Method for controlling the speed of a gas turbine based on a model-free adaptive controller

Abstract

This disclosure provides a method for controlling the speed of a gas turbine based on a model-free adaptive controller. The method includes: determining the predicted fuel quantity of the gas turbine based on the desired speed of the gas turbine; determining the actual speed of the gas turbine based on the predicted fuel quantity; inputting the error determined based on the actual speed and the desired speed into the model-free adaptive controller, and outputting a correction amount for the predicted fuel quantity so as to obtain the target fuel quantity of the gas turbine based on the correction amount; and correcting the actual speed of the gas turbine based on the target fuel quantity until the error between the actual speed and the desired speed of the gas turbine meets a preset threshold.

Description

Technical Field

[0001] This disclosure relates to the field of automatic control of power machinery, and more specifically to a method, apparatus, equipment and medium for controlling the speed of a gas turbine based on a model-free adaptive controller. Background Technology

[0002] Industrial gas turbines play a vital role in distributed power generation and industrial power drives. In controlling the speed of an industrial gas turbine, controlling the fuel quantity can alter its rotational speed. Controlling the fuel quantity not only ensures operation under specified loads but also guarantees that all components of the industrial gas turbine operate within safe limits. Related technologies typically employ model predictive control to regulate the fuel quantity of industrial gas turbines. Model predictive control is robust and can handle multiple variables and constraints.

[0003] In the process of realizing the inventive concept disclosed herein, the inventors discovered at least the following problems in the related technologies: due to the large amount of computation required for online optimization of model predictive control, offline optimization generally results in control errors, which in turn reduces the control accuracy of industrial gas turbine speed. Summary of the Invention

[0004] In view of the above problems, this disclosure provides a method, apparatus, equipment, medium and program product for controlling the speed of industrial gas turbines based on a model-free adaptive controller to improve the control accuracy of the gas turbine speed, in order to at least partially solve the above technical problems.

[0005] One aspect of this disclosure provides a method for controlling the speed of a gas turbine based on a model-free adaptive controller, comprising: determining a predicted fuel quantity of the gas turbine based on a desired speed of the gas turbine; determining the actual speed of the gas turbine based on the predicted fuel quantity; inputting the error determined based on the actual speed and the desired speed into a model-free adaptive controller, and outputting a correction amount for the predicted fuel quantity so as to obtain a target fuel quantity of the gas turbine based on the correction amount; and correcting the actual speed of the gas turbine based on the target fuel quantity until the error between the actual speed of the gas turbine and the desired speed of the gas turbine meets a preset threshold.

[0006] According to an embodiment of this disclosure, the method further includes: determining a correction amount for the predicted fuel quantity in units of time; wherein, the step of inputting the error determined based on the actual rotational speed and the desired rotational speed into a model-free adaptive controller and outputting a correction amount for the predicted fuel quantity includes: the correction amount at time k is obtained based on the error between the desired rotational speed at time k+1 and the actual rotational speed at time k, and the correction amount at time k-1, wherein k is greater than 1.

[0007] According to an embodiment of this disclosure, determining the actual speed of the gas turbine based on the predicted fuel quantity includes: inputting the predicted fuel quantity into the gas turbine control system and outputting the actual speed of the gas turbine.

[0008] According to an embodiment of this disclosure, the actual rotational speed at time k is based on the actual rotational speed at time (k-1) to time (k-1-n). y The actual rotational speed at time k-1, and the correction amount from time k-1 to time n. u The correction amount at time n is determined, where n is n. y n represents the order of the output model of the gas turbine control system. u This indicates the order of the input model of the gas turbine control system.

[0009] According to an embodiment of this disclosure, determining the predicted fuel quantity of a gas turbine based on its expected rotational speed includes: inputting the expected rotational speed of the gas turbine into a prediction model and outputting the predicted fuel quantity of the gas turbine, wherein the prediction model includes a state-space model.

[0010] According to an embodiment of this disclosure, outputting the predicted fuel quantity of the gas turbine includes: outputting the predicted fuel quantity of the gas turbine based on the output matrix of the state-space model and the state vector related to the desired rotational speed of the gas turbine.

[0011] Another aspect of this disclosure provides an apparatus for controlling the speed of a gas turbine based on a model-free adaptive controller, comprising: a first determining module for determining a predicted fuel quantity of the gas turbine based on a desired speed of the gas turbine; a second determining module for determining the actual speed of the gas turbine based on the predicted fuel quantity; a first input module for inputting an error determined based on the actual speed and the desired speed into the model-free adaptive controller and outputting a correction amount for the predicted fuel quantity so as to obtain a target fuel quantity of the gas turbine based on the correction amount; and a correction module for correcting the actual speed of the gas turbine based on the target fuel quantity until the error between the actual speed of the gas turbine and the desired speed of the gas turbine meets a preset threshold.

[0012] Another aspect of this disclosure provides an electronic device, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the above-described method for controlling the speed of a gas turbine based on a model-free adaptive controller.

[0013] Another aspect of this disclosure provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the above-described method for controlling the speed of a gas turbine based on a model-free adaptive controller.

[0014] Another aspect of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for controlling the speed of a gas turbine based on a model-free adaptive controller.

[0015] According to embodiments of this disclosure, by controlling the gas turbine speed based on a model-free adaptive controller, a target fuel quantity can be obtained by using the error between the actual and desired speed of the gas turbine as input. The actual speed of the gas turbine is then corrected based on this target fuel quantity so that the error between the actual and desired speeds meets a preset threshold. Because the control error in the gas turbine control system caused by the offline-optimized feedback control method can be further adaptively corrected based on the error between the actual and desired speeds, the control error in the speed caused by the offline optimization method can be eliminated. Therefore, this at least partially overcomes the control error problem caused by offline calculation in related technologies, thereby achieving the technical effect of improving the control accuracy of industrial gas turbine speed control. Attached Figure Description

[0016] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0017] Figure 1 A flowchart illustrating a method for controlling gas turbine speed based on a model-free adaptive controller according to an embodiment of the present disclosure is shown.

[0018] Figure 2A A schematic diagram illustrates a system architecture for controlling gas turbine speed based on a model-free adaptive controller according to an embodiment of the present disclosure;

[0019] Figure 2B This schematically illustrates a system architecture diagram for controlling the speed of a gas turbine in the related art according to embodiments of the present disclosure;

[0020] Figure 3A An upward step comparison diagram is schematically shown according to an embodiment of the present disclosure, showing an increase to the rated speed.

[0021] Figure 3B A schematic diagram illustrating a downward step reduction to rated speed according to an embodiment of the present disclosure is shown.

[0022] Figure 4A schematic diagram illustrates a structural block diagram of an apparatus for controlling gas turbine speed based on a model-free adaptive controller according to an embodiment of the present disclosure;

[0023] Figure 5 A block diagram of an electronic device suitable for implementing a method for controlling the speed of a gas turbine based on a model-free adaptive controller, according to an embodiment of the present disclosure, is shown schematically. Detailed Implementation

[0024] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0025] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0027] When using expressions such as "at least one of A, B, and C," the expression should generally be interpreted in accordance with the meaning commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.). Similarly, when using expressions such as "at least one of A, B, or C," the expression should generally be interpreted in accordance with the meaning commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, or C" should include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0028] Industrial gas turbines require long-term stable operation under varying load demands, thus necessitating stringent fuel quantity control. Currently, model predictive control (MMC) for industrial gas turbine fuel quantity control, an approach developed in the industrial sector, offers advantages such as robustness and ease of handling multiple variables and constraints, theoretically facilitating the management of gas turbine fuel quantity control. For example, a MMC-based microgrid operation method can eliminate the impact of inaccurate predictions on system optimization, reducing operational risks. Another example is the use of recursive least squares identification to construct a multi-model ensemble for the speed system. Sub-predictive controllers are designed for each model, and the weighted sum of the control outputs from these sub-predictive controllers serves as the actual control quantity for the gas turbine speed. This allows for the attainment of the optimal value for the gas turbine speed control globally, ultimately achieving multi-model adaptive generalized predictive control of the gas turbine speed system.

[0029] The aforementioned method of using predictive control models to control gas turbines is generally an online control optimization. However, such methods inherently involve significant computational overhead when the prediction and control time domains are large. Furthermore, the complex structure of gas turbine models makes it challenging to apply these methods to practical engineering projects.

[0030] To address the issue of high computational costs in online optimization of predictive control models, the computational workload can typically be shifted to offline calculations to reduce resource consumption during online computation. However, offline computation often introduces control errors in turbine speed. Therefore, a model-free adaptive controller is added during offline computation to eliminate control errors in gas turbine speed caused by offline calculations.

[0031] Specifically, embodiments of this disclosure provide a method for controlling gas turbine speed based on a model-free adaptive controller, used to reduce control errors in gas turbine speed and improve control accuracy. The method includes: determining a predicted fuel quantity for the gas turbine based on its desired speed; determining the actual speed of the gas turbine based on the predicted fuel quantity; inputting the error determined based on the actual speed and the desired speed into a model-free adaptive controller, outputting a correction amount for the predicted fuel quantity, so as to obtain a target fuel quantity for the gas turbine based on the correction amount; and correcting the actual speed of the gas turbine based on the target fuel quantity until the error between the actual speed and the desired speed of the gas turbine meets a preset threshold.

[0032] Figure 1 A flowchart illustrating a method for controlling gas turbine speed based on a model-free adaptive controller according to an embodiment of the present disclosure is shown.

[0033] like Figure 1As shown, the method includes S110 to S140.

[0034] In operation S110, the predicted fuel quantity of the gas turbine is determined based on the expected rotational speed of the gas turbine.

[0035] In operation S120, the actual rotational speed of the gas turbine is determined based on the predicted fuel quantity.

[0036] In operation S130, the error determined based on the actual speed and the desired speed is input to the model-free adaptive controller, and the correction amount for the predicted fuel quantity is output so as to obtain the target fuel quantity of the gas turbine based on the correction amount.

[0037] In operation S140, the actual speed of the gas turbine is corrected according to the target fuel quantity until the error between the actual speed of the gas turbine and the desired speed of the gas turbine meets a preset threshold.

[0038] According to embodiments of this disclosure, the desired speed can be the speed at which the gas turbine is expected to reach. For example, if it is desired that the gas turbine's current speed be increased to or decreased to its rated speed, then the rated speed can be used as the desired speed. The desired speed can also be adaptively adjusted according to actual conditions.

[0039] According to embodiments of this disclosure, the predicted fuel quantity can be the amount of fuel required to adjust the gas turbine from its current speed to a desired speed. Specifically, the desired speed is input into the prediction model, and the predicted fuel quantity can be obtained through a model predictive control (MPC) system that includes the prediction model.

[0040] According to embodiments of this disclosure, the actual rotational speed can be the rotational speed of the gas turbine adjusted based on the predicted fuel quantity, which can be close to the desired rotational speed. Specifically, the predicted fuel quantity can be input into the gas turbine control system, and the actual rotational speed of the gas turbine is obtained after adjusting the gas turbine according to the predicted fuel quantity. In one embodiment, the gas turbine control can also be other systems used for industrial control, which can be adaptively adjusted according to actual needs.

[0041] According to the embodiments of this disclosure, in order to reduce the resource consumption caused by online feedback of the MPC system, the online feedback method of the MPC system is adjusted to offline feedback. During the offline feedback process, the MPC system will still calculate the control signal in the optimization calculation method. However, when the signal is used to actually control the speed, because it is not an online feedback adjustment, the control error of the gas turbine control system may lead to the control error of the speed, resulting in an error between the expected speed and the actual speed.

[0042] According to embodiments of this disclosure, based on the aforementioned error, the Model-Free Adaptive Control (MFAC) can obtain a correction amount for the predicted fuel quantity based on the error between the desired and actual speeds. After correcting the predicted fuel quantity using this correction amount, the target fuel quantity can be obtained. The gas turbine control system can then adjust the actual speed of the gas turbine according to the target fuel quantity, so that the actual speed approaches the desired speed infinitely, until the error between the actual and desired speeds meets a preset threshold. The preset threshold can be a preset error threshold, used to characterize whether the current actual speed can be confirmed as having been adjusted to the desired speed. Preferably, the preset threshold can be 0. The preset threshold can also be adaptively set according to actual needs.

[0043] Figure 2A A schematic diagram illustrates a system architecture for controlling gas turbine speed based on a model-free adaptive controller according to an embodiment of the present disclosure; Figure 2B A schematic diagram illustrating a system architecture for controlling the speed of a gas turbine according to an embodiment of the present disclosure is provided.

[0044] like Figure 2A As shown, the system architecture 200 may include an MPC system 201, an integral module 202, a gas turbine control system 203, and an MFAC 204.

[0045] In controlling the speed of an industrial gas turbine, the input desired speed, after passing through the MPC system 201, yields the fuel component required to raise or lower the gas turbine to its rated speed. The integrator module 202 integrates this fuel component to obtain the predicted fuel quantity. The process of obtaining the predicted fuel quantity based on the desired speed can be done offline. Inputting this predicted fuel quantity to the gas turbine control system 203 yields the adjusted actual speed of the gas turbine. An error may exist between the actual and desired speeds. The MFAC 204 can provide online feedback of the correction amount for the predicted fuel quantity based on the error between the actual and desired speeds, thus correcting the predicted fuel quantity to obtain the target fuel quantity. This ensures that the error between the actual and desired speeds obtained by the gas turbine control system 203 based on the target fuel quantity meets a preset threshold. Preferably, the preset threshold can be 0, and the actual speed can be equal to the desired speed. Figure 2A The symbols in MFAC204 can represent trigger regulation.

[0046] like Figures 2A-2BAs shown, the system architecture in related technologies only includes an MPC system 201, an integral module 202, and a gas turbine control system 203, and it is an online adjustment and feedback system. Compared with related technologies, the system architecture 200 provided in this disclosure not only enables offline calculation, reducing computational consumption, but also eliminates control errors caused by offline feedback, improving the control accuracy of gas turbine speed control while reducing computational consumption. Specifically, the MFAC obtains the correction amount of the predicted fuel quantity based on the error between the actual speed and the desired speed, and then adjusts the actual speed so that the actual speed is infinitely close to the desired speed. This not only improves the control accuracy of the speed, but also allows the calculation process of MPC to be transferred to offline calculation, without having to consider the control error problem existing in offline adjustment.

[0047] According to embodiments of this disclosure, by controlling the gas turbine speed based on a model-free adaptive controller, a target fuel quantity can be obtained by using the error between the actual and desired speed of the gas turbine as input. The actual speed of the gas turbine is then corrected based on this target fuel quantity so that the error between the actual and desired speeds meets a preset threshold. Because the control error in the gas turbine control system caused by the offline-optimized feedback control method can be further adaptively corrected based on the error between the actual and desired speeds, the control error in the speed caused by the offline optimization method can be eliminated. Therefore, this at least partially overcomes the control error problem caused by offline calculation in related technologies, thereby achieving the technical effect of improving the control accuracy of industrial gas turbine speed control.

[0048] According to an embodiment of this disclosure, operation S110 may include the following operations: inputting the desired rotational speed of the gas turbine into the prediction model and outputting the predicted fuel quantity of the gas turbine, wherein the prediction model includes a state-space model.

[0049] According to embodiments of this disclosure, outputting the predicted fuel quantity of the gas turbine includes: outputting the predicted fuel quantity of the gas turbine based on the output matrix of the state-space model and the state vector related to the desired speed of the gas turbine.

[0050] According to embodiments of this disclosure, in an MPC system, the prediction model can be a state-space model. State-space models can handle multivariable control and facilitate matrix operations. Embodiments of this disclosure use a small-deviation state-space model as the prediction model. The type of prediction model can also be adaptively adjusted according to actual needs. Once the prediction model is known, the optimal predicted fuel quantity satisfying the constraints can be calculated using the MPC method based on the constraints and the desired output speed.

[0051] According to embodiments of this disclosure, the modeling and definition process of the prediction model can be as shown in formulas (1) to (4).

[0052] x m (k+1)=A m x m (k)+B m u(k) (1)

[0053] y(k)=C m x m (k)+D m u(k) (2)

[0054] Where k represents time, m represents the state model, and x m (k), x m (k+1) represents the state vector related to the desired speed, y(k) represents the output parameter, which can be the predicted fuel quantity, and u(k) represents the input parameter, which can be the desired speed. m B m C m D m The state-space matrix represents the state-space model; specifically, A m The state matrix and B can represent the state model. m It can represent the input matrix of the state model, C m The output matrix, D, can represent the state model. m It can represent the input-output matrix of a state model.

[0055] According to embodiments of this disclosure, in order to eliminate D m The matrix can be used to further construct an augmented state-space model, which can then be iteratively solved over the entire prediction time domain. The process of constructing the augmented state-space model can be shown in formulas (3) to (4).

[0056]

[0057]

[0058] In formulas (3) to (4), the same characters as those in formulas (1) to (2) can have the same meaning. m It can represent a zero matrix, Δu(k) can represent the increment of the input parameters, and Δx m (k+1) can represent the increment of the state vector. The state-space prediction model of MPC can be obtained according to formulas (1) to (4).

[0059] According to embodiments of this disclosure, the predicted quantities in the prediction time domain and control time domain of the state-space model can be written in matrix form as shown in formula (5).

[0060] Y=Fx(ki)+ΦΔU (5)

[0061] Where x(ki) can represent the state vector, Y represents the control output signal, U represents the control input signal, and F and Φ are the matrix coefficients of the predictor.

[0062] According to the embodiments of this disclosure, formula (5) can represent the system change process over a period of time; it facilitates subtraction with the desired rotational speed (Rs) in subsequent processes and optimizes the solution. The prediction time domain and the control time domain can be understood as two time periods. The prediction time domain and the control time domain are related to the size of the Y matrix and the U matrix. Specifically, Y represents the matrix composed of all y vectors in the prediction time domain, and U represents the vector composed of all u vectors in the control time domain. When it comes to the rotational speed and fuel quantity at a specific time k, it can be the last row of the Y matrix and the kth column of the U matrix.

[0063] According to the embodiments of this disclosure, since the purpose of the control process is to make the actual output speed match the expected output speed, before optimizing the control process, it can be assumed that the expected speed sequence of the state space model is r(ki), and the loss function can be as shown in formula (6).

[0064]

[0065] in, It represents the identity matrix of dimension n, r w ≥0 is a parameter used to adjust control performance. N p N represents the length of the prediction domain. c The superscript T indicates the length of the control domain.

[0066] According to the embodiments of this disclosure, a partial derivative of 0 is a necessary condition for taking an extreme value. Therefore, in order to minimize the loss function J (minimize the error between the actual rotational speed output by the system and the expected rotational speed output), formula (5) can be substituted into formula (6) and the partial derivative can be obtained. The formula after obtaining the partial derivative is shown in formula (7).

[0067]

[0068] In formula (7), the same characters in formulas (1) to (6) can have the same meaning, which will not be repeated here. Formulas (5) to (7) can be optimization solutions without constraints.

[0069] According to the embodiments of this disclosure, the control process of the MPC system needs to calculate the error between the actual speed of the feedback and the expected speed of the desired output, and further combine the prediction model and process constraints to solve the optimization calculation process, and finally obtain the control input signal. Based on the expected speed of the desired output, the prediction model used for control, and the constraints on the actual output speed, the optimization problem can be abstracted into solving the following quadratic programming problem, as shown in formulas (8) to (9).

[0070]

[0071]

[0072] stMΔU≤γ

[0073] M = [M1 M2 M3] T γ = [N1 N2 N3] T ;

[0074]

[0075]

[0076] Where st (subject to) can represent the minimum value solution required under the condition that MΔU≤γ is satisfied, and I can represent the identity matrix. Formulas (8) to (9) can be further formulaic expressions for the solution process in the general case (with constraints).

[0077] According to an embodiment of this disclosure, operation S120 may further include the following operation: inputting the predicted fuel quantity into the gas turbine control system and outputting the actual rotational speed of the gas turbine.

[0078] According to an embodiment of this disclosure, operation S130 may further include the following operation: determining a correction amount for the predicted fuel quantity in units of time; the correction amount at time k is obtained based on the error between the expected rotational speed at time k+1 and the actual rotational speed at time k, and the correction amount at time k-1, wherein k is greater than 1.

[0079] According to an embodiment of this disclosure, the actual rotational speed at time k is based on the actual rotational speed at time (k-1) to time (k-1-n). y The actual rotational speed at time k-1, and the correction amount from time k-1 to time n. u The correction amount at time n is determined, where n is n. y n represents the order of the output model of the gas turbine control system. u This indicates the order of the input model of the gas turbine control system.

[0080] According to embodiments of this disclosure, after obtaining the prediction model, the optimal predicted fuel quantity that satisfies the constraints can be calculated using model predictive control based on the constraints and the desired rotational speed of the desired output. Furthermore, based on the obtained prediction model, a modular scheme combining model-free adaptive control is needed to improve the structure of the original model predictive control, enabling the online optimization process of the MPC system to be transferred offline while still achieving steady-state error-free control.

[0081] According to embodiments of this disclosure, considering that the offline MPC system still calculates the control signal using an optimal calculation method during the pre-calculation process, and that using this signal for actual speed control will result in control errors in the gas turbine control system due to the lack of online real-time adjustment feedback, the offline calculated predicted fuel quantity remains relatively optimal when the deviation between the MPC system and the actual gas turbine control system is not too large during the adjustment of the predicted fuel quantity. Therefore, in considering the integration with MFAC, most of the control process is still completed using the predicted fuel quantity calculated by the offline MPC system. Finally, when a steady-state error arises due to the unmodeled portion, the outer-loop MFAC is triggered to eliminate the steady-state error online.

[0082] Model-free adaptive control is applicable to most gas turbine control systems. The model of a general gas turbine control system can be described by the following discrete-time SISO nonlinear system, as shown in formulas (10) to (11).

[0083] y(k)=f(y(k-1),...y(k-1-n y ),u(k-1),...u(k-1-n u (10)

[0084] y(k+1)=f(y(k),...y(kn y ),u(k),...u(kn u (11)

[0085] In formulas (10) to (11), y(k), y(k+1), and y(k-1) represent the output of the gas turbine system, which can be understood as the actual speed at different times; u(k), u(k+1), and u(k-1) represent the input of the gas turbine system, which can be the predicted fuel quantity at different times, n y n u These represent the model orders of the output and input of the gas turbine control system, respectively.

[0086] According to embodiments of this disclosure, in the modular design of MFAC, model-free adaptation is used to correct the predicted fuel quantity of the input based on the error between the actual speed and the desired speed. Therefore, y(k) and u(k) in MFAC can correspond to the amount of error in the input and the amount of correction of the fuel quantity in the output.

[0087] According to embodiments of this disclosure, for model-free adaptive controllers, dynamic linearization methods for discrete-time nonlinear systems can include compact scheme dynamic linearization (CFDL), partial scheme dynamic linearization (PFDL), and full scheme dynamic linearization (FFDL). In embodiments of this disclosure, only the compact scheme dynamic linearization method is considered as an example. The adjustable parameters of the MFAC determined based on the compact scheme dynamic linearization method are relatively few, the structure is simple, and it is easy to reduce the consumption of computational resources.

[0088] According to embodiments of this disclosure, the process of establishing a dynamic linearization model is improved by a similar improved projection algorithm, which yields the control law of the compact-format model-free adaptive control CFDL-MFAC. Based on this, the correction amount for the predicted fuel quantity can be obtained according to the error between the actual speed and the dynamic speed. The process of obtaining the correction amount based on the error is shown in formula (12).

[0089]

[0090] Here, u(k) and u(k-1) can represent the correction amount of the predicted fuel quantity at time k and time k-, respectively; y*(k+1) represents the expected speed at time k+; and y(k) represents the actual speed at time k. λ>0 is a penalty factor used to limit the change of the input quantity; ρ∈(0,1] is a step size factor; and φ(k) represents the pseudo-partial derivative in the MFAC control algorithm.

[0091] According to an embodiment of this disclosure, an effective pseudo-partial derivative estimation law given by the symmetric similar structure of the control law of formula (12) can be as shown in formula (13), which can also be an iterative process of solving pseudo-partial derivatives.

[0092]

[0093] Where μ > 0 is the penalty factor, used to limit the variation of the pseudo-partial derivative estimate, and η ∈ (0, 2] is the step size factor. For application considerations, the pseudo-partial derivative can be reset when the estimate is obviously unreasonable, where ε is an arbitrarily small constant, and ε can be as shown in formula (14).

[0094]

[0095]

[0096] or|Δu(k-1)|≤ε

[0097] Where Δu(k-1) can be the increment of the correction amount at time k-1. During the solution process of the MFAC algorithm, the pseudo-partial derivative is automatically corrected. The pseudo-partial derivative obtained is judged to be reasonable during the iterative solution of u using formulas (12) to (13). If it is unreasonable, it is reset to the initial value.

[0098] According to embodiments of this disclosure, such as Figure 2A As shown, the error between the expected output speed and the actual output speed of the offline MPC system is calculated and used as the signal input for the MFAC. In principle, the MPC system and the gas turbine control system can be considered as a whole. The outer-loop MFAC can be a modular design similar to an embedded system, allowing the addition of a control loop when the offline MPC system cannot handle steady-state errors. During the control process, the MFAC performs online corrections to the offline implementation. Considering that linear prediction models generate errors when deviating from the design point, the outer-loop correction process is triggered when the speed is adjusted to a non-design point condition. The design point can be a speed point associated with the expected speed. From a data utilization perspective, the system architecture for controlling gas turbine speed based on a model-free adaptive controller provided in this embodiment can fully utilize the signal data generated during the control process. The system structure design is reasonable and can eliminate the steady-state error of the offline MPC system when the outer-loop MFAC is triggered. This allows the optimization calculation of the MPC to be transferred offline.

[0099] Figure 3A A schematic diagram illustrating an upward step increase to rated speed according to an embodiment of the present disclosure is shown. Figure 3B A schematic diagram illustrating a downward step reduction to rated speed according to an embodiment of the present disclosure is shown.

[0100] like Figures 3A-3B As shown in the figure, the horizontal axis represents time, and the vertical axis represents the percentage of engine speed. The figure compares three scenarios: desired engine speed, actual engine speed regulated by the offline MPC system, and actual engine speed regulated by both the offline MPC system and MFAC. Based on... Figures 3A-3B The simulation results of upward and downward step movements show that the actual speed regulated by the offline MPC system differs significantly from the desired speed. However, the actual speed regulated by the offline MPC system and MFAC can effectively correct the steady-state error that exists when the offline MPC system is controlled online in the later stage when the control process of the offline MPC system tends to be stable. This makes the final actual speed close to the desired speed, and thus makes the whole control process meet the needs of industrial control applications.

[0101] The method for controlling gas turbine speed based on a model-free adaptive controller (MFAC) according to embodiments of this disclosure is applicable not only to MPC systems with linear predictive models but also to MPC systems with nonlinear predictive models. The modular MFAC in the external loop can eliminate control deviations caused by offline calculations, as well as deviations caused by noise and other issues in the actual process.

[0102] According to the embodiments of this disclosure, the system architecture for controlling gas turbine speed based on a model-free adaptive controller has a simple design structure and can easily open and close the external loop control process; it can transfer the large amount of computation of MPC to offline computation, reducing resource consumption. Although MFAC is an online adjustment method, the amount of online computation used in MFAC is small and will not consume computing resources; the control method based on a model-free adaptive controller provided in the embodiments of this disclosure is also applicable to online correction of deviations existing in the actual control process of various gas turbines.

[0103] It should be noted that, unless it is explicitly stated that there is a sequential order of execution between different operations, or that there is a sequential order of execution between different operations in terms of technical implementation, the execution order between multiple operations may not be significant, and multiple operations may be executed simultaneously.

[0104] Based on the above-described method for controlling gas turbine speed using a model-free adaptive controller, this disclosure also provides a device for controlling gas turbine speed using a model-free adaptive controller. The following will be combined with... Figure 4 The device is described in detail.

[0105] Figure 4 The diagram schematically illustrates a structural block diagram of an apparatus for controlling the speed of a gas turbine based on a model-free adaptive controller according to an embodiment of the present disclosure.

[0106] like Figure 4 As shown, the device 400 for controlling the speed of a gas turbine based on a model-free adaptive controller in this embodiment includes a first determining module 410, a second determining module 420, a first input module 430, and a correction module 440.

[0107] The first determining module 410 is used to determine the predicted fuel quantity of the gas turbine based on the expected rotational speed of the gas turbine.

[0108] The second determining module 420 is used to determine the actual rotational speed of the gas turbine based on the predicted fuel quantity.

[0109] The first input module 430 is used to input the error determined based on the actual speed and the expected speed into the model-free adaptive controller, and output the correction amount for the predicted fuel quantity so as to obtain the target fuel quantity of the gas turbine based on the correction amount.

[0110] The correction module 440 is used to correct the actual speed of the gas turbine according to the target fuel quantity until the error between the actual speed of the gas turbine and the expected speed of the gas turbine meets a preset threshold.

[0111] According to embodiments of this disclosure, by controlling the gas turbine speed based on a model-free adaptive controller, a target fuel quantity can be obtained by using the error between the actual and desired speed of the gas turbine as input. The actual speed of the gas turbine is then corrected based on this target fuel quantity so that the error between the actual and desired speeds meets a preset threshold. Because the control error in the gas turbine control system caused by the offline-optimized feedback control method can be further adaptively corrected based on the error between the actual and desired speeds, the control error in the speed caused by the offline optimization method can be eliminated. Therefore, this at least partially overcomes the control error problem caused by offline calculation in related technologies, thereby achieving the technical effect of improving the control accuracy of industrial gas turbine speed control.

[0112] According to embodiments of this disclosure, the first determining module 410 may further include a first input unit.

[0113] The first input unit is used to input the predicted fuel quantity into the gas turbine control system and output the actual speed of the gas turbine.

[0114] According to embodiments of this disclosure, the second determining module 420 may further include a second input unit.

[0115] The second input unit is used to input the desired rotational speed of the gas turbine into the prediction model and output the predicted fuel quantity of the gas turbine, wherein the prediction model includes a state-space model.

[0116] According to embodiments of this disclosure, the second input unit further includes an output subunit.

[0117] The output subunit is used to output the predicted fuel quantity of the gas turbine based on the output matrix of the state-space model and the state vector related to the expected speed of the gas turbine.

[0118] According to embodiments of this disclosure, any plurality of modules among the first determining module 410, the second determining module 420, the first input module 430, and the correction module 440 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the first determining module 410, the second determining module 420, the first input module 430, and the correction module 440 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in any one of software, hardware, and firmware methods, or in a suitable combination of any of these methods. Alternatively, at least one of the first determining module 410, the second determining module 420, the first input module 430, and the correction module 440 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0119] It should be noted that the device part for controlling gas turbine speed based on a model-free adaptive controller in the embodiments of this disclosure corresponds to the method part for controlling gas turbine speed based on a model-free adaptive controller in the embodiments of this disclosure. For a detailed description of the device part for controlling gas turbine speed based on a model-free adaptive controller, please refer to the method part for controlling gas turbine speed based on a model-free adaptive controller, which will not be repeated here.

[0120] Figure 5 A block diagram of an electronic device suitable for implementing a method for controlling the speed of a gas turbine based on a model-free adaptive controller, according to an embodiment of the present disclosure, is shown schematically.

[0121] like Figure 5 As shown, an electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage portion 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0122] RAM 503 stores various programs and data required for the operation of electronic device 500. Processor 501, ROM 502, and RAM 503 are interconnected via bus 504. Processor 501 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0123] According to embodiments of this disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to a bus 504. The electronic device 500 may also include one or more of the following components connected to the I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0124] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0125] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 502 and / or RAM 503 and / or one or more memories other than ROM 502 and RAM 503 described above.

[0126] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the method for controlling gas turbine speed based on a model-free adaptive controller provided in embodiments of this disclosure.

[0127] When the computer program is executed by the processor 501, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0128] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 509, and / or installed from a removable medium 511. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0129] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by processor 501, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0130] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0131] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0132] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0133] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A method for controlling the speed of a gas turbine based on a model-free adaptive controller, comprising: The predicted fuel quantity of the gas turbine is determined based on the expected rotational speed of the gas turbine. The actual rotational speed of the gas turbine is determined based on the predicted fuel quantity. The error determined based on the actual speed and the expected speed is input into the model-free adaptive controller, which outputs a correction amount for the predicted fuel quantity so as to obtain the target fuel quantity of the gas turbine based on the correction amount. Based on the target fuel quantity, the actual speed of the gas turbine is corrected until the error between the actual speed of the gas turbine and the desired speed of the gas turbine meets a preset threshold. The step of determining the predicted fuel quantity of the gas turbine based on the expected rotational speed of the gas turbine includes: inputting the expected rotational speed of the gas turbine into the prediction model and outputting the predicted fuel quantity of the gas turbine, wherein the prediction model includes a state-space model.

2. The method according to claim 1, further comprising: The correction amount for the predicted fuel quantity is determined in units of time. The step of inputting the error determined based on the actual rotational speed and the desired rotational speed into the model-free adaptive controller, and outputting a correction amount for the predicted fuel quantity, includes: The correction amount at time k is obtained based on the error between the expected rotational speed at time k+1 and the actual rotational speed at time k, and the correction amount at time k-1, where k is greater than 1.

3. The method according to claim 2, wherein, Determining the actual rotational speed of the gas turbine based on the predicted fuel quantity includes: The predicted fuel quantity is input into the gas turbine control system, which outputs the actual rotational speed of the gas turbine.

4. The method according to claim 3, wherein, The actual rotational speed at time k is based on the actual rotational speed at time k-1 to time k-1-n. y The actual rotational speed at time k-1, and the correction amount from time k-1 to time n. u The correction amount at time n is determined, where n is n. y n represents the order of the output model of the gas turbine control system. u This indicates the order of the input model of the gas turbine control system.

5. The method according to claim 1, wherein, The predicted fuel quantity output for the gas turbine includes: Based on the output matrix of the state-space model and the state vector related to the expected speed of the gas turbine, the predicted fuel quantity of the gas turbine is output.

6. A device for controlling the speed of a gas turbine based on a model-free adaptive controller, comprising: The first determining module is used to determine the predicted fuel quantity of the gas turbine based on the expected rotational speed of the gas turbine; The second determining module is used to determine the actual rotational speed of the gas turbine based on the predicted fuel quantity; The first input module is used to input the error determined based on the actual speed and the expected speed into the model-free adaptive controller, and output the correction amount for the predicted fuel quantity so as to obtain the target fuel quantity of the gas turbine based on the correction amount; The correction module is used to correct the actual speed of the gas turbine according to the target fuel quantity until the error between the actual speed of the gas turbine and the desired speed of the gas turbine meets a preset threshold. The first determining module includes a second input unit; the second input unit is used to input the desired rotational speed of the gas turbine into the prediction model and output the predicted fuel quantity of the gas turbine, wherein the prediction model includes a state-space model.

7. An electronic device, comprising: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 5.

9. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 5.

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