Active-disturbance-rejection control method for heavy-duty gas turbine, electronic equipment and storage medium

Abstract

The invention provides an active-disturbance-rejection control method for a heavy-duty gas turbine, electronic equipment and a storage medium. The method comprises the following steps: establishing a two-input multi-output state space model of the heavy duty gas turbine; performing high-order all-drive system model conversion on the two-input multi-output state space model so as to decouple the two-input multi-output state space model into an exhaust temperature subsystem model and a generated power subsystem model; an exhaust temperature ideal control law and a generated power ideal control law are established according to the exhaust temperature subsystem model and the generated power subsystem model respectively; and controlling the heavy duty gas turbine at least by using the ideal exhaust temperature control law and the ideal generated power control law. The method can improve the operation stability of the heavy duty gas turbine.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for heavy-duty gas turbines, and more specifically to a method for active disturbance rejection control of heavy-duty gas turbines, electronic equipment, and storage medium. Background Technology

[0002] With the continuous evolution of the global energy structure and the increasing installed capacity of renewable energy sources such as wind and solar power, heavy-duty gas turbines, as a core component of gas-steam combined cycle units, are playing an increasingly prominent role in grid regulation and renewable energy grid integration. Heavy-duty gas turbines exhibit complex dynamic characteristics during operation, including strong nonlinearity, time-varying parameters, and strong coupling. Traditional control methods, due to their performance limitations, struggle to achieve fast and accurate responses, often resulting in unsatisfactory control accuracy and response speed.

[0003] In view of this, the present invention is hereby proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a method, electronic device and storage medium for active disturbance rejection control of heavy-duty gas turbines. This method is used to solve the problems of nonlinear characteristics and uncertain disturbances faced by heavy-duty gas turbines mentioned in the background art, improve their peak-shaving capacity, and thus effectively cope with load fluctuations brought about by the grid connection of renewable energy.

[0005] To achieve the above objectives, the present invention provides a method for active disturbance rejection control of heavy-duty gas turbines, comprising: Establish a two-input multiple-output state-space model for a heavy-duty gas turbine; The two-input multi-output state space model is transformed into a high-order all-drive system model to decouple it into an exhaust temperature subsystem model and a power generation subsystem model. Based on the exhaust temperature subsystem model and the power generation subsystem model, respectively, establish the ideal control law for exhaust temperature and the ideal control law for power generation. The heavy-duty gas turbine is controlled using at least the ideal control law for exhaust temperature and the ideal control law for power generation.

[0006] The aforementioned technical solution decouples the system structure by transforming the state-space model of a heavy-duty gas turbine into a high-order all-drive system model. It then decomposes the system into two control subsystems with all-drive system characteristics. Furthermore, based on the all-drive system approach, an ideal control law is constructed, enabling intelligent active disturbance rejection control with flexible disturbance rejection capabilities. This control method outperforms traditional control methods in terms of accuracy, robustness, and dynamic response, significantly improving its ability to handle nonlinear characteristics and uncertain disturbances in heavy-duty gas turbine systems. It exhibits outstanding dynamic performance and engineering adaptability. This method meets the dual requirements of rapid deployment and high-reliability control in industrial settings, demonstrating significant technological innovation and broad application prospects. It is particularly suitable for complex industrial environments such as energy and power, and automation control, possessing significant technical value and practical significance.

[0007] For example, establishing a two-input multiple-output state-space model of a heavy-duty gas turbine includes: The compressor, combustion chamber, and turbine of the heavy-duty gas turbine are derived to obtain the two-input multi-output state-space model. The two-input multiple-output state-space model is represented by the following expression: ; ; ; ; ; Among them, state variables , It is the real-time combustion chamber pressure. It is the real-time combustion chamber temperature. It's airflow. It is the fuel flow rate. It is the exhaust temperature; input variable , It is the inlet guide vane opening. It is the fuel flow rate regulation value; It is the volume of the combustion chamber; It is the rated combustion chamber pressure; This is the rated combustion chamber temperature; It is the exhaust gas constant; It is the specific heat capacity of the exhaust gas under constant pressure; It is the specific heat capacity of air under constant pressure; It has a low calorific value; It is the air constant; It is the ambient temperature; It's environmental pressure; It refers to compressor efficiency; It is the compressor's adiabatic efficiency; and These are the time constants of the inlet guide vanes and the fuel intake system, respectively. and It is gain; and It is a constant coefficient; It is the time constant of the exhaust temperature sensor; It is the rated airflow velocity of the compressor.

[0008] The above scheme constructs a two-input multi-output state-space model by deriving the mechanism of the compressor, combustion chamber, and turbine of a heavy-duty gas turbine. This model can accurately characterize the dynamic mapping relationship between key state variables such as combustion chamber pressure, combustion chamber temperature, air flow, fuel flow, and exhaust temperature, and two major input variables: inlet guide vane opening and fuel flow regulation value. It can clearly reflect the influence of various structural parameters, efficiency parameters, time constants, and gain coefficients on the system operating state, and realize the quantitative description and dynamic tracking of the multi-physics coupled operation characteristics of the heavy-duty gas turbine. This can provide reliable mathematical support and theoretical basis for precise control in subsequent steps.

[0009] For example, the exhaust temperature subsystem model and the power generation subsystem model are represented by the following expressions: , ; in, , , , , and These represent the nonlinear components of the power generation subsystem and the exhaust temperature subsystem, respectively. ; Indicates generator efficiency; in, .

[0010] The above technical solution, by constructing and adopting the mathematical expressions of the exhaust temperature subsystem model and the power generation subsystem, can accurately characterize the nonlinear characteristics of power generation and exhaust temperature, respectively. This enables precise modeling and quantitative description of key parameters of power generation and exhaust temperature in the power generation system. Moreover, this method of setting up independent models can eliminate mutual interference between the controlled variables, exhaust temperature and power generation, simplify the modeling complexity of the overall system, improve the accuracy and convergence speed of parameter identification of each subsystem model, and make the dynamic characteristic description of exhaust temperature and power generation more in line with actual operating laws. This facilitates the establishment of subsequent control laws and the precise regulation of power generation and exhaust temperature.

[0011] For example, establishing the ideal control law for exhaust temperature and the ideal control law for power generation based on the exhaust temperature subsystem model and the power generation subsystem model respectively includes: Based on the exhaust temperature subsystem model and the power generation subsystem model, a full-drive state model of the exhaust temperature subsystem and a full-drive state model of the power generation subsystem are established respectively. The nonlinear and uncertain disturbances in the exhaust temperature subsystem and the power generation subsystem are combined into a total disturbance; The full-drive state models of the exhaust temperature subsystem and the power generation subsystem are rewritten based on the total disturbance. Based on the rewritten full-drive state models of the exhaust temperature subsystem and the power generation subsystem, control laws are established respectively.

[0012] The above technical solution constructs full-drive-state models for exhaust temperature and power generation subsystems respectively, unifies the nonlinear characteristics and uncertain disturbances in the system into a total disturbance, and reconstructs the full-drive-state model based on the total disturbance. Then, based on the reconstructed model, ideal control laws for exhaust temperature and power generation are designed respectively. This can effectively suppress the influence of system nonlinearity and external disturbances on control accuracy, and improve the stability, robustness and dynamic response performance of exhaust temperature and power generation control.

[0013] For example, the full-drive state model of the exhaust temperature subsystem and the full-drive state model of the power generation subsystem can be represented by the following expressions: ; in, , , ; The rewritten full-drive-state models of the power generation subsystem and the exhaust temperature subsystem can be represented by the following expressions: ; in, , ; The ideal control laws for the power generation subsystem and the exhaust temperature subsystem are expressed by the following expressions: ; in, and These represent the power generation tracking error and the exhaust temperature tracking error, respectively. and These are the preset values ​​for power generation and exhaust temperature, respectively. and These represent the observed values ​​of power generation and exhaust temperature tracking errors, respectively. and These represent the observed values ​​of the second and third derivatives of the exhaust temperature tracking error, respectively. and These represent the observed values ​​of the total disturbance of the power generation subsystem and the total disturbance of the exhaust temperature subsystem, respectively. The first derivative of the preset value of power generation; The third derivative of the preset exhaust temperature.

[0014] The above technical solution constructs and solves the full-drive-state models of the exhaust temperature subsystem and the power generation subsystem. Combined with model rewriting and error observation, total disturbance observation and other processing methods, it can accurately obtain the tracking error of power generation and exhaust temperature, its higher-order derivative observation values ​​and total disturbance observation values. Based on the ideal control law, it realizes high-precision tracking control of power generation and exhaust temperature, while effectively compensating for system disturbances, improving control response speed and steady-state accuracy, and ensuring that the system still has stable and reliable dynamic control performance under changes in preset values ​​of power generation and temperature.

[0015] Exemplarily, the method further includes: The power generation tracking error and exhaust temperature tracking error in the control law are derived respectively to obtain the dynamic characteristics of the tracking error of the power generation subsystem and the exhaust temperature subsystem. Based on the control laws and tracking error dynamic characteristics of the power generation subsystem and the exhaust temperature subsystem, respectively, linear extended state observers for the power generation subsystem and the exhaust temperature subsystem are constructed. The control of the heavy-duty gas turbine using at least the ideal control law for exhaust temperature and the ideal control law for power generation includes: Using the ideal control law for exhaust temperature, the ideal control law for power generation, and the linear extended state observers for the power generation subsystem and the exhaust temperature subsystem, real-time disturbance observation and compensation control are performed on the power generation subsystem and the exhaust temperature subsystem of the heavy-duty gas turbine.

[0016] The above technical solution derives the power generation tracking error and exhaust temperature tracking error in the control law separately, clarifies the dynamic characteristics of the tracking errors of the power generation subsystem and the exhaust temperature subsystem, and then constructs linear extended state observers based on the corresponding control law and tracking error dynamic characteristics. Finally, by combining the ideal control law and the linear extended state observer, real-time disturbance observation and compensation control of the power generation subsystem and exhaust temperature subsystem of the heavy-duty gas turbine can be realized. This can effectively improve the system's ability to suppress disturbances and its tracking accuracy, and enhance the stability, robustness and control accuracy of the heavy-duty gas turbine operation.

[0017] For example, the tracking error dynamic characteristics of the power generation subsystem are represented by the following expression: ; The linear extended state observer of the power generation subsystem is represented by the following expression: ; in, This represents the estimated value of the state variable s1. This represents the gain vector of the linear extended state observer of the power generation subsystem.

[0018] The above scheme constructs a dynamic characteristic expression for the tracking error of the power generation subsystem and designs a corresponding linear extended state observer. By introducing state variable estimates and observer gain vectors, it can achieve real-time and accurate observation and dynamic compensation of the internal state and disturbances of the power generation subsystem, effectively reducing the system tracking error, improving the response speed, anti-interference ability and control accuracy of the power generation subsystem, and ensuring that the system outputs the target power stably and efficiently under complex operating conditions.

[0019] For example, the tracking error dynamic characteristics of the exhaust temperature subsystem are represented by the following expression: ; The linear expansion state observer of the exhaust temperature subsystem is represented by the following expression: ; in, This represents the estimated value of the state variable s2. This represents the gain vector of the linear extended state observer of the exhaust temperature subsystem.

[0020] The above scheme constructs a dynamic characteristic expression for the tracking error of the exhaust temperature subsystem and designs a corresponding linear extended state observer to clarify the representational relationship between the estimated state variable and the observer gain vector. This enables accurate observation and dynamic tracking of the operating state of the exhaust temperature subsystem, effectively improving the system state identification accuracy and dynamic response stability, and enhancing the robustness and control accuracy of the exhaust temperature control process.

[0021] According to another aspect of the present invention, an electronic device is provided, including a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the method as described above.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program / instructions that, when executed by a processor, implement the method described above.

[0023] In the aforementioned technical solution, the state-space model of a heavy-duty gas turbine is transformed into a high-order all-drive system model, achieving decoupling of the system structure. This decomposes the system into two control subsystems with all-drive system characteristics. Furthermore, an ideal control law is constructed based on the all-drive system approach, enabling intelligent active disturbance rejection control with flexible disturbance rejection capabilities. This control method outperforms traditional control methods in terms of accuracy, robustness, and dynamic response, significantly improving its ability to handle nonlinear characteristics and uncertain disturbances in heavy-duty gas turbine systems, exhibiting outstanding dynamic performance and engineering adaptability. This method meets the dual requirements of rapid deployment and high-reliability control in industrial settings, demonstrating significant technological innovation and broad application prospects. It is particularly suitable for complex industrial environments such as energy and power, and automation control, possessing significant technical value and practical significance.

[0024] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0025] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0026] Figure 1 A schematic flowchart of a heavy-duty gas turbine active disturbance rejection control method according to an embodiment of the present invention is shown; Figure 2 A control strategy structure diagram according to an embodiment of the present invention is shown; Figure 3 This diagram illustrates the load tracking instruction for a preset value tracking test in an experimental example according to an embodiment of the present invention. Figure 4 This diagram shows a comparison of the dynamic power generation response of the PG9351FA model in an experimental example according to an embodiment of the present invention. Figure 5 This diagram shows a comparison of the dynamic response of exhaust temperature of the PG9351FA model according to an experimental example in an embodiment of the present invention. Figure 6 This diagram shows a comparison of the dynamic power generation response of the MS109FA model in an experimental example according to an embodiment of the present invention. Figure 7 This diagram shows a comparison of the dynamic response of exhaust temperature of the MS109FA model according to an experimental example in an embodiment of the present invention. Figure 8 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of the present invention.

[0028] As mentioned above, traditional control methods, due to their performance limitations, struggle to achieve fast and accurate responses, often resulting in unsatisfactory control accuracy and response speed in heavy-duty gas turbines. To overcome these shortcomings, exploring advanced control methods to address the challenges faced by heavy-duty gas turbine control systems has gradually become a research hotspot in recent years. The all-drive system approach was proposed in 2020, providing a new perspective for the design of control methods for heavy-duty gas turbine control systems. Active disturbance rejection control (ADRC), as an advanced control technology, has become an important tool for solving complex dynamic system problems due to its strong anti-interference capability and fast dynamic response. Based on this, the inventors considered constructing ADRC for heavy-duty gas turbines by introducing an all-drive system to achieve stable operation. For detailed explanation, the following description uses an embodiment.

[0029] Example This embodiment provides a method for active disturbance rejection control of heavy-duty gas turbines. Figure 1 A schematic flowchart illustrating a heavy-duty gas turbine active disturbance rejection control method according to an embodiment of the present invention is shown. Figure 1 As shown, the method includes the following steps: S110, S120, S130 and S140.

[0030] In step S110, a two-input multiple-output state-space model of a heavy-duty gas turbine is established.

[0031] In this embodiment, a two-input multiple-output (MIMO) state-space model of a heavy-duty gas turbine is established, including: deriving the compressor, combustion chamber, and turbine of the heavy-duty gas turbine to obtain the two-input multiple-output state-space model. Specifically, a generalized nonlinear state-space model of the heavy-duty gas turbine, i.e., a two-input multiple-output (MIMO) state-space model, can be derived from the compressor, combustion chamber, and turbine based on the laws of conservation of mass and momentum.

[0032] This two-input multiple-output state-space model can be represented by the following expression: ; ; ; ; ; Among them, state variables , It is the real-time combustion chamber pressure. It is the real-time combustion chamber temperature. It's airflow. It is the fuel flow rate. It is the exhaust temperature; input variable , It is the inlet guide vane opening. It is the fuel flow rate regulation value; It is the volume of the combustion chamber; It is the rated combustion chamber pressure; This is the rated combustion chamber temperature; It is the exhaust gas constant; It is the specific heat capacity of the exhaust gas under constant pressure. It is the specific heat capacity of air under constant pressure, and the unit is J / kgK; It is the lower calorific value of fuel, measured in J / kg; It is the air constant; This is the ambient temperature, expressed in Kelvin (K). This is environmental pressure, measured in Pa. It refers to compressor efficiency; It is the compressor's adiabatic efficiency; and These are the time constants of the inlet guide vanes and the fuel intake system, respectively. and It's gain. and It is a constant coefficient. , , , , and All of these can be obtained through fitting experimental or simulation data; It is the time constant of the exhaust temperature sensor, measured in seconds (s). It is the rated airflow velocity of the compressor.

[0033] In step S120, the two-input multi-output state space model is transformed into a high-order all-drive system model to decouple the two-input multi-output state space model into an exhaust temperature subsystem model and a power generation subsystem model.

[0034] To facilitate the elimination of mutual interference between the controlled variables, exhaust temperature and power generation, in this embodiment, the two-input multi-output state-space model is decoupled into an exhaust temperature subsystem and a power generation subsystem in the form of a full-drive system model, i.e., an exhaust temperature subsystem model and a power generation subsystem model.

[0035] Among them, the power generation capacity of the gas turbine The specific expression is as follows: .

[0036] Differentiate the expression for power generation and substitute it into... The expression is: .

[0037] Take the second derivative of the exhaust temperature equation and substitute it into... The expression is: .

[0038] Continue to differentiate and substitute. , , The expression is: .

[0039] make ,have to: .

[0040] Further refining the model into an all-drive system, the exhaust temperature subsystem model and the power generation subsystem model are represented by the following expressions: , ; in, , , , , and These represent the nonlinear components of the power generation subsystem and the exhaust temperature subsystem, respectively. ; Power generation capacity; Indicates generator efficiency; express of Derivative order; express of The first derivative.

[0041] In step S130, the ideal control law for exhaust temperature and the ideal control law for power generation are established based on the exhaust temperature subsystem model and the power generation subsystem model, respectively.

[0042] After obtaining the power generation subsystem model and the exhaust temperature subsystem model, we consider designing ideal control laws that integrate all-drive characteristics for each model to achieve intelligent active disturbance rejection control of the exhaust temperature and power generation of the heavy-duty gas turbine. In this embodiment, step S130 can be specifically represented by the following steps S131, S132, S133, and S134.

[0043] In step S131, based on the exhaust temperature subsystem model and the power generation subsystem model, the full-drive state model of the exhaust temperature subsystem and the full-drive state model of the power generation subsystem are established respectively.

[0044] After obtaining the subsystem models for exhaust temperature and power generation in full-drive mode, we first consider establishing the dynamic characteristics of the two subsystems, i.e., the full-drive models. In this embodiment, the full-drive models of the exhaust temperature subsystem and the power generation subsystem can be represented by the following expressions: ; in, , , .

[0045] In step S132, the nonlinear and uncertain disturbances in the exhaust temperature subsystem and the power generation subsystem are combined into a total disturbance. In this embodiment, the total disturbance is taken. , . This represents the total disturbance related to power generation. This represents the total disturbance related to exhaust temperature. k 11 , k 21 , k 22 , k 23 These are all controller parameters.

[0046] Step S133: Rewrite the full-drive-state models of the exhaust temperature subsystem and the power generation subsystem based on the total disturbance. According to the total disturbance, the above dynamic characteristics can be rewritten as follows: .

[0047] In step S134, control laws are established based on the rewritten full-drive-state models of the exhaust temperature subsystem and the power generation subsystem. In this embodiment, the ideal control laws for the power generation subsystem and the exhaust temperature subsystem are expressed by the following expressions: ; in, and These represent the power generation tracking error and the exhaust temperature tracking error, respectively. and These are the preset values ​​for power generation and exhaust temperature, respectively. and These represent the observed values ​​of power generation and exhaust temperature tracking errors, respectively. and These represent the observed values ​​of the second and third derivatives of the exhaust temperature tracking error, respectively. and These represent the observed values ​​of the total disturbance of the power generation subsystem and the total disturbance of the exhaust temperature subsystem, respectively. The first derivative of the preset value of power generation is obtained by a second-order linear tracking differentiator. The third derivative of the preset exhaust temperature is obtained by a fourth-order linear tracking differentiator.

[0048] In step S140, the heavy-duty gas turbine is controlled using at least the ideal control law for exhaust temperature and the ideal control law for power generation.

[0049] In this embodiment, after obtaining the control law, the system's anti-interference capability is further enhanced by extending the state observer. Specifically, the method further includes steps S150 and S160.

[0050] In step S150, the power generation tracking error and exhaust temperature tracking error in the control law are derived respectively to obtain the dynamic characteristics of the tracking error of the power generation subsystem and the exhaust temperature subsystem.

[0051] The specific process is as follows: First, assume... Since the designed control method is error-driven, , Therefore, the dynamic characteristics of the tracking error of the power generation subsystem are obtained as follows: .

[0052] The tracking error dynamic characteristics of the power generation subsystem are rewritten in the form of a state-space expression. Let... ,have to ,in, .

[0053] Similarly, assuming Since the designed control method is error-driven, , , , Therefore, the dynamic characteristics of the tracking error of the exhaust temperature subsystem are obtained as follows: .

[0054] The tracking error dynamic characteristics of the exhaust temperature subsystem described above are rewritten in the form of a state-space expression. Let... ,have to .in, .

[0055] In step S160, linear extended state observers for the power generation subsystem and the exhaust temperature subsystem are constructed based on their control laws and tracking error dynamic characteristics. For ease of description, the linear extended state observer for the power generation subsystem is referred to as LESO1, and the linear extended state observer for the exhaust temperature subsystem is referred to as LESO2.

[0056] In this embodiment, LESO1 is introduced to estimate s1. The specific LESO1 construction is as follows: ; in, This represents the estimated value of the state variable s1. This represents the gain vector of LESO1.

[0057] The bandwidth of LESO1 above is defined as follows: Let the characteristic polynomial of the estimation error satisfy: This allows the gain of LESO1 to be determined as follows: .

[0058] Define the bandwidth of the power generation subsystem control strategy as: Let the characteristic equation of the dynamic characteristics of the power generation subsystem error satisfy: This makes the design parameters of the power generation subsystem control strategy as follows: .

[0059] Similarly, LESO2 is introduced to estimate s2. The specific LESO2 construction is as follows: ; in, This represents the estimated value of the state variable s2. This represents the gain vector of LESO2.

[0060] The bandwidth of LESO2 is defined as follows: Let the characteristic polynomial of the estimation error satisfy: This allows the gain of LESO2 to be determined as follows: .

[0061] Define the bandwidth of the exhaust temperature subsystem control strategy as follows: Let the characteristic equation of the dynamic characteristics of the exhaust temperature subsystem error satisfy: This makes the design parameters for the exhaust temperature subsystem control strategy as follows: .

[0062] In this case, step S140 involves controlling the heavy-duty gas turbine using at least the ideal control law for exhaust temperature and the ideal control law for power generation. This includes using the ideal control law for exhaust temperature, the ideal control law for power generation, and linearly extended state observers for the power generation subsystem and the exhaust temperature subsystem to perform real-time disturbance observation and compensation control on the power generation subsystem and the exhaust temperature subsystem of the heavy-duty gas turbine. Those skilled in the art will understand the specific implementation of this control method, which will not be elaborated upon here.

[0063] Figure 2 A control strategy structure diagram according to an embodiment of the present invention is shown. Figure 2 As shown, a heavy-duty gas turbine consists of a compressor, combustion chamber, turbine, and coaxial generator. It includes two control inputs: inlet guide vane opening and gas / fuel flow rate, and two core controlled outputs: generator output power and turbine outlet exhaust temperature. Through decoupling design, the original coupled system is broken down into two independent single-input, single-output closed-loop control loops: exhaust temperature and generator output power. Both loops first calculate the tracking deviation between the preset values ​​of the controlled variables (preset values ​​for exhaust temperature and generator output power) and the actual outputs (exhaust temperature and generator output power) using a comparison circuit. This deviation is then fed into an active disturbance rejection controller (ADRC) integrating three core units: feedforward compensation, extended state observer, and error feedback. The controller outputs and adjusts the inlet guide vane opening and gas / fuel flow rate, thereby achieving ADRC.

[0064] To illustrate the effectiveness of the solution in this embodiment, an experimental example is used below to demonstrate its control performance. In this example, the PG9351FA of a gas turbine power plant is used as a case study. Based on the mechanistic model, actual operating data is collected and analyzed under rapid load fluctuations of 150MW to 255.6MW. Taking the MS109FA as an example, the performance of the control strategy in this embodiment is evaluated under the conditions of rated power of 275MW and exhaust temperature of 597℃. The MOIEDO intelligent optimization algorithm is used under both the PG9351FA and MS109FA models, with ITAE as the optimization objective, to iteratively find the optimal parameters of the control strategy. Simulation tests are performed on the solution in this embodiment to evaluate the tracking, disturbance rejection, and robustness performance of the method shown in this embodiment. To compare and analyze the control performance of the proposed method in this embodiment, PID and LADRC controllers are also selected for comparison. The parameter optimization results of the control method under different gas turbine models are shown in Table 1.

[0065] Table 1. Optimal parameter settings for control methods under different gas turbine models

[0066] Next, using MATLAB simulations, preset value tracking and disturbance rejection tests were conducted under varying load conditions to verify the effectiveness of the proposed control strategy. These tests included preset value tracking and disturbance rejection tests. The load tracking command for the preset value tracking test is as follows: Figure 3 ( Figure 3 The horizontal axis represents time, and the vertical axis represents the preset power setpoint, where the preset exhaust temperature is fixed at 650℃. This scheme evaluates the regulation effect of heavy-duty gas turbine power generation and exhaust temperature, and compares it with LADRC and PID. The results are shown in [data missing]. Figure 4 ( Figure 4 The horizontal axis represents time, and the vertical axis represents power generation. Figure 5 ( Figure 5 The horizontal axis represents time, and the vertical axis represents exhaust temperature. Figure 4 The results show a comparison between the proposed control strategy and conventional LADRC and PID control during load command tracking. Figure 4 It is evident that the control strategy proposed in this scheme responds more rapidly to commands and can enter steady state more quickly, while meeting the design requirements in terms of control accuracy and overshoot. Figure 5 The comparison results during the temperature command tracking process are presented. For example... Figure 5 It can be seen that the control strategy proposed in this scheme has a faster dynamic response capability, can quickly reach a steady state, and performs well in terms of control accuracy and overshoot control, while also showing strong anti-disturbance performance.

[0067] In disturbance rejection testing, factors such as system nonlinearity, thermal inertia, and signal noise can significantly affect the performance of the gas fuel valve and the accuracy of temperature sensor measurements. The normalized power is 1×10⁻⁶. -5 White noise was added to the gas fuel input to evaluate the control performance of the proposed control strategy under rated operating conditions, and compared with LADRC and PID. The comparison results are as follows: Figure 6 ( Figure 6 The horizontal axis represents time, and the vertical axis represents power generation. Figure 7 ( Figure 7 The horizontal axis represents time, and the vertical axis represents exhaust temperature. Figure 4-7In this embodiment, the solution is referred to as Proposed Method. Figure 6 The response of the MS109FA model under load command changes is compared. The results show that the control method proposed in this invention has better dynamic adjustment performance, can smoothly achieve target tracking, and maintains good performance in terms of accuracy and overshoot control. Figure 7 The control performance of the MS109FA model in a temperature tracking scenario is compared. It can be seen that the control strategy proposed in this invention not only has a faster convergence speed but also exhibits stronger stability and disturbance rejection capability, with overall control performance superior to the comparative methods.

[0068] The comparison shows that the solution in this embodiment has high reliability, and all performance indicators are better than LADRC and PID, with better preset value tracking ability and robustness.

[0069] In summary, the solution presented in this embodiment, employing a high-order all-drive system model and intelligent active disturbance rejection control, enables flexible disturbance rejection control of the exhaust temperature and power generation of a heavy-duty gas turbine. Compared to traditional control methods, the proposed control strategy significantly improves accuracy, robustness, and dynamic response. Specifically, the high-order all-drive system effectively decouples the nonlinear characteristics and uncertainties of the heavy-duty gas turbine and enhances the system's anti-interference capability through an extended state observer. Furthermore, the control law based on this method enables the exhaust temperature and power generation to accurately track preset values ​​and maintain efficient operation under disturbance and nonlinear system environments. The use of these techniques makes the operation of the heavy-duty gas turbine more stable and allows for rapid response to load fluctuations, meeting the dual requirements of rapid deployment and high-reliability control in industrial settings.

[0070] According to another aspect of the present invention, an electronic device is also provided. Figure 8 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. Figure 8 As shown, the electronic device 800 includes a processor 810 and a memory 820. The memory 820 stores a computer program, which the processor 810 executes to implement the method described above.

[0071] According to another aspect of the present invention, a computer-readable storage medium is also provided. The storage medium stores a computer program / instructions that, when executed by a processor, implement the method described above. The storage medium may, for example, include a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0072] Those skilled in the art will readily understand the implementation structure, working principle, and beneficial effects of electronic devices and computer-readable storage media by reading the above methods. For the sake of brevity, further details will not be elaborated upon here.

[0073] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.

[0074] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0075] In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0076] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0077] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention. However, this approach should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with fewer features than all of those in a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0078] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or elements of any method or apparatus so disclosed may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0079] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0080] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules in the electronic device according to embodiments of the present invention. The present invention can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing some or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0081] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0082] The above description is merely a specific embodiment of the present invention or an explanation of that embodiment. The scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for active disturbance rejection control of a heavy-duty gas turbine, characterized in that, include: Establish a two-input multiple-output state-space model for a heavy-duty gas turbine; The two-input multi-output state space model is transformed into a high-order all-drive system model to decouple it into an exhaust temperature subsystem model and a power generation subsystem model. Based on the exhaust temperature subsystem model and the power generation subsystem model, respectively, establish the ideal control law for exhaust temperature and the ideal control law for power generation. The heavy-duty gas turbine is controlled using at least the ideal control law for exhaust temperature and the ideal control law for power generation.

2. The method according to claim 1, characterized in that, The establishment of a two-input multiple-output state-space model for a heavy-duty gas turbine includes: The compressor, combustion chamber, and turbine of the heavy-duty gas turbine are derived to obtain the two-input multi-output state-space model. The two-input multiple-output state-space model is represented by the following expression: ； ； ； ； ； Among them, state variables , It is the combustion chamber pressure. It is the combustion chamber temperature. It's airflow. It is the fuel flow rate. It is the exhaust temperature; input variable , It refers to the inlet guide vane opening. It is the fuel flow rate regulation value; It is the volume of the combustion chamber; It is the rated combustion chamber pressure; This is the rated combustion chamber temperature; It is the exhaust gas constant; It is the specific heat capacity of the exhaust gas under constant pressure; It is the specific heat capacity of air under constant pressure; It is the low calorific value of the fuel; It is the air constant; It is the ambient temperature; It is environmental pressure; It refers to compressor efficiency; It is the compressor's adiabatic efficiency; and These are the time constants of the inlet guide vanes and the fuel intake system, respectively. and It is gain; and It is a constant coefficient; It is the time constant of the exhaust temperature sensor; It is the rated airflow velocity of the compressor.

3. The method according to claim 2, characterized in that, The exhaust temperature subsystem model and the power generation subsystem model are represented by the following expressions: , ； in, , , , , and These represent the nonlinear components of the power generation subsystem and the exhaust temperature subsystem, respectively. ; Indicates generator efficiency; in, 。 4. The method according to any one of claims 1-3, characterized in that, The establishment of ideal control laws for exhaust temperature and power generation based on the exhaust temperature subsystem model and the power generation subsystem model, respectively, includes: Based on the exhaust temperature subsystem model and the power generation subsystem model, a full-drive state model of the exhaust temperature subsystem and a full-drive state model of the power generation subsystem are established respectively. The nonlinear and uncertain disturbances in the exhaust temperature subsystem and the power generation subsystem are combined into a total disturbance; The full-drive state models of the exhaust temperature subsystem and the power generation subsystem are rewritten based on the total disturbance. Based on the rewritten full-drive state models of the exhaust temperature subsystem and the power generation subsystem, control laws are established respectively.

5. The method according to claim 4, characterized in that, The full-drive state model of the exhaust temperature subsystem and the full-drive state model of the power generation subsystem can be represented by the following expressions: ； in, , , ; The rewritten full-drive-state models of the power generation subsystem and the exhaust temperature subsystem can be represented by the following expressions: ； in, , ; The ideal control laws for the power generation subsystem and the exhaust temperature subsystem are expressed by the following expressions: ； in, and These represent the power generation tracking error and the exhaust temperature tracking error, respectively. and These are the preset values ​​for power generation and exhaust temperature, respectively. and These represent the observed values ​​of power generation and exhaust temperature tracking errors, respectively. and These represent the observed values ​​of the second and third derivatives of the exhaust temperature tracking error, respectively. and These represent the observed values ​​of the total disturbance of the power generation subsystem and the total disturbance of the exhaust temperature subsystem, respectively. The first derivative of the preset value of power generation; The third derivative of the preset exhaust temperature.

6. The method according to claim 4, characterized in that, The method further includes: The power generation tracking error and exhaust temperature tracking error in the control law are derived respectively to obtain the dynamic characteristics of the tracking error of the power generation subsystem and the exhaust temperature subsystem. Based on the control laws and tracking error dynamic characteristics of the power generation subsystem and the exhaust temperature subsystem, respectively, linear extended state observers for the power generation subsystem and the exhaust temperature subsystem are constructed. The control of the heavy-duty gas turbine using at least the ideal control law for exhaust temperature and the ideal control law for power generation includes: Using the ideal control law for exhaust temperature, the ideal control law for power generation, and the linear extended state observers for the power generation subsystem and the exhaust temperature subsystem, real-time disturbance observation and compensation control are performed on the power generation subsystem and the exhaust temperature subsystem of the heavy-duty gas turbine.

7. The method according to claim 6, characterized in that, The dynamic characteristics of the tracking error of the power generation subsystem are expressed by the following expression: ； The linear extended state observer of the power generation subsystem is represented by the following expression: ； in, This represents the estimated value of the state variable s1. This represents the gain vector of the linear extended state observer of the power generation subsystem.

8. The method according to claim 6, characterized in that, The dynamic characteristics of the tracking error of the exhaust temperature subsystem are expressed by the following expression: ； The linear expansion state observer of the exhaust temperature subsystem is represented by the following expression: ； in, This represents the estimated value of the state variable s2. This represents the gain vector of the linear extended state observer of the exhaust temperature subsystem.

9. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The system stores a computer program / instructions that, when executed by a processor, implement the method as described in any one of claims 1-8.

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