Method, apparatus and electronic device for determining component characteristics of a gas turbine

Abstract

The present disclosure provides a method, device, electronic device and storage medium for determining component characteristics of a gas turbine, relating to the technical field of gas turbines. The method includes: obtaining an initial migration coefficient matrix based on existing component characteristic curves; establishing a simulation model of a component of the gas turbine to be estimated based on the migration coefficient matrix and the existing component characteristic curves, and determining an objective function; determining a simulation loss value based on the full operating condition data of the gas turbine to be estimated, the component simulation model and a constrained optimization algorithm, and obtaining the migration coefficient matrix; when the simulation loss value is less than a loss threshold, determining component characteristic data based on the above data. By establishing a simulation model of a component of the gas turbine to be estimated to determine whether the component simulation model matches the operating data of the gas turbine to be estimated, thus, based on the component characteristics of an existing gas turbine and the operating data of the gas turbine to be estimated, the component characteristic data of the gas turbine to be estimated is determined. This method is more operable, more efficient and more accurate.

Description

Technical Field

[0001] The present disclosure relates to the technical field of gas turbines, and particularly to a method, device, electronic device, and storage medium for determining component characteristics of a gas turbine. Background Art

[0002] The component characteristics of a gas turbine, namely the compressor flow characteristic curve, the compressor efficiency characteristic curve, the turbine flow characteristic curve, and the turbine efficiency characteristic curve, are important parameters for analyzing the performance of a gas turbine and establishing a method for the mechanism model of a gas turbine.

[0003] In the current technology, the main way to obtain the component characteristics of a gas turbine is to calculate the initial gas turbine component characteristic curve step by step through the design parameters provided by the gas turbine manufacturer, and then correct the component characteristic curve through experiments. This method has a high cost and the gas turbine manufacturer is reluctant to provide the specific design parameters of the gas turbine components.

[0004] Another method for obtaining the component characteristics of a gas turbine is a method for obtaining the gas turbine component characteristic curve based on an elliptic equation. This method assumes that the component characteristics of the gas turbine are in an elliptical shape. First, the initial elliptic equation is used to represent the component characteristics of the gas turbine, and then by defining the rotation, scaling, and translation transformation coefficients of the elliptic equation, and finally, the transformation coefficients are corrected through steady-state data. The component characteristics of the gas turbine cannot be fully represented by an elliptic curve. Even if the ellipse is scaled, translated, and rotated, the transformed curve is still an elliptic curve. The error is large when representing the component characteristics of the gas turbine in the full range with an elliptic curve, and this method cannot be applied to the case of different IGV opening degrees.

[0005] Disclosure Content

[0006] The present disclosure aims to at least partly solve one of the technical problems in the related art.

[0007] To this end, one object of the present disclosure is to propose a method for determining component characteristics of a gas turbine.

[0008] The second object of the present disclosure is to propose a device for determining component characteristics of a gas turbine.

[0009] The third object of the present disclosure is to propose an electronic device.

[0010] The fourth object of the present disclosure is to propose a non-transitory computer-readable storage medium.

[0011] The fifth object of the present disclosure is to propose a computer program product.

[0012] To achieve the above object, an embodiment of the first aspect of the present disclosure provides a method for determining component characteristics of a gas turbine, including: obtaining a set of transformation strategies, and selecting a target transformation strategy from the set of transformation strategies; based on the target transformation strategy, performing a migration transformation on the component characteristic curve of an existing gas turbine to obtain an initial migration coefficient matrix of the component characteristics of the gas turbine to be estimated; based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine, establishing a simulation model of the component of the gas turbine to be estimated, and determining an objective function of the simulation model of the component of the gas turbine to be estimated; obtaining full-condition data of the gas turbine to be estimated, and based on the full-condition data, the simulation model of the component of the gas turbine to be estimated, and a constrained optimization algorithm, determining a simulation loss value of the objective function, and obtaining a target migration coefficient matrix corresponding to the simulation loss value; in response to the simulation loss value being less than a loss threshold, 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.

[0013] According to an embodiment of the present disclosure, the method for determining component characteristics of a gas turbine further includes: establishing at least one component model based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine; combining the component models to generate a simulation model of the component of the gas turbine to be estimated.

[0014] According to an embodiment of the present disclosure, performing a migration transformation on the component characteristic curve of an existing gas turbine to obtain an initial migration coefficient matrix of the component characteristics of the gas turbine to be estimated includes: performing a 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 a quadratic polynomial migration to generate an initial migration coefficient matrix of the characteristic lines at a specific rotational speed and a specific inlet guide vane opening of the gas turbine to be estimated.

[0015] According to an embodiment of the present disclosure, the full-condition data includes the first inlet guide vane (IGV) opening data of the existing gas turbine, and the method further includes: obtaining the second IGV opening data in the component characteristic curve; determining the third IGV opening data based on the first IGV opening data and the second IGV opening data, and replacing the first IGV opening data in the full-condition data with the third IGV opening data.

[0016] According to an embodiment of the present disclosure, the third IGV opening data is determined using the following formula:

[0017]

[0018] where IGV o is the second IGV opening data of the component characteristic curve, is the minimum value of the second IGV opening data, is the maximum value of the second IGV opening data, is the minimum value of the first IGV opening data, is the maximum value of the first IGV opening data, IGV t is the third IGV.

[0019] According to an embodiment of the present disclosure, determining the objective function of the gas turbine component simulation model to be estimated includes: respectively determining the loss functions of the compressor outlet temperature, the turbine outlet temperature, the output power, and the gas turbine flow balance of the gas turbine component simulation model to be estimated; determining the objective function based on the loss function of the compressor outlet temperature, the loss function of the turbine outlet temperature, the loss function of the output power, and the loss function of the gas turbine flow balance and a preset weight; wherein, the following formula is used to determine the objective function:

[0020]

[0021] wherein is the compressor outlet temperature calculated by the i-th input to the gas turbine component simulation model to be estimated, is the actually measured compressor outlet temperature of the i-th input, is the maximum measured value of the compressor outlet temperature, is the turbine outlet temperature calculated by the i-th input to the gas turbine component simulation model to be estimated, is the actually measured turbine outlet temperature of the i-th input, is the maximum measured value of the turbine outlet temperature, is the output power calculated by the i-th input to the gas turbine component simulation model to be estimated, is the actually measured output power of the i-th input, P Max is the maximum measured value of the output power, is the compressor outlet flow calculated by the i-th input to the gas turbine component simulation model to be estimated, is the fuel quantity of the i-th input, is the turbine inlet flow calculated by the i-th input to the gas turbine component simulation model to be estimated through the turbine flow characteristic curve, is the estimated maximum value of the turbine inlet flow, α, β, γ, λ are weight coefficients.

[0022] According to an embodiment of the present disclosure, the method further includes: in response to the simulation loss value being greater than or equal to the loss threshold; adjusting the constraint optimization algorithm, recalculating the simulation loss value, and obtaining a new target transfer coefficient matrix; in response to the adjusted simulation loss value being less than the loss threshold, determining the component characteristics of the gas turbine to be estimated based on the target transformation strategy, the adjusted target transfer coefficient matrix, the component characteristics of the existing gas turbine, and the adjusted constraint optimization algorithm.

[0023] According to an embodiment of the present disclosure, the method further includes: in response to the recalculated simulation loss value being greater than or equal to the loss threshold; re-determining a target transformation strategy from the transformation strategies not adopted in 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.

[0024] To achieve the above object, an embodiment of the second aspect of the present disclosure provides a device for determining component characteristics of a gas turbine, including: a first acquisition module, configured to acquire a transformation strategy set and select a target transformation strategy from the transformation strategy set; a second acquisition module, configured to perform a 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 characteristics of the gas turbine to be estimated; a determination module, 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; a third acquisition module, configured to acquire full operating condition data of the gas turbine to be estimated, and determine a simulation loss value of the objective function based on the full operating condition data, the simulation model of the gas turbine component to be estimated, and a constrained optimization algorithm, and obtain a target migration coefficient matrix corresponding to the simulation loss value; a calculation module, configured to, in response to the simulation loss value being less than the loss threshold, determine 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.

[0025] According to an embodiment of the present disclosure, the determination module is further configured to: establish at least one component model based on the initial migration coefficient matrix and the component characteristic curve of the existing gas turbine; combine the component models to generate a simulation model of the gas turbine component to be estimated.

[0026] According to an embodiment of the present disclosure, the second acquisition module is further configured to: perform a 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 a quadratic polynomial migration to generate an initial migration coefficient matrix of the characteristic lines at a specific speed and a specific inlet guide vane opening degree of the gas turbine to be estimated.

[0027] According to an embodiment of the present disclosure, the third acquisition module is further configured to: acquire second IGV opening degree data in the component characteristic curve; determine third IGV opening degree data based on the first IGV opening degree data and the second IGV opening degree data, and replace the first IGV opening degree data in the full operating condition data with the third IGV opening degree data.

[0028] According to an embodiment of the present disclosure, the determination module is further configured to: respectively determine the loss functions of the compressor outlet temperature, the turbine outlet temperature, the output power, and the gas turbine flow balance of the gas turbine component simulation model to be estimated; determine an objective function based on the loss function of the compressor outlet temperature, the loss function of the turbine outlet temperature, the loss function of the output power, the loss function of the gas turbine flow balance, and a preset weight; wherein, the objective function is determined by using the following formula:

[0029]

[0030] wherein is the compressor outlet temperature calculated for the i-th input to the gas turbine component simulation model to be estimated, is the actually measured compressor outlet temperature for the i-th input, is the maximum measured value of the compressor outlet temperature, is the turbine outlet temperature calculated for the i-th input to the gas turbine component simulation model to be estimated, is the actually measured turbine outlet temperature for the i-th input, is the maximum measured value of the turbine outlet temperature, is the output power calculated for the i-th input to the gas turbine component simulation model to be estimated, is the actually measured output power for the i-th input, P Max is the maximum measured value of the output power, is the compressor outlet flow calculated for the i-th input to the gas turbine component simulation model to be estimated, is the fuel quantity for the i-th input, is the turbine inlet flow calculated for the i-th input to the gas turbine component simulation model through the turbine flow characteristic curve, is the estimated maximum value of the turbine inlet flow, and α, β, γ, and λ are weight coefficients.

[0031] According to an embodiment of the present disclosure, the calculation module is further configured to: in response to the simulation loss value being greater than or equal to the loss threshold; adjust the constrained optimization algorithm, recalculate the simulation loss value, and obtain a new target migration coefficient matrix; in response to the adjusted simulation loss value being less than the loss threshold, determine 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 constrained optimization algorithm.

[0032] According to an embodiment of the present disclosure, the calculation module is further configured to: in response to the recalculated simulation loss value being greater than or equal to the loss threshold; re-determine the target transformation strategy from the transformation strategies not adopted in the transformation strategy set, and re-establish the gas turbine component simulation model to be estimated based on the re-determined target transformation strategy.

[0033] To achieve the above object, an embodiment of the third aspect of the present disclosure provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the method for determining the component characteristics of a gas turbine according to the embodiment of the first aspect of the present disclosure.

[0034] To achieve the above object, an embodiment of the fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the method for determining the component characteristics of a gas turbine according to the embodiment of the first aspect of the present disclosure.

[0035] To achieve the above object, an embodiment of the fifth aspect of the present disclosure provides a computer program product, including a computer program, which is used to implement the method for determining the component characteristics of a gas turbine according to the embodiment of the first aspect of the present disclosure when executed by a processor. Description of the Drawings

[0036] Figure 1 is a schematic diagram of a method for determining the component characteristics of a gas turbine according to an embodiment of the present disclosure;

[0037] Figure 2 is a schematic diagram of another method for determining the component characteristics of a gas turbine according to an embodiment of the present disclosure;

[0038] Figure 3 is an overall flowchart of another method for determining the component characteristics of a gas turbine according to an embodiment of the present disclosure;

[0039] Figure 4 is a schematic diagram of a device for determining the component characteristics of a gas turbine according to an embodiment of the present disclosure;

[0040] Figure 5 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. Detailed Embodiments

[0041] The embodiments of the present disclosure will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present disclosure, but should not be construed as limiting the present disclosure.

[0042] Figure 1 is a schematic diagram of an exemplary embodiment of a method for determining the component characteristics of a gas turbine proposed by the present disclosure, as Figure 1 shown, the method for determining the component characteristics of the gas turbine includes the following steps:

[0043] S101. Obtain a set of transformation strategies and select a target transformation strategy from the set of transformation strategies.

[0044] Gas turbines have high energy comprehensive utilization efficiency. The fuel they use is clean energy, with the advantages of no soot and low emissions. Moreover, the equipment has high reliability and a high safety factor. At the same time, gas turbines operate flexibly, can adapt to various power generation demands, and have a robust regulation ability, including the abilities of rapid start-up, rapid loading, rapid load change, and deep peak shaving under the premise of meeting robustness, etc.

[0045] It should be noted that the component characteristics of gas turbines include compressor flow characteristic curves, compressor efficiency characteristic curves, turbine flow characteristic curves, turbine efficiency characteristic curves, etc. The compressor flow characteristic curves, compressor efficiency characteristic curves, turbine flow characteristic curves, and turbine efficiency characteristic curves of different models of gas turbines can be different. Correspondingly, the transformation algorithms for the component characteristic curves of each model of gas turbine can also be different. For example, the transformation algorithm can be a binomial migration algorithm, a trinomial migration algorithm, etc.

[0046] Taking an example through a formula, the flow characteristic of the compressor can be expressed by the following formula:

[0047]

[0048]

[0049] where p1 is the compressor inlet pressure, π c is the compressor pressure ratio, N is the compressor speed, T1 is the compressor inlet temperature, IGV is the compressor inlet guide vane opening, w c is the compressor flow rate, η c is the compressor efficiency.

[0050] The flow characteristic of the turbine can be expressed by the following formula:

[0051]

[0052]

[0053] where p3 is the turbine inlet pressure, π t is the turbine expansion ratio, N is the compressor speed, T3 is the turbine inlet temperature, w t is the turbine flow rate, η t is the turbine efficiency.

[0054] In the embodiments of the present disclosure, the transformation strategy set may be a set of different migration algorithm combinations for each gas turbine component. These algorithm combinations may include the same algorithm or different algorithms, without any limitation here, and specifically need to be set according to actual needs.

[0055] In the embodiments of the present disclosure, selecting a target transformation strategy from the transformation strategy set may be random selection or selection according to certain rules.

[0056] Optionally, the transformation strategy may be selected based on the model of the gas turbine. The same gas turbine model or similar gas turbine models may select the same transformation strategy.

[0057] Optionally, the transformation strategy may also be selected based on the main function of the gas turbine, that is, gas turbines with different functions may have corresponding transformation strategies.

[0058] S102. Based on the target transformation strategy, perform migration transformation on the component characteristic curves of the existing gas turbine to obtain the initial migration coefficient matrix of the component characteristics of the gas turbine to be estimated.

[0059] After obtaining the target transformation strategy, the component characteristic curves of the existing gas turbine can be subjected to migration transformation based on the target transformation strategy to obtain the initial migration coefficient matrix of the component characteristics of the gas turbine to be estimated. It should be noted that the gas turbine to be estimated and the existing gas turbine are gas turbines of the same model or similar models. Optionally, the gas turbine to be estimated and the existing gas turbine may also be gas turbines that can achieve similar functions or the same function.

[0060] Through the method of migration transformation, the component characteristic curves of the existing gas turbine are transformed into an initial migration coefficient matrix close to the component characteristics of the gas turbine to be estimated, so as to achieve the purpose of predicting the characteristics of the gas turbine to be estimated.

[0061] In the embodiments of the present disclosure, the existing gas turbine component characteristic lines are usually represented by data tables. When used, the characteristic data outside the data table can be obtained by interpolating the data table. For the compressor component characteristics, the corresponding relationships between the pressure ratio and the reduced flow rate, and the pressure ratio and the efficiency are given under different reduced speeds and different IGV opening combinations; for the turbine component characteristics, the corresponding relationships between the expansion ratio and the reduced flow rate, and the expansion ratio and the efficiency are given under different reduced speeds. When performing migration transformation on the gas turbine component characteristic lines, polynomial nonlinear transformation is adopted for a single characteristic line, which can retain the basic characteristics of the original component characteristic line and perform transformation on the basis of this characteristic line to obtain the predicted component characteristic data.

[0062] S103. Based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine, establish a simulation model for the components of the gas turbine to be estimated, and determine the objective function of the simulation model for the components of the gas turbine to be estimated.

[0063] After obtaining the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine, a simulation model for the components of the gas turbine to be estimated can be established. In the embodiments of the present disclosure, a complete simulation model for the components of the gas turbine to be estimated can be established through the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine.

[0064] Optionally, a training model for the characteristics of the gas turbine components can also be formed by separately establishing a compressor model, a combustor model, and a turbine model and then combining them.

[0065] It can be understood that the simulation model for the components of the gas turbine to be estimated does not necessarily include all the components of the gas turbine to be estimated. It can only simulate one of the components to obtain the simulation results of the component, or can be combined by several components. There is no specific limitation here, and it can be specifically designed according to actual needs.

[0066] It should be noted that the inputs of the training model are the compressor inlet temperature, compressor inlet pressure, compressor pressure ratio, fuel quantity, rotational speed, IGV opening, etc., and the outputs of the training model are the compressor outlet temperature, compressor outlet flow rate, turbine outlet temperature, turbine inlet flow rate calculated by the turbine flow rate characteristic curve, output power, etc. There is no specific limitation here, and it can be specifically set according to the actual situation.

[0067] The goal of transfer learning is a multi-objective problem. On the one hand, it reflects the deviation between the key parameters of the gas turbine calculated by the model and the actually measured parameters, that is, the relative deviation of the compressor outlet temperature, the relative deviation of the turbine outlet temperature, and the relative deviation. On the other hand, it also needs to reflect the self-balancing problem within the gas turbine model itself, that is, the matching between the compressor flow rate characteristic and the turbine flow rate characteristic, so as to determine the objective function of the simulation model for the components of the gas turbine to be estimated.

[0068] Optionally, the multi-objective can also be transformed into a single-objective problem by using the method of weight weighting, so as to determine the objective function of the simulation model for the components of the gas turbine to be estimated, which can increase the accuracy of the objective function.

[0069] S104. Obtain the full operating condition data of the gas turbine to be estimated, and based on the full operating condition data, the simulation model for the components of the gas turbine to be estimated, and the constrained optimization algorithm, determine the simulation loss value of the objective function, and obtain the target migration coefficient matrix corresponding to the simulation loss value.

[0070] In the embodiments of the present disclosure, the full operating condition data of the gas turbine to be estimated may be the whole process operating data of the gas turbine from ignition, warm-up, speed increase, full speed no-load, grid connection, load increase, full load, load decrease, disconnection, and shutdown at different ambient temperatures. The required operating data includes compressor inlet temperature, compressor inlet pressure, compressor outlet temperature, compressor pressure ratio, turbine outlet temperature, turbine outlet pressure, rotational speed, power, IGV opening, and total fuel quantity.

[0071] It should be noted that the method for obtaining the full operating condition data of the gas turbine to be estimated may be to record the working data of an existing gas turbine during actual operation and perform targeted screening on the working data to generate the full operating condition data.

[0072] Optionally, it may also be the data obtained through simulation experiments based on the component simulation model of the gas turbine to be estimated.

[0073] Optionally, it may also be to analyze and process the past operating data of the gas turbine to be estimated to determine the full operating condition data.

[0074] In the embodiments of the present disclosure, after obtaining the full operating condition data, since the actually collected operating data contains signal noise, the signal noise needs to be processed. For example, a Gaussian filter can be used to filter the operating data.

[0075] In the embodiments of the present disclosure, based on the constraint conditions and the objective function, a sequential quadratic programming algorithm is used to solve the constrained nonlinear problem to obtain the target transfer coefficient matrix when the objective function is minimized. It should be noted that this constrained optimization algorithm may be to solve the constrained nonlinear problem through the sequential quadratic programming algorithm to obtain the target transfer coefficient matrix when the objective function is minimized. Optionally, a particle swarm algorithm or a genetic algorithm can also be used. When selecting the optimization algorithm, the algorithm can be selected according to the actual optimization accuracy and optimization speed to avoid the optimization problem falling into a local optimum.

[0076] Apply the optimized target transfer coefficient matrix to the simulation model of the gas turbine to be estimated, simulate and calculate the key parameters of the gas turbine, including compressor outlet temperature, compressor outlet pressure, turbine outlet temperature, and power, and compare the simulation results with the operating data to calculate the steady-state deviation of each key parameter. Then compare the simulation data with the true output data of the full operating condition data, and calculate the simulation loss value based on the preset loss function.

[0077] The loss function in the embodiments 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, etc., and can be specifically selected according to actual needs, and no restrictions are made here.

[0078] S105. In response to the simulation loss value being less than the loss threshold, determine 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.

[0079] In the embodiment of the present disclosure, when the obtained simulation loss value is less than the loss threshold, it can be considered that the simulation model of the components of the gas turbine to be estimated is applicable to the data simulation of the gas turbine to be estimated. Similarly, the objective function and the target transformation strategy that make up the simulation model of the components of the gas turbine to be estimated are also applicable to the component characteristic data of the existing gas turbine. The component characteristic data of the existing gas turbine can be calculated through the constraint optimization algorithm by means of the objective function and the target transformation strategy to obtain the component characteristic data of the gas turbine to be estimated. Through this reverse conversion of data, it is possible to determine the component characteristic data of the gas turbine to be estimated based on the component characteristic data of the existing gas turbine.

[0080] In the embodiment of the present disclosure, first, obtain a set of transformation strategies, and select a target transformation strategy from the set of transformation strategies. Then, perform a migration transformation on the component characteristic curves of the existing gas turbine based on the target transformation strategy to obtain the initial migration coefficient matrix of the component characteristics of the gas turbine to be estimated. Then, establish a simulation model of the components of the gas turbine to be estimated based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine, and determine the objective function of the simulation model of the components of the gas turbine to be estimated. After that, obtain the full-condition data of the gas turbine to be estimated, and determine the simulation loss value based on the full-condition data, the simulation model of the components of the gas turbine to be estimated, and the constraint optimization algorithm. Determine the component characteristic data of the gas turbine to be estimated based on the target transformation strategy, the initial migration coefficient matrix, and the component characteristic data of the existing gas turbine. Thus, by performing a transformation on the component characteristic curves of the gas turbine, establishing a simulation model of the components of the gas turbine to be estimated, and performing a simulation comparison on the gas turbine to be estimated, if the simulation model of the components of the gas turbine to be estimated is applicable to the operation data of the gas turbine to be estimated, the component characteristic data of the gas turbine to be estimated can be obtained through reverse calculation of the component characteristic data of the existing gas turbine by means of the objective function and the target transformation strategy. Compared with the traditional method of determining component characteristic data, the determination method of the present disclosure is more accurate, more operable, and more efficient.

[0081] In an embodiment of the present disclosure, since the fully open and fully closed angles of the IGV of different gas turbines are different, it is necessary to convert the IGV opening in the existing compressor component line to the IGV opening range in the gas turbine to be estimated. The full-condition data includes obtaining the first inlet guide vane (IGV) opening data of the existing gas turbine, obtaining the second IGV opening data in the component characteristic curve, determining the third IGV opening data based on the first IGV opening data and the second IGV opening data, and replacing the first IGV opening data in the full-condition data with the third IGV opening data. The conversion can be performed through the following formula:

[0082]

[0083] Among them, IGV o is the second IGV opening data of the component characteristic curve, is the minimum value of the second IGV opening data, is the maximum value of the second IGV opening data, is the minimum value of the first IGV opening data, is the maximum value of the first IGV opening data, IGV t is the third IGV.

[0084] In the above embodiment, based on the target transformation strategy, the component characteristic curves of the existing gas turbine are migrated and transformed to obtain the initial migration coefficient matrix of the component characteristics of the gas turbine to be estimated. It can also be passed through Figure 2 For further explanation, it includes:

[0085] S201. Based on the quadratic polynomial migration, the compressor flow characteristic line, turbine flow characteristic line, compressor efficiency characteristic line, and turbine efficiency characteristic line of the existing gas turbine are migrated and transformed to generate the initial migration coefficient matrix of the characteristic lines at the specific rotational speed and specific inlet guide vane opening data of the gas turbine to be estimated.

[0086] Using the quadratic polynomial migration for the non-linear transformation of a single characteristic line can retain the basic characteristics of the original component characteristic line, better analyze and calculate the characteristic parameters of the gas turbine, and provide a basis for establishing a more accurate component simulation model of the gas turbine to be estimated in the future.

[0087] In an embodiment of the present disclosure, the migration formula of the compressor flow characteristic line is as follows:

[0088]

[0089] Among them, g_c1 is the compressor flow characteristic line of the existing gas turbine at the same rotational speed N1 and IGV1; g_c n is the compressor flow characteristic line of the existing gas turbine at the same rotational speed N n and IGV n ; g_c′1 is the compressor flow characteristic line of the gas turbine to be estimated at the same rotational speed N1 and IGV′1; g_c′ n is the compressor flow characteristic line of the gas turbine to be estimated at the same rotational speed N n and IGV′ n . A_c, B_c, and C_c are the migration coefficient matrices, and each characteristic line corresponds to a set of migration parameters (A_c i , B_c i , C_c i ).

[0090] In one embodiment of the present disclosure, the migration formula of the compressor efficiency characteristic line is as follows:

[0091]

[0092] where f_c1 is the compressor efficiency characteristic line of the existing gas turbine at the same speed N1 and IGV1; f_c n is the compressor efficiency characteristic line of the existing gas turbine at the same speed N n and IGV n ; f_c′1 is the compressor efficiency characteristic line of the gas turbine to be estimated at the same speed N1 and IGV′1; f_c′ n is the compressor efficiency characteristic line of the gas turbine to be estimated at the same speed N n and IGV′ n . D_c, E_c, and F_c are migration coefficient matrices, and each characteristic line corresponds to a set of migration parameters (D_c i , E_c i , F_c i ).

[0093] In one embodiment of the present disclosure, the migration formula of the turbine flow characteristic line is as follows:

[0094]

[0095] where g_t1 is the turbine flow characteristic line of the existing gas turbine at the same speed N1; g_t n is the turbine flow characteristic line of the existing gas turbine at the same speed N n ; g_t′1 is the turbine flow characteristic line of the gas turbine to be estimated at the same speed N1; g_t′ n is the turbine flow characteristic line of the gas turbine to be estimated at the same speed N n . A_t, B_t, and C_t are migration coefficient matrices, and each characteristic line corresponds to a set of migration parameters (A_t i , B_t i , C_t i ).

[0096] In one embodiment of the present disclosure, the migration formula of the turbine efficiency characteristic line is as follows:

[0097]

[0098] where f_t1 is the turbine efficiency characteristic line of the existing gas turbine at the same speed N1; f_t n is the turbine efficiency characteristic line of the existing gas turbine at the same speed N n ; f_t′1 is the turbine efficiency characteristic line of the gas turbine to be estimated at the same speed N1; f_t′ nFor the gas turbine to be estimated, at a constant speed N n The turbine efficiency characteristic curve under this condition. D_t, E_t, and F_t are migration coefficient matrices, and each characteristic curve corresponds to a set of migration parameters (D_t i , E_t i , F_t i ).

[0099] In the above embodiments, to determine the objective function of the simulation model of the gas turbine component to be estimated, it can be obtained based on the following steps: respectively determine the loss functions of the compressor outlet temperature, turbine outlet temperature, output power, and gas turbine flow balance of the simulation model of the gas turbine component to be estimated, and determine the objective function based on the loss function of the compressor outlet temperature, the loss function of the turbine outlet temperature, the loss function of the output power, the loss function of the gas turbine flow balance, and the preset weights. It should be noted that the preset weights are set in advance and can be adjusted according to actual needs. The objective function can also be determined using the following formula:

[0100]

[0101] Where is the compressor outlet temperature calculated for the i-th input to the simulation model of the gas turbine component to be estimated, is the actually measured compressor outlet temperature for the i-th input, is the maximum measured value of the compressor outlet temperature, is the turbine outlet temperature calculated for the i-th input to the simulation model of the gas turbine component to be estimated, is the actually measured turbine outlet temperature for the i-th input, is the maximum measured value of the turbine outlet temperature, is the output power calculated for the i-th input to the simulation model of the gas turbine component to be estimated, is the actually measured output power for the i-th input, P Max is the maximum measured value of the output power, is the compressor outlet flow calculated for the i-th input to the simulation model of the gas turbine component to be estimated, is the fuel quantity for the i-th input, is the turbine inlet flow calculated for the i-th input to the simulation model of the gas turbine component to be estimated through the turbine flow characteristic curve, is the estimated maximum value of the turbine inlet flow, and α, β, γ, and λ are weight coefficients.

[0102] It should be noted that during the optimization training, to avoid the component characteristics after migration from violating the actual physical process, the data of the component characteristics are constrained to ensure that the migration matrix coefficients are within a reasonable range. For example, the constraint equation is expressed as follows:

[0103] constraint1 = max(g_c′) ≤ Const1

[0104] constraint2 = min(g_c′) ≥ Const2

[0105] constraint3 = max(f_c′) ≤ Const3

[0106] constraint4 = min(f_c′) ≥ Const4

[0107] constraint5 = max(g_t′) ≤ Const5

[0108] constraint6 = min(g_t′) ≥ Const6

[0109] constraint7 = max(f_t′) ≤ Const7

[0110] constraint8 = min(f_t′) ≥ Const8

[0111] constraint9 = fcool_cor ≤ Const9

[0112] constraint10 = fcool_cor ≥ Const10

[0113] Among them, constraint1 and constraint2 are the constraints on the compressor flow rate; constraint3 and constraint4 are the constraints on the compressor efficiency; constraint5 and constraint6 are the constraints on the turbine flow rate; constraint7 and constraint8 are the constraints on the turbine efficiency; constraint9 and constraint10 are the constraints on the cooling air volume of the first stage of the turbine. It should be noted that Const1, Const2, Const3, Const4, Const5, Const6, Const7, Const8,

[0114] Const9, and Const10 are set in advance and can be changed according to the actual design requirements, and no specific limitations are made here.

[0115] In another embodiment of the present disclosure, the objective function can also optimize the component characteristic curves of the gas turbine to be estimated item by item. First, the optimization of the compressor efficiency characteristic curve can be carried out, then the joint optimization of the compressor flow characteristic curve and the turbine efficiency characteristic curve can be carried out, and finally the optimization of the turbine flow characteristic curve can be carried out. Compared with the above method for determining the objective function, the objective function generated by this method has higher accuracy and faster optimization speed.

[0116] Taking the power deviation as an example of the objective function, the objective function can be determined by the following formula:

[0117]

[0118] Wherein, is the output power calculated by the model at the i-th input, is the actually measured output power at the i-th input, and P Max is the maximum measured value of the output power.

[0119] Correspondingly, the constraint conditions also need to be transformed, where

[0120]

[0121]

[0122]

[0123] Const11 , Const12 , Const13 are set in advance and can be transformed according to actual design requirements, and no specific limitations are made here.

[0124] When optimizing the compressor efficiency characteristic curve, a training model for the compressor efficiency characteristic curve needs to be established. The inputs of the model are the compressor inlet temperature, the compressor inlet pressure, the rotational speed, the IGV opening, and the pressure ratio, and the output of the model is the compressor outlet temperature. The objective function can be determined by the following formula:

[0125]

[0126] Wherein, is the compressor outlet temperature calculated by the simulation model of the component of the gas turbine to be estimated at the i-th input, is the actually measured compressor outlet temperature at the i-th input, is the maximum measured value of the compressor outlet temperature. It should be noted that the constraint function of this objective function is only applicable to the constraint conditions related to the compressor efficiency characteristic curve.

[0127] When conducting the combined optimization of the compressor flow characteristic curve and the turbine flow characteristic curve, a training model for the compressor flow characteristic curve and the turbine efficiency characteristic curve needs to be established. The difference from the original training model lies in that when modeling the turbine, the inlet flow rate of the turbine uses the compressor outlet flow rate plus the fuel flow rate, rather than the turbine inlet flow rate obtained from the turbine characteristic curve. The objective function can be determined using the following formula:

[0128]

[0129] Among them, is the turbine outlet temperature calculated by the simulation model of the i-th input to the gas turbine component to be estimated, is the actually measured turbine outlet temperature of the i-th input, is the maximum measured value of the turbine outlet temperature, is the output power calculated by the model at the i-th input, is the actually measured output power at the i-th input, P Max is the maximum measured value of the output power. It should be noted that the constraint function of this objective function is only applicable to the constraint conditions related to the compressor flow characteristic curve and the turbine efficiency characteristic curve.

[0130] When optimizing the turbine flow characteristic curve, a training model for the turbine flow characteristic curve needs to be established. First, use the optimized compressor flow characteristic curve to calculate the compressor outlet flow rate at each pressure ratio. The training model takes the compressor pressure ratio, the compressor outlet flow rate, and the fuel flow rate as inputs, and the turbine inlet flow rate obtained from the turbine characteristic curve as the output. The objective function can be determined using the following formula:

[0131]

[0132] Among them, is the compressor outlet flow rate calculated by the simulation model of the i-th input to the gas turbine component to be estimated, is the fuel flow rate of the i-th input, is the turbine inlet flow rate calculated by the simulation model of the i-th input to the gas turbine component to be estimated through the turbine flow characteristic curve, is the maximum value of the estimated turbine inlet flow rate. The constraint function of this objective function is only applicable to the constraint conditions related to the turbine flow characteristic curve.

[0133] Furthermore, when the simulation loss value obtained after inputting the full operating condition data into the simulation model of the gas turbine component to be estimated is greater than or equal to the loss threshold, at this time, it can be considered that the simulation model of the gas turbine component to be estimated is not applicable to the operating data of the gas turbine to be estimated. The constraint optimization algorithm can be adjusted to re-obtain the simulation loss value, and continue to compare the simulation loss value with the loss threshold.

[0134] In response to the recalculated simulation loss value being greater than the loss threshold, re-determine the target transformation strategy from the transformation strategies not adopted in the transformation strategy set, and re-establish the simulation model of the gas turbine component to be estimated based on the re-determined target transformation strategy. Until the training is completed, the simulation loss value is less than the loss threshold.

[0135] Figure 3 This is a schematic diagram of the overall process of the embodiment of the present disclosure. As Figure 3 shown, the method includes: first, obtaining a set of transformation strategies, and selecting a target transformation strategy from the set of transformation strategies; then, performing a migration transformation on the component characteristic curves of the existing gas turbine based on the target transformation strategy to obtain an initial migration coefficient matrix of the component characteristics 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 curves of the existing gas turbine, and determining the objective function of the simulation model of the gas turbine component to be estimated; then, obtaining the full operating condition data of the gas turbine to be estimated, and determining the simulation loss value of the objective function based on the full operating condition data and the simulation model of the gas turbine component to be estimated. In response to the simulation loss value being less than the loss threshold, determine 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 greater than or equal to the loss threshold, adjust the constrained optimization algorithm, recalculate the simulation loss value, and obtain a new target migration coefficient matrix. In response to the adjusted simulation loss value being less than the loss threshold, determine 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 constrained optimization algorithm; in response to the recalculated simulation loss value being greater than the loss threshold, re-determine the target transformation strategy from the transformation strategies not adopted in the transformation strategy set, and re-establish the simulation model of the gas turbine component to be estimated based on the re-determined target transformation strategy. Repeat the above steps until the simulation loss value is less than the loss threshold.

[0136] Corresponding to the methods for determining the component characteristics of a gas turbine provided in the above several embodiments, an embodiment of the present disclosure also provides a device for determining the component characteristics of a gas turbine. Since the device for determining the component characteristics of a gas turbine provided in the embodiment of the present disclosure corresponds to the methods for determining the component characteristics of a gas turbine provided in the above several embodiments, the implementation manners of the above methods for determining the component characteristics of a gas turbine are also applicable to the device for determining the component characteristics of a gas turbine provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0137] Figure 4 This is a schematic diagram of a device for determining the component characteristics of a gas turbine proposed by the present disclosure. As Figure 4As shown in the figure, the component characteristic determination device 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.

[0138] Among them, the first acquisition module 410 is used to acquire a set of transformation strategies and select a target transformation strategy from the set of transformation strategies.

[0139] The second acquisition module 420 is used to perform a migration transformation on the component characteristic curves of the existing gas turbine based on the target transformation strategy to obtain an initial migration coefficient matrix of the component characteristics of the gas turbine to be estimated.

[0140] The determination module 430 is used to establish a simulation model of the components of the gas turbine to be estimated based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine, and determine the objective function of the simulation model of the components of the gas turbine to be estimated.

[0141] The third acquisition module 440 is used to acquire the full operating condition data of the gas turbine to be estimated, and determine the simulation loss value of the objective function based on the full operating condition data, the simulation model of the components of the gas turbine to be estimated, and the constraint optimization algorithm, and obtain the target migration coefficient matrix corresponding to the simulation loss value.

[0142] The calculation module 450 is used to, in response to the simulation loss value being less than the loss threshold, determine 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.

[0143] In an embodiment of the present disclosure, the determination module 430 is further used to: establish at least one component model based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine; combine the component models to generate a simulation model of the components of the gas turbine to be estimated.

[0144] In an embodiment of the present disclosure, the second acquisition module 420 is further used to: perform a 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 to generate an initial migration coefficient matrix of the characteristic lines under specific rotational speed and specific inlet guide vane opening data of the gas turbine to be estimated.

[0145] In an embodiment of the present disclosure, the third acquisition module 440 is further used to: acquire the second IGV opening data in the component characteristic curve; determine the third IGV opening data based on the first IGV opening data and the second IGV opening data, and replace the first IGV opening data in the full operating condition data with the third IGV opening data.

[0146] In one embodiment of the present disclosure, the determination module 430 is further configured to: respectively determine the loss functions of the compressor outlet temperature, the turbine outlet temperature, the output power, and the gas turbine flow balance of the gas turbine component simulation model to be estimated; determine an objective function based on the loss function of the compressor outlet temperature, the loss function of the turbine outlet temperature, the loss function of the output power, the loss function of the gas turbine flow balance, and a preset weight; wherein, the objective function is determined by using the following formula:

[0147]

[0148] Wherein is the compressor outlet temperature calculated for the i-th input to the gas turbine component simulation model to be estimated, is the actually measured compressor outlet temperature for the i-th input, is the maximum measured value of the compressor outlet temperature, is the turbine outlet temperature calculated for the i-th input to the gas turbine component simulation model to be estimated, is the actually measured turbine outlet temperature for the i-th input, is the maximum measured value of the turbine outlet temperature, is the output power calculated for the i-th input to the gas turbine component simulation model to be estimated, is the actually measured output power for the i-th input, P Max is the maximum measured value of the output power, is the compressor outlet flow calculated for the i-th input to the gas turbine component simulation model to be estimated, is the fuel quantity for the i-th input, is the turbine inlet flow calculated for the i-th input to the gas turbine component simulation model to be estimated through the turbine flow characteristic curve, is the estimated maximum value of the turbine inlet flow, and α, β, γ, and λ are weight coefficients.

[0149] In one embodiment of the present disclosure, the calculation module 450 is further configured to: in response to the simulation loss value being greater than or equal to the loss threshold; adjust the constrained optimization algorithm and recalculate the simulation loss value; in response to the adjusted simulation loss value being less than the loss threshold, determine 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 constrained optimization algorithm.

[0150] In one embodiment of the present disclosure, the calculation module 450 is further configured to: in response to the recalculated simulation loss value being greater than or equal to the loss threshold; re-determine the target transformation strategy from the transformation strategies not adopted in the transformation strategy set, and re-establish the gas turbine component simulation model to be estimated based on the re-determined target transformation strategy.

[0151] To implement the above embodiments, the embodiments of the present disclosure also propose an electronic device 500, as Figure 5 shown. The electronic device 500 includes: a processor 501 and a memory 502 communicatively connected to the processor. The memory 502 stores instructions executable by at least one processor. The instructions are executed by at least one processor 501 to implement the method for determining the component characteristics of a gas turbine as in the embodiments of the first aspect of the present disclosure.

[0152] To implement the above embodiments, the embodiments of the present disclosure also propose a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to implement the method for determining the component characteristics of a gas turbine as in the embodiments of the first aspect of the present disclosure.

[0153] To implement the above embodiments, the embodiments of the present disclosure also propose a computer program product, including a computer program, which implements the method for determining the component characteristics of a gas turbine as in the embodiments of the first aspect of the present disclosure when executed by a processor.

[0154] In the description of the present disclosure, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present disclosure and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present disclosure.

[0155] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the number of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present disclosure, "a plurality" means two or more, unless otherwise specifically defined.

[0156] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0157] Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for determining component characteristics of a gas turbine, characterized in that Including: Obtain a set of transformation strategies, and select a target transformation strategy from the set of transformation strategies; Based on the target transformation strategy, perform a migration transformation on the component characteristic curves of the existing gas turbine to obtain an initial migration coefficient matrix of the component characteristics of the gas turbine to be estimated; Based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine, establish a simulation model of the components of the gas turbine to be estimated, and determine the objective function of the simulation model of the components of the gas turbine to be estimated; Obtain the full operating condition data of the gas turbine to be estimated, and based on the full operating condition data, the simulation model of the components of the gas turbine to be estimated, and a constrained optimization algorithm, determine the simulation loss value of the objective function, and obtain the target migration coefficient matrix corresponding to the simulation loss value; In response to the simulation loss value being less than the loss threshold, based on the target transformation strategy, the target migration coefficient matrix, and the component characteristic data of the existing gas turbine, determine the component characteristic data of the gas turbine to be estimated; The performing a migration transformation on the component characteristic curves of the existing gas turbine to obtain an initial migration coefficient matrix of the component characteristics of the gas turbine to be estimated includes: Based on quadratic polynomial migration, perform a 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 to generate the initial migration coefficient matrix of the characteristic lines at specific speeds and specific inlet guide vane opening degrees of the gas turbine to be estimated.

2. The method according to claim 1, characterized in that, The establishing a simulation model of the components of the gas turbine to be estimated based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine includes: Establish at least one component model based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine; Combine the component models to generate the simulation model of the components of the gas turbine to be estimated.

3. The method according to claim 1, wherein The full operating condition data includes the first IGV opening data of the existing gas turbine, and the method further includes: Obtain the second IGV opening data in the component characteristic curves; Based on the first IGV opening data and the second IGV opening data, determine the third IGV opening data, and replace the first IGV opening data in the full operating condition data with the third IGV opening data.

4. The method according to claim 3, characterized in that, The third IGV opening data is also determined using the following formula: Among them, the is the second IGV opening data of the component characteristic curve, the is the minimum value of the second IGV opening data, the is the maximum value of the second IGV opening data, the is the minimum value of the first IGV opening data, the is the maximum value of the first IGV opening data, the is the third IGV opening data.

5. The method according to claim 1, characterized in that The determining the objective function of the simulation model of the components of the gas turbine to be estimated includes: Respectively determine the loss functions of the compressor outlet temperature, the turbine outlet temperature, the output power, and the gas turbine flow balance of the simulation model of the components of the gas turbine to be estimated; Based on the loss function of the compressor outlet temperature, the loss function of the turbine outlet temperature, the loss function of the output power, the loss function of the gas turbine flow balance, and a preset weight, determine the objective function; Wherein, the objective function is determined using the following formula: wherein the is the i th compressor outlet temperature input to the simulation model calculation of the gas turbine component to be estimated, the is the i th actually measured compressor outlet temperature, the is the maximum measured value of the compressor outlet temperature, the is the i th turbine outlet temperature input to the simulation model calculation of the gas turbine component to be estimated, the is the i th actually measured turbine outlet temperature, the is the maximum measured value of the turbine outlet temperature, the is the i th output power input to the simulation model calculation of the gas turbine component to be estimated, the is the i th actually measured output power, the is the maximum measured value of the output power, the is the i th compressor outlet flow rate input to the simulation model calculation of the gas turbine component to be estimated, the is the i th input fuel quantity, the is the i th turbine inlet flow rate input to the simulation model of the gas turbine component to be estimated calculated through the turbine flow characteristic curve, the is the estimated maximum value of the turbine inlet flow rate, the is the weight coefficient.

6. The method according to claim 1, characterized in that, The method further includes: In response to the simulation loss value being greater than or equal to the loss threshold, adjust the constrained optimization algorithm, recalculate the simulation loss value, and obtain a new target migration coefficient matrix; In response to the adjusted simulation loss value being less than the loss threshold, determine 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.

7. The method according to claim 6, wherein The method further includes: In response to the recalculated simulation loss value being greater than or equal to the loss threshold; Redetermine the target transformation strategy from the transformation strategies not adopted in the transformation strategy set, and re-establish the simulation model of the components of the gas turbine to be estimated based on the re-determined target transformation strategy.

8. A device for determining component characteristics of a gas turbine, characterized in that, It includes: A first acquisition module, configured to acquire a transformation strategy set and select a target transformation strategy from the transformation strategy set; A second acquisition module, configured to perform migration transformation on the component characteristic curves of the existing gas turbine based on the target transformation strategy to obtain an initial migration coefficient matrix of the component characteristics of the gas turbine to be estimated; A determination module, configured to establish a simulation model of the components of the gas turbine to be estimated based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine, and determine the objective function of the simulation model of the components of the gas turbine to be estimated; A third acquisition module, configured to acquire the full operating condition data of the gas turbine to be estimated, and determine the simulation loss value of the objective function based on the full operating condition data, the simulation model of the components of the gas turbine to be estimated, and the constraint optimization algorithm, and obtain the target migration coefficient matrix corresponding to the simulation loss value; A calculation module, configured to, in response to the simulation loss value being less than the loss threshold, determine 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; The second acquisition module is further configured to: Perform 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 to generate an initial migration coefficient matrix of the characteristic lines at specific rotational speeds and specific inlet guide vane opening degrees of the gas turbine to be estimated.

9. The device according to claim 8, characterized in that, The determination module is further configured to: Establish at least one component model based on the initial migration coefficient matrix and the component characteristic curves of the existing gas turbine; Combine the component models to generate the simulation model of the components of the gas turbine to be estimated.

10. The device according to claim 8, characterized in that, The third acquisition module is further configured to: Acquire the second IGV opening degree data in the component characteristic curves; Determine the third IGV opening degree data based on the first IGV opening degree data and the second IGV opening degree data, and replace the first IGV opening degree data in the full operating condition data with the third IGV opening degree data.

11. The device according to claim 8, characterized in that, The determination module is further configured to: Respectively determine the loss functions of the compressor outlet temperature, the turbine outlet temperature, the output power, and the gas turbine flow balance of the simulation model of the components of the gas turbine to be estimated; Determine the objective function based on the loss function of the compressor outlet temperature, the loss function of the turbine outlet temperature, the loss function of the output power, the loss function of the gas turbine flow balance, and a preset weight; Wherein, the following formula is used to determine the objective function: wherein the is the i th compressor outlet temperature input to the calculation of the gas turbine component simulation model to be estimated, the is the i th actually measured compressor outlet temperature, the is the maximum measured value of the compressor outlet temperature, the is the i th turbine outlet temperature input to the calculation of the gas turbine component simulation model to be estimated, the is the i th actually measured turbine outlet temperature, the is the maximum measured value of the turbine outlet temperature, the is the i th output power input to the calculation of the gas turbine component simulation model to be estimated, the is the i th actually measured output power, the is the maximum measured value of the output power, the is the i th compressor outlet flow rate input to the calculation of the gas turbine component simulation model to be estimated, the is the i th input fuel quantity, the is the i th turbine inlet flow rate input to the calculation of the gas turbine component simulation model to be estimated through the turbine flow characteristic curve, the is the estimated maximum value of the turbine inlet flow rate, the is the weighting coefficient.

12. The device according to claim 8, wherein, The calculation module is further configured to: In response to the simulation loss value being greater than or equal to the loss threshold, adjust the constrained optimization algorithm, recalculate the simulation loss value, and obtain a new target migration coefficient matrix; In response to the adjusted simulation loss value being less than the loss threshold, determine 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 constrained optimization algorithm.

13. The device according to claim 12, characterized in that, The calculation module is further configured to: In response to the recalculated simulation loss value being greater than or equal to the loss threshold; Redetermine a target transformation strategy from the transformation strategies not adopted in the transformation strategy set, and re-establish a simulation model of the components of the gas turbine to be estimated based on the redetermined target transformation strategy.

14. An electronic device, characterized in that, Comprising a memory and a processor; Wherein, the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method according to any one of claims 1-7.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, the method according to any one of claims 1-7 is implemented.

16. A computer program product, comprising a computer program, which when executed by a processor implements the method according to any one of claims 1-7.

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