transformation, the characteristic lines of a single component are subjected to polynomial nonlinear change, so that the basic characteristics of the characteristic lines of the original components can be reserved, and transformation is performed on the basis of the characteristic lines to obtain predicted component characteristic data.
S103, establishing a simulation model of the gas turbine component to be estimated based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine, and determining an objective function of the simulation model of the gas turbine component to be estimated.
After the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine are obtained, a simulation model of the component of the gas turbine to be estimated can be established. In the disclosed embodiment, a complete simulation model of the gas turbine component to be estimated can be established through the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine.
Alternatively, a training model of the characteristics of the gas turbine components may be constructed by separately constructing the compressor, combustor and turbine models, which are then combined.
It will be understood that the gas turbine component simulation model to be estimated does not necessarily contain all the components of the gas turbine to be estimated, it may simulate only one of the components to obtain the simulation result of the component, or it may be a combination of several components. The method is not limited in any way, and the method can be designed according to actual needs.
It should be noted that the input of the training model is the compressor inlet temperature, the compressor inlet pressure, the compressor pressure ratio, the fuel quantity, the rotation speed, the IGV opening, etc., and the output of the training model is the compressor outlet temperature, the compressor outlet flow, the turbine outlet temperature, the turbine inlet flow calculated by the turbine flow characteristic line, the output power, etc. The present invention is not limited to the above embodiments, and may be set according to the actual situation.
The migration learning aims at a multi-target problem, on one hand, the deviation of the key parameters of the gas turbine calculated by the model and actual measured parameters, namely the relative deviation of the outlet temperature of the gas compressor, the relative deviation of the outlet temperature of the turbine and the relative deviation, is reflected, on the other hand, the self-balancing problem in the gas turbine model, namely the matching of the flow characteristic of the gas compressor and the flow characteristic of the turbine, is reflected, and therefore the target function of the simulation model of the gas turbine component to be estimated is determined.
Optionally, multiple targets can be converted into a single-target problem by adopting a weight weighting mode, so that an objective function of the simulation model of the gas turbine component to be estimated is determined, and the accuracy of the objective function can be increased.
S104, acquiring all-condition data of the gas turbine to be estimated, determining a simulation loss value of the objective function based on the all-condition data, the simulation model of the gas turbine component to be estimated and a constraint optimization algorithm, and acquiring a target migration coefficient matrix corresponding to the simulation loss value.
In the embodiment of the disclosure, the full condition data of the gas turbine to be estimated may be full process operation data of the combustion engine from ignition, warming up, speed increasing, full speed no-load, grid connection, load increasing, full load, load decreasing, splitting and shutdown under different environmental temperatures, and the required operation data includes a compressor inlet temperature, a compressor inlet pressure, a compressor outlet temperature, a compressor pressure ratio, a turbine outlet temperature, a turbine outlet pressure, a rotation speed, power, an IGV opening degree and a total fuel quantity.
It should be noted that the method for acquiring the full condition data of the gas turbine to be estimated may be to perform targeted screening on the working data to generate the full condition data by recording the working data of the existing gas turbine actually operated.
Optionally, the data obtained by simulation based on the simulation model of the gas turbine component to be estimated can also be obtained.
Optionally, past operating data of the gas turbine to be estimated may also be analyzed to determine full-operating-condition data.
In the embodiment of the present disclosure, after the full condition data is acquired, since the actually acquired operation data contains signal noise, the signal noise needs to be processed. For example, the operational data may be filtered using a gaussian filter.
In the embodiment of the disclosure, based on the constraint condition and the objective function, a sequential quadratic programming algorithm is adopted to solve the nonlinear problem with constraint, and a target migration coefficient matrix when the objective function is minimum is obtained. It should be noted that the constraint optimization algorithm may solve the nonlinear problem with constraints through a sequential quadratic programming algorithm, and obtain a target migration coefficient matrix when the target function is the minimum. Optionally, a particle swarm algorithm or a genetic algorithm can be adopted, and when the optimization algorithm is selected, the algorithm can be selected according to the actual optimization precision and the optimization speed, so that the optimization problem is prevented from falling into local optimization.
And applying the optimized target migration coefficient matrix to a gas turbine simulation model to be estimated, performing simulation calculation on key parameters of the gas turbine, including the outlet temperature of the gas compressor, the outlet pressure of the gas compressor, the outlet temperature of the turbine and the power, comparing a simulation result with operation data, and calculating the steady state deviation of each key parameter. And then comparing the simulation data with the real output data of the all-condition data, and solving a simulation loss value based on a preset loss function.
The loss function in the embodiment of the present disclosure is set in advance, and can be set according to actual needs. For example, the loss function may be a hinge loss function, a cross entropy loss function, an exponential loss function, and the like, and may be specifically selected according to actual needs, which is not limited herein.
And S105, responding to the fact that the simulation loss value is smaller than the loss threshold value, and determining the component characteristic data of the gas turbine to be estimated based on the target transformation strategy, the target migration coefficient matrix, the component characteristic data of the existing gas turbine and the constraint optimization algorithm.
In the disclosed embodiment, when the obtained simulation loss value is less than the loss threshold value, the gas turbine component simulation model to be estimated may be considered to be suitable for data simulation of the gas turbine to be estimated. Similarly, the objective function and the objective transformation strategy which form the simulation model of the gas turbine component to be estimated are also suitable for calculating the component characteristic data of the existing gas turbine through the constraint optimization algorithm by the objective function and the objective transformation strategy so as to obtain the component characteristic data of the gas turbine to be estimated. By means of this inverse transformation of the data, it is possible to determine the component property data of the gas turbine to be estimated on the basis of the component property data of the existing gas turbine.
In the embodiment of the disclosure, a transformation policy set is obtained first, and a target transformation policy is selected from the transformation policy set, then, based on the target transformation strategy, carrying out migration transformation on the component characteristic curve of the existing gas turbine so as to obtain an initial migration coefficient matrix of the component characteristic of the gas turbine to be estimated, then establishing a simulation model of the gas turbine component to be estimated based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine, determining a target function of a simulation model of a gas turbine component to be estimated, then acquiring all-condition data of the gas turbine to be estimated, and determining a simulation loss value based on the all-condition data, the simulation model of the gas turbine component to be estimated and a constraint optimization algorithm, and determining the component characteristic data of the gas turbine to be estimated based on a target transformation strategy, an initial migration coefficient matrix and the component characteristic data of the existing gas turbine. Therefore, the method is more accurate, strong in operability and higher in efficiency compared with the traditional method for determining the characteristic data of the component.
In one embodiment of the present disclosure, since the IGV full-on and full-off angles of different gas turbines are different, the IGV opening degree in the existing compressor component line needs to be converted into the IGV opening degree range in the gas turbine to be estimated. The full-operating-condition data comprises IGV opening data of a first inlet guide vane of the existing gas turbine, second IGV opening data in a component characteristic curve is obtained, third IGV opening data is determined based on the first IGV opening data and the second IGV opening data, and the third IGV opening data is replaced by the first IGV opening data in the full-operating-condition data. The conversion can be done by the following equation:
Figure BDA0003686595030000131
wherein, IGV o Second IGV opening degree data that is a characteristic curve of the component,
Figure BDA0003686595030000132
is the minimum value of the second IGV opening degree data,
Figure BDA0003686595030000133
is the maximum value of the second IGV opening degree data,
Figure BDA0003686595030000134
is the minimum value of the first IGV opening degree data,
Figure BDA0003686595030000135
is the maximum value of the first IGV opening degree data, IGV t Is the third IGV.
In the above embodiment, the migration transformation is performed on the component characteristic curve of the existing gas turbine based on the target transformation strategy to obtain the initial migration coefficient matrix of the component characteristic of the gas turbine to be estimated, which can be further explained by fig. 2, and includes:
s201, performing migration transformation on a compressor flow characteristic line, a turbine flow characteristic line, a compressor efficiency characteristic line and a turbine efficiency characteristic line of the existing gas turbine based on quadratic polynomial migration to generate an initial migration coefficient matrix of the characteristic lines of the gas turbine to be estimated under the specific rotating speed and specific inlet guide vane opening data.
The single characteristic line is subjected to nonlinear change by adopting quadratic polynomial migration, so that the basic characteristics of the characteristic line of the original component can be reserved, the characteristic parameters of the gas turbine can be better analyzed and calculated, and a basis is provided for subsequently establishing a more accurate simulation model of the component of the gas turbine to be estimated.
In one embodiment of the present disclosure, the compressor flow characteristic line migration formula is as follows:
Figure BDA0003686595030000141
wherein g _ c 1 For existing gas turbine at equal rotation speed N 1 And IGV 1 A lower compressor flow characteristic line; g _ c n For existing gas turbines at equal rotational speed N n And IGV n A lower compressor flow characteristic line; g _ c' 1 For N at equal rotational speed of the gas turbine to be estimated 1 And IGV' 1 A lower compressor flow characteristic line; g _ c' n For N at equal rotational speed of the gas turbine to be estimated n And IGV' n And (5) a compressor flow characteristic line. A _ C, B _ C and C _ C are migration coefficient matrixes, and each characteristic line corresponds to one set of migration parameters (A _ C) i ,B_c i ,C_c i )。
In one embodiment of the present disclosure, the compressor efficiency characteristic line migration formula is as follows:
Figure BDA0003686595030000142
wherein f _ c 1 For existing gas turbine at equal rotation speed N 1 And IGV 1 The efficiency characteristic line of the lower gas compressor; f _ c n For existing gas turbine at equal rotation speed N n And IGV n The efficiency characteristic line of the lower gas compressor; f _ c' 1 For N at equal rotational speed of the gas turbine to be estimated 1 And IGV' 1 The efficiency characteristic line of the lower gas compressor; f _ c' n For N at equal rotational speed of the gas turbine to be estimated n And IGV' n And (5) efficiency characteristic line of the compressor. D _ c, E _ c and F _ c are migration coefficient matrixes, and each characteristic line corresponds to one group of migration parameters (D _ c) i ,E_c i ,F_c i )。
In one embodiment of the present disclosure, the turbine flow characteristic line migration equation is as follows:
Figure BDA0003686595030000151
where g _ t 1 For existing gas turbine at equal rotation speed N 1 A lower turbine flow characteristic line; g _ t n For existing gas turbine at equal rotation speed N n A lower turbine flow characteristic line; g _ t' 1 For N at equal speed of the gas turbine to be estimated 1 A lower turbine flow characteristic line; g _ t' n For N at equal rotational speed of the gas turbine to be estimated n Lower turbine flow characteristic line. A _ t, B _ t and C _ t are migration coefficient matrixes, and each characteristic line corresponds to a group of migration parameters (A _ t) i ,B_t i ,C_t i )。
In one embodiment of the present disclosure, the turbine efficiency characteristic line migration equation is as follows:
Figure BDA0003686595030000152
wherein f _ t 1 Equal rotational speed N for existing gas turbines 1 Lower turbine efficiency characteristic line; f _ t n Equal rotational speed N for existing gas turbines n Lower turbine efficiency characteristic line; f _ t' 1 For equal rotational speed N of gas turbine to be estimated 1 Lower turbine efficiency characteristic line; f _ t' n For equal rotational speed N of gas turbine to be estimated n Lower turbine efficiency characteristic line. D _ t, E _ t and F _ t are migration coefficient matrixes, and each characteristic line corresponds to a group of migration parameters (D _ t) i ,E_t i ,F_t i )。
In the above embodiment, the determining of the objective function of the simulation model of the gas turbine component to be estimated may be performed based on the following steps: the method comprises the steps of respectively determining the outlet temperature of a gas compressor, the outlet temperature of a turbine, the output power and the loss function of the flow balance of the gas turbine of a simulation model of a gas turbine component to be estimated, and determining a target function based on the loss function of the outlet temperature of the gas compressor, the loss function of the outlet temperature of the turbine, the loss function of the output power, the loss function of the flow balance of the gas turbine and preset weight. It should be noted that the preset weight is set in advance, and can be adjusted according to actual needs. The objective function may also be determined using the following formula:
Figure BDA0003686595030000161
wherein
Figure BDA0003686595030000162
The outlet temperature of the compressor calculated for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000163
for the ith input the actual measured compressor outlet temperature,
Figure BDA0003686595030000164
is the maximum measure of the compressor outlet temperature,
Figure BDA0003686595030000165
for the turbine outlet temperature calculated for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000166
for the ith input the actual measured turbine outlet temperature,
Figure BDA0003686595030000167
is the maximum measure of the turbine outlet temperature,
Figure BDA0003686595030000168
for the output power calculated for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000169
actual measured output power for the ith input, P Max Is the maximum measure of the output power,
Figure BDA00036865950300001610
compressor outlet calculated for the ith input to the simulation model of the gas turbine component to be estimatedThe flow rate of the port is controlled,
Figure BDA00036865950300001611
for the amount of fuel of the ith input,
Figure BDA00036865950300001612
for the turbine inlet flow calculated by a turbine flow characteristic line for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA00036865950300001613
for the estimated maximum turbine inlet flow, α, β, γ, λ are weighting coefficients.
It should be noted that, during optimization training, in order to avoid that the characteristics of the migrated component violate the actual physical process, data of the characteristics of the component is constrained, and the coefficients of the migration matrix are ensured to be within a reasonable range. For example, the constraint equation is expressed as follows:
constraint1＝max(g_c′)≤Const1
constraint2＝min(g_c′)≥Const2
constraint3＝max(f_c′)≤Const3
constraint4＝min(f_c′)≥Const4
constraint5＝max(g_t′)≤Const5
constraint6＝min(g_t′)≥Const6
constraint7＝max(f_t′)≤Const7
constraint8＝min(f_t′)≥Const8
constraint9＝fcool_cor≤Const9
constraint10＝fcool_cor≥Const10
constraint1 and constraint2 are constraints on the flow of the compressor; constraint3 and constraint4 are constraints on compressor efficiency; constraint5 and constraint6 are constraints on turbine flow; constraint7 and constraint8 are constraints on turbine efficiency; constraint9 and constraint10 are constraints on the turbine first-stage cooling air amount, and Const1, Const2, Const3, Const4, Const5, Const6, Const7, Const8, Const10,
Const9 and Const10 are set in advance, and may be changed according to actual design requirements, and are not limited herein.
In another embodiment of the present disclosure, the objective function may also be optimized item by item for the component characteristics of the gas turbine to be estimated. The method can optimize the efficiency characteristic line of the gas compressor, then jointly optimize the flow characteristic line of the gas compressor and the efficiency characteristic line of the turbine, and finally optimize the flow characteristic line of the turbine.
Taking the power deviation as an example of the objective function, the objective function can be determined by using the following formula:
Figure BDA0003686595030000171
wherein the content of the first and second substances,
Figure BDA0003686595030000172
output power calculated for the model at the i-th input,
Figure BDA0003686595030000173
For the actually measured output power, P, at the i-th input Max Is the maximum measure of output power.
Correspondingly, the constraints also need to be transformed, wherein
Figure BDA0003686595030000174
Figure BDA0003686595030000175
Figure BDA0003686595030000181
Const11 、 Const12 、 Const13 The design is not limited herein, and the design can be changed according to the actual design requirement.
When the efficiency characteristic line of the compressor is optimized, a training model of the efficiency characteristic line of the compressor needs to be established. The input of the model is the inlet temperature of the compressor, the inlet pressure of the compressor, the rotating speed, the IGV opening degree and the pressure ratio, and the output of the model is the outlet temperature of the compressor. The objective function may be determined using the following formula:
Figure BDA0003686595030000182
wherein the content of the first and second substances,
Figure BDA0003686595030000183
for the compressor outlet temperature calculated by the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000184
for the ith input the actual measured compressor outlet temperature,
Figure BDA0003686595030000185
is the maximum measure of the compressor outlet temperature. It should be noted that the constraint function of the objective function is only applicable to the constraint condition related to the efficiency characteristic line of the compressor.
When the combined optimization of the compressor flow characteristic line and the turbine flow characteristic line is carried out, a training model of the compressor flow characteristic line and the turbine efficiency characteristic line needs to be established. The difference from the original training model is that when the turbine is modeled, the inlet flow of the turbine uses the outlet flow of the compressor to add fuel quantity, and the inlet flow of the turbine obtained by the checking of the turbine characteristic line is not used. The objective function may be determined using the following formula:
Figure BDA0003686595030000186
wherein the content of the first and second substances,
Figure BDA0003686595030000187
for the turbine outlet temperature calculated for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000188
for the ith input the actual measured turbine outlet temperature,
Figure BDA0003686595030000189
is the maximum measure of the turbine outlet temperature,
Figure BDA00036865950300001810
output power calculated for the model at the i-th input,
Figure BDA00036865950300001811
For the actually measured output power, P, at the i-th input Max Is the maximum measure of output power. It should be noted that the constraint function of the objective function is only applicable to the constraint condition that the constraint function only relates to the compressor flow characteristic line and the turbine efficiency characteristic line.
When optimizing the turbine flow characteristic line, a training model of the turbine flow characteristic line needs to be established. Firstly, calculating the outlet flow of the compressor under each pressure ratio by using the optimized compressor flow characteristic line. The training model takes the pressure ratio of the compressor, the outlet flow of the compressor and the fuel quantity as input, and takes the inlet flow of the turbine obtained by the inspection of the characteristic line of the turbine as output. The objective function may be determined using the following formula:
Figure BDA0003686595030000191
wherein the content of the first and second substances,
Figure BDA0003686595030000192
for the compressor outlet flow calculated by the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000193
for the amount of fuel of the ith input,
Figure BDA0003686595030000194
the flow of the turbine inlet calculated by a turbine flow characteristic line for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000195
is the estimated maximum turbine inlet flow. The constraint function of the objective function is only applicable to the constraint condition related to the turbine flow characteristic line.
Further, when the simulation loss value obtained after the full condition data is input into the gas turbine component simulation model to be estimated is greater than or equal to the loss threshold value, at this time, it can be considered that the gas turbine component simulation model to be estimated is not suitable for the operation data of the gas turbine to be estimated. The simulated loss value can be re-solved by adjusting the constraint optimization algorithm, and the simulated loss value is continuously compared with the loss threshold value.
And in response to the recalculated simulation loss value being greater than the loss threshold, re-determining the target transformation strategy from the transformation strategies not adopted by the transformation strategy set, and re-establishing the gas turbine component simulation model to be estimated based on the re-determined target transformation strategy. And until the training is finished, the simulation loss value is smaller than the loss threshold value.
Fig. 3 is a schematic diagram of an overall flow of an embodiment of the disclosure, and as shown in fig. 3, the method includes, first, obtaining a transformation policy set, and selecting a target transformation policy from the transformation policy set, then, performing migration transformation on a component characteristic curve of an existing gas turbine based on the target transformation policy to obtain an initial migration coefficient matrix of component characteristics of the gas turbine to be estimated, then, establishing a gas turbine component simulation model to be estimated based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine, and determining an objective function of the gas turbine component simulation model to be estimated, then, obtaining full condition data of the gas turbine to be estimated, and determining a simulation loss value of the objective function based on the full condition data and the gas turbine component simulation model to be estimated, and determining component characteristic data of the gas turbine to be estimated based on the target transformation policy, the target migration coefficient matrix and the component characteristic data of the existing gas turbine in response to the simulation loss value being smaller than a loss threshold value; and responding to the simulation loss value being larger than or equal to the loss threshold value, adjusting the constraint optimization algorithm, recalculating the simulation loss value, and acquiring a new target migration coefficient matrix. In response to the adjusted simulation loss value being smaller than the loss threshold value, determining the component characteristics of the gas turbine to be estimated based on the adjusted target transformation strategy, the target migration coefficient matrix, the component characteristics of the existing gas turbine and the adjusted constraint optimization algorithm; and in response to the fact that the recalculated simulation loss value is larger than the loss threshold value, re-determining the target transformation strategy from the transformation strategies which are not adopted in the transformation strategy set, re-establishing the simulation model of the gas turbine component to be estimated based on the re-determined target transformation strategy, and repeating the steps until the simulation loss value is smaller than the loss threshold value.
Corresponding to the method for determining the component characteristics of the gas turbine provided in the foregoing several embodiments, an embodiment of the present disclosure further provides a device for determining the component characteristics of the gas turbine, and since the device for determining the component characteristics of the gas turbine provided in the embodiment of the present disclosure corresponds to the method for determining the component characteristics of the gas turbine provided in the foregoing several embodiments, the implementation of the method for determining the component characteristics of the gas turbine provided in the embodiment of the present disclosure is also applicable to the device for determining the component characteristics of the gas turbine provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.
Fig. 4 is a schematic diagram of a component characteristic determining apparatus of a gas turbine according to the present disclosure, as shown in fig. 4, the component characteristic determining apparatus 400 of the gas turbine includes: a first acquisition module 410, a second acquisition module 420, a determination module 430, a third acquisition module 440, and a calculation module 450.
The first obtaining module 410 is configured to obtain a transformation policy set, and select a target transformation policy from the transformation policy set.
The second obtaining module 420 is configured to perform migration transformation on the component characteristic curve of the existing gas turbine based on the target transformation strategy to obtain an initial migration coefficient matrix of the component characteristic of the gas turbine to be estimated.
The determining module 430 is configured to establish a simulation model of the gas turbine component to be estimated based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine, and determine an objective function of the simulation model of the gas turbine component to be estimated.
The third obtaining module 440 is configured to obtain full condition data of the gas turbine to be estimated, determine a simulation loss value of the objective function based on the full condition data, the simulation model of the gas turbine to be estimated, and the constraint optimization algorithm, and obtain a target migration coefficient matrix corresponding to the simulation loss value.
And the calculating module 450 is used for determining the component characteristic data of the gas turbine to be estimated based on the target transformation strategy, the target migration coefficient matrix and the component characteristic data of the existing gas turbine in response to the simulation loss value being smaller than the loss threshold value.
In an embodiment of the disclosure, the determining module 430 is further configured to: establishing at least one component model based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine; and combining the component models to generate a simulation model of the gas turbine component to be estimated.
In an embodiment of the disclosure, the second obtaining module 420 is further configured to: and carrying out migration transformation on a compressor flow characteristic line, a turbine flow characteristic line, a compressor efficiency characteristic line and a turbine efficiency characteristic line of the existing gas turbine based on quadratic polynomial migration to generate an initial migration coefficient matrix of the characteristic lines of the gas turbine to be estimated under the specific rotating speed and specific inlet guide vane opening data.
In an embodiment of the disclosure, the third obtaining module 440 is further configured to: acquiring second IGV opening data in the component characteristic curve; and determining third IGV opening degree data based on the first IGV opening degree data and the second IGV opening degree data, and replacing the third IGV opening degree data with the first IGV opening degree data in the full-operating-condition data.
In an embodiment of the disclosure, the determining module 430 is further configured to: respectively determining the outlet temperature of a gas compressor, the outlet temperature of a turbine, the output power and the loss function of the flow balance of the gas turbine of a simulation model of a gas turbine component to be estimated; determining a target function based on a loss function of the outlet temperature of the gas compressor, a loss function of the outlet temperature of the turbine, a loss function of the output power, a loss function of the flow balance of the gas turbine and a preset weight; wherein, the objective function is determined by adopting the following formula:
Figure BDA0003686595030000221
wherein
Figure BDA0003686595030000222
For the compressor outlet temperature calculated by the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000223
for the ith input the actual measured compressor outlet temperature,
Figure BDA0003686595030000224
is the maximum measure of the compressor outlet temperature,
Figure BDA0003686595030000225
for the turbine outlet temperature calculated for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000226
for the ith input the actual measured turbine outlet temperature,
Figure BDA0003686595030000227
is the maximum measure of the turbine outlet temperature,
Figure BDA0003686595030000228
for the output power calculated for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA0003686595030000229
actual measured output power for the ith input, P Max Is the maximum measure of the output power,
Figure BDA00036865950300002210
for the compressor outlet flow calculated by the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA00036865950300002211
for the amount of fuel of the ith input,
Figure BDA00036865950300002212
for the turbine inlet flow calculated by a turbine flow characteristic line for the ith input to the simulation model of the gas turbine component to be estimated,
Figure BDA00036865950300002213
for the maximum estimated turbine inlet flow, α, β, γ, λ are weighting coefficients.
In an embodiment of the disclosure, the calculation module 450 is further configured to: in response to the simulated loss value being greater than or equal to the loss threshold; adjusting a constraint optimization algorithm and recalculating a simulation loss value; and determining the component characteristics of the gas turbine to be estimated based on the target transformation strategy, the target migration coefficient matrix, the component characteristics of the existing gas turbine and the adjusted constraint optimization algorithm in response to the adjusted simulation loss value being smaller than the loss threshold value.
In an embodiment of the disclosure, the calculation module 450 is further configured to: in response to the recalculated simulated loss value being greater than or equal to the loss threshold; and re-determining a target transformation strategy from the transformation strategies which are not adopted in the transformation strategy set, and re-establishing the simulation model of the gas turbine component to be estimated based on the re-determined target transformation strategy.
In order to implement the foregoing embodiment, an embodiment of the present disclosure further provides an electronic device 500, as shown in fig. 5, where the electronic device 500 includes: the processor 501 and the memory 502 are communicatively connected, and the memory 502 stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor 501 to implement the method for determining the characteristic of the component of the gas turbine according to the embodiment of the first aspect of the present disclosure.
In order to implement the above embodiments, the embodiments of the present disclosure also propose a non-transitory computer-readable storage medium storing computer instructions for causing a computer to implement the component characteristic determination method of a gas turbine as embodied in the first aspect of the present disclosure.
In order to implement the above embodiments, the embodiments of the present disclosure also propose a computer program product comprising a computer program which, when executed by a processor, implements a component characteristic determination method of a gas turbine as an embodiment of the first aspect of the present disclosure.
In the description of the present disclosure, it is to be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the present disclosure and to simplify the description, but are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the present disclosure.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present disclosure, "a plurality" means two or more unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
While embodiments of the present disclosure have been shown and described above, it will be understood that the above embodiments are exemplary and not to be construed as limiting the present disclosure, and that changes, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present disclosure.

Claims (18)

1. A method of determining a characteristic of a component of a gas turbine, comprising:
acquiring a transformation strategy set, and selecting a target transformation strategy from the transformation strategy set;
based on the target transformation strategy, carrying out migration transformation on the component characteristic curve of the existing gas turbine so as to obtain an initial migration coefficient matrix of the component characteristic of the gas turbine to be estimated;
establishing a simulation model of the gas turbine component to be estimated based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine, and determining an objective function of the simulation model of the gas turbine component to be estimated;
acquiring all-condition data of the gas turbine to be estimated, determining a simulation loss value of the objective function based on the all-condition data, a simulation model of the gas turbine component to be estimated and a constraint optimization algorithm, and acquiring a target migration coefficient matrix corresponding to the simulation loss value;
and in response to the simulation loss value being smaller than a loss threshold value, determining component characteristic data of the gas turbine to be estimated based on the target transformation strategy, the target migration coefficient matrix and the component characteristic data of the existing gas turbine.
2. The method of claim 1, wherein the building a gas turbine component simulation model to be estimated based on the initial migration coefficient matrix and the existing gas turbine component characteristic curve comprises:
establishing at least one component model based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine;
and combining the component models to generate the simulation model of the gas turbine component to be estimated.
3. The method of claim 1, wherein the performing a migration transformation on the component characteristic curve of the existing gas turbine to obtain an initial migration coefficient matrix of the component characteristic of the gas turbine to be estimated comprises:
and carrying out migration transformation on the compressor flow characteristic line, the turbine flow characteristic line, the compressor efficiency characteristic line and the turbine efficiency characteristic line of the existing gas turbine based on quadratic polynomial migration so as to generate the initial migration coefficient matrix of the characteristic lines of the gas turbine to be estimated under the specific rotating speed and specific inlet guide vane opening data.
4. The method of claim 1, wherein the full condition data comprises first Inlet Guide Vane (IGV) opening data for the existing gas turbine engine, the method further comprising:
acquiring second IGV opening data in the component characteristic curve;
and determining third IGV opening degree data based on the first IGV opening degree data and the second IGV opening degree data, and replacing the first IGV opening degree data in the full-operating-condition data with the third IGV opening degree data.
5. The method of claim 4, wherein the third IGV opening data is also determined using the following equation:
Figure FDA0003686595020000021
wherein, the IGV o Second IGV opening data being a characteristic curve of the component, said
Figure FDA0003686595020000022
Is the minimum value of the second IGV opening degree data, the
Figure FDA0003686595020000023
Is the maximum value of the second IGV opening degree data, the
Figure FDA0003686595020000024
Is the minimum value of the first IGV opening degree data, the
Figure FDA0003686595020000025
Is the maximum value of the first IGV opening degree data, the IGV t Is the third IGV.
6. The method of claim 1, wherein the determining an objective function of the simulation model of the gas turbine component to be estimated comprises:
respectively determining the gas compressor outlet temperature, the turbine outlet temperature, the output power and the loss function of the gas turbine flow balance of the gas turbine part simulation model to be estimated;
determining the target function based on the loss function of the outlet temperature of the compressor, the loss function of the outlet temperature of the turbine, the loss function of the output power, the loss function of the flow balance of the gas turbine and a preset weight;
wherein the objective function is determined using the following formula:
Figure FDA0003686595020000031
wherein said
Figure FDA0003686595020000032
The calculated compressor outlet temperature for the ith input to the simulation model of the gas turbine component to be estimated, the
Figure FDA0003686595020000033
Inputting the actual measured compressor outlet temperature for the ith
Figure FDA0003686595020000034
Is a maximum measure of the compressor outlet temperature, the
Figure FDA0003686595020000035
The turbine outlet temperature calculated for the ith input to the simulation model of the gas turbine component to be estimated, the
Figure FDA0003686595020000036
For the ith input, the actual measured turbine outlet temperature, said
Figure FDA0003686595020000037
Is the maximum measure of the turbine outlet temperature, said
Figure FDA0003686595020000038
Calculated output power for the ith input to the simulation model of the gas turbine component to be estimated, the
Figure FDA0003686595020000039
Actual measured output power for the ith input, P Max Is the maximum measure of output power, said
Figure FDA00036865950200000310
Simulation model for ith input to gas turbine component to be estimatedModeling the calculated compressor outlet flow, said
Figure FDA00036865950200000311
Is the amount of fuel of the ith input
Figure FDA00036865950200000312
The turbine inlet flow calculated by the turbine flow characteristic line is input to the ith simulation model of the gas turbine part to be estimated, and the flow is measured
Figure FDA00036865950200000313
For the estimated maximum turbine inlet flow, the α, β, γ, λ are weighting coefficients.
7. The method of claim 1, further comprising:
responding to the simulation loss value being larger than or equal to the loss threshold value, adjusting the constraint optimization algorithm, recalculating the simulation loss value, and acquiring a new target migration coefficient matrix;
and in response to the adjusted simulation loss value being smaller than a loss threshold value, determining the component characteristics of the gas turbine to be estimated based on the target transformation strategy, the adjusted target migration coefficient matrix, the component characteristics of the existing gas turbine and the adjusted constraint optimization algorithm.
8. The method of claim 7, further comprising:
in response to the recalculated simulated loss value being greater than or equal to the loss threshold;
and re-determining a target transformation strategy from the transformation strategies which are not adopted by the transformation strategy set, and re-establishing a simulation model of the gas turbine component to be estimated based on the re-determined target transformation strategy.
9. A component characteristic determination apparatus of a gas turbine, characterized by comprising:
the first acquisition module is used for acquiring a transformation strategy set and selecting a target transformation strategy from the transformation strategy set;
the second obtaining module is used for carrying out migration transformation on the component characteristic curve of the existing gas turbine based on the target transformation strategy so as to obtain an initial migration coefficient matrix of the component characteristic of the gas turbine to be estimated;
the determining module is used for establishing a simulation model of the gas turbine component to be estimated based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine, and determining an objective function of the simulation model of the gas turbine component to be estimated;
the third acquisition module is used for acquiring all-working-condition data of the gas turbine to be estimated, determining a simulation loss value of the objective function based on the all-working-condition data, the simulation model of the gas turbine component to be estimated and a constraint optimization algorithm, and acquiring a target migration coefficient matrix corresponding to the simulation loss value;
and the calculation module is used for responding to the simulation loss value being smaller than a loss threshold value, and determining the component characteristic data of the gas turbine to be estimated based on the target transformation strategy, the target migration coefficient matrix and the component characteristic data of the existing gas turbine.
10. The apparatus of claim 9, wherein the determining module is further configured to:
establishing at least one component model based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine;
and combining the component models to generate the simulation model of the gas turbine component to be estimated.
11. The apparatus of claim 9, wherein the second obtaining module is further configured to:
and carrying out migration transformation on the compressor flow characteristic line, the turbine flow characteristic line, the compressor efficiency characteristic line and the turbine efficiency characteristic line of the existing gas turbine based on quadratic polynomial migration so as to generate an initial migration coefficient matrix of the characteristic lines of the gas turbine to be estimated under the specific rotating speed and specific inlet guide vane opening data.
12. The apparatus of claim 11, wherein the third obtaining module is further configured to:
acquiring second IGV opening data in the component characteristic curve;
and determining third IGV opening degree data based on the first IGV opening degree data and the second IGV opening degree data, and replacing the first IGV opening degree data in the full-operating-condition data with the third IGV opening degree data.
13. The apparatus of claim 9, wherein the determining module is further configured to:
respectively determining the gas compressor outlet temperature, the turbine outlet temperature, the output power and the loss function of the gas turbine flow balance of the gas turbine part simulation model to be estimated;
determining the target function based on the loss function of the outlet temperature of the compressor, the loss function of the outlet temperature of the turbine, the loss function of the output power, the loss function of the flow balance of the gas turbine and a preset weight;
wherein the objective function is determined using the following formula:
Figure FDA0003686595020000061
wherein said
Figure FDA0003686595020000062
The calculated compressor outlet temperature for the ith input to the simulation model of the gas turbine component to be estimated, the
Figure FDA0003686595020000063
Is as followsi inputs the actual measured compressor outlet temperature, said
Figure FDA0003686595020000064
Is a maximum measure of the compressor outlet temperature, the
Figure FDA0003686595020000065
The turbine outlet temperature calculated for the ith input to the simulation model of the gas turbine component to be estimated, the
Figure FDA0003686595020000066
For the ith input, the actual measured turbine outlet temperature, said
Figure FDA0003686595020000067
Is the maximum measure of the turbine outlet temperature, said
Figure FDA0003686595020000071
Calculated output power for the ith input to the simulation model of the gas turbine component to be estimated, the
Figure FDA0003686595020000072
Actual measured output power for the ith input, P Max Is the maximum measure of output power, said
Figure FDA0003686595020000073
The compressor outlet flow calculated for the ith input to the simulation model of the gas turbine component to be estimated, the
Figure FDA0003686595020000074
Is the amount of fuel of the ith input
Figure FDA0003686595020000075
Passing the turbine flow through the simulation model for the ith input to the gas turbine component to be estimatedTurbine inlet flow calculated from a characteristic curve, said
Figure FDA0003686595020000076
For the estimated maximum turbine inlet flow, the α, β, γ, λ are weighting coefficients.
14. The apparatus of claim 9, wherein the computing module is further configured to:
responding to the simulation loss value being larger than or equal to the loss threshold value, adjusting the constraint optimization algorithm, recalculating the simulation loss value, and acquiring a new target migration coefficient matrix;
and in response to the adjusted simulation loss value being smaller than a loss threshold value, determining the component characteristics of the gas turbine to be estimated based on the target transformation strategy, the adjusted target migration coefficient matrix, the component characteristics of the existing gas turbine and the adjusted constraint optimization algorithm.
15. The apparatus of claim 14, wherein the computing module is further configured to:
in response to the recalculated simulated loss value being greater than or equal to the loss threshold;
and re-determining a target transformation strategy from the transformation strategies which are not adopted by the transformation strategy set, and re-establishing a simulation model of the gas turbine component to be estimated based on the re-determined target transformation strategy.
16. An electronic device comprising a memory, a processor;
wherein the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory for implementing the method according to any one of claims 1 to 8.
17. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-8.
18. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-8.
CN202210647512.1A 2022-06-09 2022-06-09 Method and device for determining component characteristics of gas turbine and electronic equipment Pending CN114970364A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118013892A (en) * 2024-04-07 2024-05-10 杭州汽轮动力集团股份有限公司 Gas turbine state real-time monitoring method and device based on multiple physical fields

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118013892A (en) * 2024-04-07 2024-05-10 杭州汽轮动力集团股份有限公司 Gas turbine state real-time monitoring method and device based on multiple physical fields

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