Turbine outlet temperature self-adaptive limiting protection method, device, equipment and medium

Abstract

The invention discloses a turbine outlet temperature self-adaptive limiting protection method, device and equipment and a medium. The method comprises the steps that S1, turboshaft engine state parameters are obtained, and a corresponding characteristic value matrix is calculated; s2, the turboshaft engine airborne model predicts engine performance parameters online according to the state parameters and inputs the engine performance parameters to the unscented Kalman state estimator, wherein the turboshaft engine airborne model comprises an input-feedback dual-adaptive long-short-term memory network; s3, the unscented Kalman state estimator estimates the outlet temperature of the gas turbine on line according to the predicted engine performance parameters; and S4, when the estimated outlet temperature of the gas turbine exceeds the temperature limit value, the fuel flow of the engine is corrected online, and self-adaptive limit protection control over the outlet temperature of the gas turbine is achieved. According to the method, the probability that part of strongly correlated input parameters are misjudged and forgotten due to the measurement error and uncertain time-delay sudden change of the thermocouple temperature sensor at the outlet of the gas turbine is reduced, and the control precision and the real-time performance are improved.

Description

Technical Field

[0001] This application relates to the field of aero-engine technology, and in particular to a method, apparatus, device, and medium for adaptive limiting protection of turbine outlet temperature. Background Technology

[0002] In recent years, driven by both the accelerated modernization of national defense and the growing demand for civil aviation, aero-engine technology has experienced leapfrog development. However, with the continuous improvement of high-performance requirements, aero-engines are operating at their performance limits, and performance improvements often involve pushing beyond existing safety boundaries. Performance and safety are mutually restrictive; pursuing extreme performance can easily lead to engine safety issues, making over-limit safety protection crucial.

[0003] For aero-engine control systems, the goal is to ensure stable and reliable engine operation under any environment and operating condition, maximizing performance benefits. Besides controlled variables, aero-engine control systems contain many limiting variables, among which the most critical are the low-pressure rotor's physical speed, compressor outlet static pressure, and turboshaft engine gas turbine outlet temperature. In certain situations, these physical quantities may exceed their maximum limits, leading to serious consequences. For example, exceeding the limit for turboshaft engine gas turbine outlet temperature can cause turbine blade ablation, severely impacting turbine blade lifespan and threatening engine operational safety. Currently, turboshaft engine gas turbine outlet temperature is often controlled by incorporating a limiting protection control module based on sensor-measured temperature signals into the control structure. However, due to the inherent thermal inertia of thermocouple sensors, the steady-state error of temperature measurements is large, and dynamic characteristics exhibit a certain lag. Protection against turbine outlet temperature is often based on compensation feedback, at which point the turbine is already in an over-temperature state, failing to provide early protection and exhibiting a time lag. Summary of the Invention

[0004] This application provides a turbine outlet temperature adaptive limiting protection method to solve the technical problems of existing turboshaft engine gas turbine outlet temperature protection methods, which do not provide advance protection for the turbine and have large errors and time lag.

[0005] This application is achieved through the following solution: The turbine outlet temperature adaptive limiting protection method includes the following steps: S1. Obtain the state parameters of the turboshaft engine and calculate the corresponding eigenvalue matrix. The eigenvalues ​​are used to characterize the correlation between the state parameters and the output, and the magnitude of the eigenvalues ​​is proportional to the correlation. S2, the turbo-shaft engine on-board model predicts the engine performance parameters according to the turbo-shaft engine state parameters and inputs the engine performance parameters into the unscented Kalman state estimator, the turbo-shaft engine on-board model comprises an input-feedback double adaptive long short-term memory network, wherein the input-feedback double adaptive long short-term memory network adaptively allocates the weight of the input gate according to the eigenvalue size, and adaptively corrects the feedback weight corresponding to each input parameter in the forgetting gate according to the eigenvalue size; S3, the unscented Kalman state estimator estimates the gas turbine outlet temperature T 45 online according to the predicted engine performance parameters; S4, when the online estimated gas turbine outlet temperature T 45 exceeds the temperature limit value, a control amount is obtained by using a PI controller to take the temperature limit value as a reference instruction and the temperature estimation value as a controlled parameter, the fuel flow of the engine is corrected online, and adaptive limit protection control of the gas turbine outlet temperature is realized.

[0006] Further, the step S1 specifically comprises the steps of: S11, acquiring turbo-shaft engine state parameters, including flight height, flight speed, gas turbine speed, power turbine speed, total distance, fuel flow, and bleed air flow; S12, normalizing the acquired turbo-shaft engine state parameters; S13, fitting the normalized turbo-shaft engine state parameters to obtain a mapping matrix between input and output, and obtaining an eigenvalue matrix through singular value decomposition.

[0007] Further, the step S12 specifically comprises the steps of: According to the mathematical expectation and the standard deviation of the signal, the input and output sample sequences are normalized to unify the evaluation standard: ; In the formula, X represents the input data ，X out represents the normalized input data, μ and σ are the expectation and standard deviation of X , and ε is a small normal number added for the stability of division.

[0008] Further, S13 specifically comprises the steps of: S131, fitting the input data X to the output data Y to obtain a mapping matrix: ; S132, decompose the above formula using singular value to obtain eigenvalue: ; In the formula, U M Indicates M An orthogonal matrix of order, V N Indicates N An orthogonal matrix of order, r =rank( C M ), represents the rank of the mapping matrix C m , κ 1、 κ 2…、 κ r There are r Eigenvalues.

[0009] Further, in step S2, the input-feedback double adaptive long short-term memory network adaptively allocates the weight of the input gate according to the eigenvalue size. The specific strategy is: ; In the formula, tanh(•) is the hyperbolic tangent function, W ij The initial weight value corresponding to the j The input parameter in the input gate of the long short-term memory network, The weight of the j Input parameter in the input gate after adaptive allocation; The input-feedback double adaptive long short-term memory network adaptively corrects the corresponding feedback weight of each input parameter in the forget gate according to the eigenvalue size. The specific strategy is: ; In the formula, W fj The initial feedback weight value corresponding to the j Input in the forget gate of the long short-term memory network, The correction value of the corresponding feedback weight value.

[0010] Further, in step S2, the forward propagation process of the input-feedback double adaptive long short-term memory network is: ; In the formula, f t , i t , o t Respectively represent the activation value of the forget gate, the input gate and the output gate, ,C t represents the candidate cell state and the current cell state; h t represents the hidden state output; σ represents the sigmoid activation function; represents the Hadamard product, W i 、 W f 、 W C are input, feedback, and cell weight matrices, respectively.

[0011] Further, in step S2, the turboshaft engine on-board model is: ; wherein the turboshaft engine on-board model takes the current k time and the fuel flow rates of the previous two time k -1, k -2, W f , the total pitch of the rotor θ 0 , the intermediate stage bleed of the compressor L ct , the gas turbine speed of the previous two time N g , the power turbine speed N p , the gas turbine outlet temperature T 45 , the flight altitude H , and the forward speed V c as input; the current k time gas turbine speed N g , the power turbine speed N p , the engine output torque T qe as output, f UAFLSTM (•) is the input-output mapping function of the turboshaft engine on-board model based on the input-feedback double adaptation of the long short-term memory network.

[0012] Another aspect of the present application also provides a turbine outlet temperature adaptive limiting protection device, comprising: a characteristic value calculation module configured to obtain state parameters of the turboshaft engine and calculate a corresponding characteristic value matrix, wherein the characteristic value is used to represent the correlation between the state parameters and the output, and the size of the characteristic value is proportional to the correlation; An engine performance prediction module is configured to predict engine performance parameters online according to a turboshaft engine state parameter by a turboshaft engine on-board model and input the engine performance parameters to a Kalman state estimator, the turboshaft engine on-board model comprising an input-feedback double adaptive long short-term memory network, wherein the input-feedback double adaptive long short-term memory network is configured to adaptively distribute weights of input gates according to eigenvalues and adaptively correct feedback weights corresponding to each input parameter in a forgetting gate according to the eigenvalues; A gas turbine outlet temperature estimation module is configured to estimate a gas turbine outlet temperature T 45 online by the Kalman state estimator according to the predicted engine performance parameters; A fuel flow control module is configured to correct fuel flow of the engine online by using a PI controller to calculate a control amount when the online estimated gas turbine outlet temperature T 45 exceeds a temperature limit value, taking the temperature limit value as a reference instruction and the temperature estimation value as a controlled parameter.

[0013] Another aspect of the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements steps of the turbine outlet temperature adaptive limit protection method when executing the computer program.

[0014] Another aspect of the present application provides a storage medium comprising a stored program, wherein the program controls a device where the storage medium is located to implement steps of the turbine outlet temperature adaptive limit protection method when running.

[0015] Compared with the prior art, the present application has the following beneficial effects: The application provides a turbine outlet temperature adaptive limiting protection method, device, equipment and medium. The turbine outlet temperature adaptive limiting protection method introduces an input-feedback double-adaptive long short-term memory network in an on-board model of a turboshaft engine. The long short-term memory network is double-adaptively corrected through setting a feedback weight adaptive correction based on an eigenvalue and a double-adaptive correction of a feedback weight, so that the long short-term memory network can automatically analyze and identify input features strongly related to a target output under different operating conditions and different input combinations (fuel, guide vane, intermediate stage bleeder) of the turboshaft engine, and can more quickly and accurately construct a mapping relationship between the input and the output in combination with a weight adaptive correction strategy, so that the model can dynamically adjust the feedback strength according to the importance (eigenvalue size) of the input features, thereby adaptively correcting the feedback weight corresponding to each input parameter in the forget gate, having high dynamic precision, thereby reducing the measurement error of a gas turbine outlet thermocouple temperature sensor, and the probability of misjudgment of some strongly related input parameters forgotten due to sudden changes in uncertain time lag. In summary, the application can automatically analyze and identify input features strongly related to a target output, and more quickly and accurately construct a mapping model between the input and the output in combination with a weight adaptive correction strategy of an input-feedback matrix, and has high dynamic precision.

[0016] In addition to the purposes, features and advantages described above, the application has other purposes, features and advantages. The application will be described in further detail below with reference to the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0017] The drawings incorporated into the specification and forming a part thereof show embodiments consistent with the application and, together with the specification, serve to explain the principles of the application.

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, for those skilled in the field, other drawings can also be obtained from these drawings without creative labor. Figure 1 is a flowchart of the turbine outlet temperature adaptive limiting protection method of the preferred embodiment of the application; Figure 2 is a long short-term memory network structure diagram based on input-feedback double-adaptive of the preferred embodiment of the application; Figure 3 is a principle diagram of the turbine outlet temperature adaptive limiting protection method of the preferred embodiment of the application; Figure 4 is a relative test error diagram of the off-line training engine after-gas turbine speed of the application; Figure 5 is a schematic diagram of the relative test error of the off-line training engine after power turbine speed of the application; Figure 6 is a schematic diagram of the relative test error of the off-line training engine after engine output torque of the application; Figure 7 is a comparison diagram of the estimated value and the true value of the gas turbine outlet temperature at a flight height of 2000m and a flight speed of 10m / s of the application; Figure 8 is an error distribution diagram of the estimated value and the true value of the gas turbine outlet temperature at a flight height of 2000m and a flight speed of 10m / s of the application; Figure 9 is a comparison diagram of the estimated value and the true value of the gas turbine outlet temperature at a flight height of 1000m and a flight speed of 10m / s of the application; Figure 10 is an error distribution diagram of the estimated value and the true value of the gas turbine outlet temperature at a flight height of 1000m and a flight speed of 10m / s of the application; Figure 11 is a comparison diagram of the estimated value and the true value of the gas turbine outlet temperature at a flight height of 500m and a flight speed of 10m / s of the application; Figure 12 is an error distribution diagram of the estimated value and the true value of the gas turbine outlet temperature at a flight height of 500m and a flight speed of 10m / s of the application; Figure 13 is a comparison diagram of the estimated value and the true value of the gas turbine outlet temperature at a flight height of 1500m and a flight speed of 20m / s of the application; Figure 14 is an error distribution diagram of the estimated value and the true value of the gas turbine outlet temperature at a flight height of 1500m and a flight speed of 20m / s of the application; Figure 15 is a schematic diagram of the change of each parameter before and after the limit protection in different flight states of the application; Figure 16 is a schematic diagram of the turbine outlet temperature adaptive limit protection device module of the preferred embodiment of the application; Figure 17 is a schematic block diagram of the electronic device entity of the preferred embodiment of the application; Figure 18 is an internal structure diagram of the computer device of the preferred embodiment of the application. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are merely intended to explain the technical solutions of the present application, and are not intended to limit the present application.

[0020] In order to better understand the technical solutions of the present application, the following will be described in detail in combination with the drawings of the specification and specific embodiments.

[0021] It should be noted that the execution subject of the present embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a turbine outlet temperature adaptive limiting protection device capable of realizing the above functions. The following takes the turbine outlet temperature adaptive limiting protection device as an example to describe the present embodiment and the following embodiments.

[0022] As shown in Figure 1 , the preferred embodiment of the present application provides a turbine outlet temperature adaptive limiting protection method, comprising the steps of: S1, obtaining a turboshaft engine state parameter and calculating a corresponding eigenvalue matrix, wherein the eigenvalue is used to represent the correlation between the state parameter and the output, and the size of the eigenvalue is proportional to the correlation; S2, the turboshaft engine on-board model predicts the engine performance parameter online according to the turboshaft engine state parameter and inputs it to the unscented Kalman state estimator, wherein the turboshaft engine on-board model includes an input-feedback double adaptive long short-term memory network (see Figure 2 ), wherein the input-feedback double adaptive long short-term memory network adaptively allocates the weight of the input gate according to the size of the eigenvalue, and adaptively corrects the feedback weight corresponding to each input parameter in the forgetting gate according to the size of the eigenvalue; S3, the unscented Kalman state estimator estimates the gas turbine outlet temperature T 45 online according to the predicted engine performance parameter; S4, when the online estimated gas turbine outlet temperature T 45 exceeds the temperature limit value, taking the temperature limit value as the reference instruction and the temperature estimation value as the controlled parameter, using a PI controller to calculate the control amount, online correcting the fuel flow of the engine, and realizing the adaptive limiting protection control of the gas turbine outlet temperature.

[0023] Based on this, the present embodiment quickly establishes a high-precision turboshaft engine on-board model for online prediction of engine performance parameters, and uses an unscented Kalman state estimator to estimate the gas turbine outlet temperature T 45Online estimation. Finally, on the basis of the closed-loop feedback controller, when the gas turbine outlet temperature exceeds the limit value, the temperature limit value is taken as the reference command, the temperature estimation value is taken as the controlled parameter, and a PI controller is used to calculate the control amount to online correct the engine fuel flow, otherwise, no correction is made, so as to realize adaptive limit protection control of the gas turbine outlet temperature. The control principle is as shown in Figure 3 In addition, the power turbine speed instruction is taken as an external set value to require the operation condition of the engine power turbine. The inner and outer loop controllers are PI control algorithms. The outer loop controller is used to adjust the power turbine speed to maintain the power turbine speed instruction operation; and the inner loop controller is used to adjust the gas turbine speed to improve the anti-interference performance of the outer loop controller. In actual operation, the actual speeds of the gas turbine and the power turbine can be collected by corresponding sensors.

[0024] The embodiment provides a turbine outlet temperature adaptive limit protection method. The turbine outlet temperature adaptive limit protection method introduces an input-feedback double-adaptive long short-term memory network in an on-board model of a turboshaft engine. The long short-term memory network is double-adaptively corrected by setting a feedback weight adaptive correction based on characteristic values and a double-adaptive correction of a feedback weight, so that the long short-term memory network can automatically analyze and identify input characteristics strongly related to a target output under different operating conditions of the turboshaft engine (fuel, guide vanes, and intermediate stage bleeds), and can more quickly and accurately construct a mapping relationship between the input and the output in combination with a weight adaptive correction strategy, so that the model can dynamically adjust the feedback strength according to the importance (characteristic value size) of the input characteristics, thereby adaptively correcting the feedback weight corresponding to each input parameter in the forget gate, having high dynamic precision, thereby reducing the measurement error of a gas turbine outlet thermocouple temperature sensor and the probability of misjudgment of the forgetting of part of the strongly related input parameters caused by an uncertain time lag mutation. In summary, the embodiment can automatically analyze and identify input characteristics strongly related to a target output, and more quickly and accurately construct a mapping model between the input and the output in combination with a weight adaptive correction strategy of an input and a feedback matrix, and has high dynamic precision.

[0025] As a preference, the unscented Kalman state estimator can be replaced by other kinds of filters, such as an extended Kalman state estimator.

[0026] Preferably, the step S1 specifically comprises the following steps: S11, acquiring turboshaft engine state parameters, including flight height, flight speed, gas turbine speed, power turbine speed, total distance, fuel flow, and bleed air flow; S12, performing normalization processing on the acquired turboshaft engine state parameters; S13. Fit the normalized state parameters of the turboshaft engine to obtain the mapping matrix between input and output, and obtain the eigenvalue matrix through singular value decomposition.

[0027] Preferably, step S12 specifically includes the following steps: The input and output sample sequences are normalized based on the mathematical expectation and standard deviation of the signal to unify the evaluation criteria. ; In the formula, X Indicates input data ，X out This represents the normalized input data. μ and σ yes X Expected value and standard deviation ε These are small positive numbers added for the stability of division.

[0028] Preferably, S13 specifically includes the following steps: S131. Obtain input data through least squares fitting. X To output data Y Mapping matrix: ; S132. Obtain the eigenvalues ​​by decomposing the above equation using singular values: ; In the formula, U M express M orthogonal matrix of order 1, V N express N orthogonal matrix of order 1, r =rank( C M ), representing the mapping matrix C m rank, κ 1. κ 2…、 κ r for r Each feature value.

[0029] Considering that during the operation of a turboshaft engine, the guide vane angle, the compressor intermediate stage bleed gas volume, and the timing of fuel flow are coordinated to regulate the engine, such as opening the compressor intermediate stage bleed gas valve at low speeds and closing the bleed gas valve at high speeds, different input combinations (fuel, fuel-guide vane, fuel-guide vane-bleed gas, fuel-bleed gas, etc.) exist throughout the engine's operation. Furthermore, the correlation or relationship between inputs and outputs varies significantly under different input combinations. To address this, this application introduces an adaptive input weight allocation and adaptive feedback weight correction mechanism, specifically including: In step S2, the specific strategy for the input-feedback dual adaptive long short-term memory network to adaptively allocate the weights of the input gates based on the feature values ​​is as follows: ; In the formula, tanh(•) is the hyperbolic tangent function. W ij The first input gate in the Long Short-Term Memory network j The initial weight values ​​corresponding to each input parameter. For the input gate, the first j The weights of the input parameters are adaptively assigned; The input-feedback dual adaptive long short-term memory network adaptively adjusts the feedback weights corresponding to each input parameter in the forget gate based on the feature value. The specific strategy is as follows: ; In the formula, W fj The first of the forgetting gates in the Long Short-Term Memory Network j The initial feedback weight values ​​corresponding to each input. This is the correction value for the corresponding feedback weight value.

[0030] Eigenvalues ​​can characterize the correlation between an input and an output to a certain extent; the larger the eigenvalue, the stronger the correlation, and vice versa. Based on this, state parameters with large eigenvalues ​​are preferred as inputs, and the weights of the input gates are adjusted using these eigenvalues. Input parameters with stronger correlation receive larger weights, and vice versa. Larger weights for highly correlated parameters act as a "reward" mechanism, leading to more accurate predictions. Furthermore, considering the measurement errors and uncertain time delays of the gas turbine outlet thermocouple temperature sensor, this embodiment introduces an adaptive feedback weight correction function. This allows the recording model to dynamically adjust the feedback strength based on the importance (eigenvalue) of the input features, thereby adaptively correcting the feedback weights corresponding to each input parameter in the forget gate.

[0031] In this embodiment, the feedback weights corresponding to each input parameter in the forget gate are adaptively adjusted based on the magnitude of the eigenvalues. The larger the eigenvalue, the more significant the enhancement of the feedback weights. The forget gate of the Long Short-Term Memory network determines which information from the previous time step needs to be retained or forgotten through σ (the sigmoid activation function, with an output value between [0,1]). By introducing the aforementioned adaptive feedback weight adjustment module, the feedback weights are adaptively strengthened based on the correlation of the input features, reducing the probability of some strongly correlated input parameters being misjudged and forgotten due to sensor measurement errors and uncertain time delay abrupt changes.

[0032] Based on the above formula, in step S2, the forward propagation process of the input-feedback dual adaptive long short-term memory network is as follows: ; In the formula, f t , i t , o t These represent the activation values ​​of the forget gate, input gate, and output gate, respectively. , C t This indicates the candidate cell state and the current cell state; h t Indicates output in hidden state; σ Represents the sigmoid activation function; ⊙ represents the Hadamard product. W i , W f , W C These are the input, feedback, and cell weight matrices, respectively.

[0033] The introduction of the input-feedback weighted dual adaptive module enables the long short-term memory network to automatically analyze and identify input features strongly correlated with the target output under different operating conditions and for different input combinations (fuel, guide vanes, intermediate stage venting) of the turboshaft engine. Combined with the aforementioned weighted adaptive correction strategy, it can more quickly and accurately construct the mapping relationship between input and output, and has high dynamic accuracy.

[0034] Preferably, in step S2, the airborne model of the turboshaft engine is: ; Among them, the airborne model of the turboshaft engine is based on the current k Moment and the two previous moments in history k -1、 k fuel flow at -2 W f Collective rotor pitch θ0 Intermediate stage exhaust volume of the compressor L ct Gas turbine speeds at the two most recent historical moments N g Power turbine speed N p Gas turbine outlet temperature T 45 Flight altitude H and forward flight speed V c For input; current k Gas turbine speed at any time N g Power turbine speed N p Engine output torque T qe For output, f UAFLSTM (•) is the input and output mapping function of the turboshaft engine airborne model established based on the input-feedback dual adaptive long short-term memory network. Since the turboshaft engine can be approximated as a second-order system, the above-mentioned turboshaft engine airborne model can be constructed.

[0035] Selecting the relative speed of the power turbine N p relative speed of gas turbine N g Engine output torque T qe As state variables in the component-level model of the turboshaft engine, the input to the unscented Kalman state estimator is the error between the gas turbine speed output from the airborne model and the measured speed, and the output is the gas turbine outlet temperature T. 45 The unscented Kalman state estimator is based on the engine k The state variables and inputs at time -1 can be estimated. k The state variable at any given time.

[0036] Based on a component-level model used to simulate the operating characteristics of a real turboshaft engine, input and output sample data for training the airborne model of the turboshaft engine were collected at different flight envelope points. The relative test error is as follows: Figures 4 to 6 As shown. Figure 4 and Figure 5 As shown, the gas turbine speed N g With power turbine speed N p The relative error does not exceed 1%, such as Figure 6 As shown, the engine output torque T qeThe relative error is basically no more than 2%, confirming that the established airborne model has high accuracy. The unscented Kalman filter (UKF) estimates the state variables based on the residual vector between the output parameters of the engine and the airborne nonlinear model. It is an observation algorithm that directly applies the principle of unscented transformation to the integral calculation in Bayes recursive estimation.

[0037] Based on the component-level model and airborne model of the turboshaft engine, the accuracy of the established unscented Kalman state estimator was verified under different flight conditions (flight altitude and flight speed). The results are as follows: Figures 7 to 14 As shown. Under different operating conditions, T based on the unscented Kalman state estimator 45 The estimated value has an error of less than 1.4% compared with the output value of the turboshaft engine component-level model, indicating high estimation accuracy.

[0038] When the estimated gas turbine outlet temperature exceeds the limit, to achieve the purpose of limiting protection, the limit is used as a reference command, and the estimated gas turbine outlet temperature is used as the controlled variable. This allows the PI controller to generate a virtual control variable and correct the engine's fuel flow online. The structure diagram of this limiting protection method is shown below. Figure 1 As shown.

[0039] Figure 15 The results of over-limit protection control under two flight conditions are presented. In state A, overheating occurs at t=35s. Under the action of the limiting protection controller, the fuel flow rate W... fb Decrease, N p The sag also increases accordingly, and the gas turbine outlet temperature T 45 The design temperature was reduced from 118% to the limit of 110%, and the over-temperature time was shortened by more than 50%; in state B, under the action of the limit protection controller, T 45 The temperature at that point was reduced from 123% to 110% of the design temperature, which demonstrates the superiority and robustness of the turbine outlet temperature adaptive limiting protection method of this application.

[0040] like Figure 16 As shown, another preferred embodiment of this application also provides a turbine outlet temperature adaptive limiting protection device, including: The eigenvalue calculation module is used to obtain the state parameters of the turboshaft engine and calculate the corresponding eigenvalue matrix. The eigenvalues ​​are used to characterize the correlation between the state parameters and the output, and the magnitude of the eigenvalues ​​is proportional to the correlation. An engine performance prediction module is used to predict engine performance parameters online based on the turboshaft engine state parameters of the airborne model and input them into an unscented Kalman state estimator. The airborne model of the turboshaft engine includes an input-feedback dual adaptive long short-term memory network. The input-feedback dual adaptive long short-term memory network adaptively allocates the weights of the input gates according to the size of the feature values, and adaptively corrects the feedback weights corresponding to each input parameter in the forget gate according to the size of the feature values. A gas turbine outlet temperature estimation module is used by the unscented Kalman state estimator to estimate the gas turbine outlet temperature T online based on predicted engine performance parameters. 45 ; The fuel flow control module is used to control the gas turbine outlet temperature T online. 45 When the temperature limit is exceeded, the temperature limit is used as a reference command and the temperature estimate is used as the controlled parameter. The control quantity is calculated using a PI controller to correct the fuel flow of the engine online and realize adaptive limit protection control of the gas turbine outlet temperature.

[0041] The turbine outlet temperature adaptive limiting protection device provided in this embodiment adopts the turbine outlet temperature adaptive limiting protection method in the above embodiments, solving the technical problems of existing turboshaft engine gas turbine outlet temperature protection methods that do not provide early protection for the turbine, resulting in large errors and time lags. Compared with the prior art, the beneficial effects of the turbine outlet temperature adaptive limiting protection device provided in this application are the same as those of the turbine outlet temperature adaptive limiting protection method provided in the above embodiments, and other technical features in the turbine outlet temperature adaptive limiting protection device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0042] like Figure 17 As shown, a preferred embodiment of this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the turbine outlet temperature adaptive limiting protection method in the above embodiments.

[0043] This application provides an electronic device that employs the turbine outlet temperature adaptive limiting protection method described in the above embodiments. This addresses the technical problems of existing turboshaft engine gas turbine outlet temperature protection methods, which lack advance turbine protection, resulting in large errors and time lags. Compared to the prior art, the electronic device provided in this application has the same beneficial effects as the turbine outlet temperature adaptive limiting protection method provided in the above embodiments, and other technical features of the electronic device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0044] likeFigure 18 As shown, a preferred embodiment of this application also provides a computer device, which may be a terminal or a liveness detection server, and its internal structure diagram may be as follows. Figure 18 As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, it implements the steps of the aforementioned turbine outlet temperature adaptive limiting protection method.

[0045] Those skilled in the art will understand that Figure 18 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0046] The computer device provided in this application employs the turbine outlet temperature adaptive limiting protection method described in the above embodiments, solving the technical problems of existing turboshaft engine gas turbine outlet temperature protection methods that do not provide advance protection for the turbine, resulting in large errors and time lags. Compared with the prior art, the beneficial effects of the computer device provided in this application are the same as those of the turbine outlet temperature adaptive limiting protection method provided in the above embodiments, and other technical features in the electronic device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0047] A preferred embodiment of this application also provides a storage medium including a stored program that, when the program is executed, controls the device containing the storage medium to perform the steps of the turbine outlet temperature adaptive limiting protection method in the above embodiments.

[0048] It is evident that excessively high turbine outlet temperatures in turboshaft engines severely impact turbine blade lifespan, posing a threat to engine operational safety. Therefore, existing technologies employ a temperature-sensing sensor-based limit protection control module within the control structure. However, the inherent thermal inertia of thermocouple sensors results in significant steady-state errors and dynamic hysteresis in temperature measurements. Therefore, rapidly and accurately sensing transient changes in turbine outlet temperature and applying them to fuel control would significantly contribute to the safe, stable, and reliable operation of the engine.

[0049] Therefore, this application provides an airborne model of a temporal convolutional attention mechanism network, combined with a state estimator based on an unscented Kalman filter, to estimate the gas turbine outlet temperature online. When the gas turbine outlet temperature exceeds the limit, the temperature limit is used as a reference command, and the temperature estimate is used as the controlled parameter. A PI controller is used to calculate the control quantity to correct the engine's fuel flow online; otherwise, no correction is made, achieving the purpose of adaptive limit protection for the gas turbine outlet temperature. The same principle can also be used to achieve speed limit protection.

[0050] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0051] If the functions described in this embodiment are implemented as software functional units and sold or used as independent products, they can be stored in one or more computing device-readable storage media. Based on this understanding, the parts of this application's embodiments that contribute to the prior art or the technical solutions can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computing device (which may be a personal computer, server, mobile computing device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage media include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0052] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language C++ and the embedded programming language C.

[0053] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0054] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0056] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the turbine outlet temperature adaptive limiting protection method as described above.

[0057] The computer program product provided in this application solves the technical problem that existing turboshaft engine gas turbine outlet temperature protection methods do not provide advance protection for the turbine, resulting in large errors and time lags. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the turbine outlet temperature adaptive limiting protection method provided in the above embodiments, and will not be repeated here.

[0058] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0059] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A turbine outlet temperature adaptive limiting protection method, characterized in that, Including the following steps: S1. Obtain the state parameters of the turboshaft engine and calculate the corresponding eigenvalue matrix. The eigenvalues ​​are used to characterize the correlation between the state parameters and the output, and the magnitude of the eigenvalues ​​is proportional to the correlation. S2. The airborne model of the turboshaft engine predicts the engine performance parameters online based on the state parameters of the turboshaft engine and inputs them into the unscented Kalman state estimator. The airborne model of the turboshaft engine includes an input-feedback dual adaptive long short-term memory network. The input-feedback dual adaptive long short-term memory network adaptively allocates the weights of the input gates according to the size of the eigenvalues, and adaptively corrects the feedback weights corresponding to each input parameter in the forget gate according to the size of the eigenvalues. S3. The unscented Kalman state estimator estimates the gas turbine outlet temperature T online based on the predicted engine performance parameters. 45 ; S4, when the online estimated gas turbine outlet temperature T 45 When the temperature limit is exceeded, the temperature limit is used as a reference command and the temperature estimate is used as the controlled parameter. The control quantity is calculated using a PI controller to correct the fuel flow of the engine online and realize adaptive limit protection control of the gas turbine outlet temperature.

2. The turbine outlet temperature adaptive limiting protection method according to claim 1, characterized in that, Step S1 specifically includes the following steps: S11. Obtain the status parameters of the turboshaft engine, including flight altitude, flight speed, gas turbine speed, power turbine speed, collective pitch, fuel flow rate, and exhaust volume. S12. Normalize the acquired turboshaft engine state parameters. S13. Fit the normalized state parameters of the turboshaft engine to obtain the mapping matrix between input and output, and obtain the eigenvalue matrix through singular value decomposition.

3. The turbine outlet temperature adaptive limiting protection method according to claim 2, characterized in that, Step S12 specifically includes the following steps: The input and output sample sequences are normalized based on the mathematical expectation and standard deviation of the signal to unify the evaluation criteria. ； In the formula, X Indicates input data ，X out This represents the normalized input data. μ and σ yes X Expected value and standard deviation ε These are small positive numbers added for the stability of division.

4. The turbine outlet temperature adaptive limiting protection method according to claim 3, characterized in that, S13 specifically includes the following steps: S131. Obtain input data through least squares fitting. X To output data Y Mapping matrix: ； S132. Obtain the eigenvalues ​​by decomposing the above equation using singular values: ； In the formula, U M express M orthogonal matrix of order 1, V N express N orthogonal matrix of order, r =rank( C M ) represents the mapping matrix C m rank, κ 1. κ 2…、 κ r for r Each feature value.

5. The turbine outlet temperature adaptive limiting protection method according to claim 4, characterized in that, In step S2, the specific strategy for the input-feedback dual adaptive long short-term memory network to adaptively allocate the weights of the input gates based on the feature values ​​is as follows: ； In the formula, tanh(•) is the hyperbolic tangent function. W ij The first input gate in the Long Short-Term Memory network j The initial weight values ​​corresponding to each input parameter. For the input gate, the first j The weights of the input parameters are adaptively assigned; The input-feedback dual adaptive long short-term memory network adaptively adjusts the feedback weights corresponding to each input parameter in the forget gate based on the feature value. The specific strategy is as follows: ； In the formula, W fj The first of the forgetting gates in the Long Short-Term Memory Network j The initial feedback weight values ​​corresponding to each input. This is the correction value for the corresponding feedback weight value.

6. The turbine outlet temperature adaptive limiting protection method according to claim 5, characterized in that, In step S2, the forward propagation process of the input-feedback dual adaptive long short-term memory network is as follows: ； In the formula, f t , i t , o t These represent the activation values ​​of the forget gate, input gate, and output gate, respectively. , C t This indicates the candidate cell state and the current cell state; h t Indicates output in hidden state; σ Represents the sigmoid activation function; ⊙ represents the Hadamard product. W i , W f , W C These are the input, feedback, and cell weight matrices, respectively.

7. The turbine outlet temperature adaptive limiting protection method according to claim 6, characterized in that, In step S2, the specific strategy for the airborne model of the turboshaft engine is as follows: ； Among them, the airborne model of the turboshaft engine is based on the current k Moment and the two previous moments in history k -1、 k fuel flow at time -2 W f rotor collective pitch θ 0 Intermediate stage exhaust volume of the compressor L ct Gas turbine speeds at the two most recent historical moments N g Power turbine speed N p Gas turbine outlet temperature T 45 Flight altitude H and forward flight speed V c For input; current k Gas turbine speed at any time N g Power turbine speed N p Engine output torque T qe For output, f UAFLSTM (•) is the input and output mapping function of the airborne model of the turboshaft engine based on the input-feedback dual adaptive long short-term memory network.

8. A turbine outlet temperature adaptive limiting protection device, characterized in that, include: The eigenvalue calculation module is used to obtain the state parameters of the turboshaft engine and calculate the corresponding eigenvalue matrix. The eigenvalues ​​are used to characterize the correlation between the state parameters and the output, and the magnitude of the eigenvalues ​​is proportional to the correlation. An engine performance prediction module is used to predict engine performance parameters online based on the turboshaft engine state parameters of the airborne model and input them into an unscented Kalman state estimator. The airborne model of the turboshaft engine includes an input-feedback dual adaptive long short-term memory network. The input-feedback dual adaptive long short-term memory network adaptively allocates the weights of the input gates according to the size of the feature values, and adaptively corrects the feedback weights corresponding to each input parameter in the forget gate according to the size of the feature values. A gas turbine outlet temperature estimation module is used by the unscented Kalman state estimator to estimate the gas turbine outlet temperature T online based on predicted engine performance parameters. 45 ; The fuel flow control module is used to control the gas turbine outlet temperature T online. 45 When the temperature limit is exceeded, the temperature limit is used as a reference command and the temperature estimate is used as the controlled parameter. The control quantity is calculated using a PI controller to correct the fuel flow of the engine online and realize adaptive limit protection control of the gas turbine outlet temperature.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the turbine outlet temperature adaptive limiting protection method as described in any one of claims 1 to 7.

10. A storage medium comprising a stored program, characterized in that, When the program is running, it controls the device containing the storage medium to perform the steps of the turbine outlet temperature adaptive limiting protection method as described in any one of claims 1 to 7.

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