A single-input multiple-output robust speed regulator design method, system and medium

Abstract

This application relates to a design method, system, and medium for a single-input multi-output robust speed controller. The method includes: Step 1, establishing a system model; Step 2, constructing a single-input multi-output controlled object; Step 3, robust speed controller design; Step 4, robustness verification; and Step 5, control command execution. This method, by employing a single-input multi-output structure, fully utilizes multi-dimensional state information such as guide vane position, mechanical power, and load angle as auxiliary feedback, and achieves robustness through systematic H... ∞ The optimized design achieves coordinated optimization of each control channel, resulting in overall performance far exceeding that of a single-variable design.

Description

Technical Field

[0001] This invention relates to the field of power system control technology, specifically to a design method, system, and medium for a single-input multi-output robust speed governor. Background Technology

[0002] In power systems, turbine governors are used to control the turbine speed to maintain the stability of the generator output frequency. With the expansion of power grid interconnection, the increase in large generator units, and the continuous growth in electricity demand, the performance requirements for governors are becoming increasingly stringent. Traditional governor designs often employ classic single-input single-output (SISO), proportional-integral (PI), or proportional-integral-derivative (PID) control strategies. These designs are based on nominalized linear time-invariant system models, which are insufficient to effectively address the inherent nonlinear characteristics, time-varying parameters, external load disturbances, and complex hydraulic dynamics such as water hammer and water column elasticity in turbine systems. Therefore, there is an urgent need for a turbine governor system and method that can overcome these shortcomings and possess strong robustness, excellent disturbance rejection capability, and dynamic performance. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention proposes a design method and system for a single-input multi-output robust speed controller, thereby solving the problems of poor robustness, weak anti-interference capability, and performance limitations of existing speed controllers.

[0004] To achieve the above objectives, this application provides the following technical solution:

[0005] In a first aspect, embodiments of this application provide a design method for a single-input multi-output robust speed controller, including:

[0006] Step 1: Establish system model; establish a nonlinear turbine model, generator model, guide vane servo motor model, and disturbance model that include water hammer effect and traveling wave effect;

[0007] Step 2: Construct a single-input multiple-output controlled object; reconstruct the system into a single-input multiple-output form: output vector Y(s)=[ΔG(s),ΔPm(s),Δω(s),Δδ(s)]^T, input is the control signal U(s);

[0008] Step 3: Robust speed controller design; using H ∞ A robust optimization method is used to design a single-input multi-output speed controller. The frequency response characteristics of the speed controller are adjusted through a dynamic weighting function to ensure the stability and performance of the system under uncertain conditions. The specific design steps are as follows:

[0009] Parameter range determination: Determine the range of variation of system parameters based on the actual operating parameters of the turbine;

[0010] Nominal parameter selection: Select a set of nominal parameters within the parameter range as the design basis;

[0011] Uncertainty boundary setting: Define the uncertainty boundary of system parameters and construct a generalized controlled object model that includes uncertainty;

[0012] Dynamic weighting function design: Design error weighting function, control weighting function and cross weighting function to optimize the frequency response characteristics of the speed governor;

[0013] Optimal speed governor calculation: using H ∞ The optimization algorithm solves for the optimal governor parameters to ensure the robust stability of the system under uncertain conditions;

[0014] Step 4: Robustness Verification; Construct a closed-loop system containing the nominal model Wo and the uncertainty Δ, and verify the robust stability condition: ||ToΔ|| ∞ <1, where To is the nominal closed-loop system transfer function matrix. By calculating the frequency response of this norm, we ensure that its maximum value is less than 1, so as to guarantee that the system is asymptotically stable under all norm bounded uncertainties.

[0015] Step 5: Execution of control commands; The designed governor is put into operation. The system collects the set speed and actual speed in real time, calculates the deviation, and the governor outputs a unified control signal based on the deviation and auxiliary feedback signal. The governor adjusts the turbine guide vane opening and generator excitation current through the actuator to regulate the speed of the hydro-generator unit.

[0016] Preferably, the single-input multi-output robust speed controller simultaneously generates four parallel control channels:

[0017] Main control channel: used to generate speed regulation signals, including an integral element to ensure that the steady-state speed error is zero;

[0018] Auxiliary control channel 1: Used to generate guide vane position adjustment signals;

[0019] Auxiliary control channel two: used to generate mechanical power adjustment signals;

[0020] Auxiliary control channel three: used to generate load angle adjustment signals.

[0021] Preferably, the error weighting function adopts a variable parameter design, adjusting the error weight in real time according to the system operating status. The error weight is increased during unit startup and shutdown to improve tracking accuracy, and decreased during steady-state operation to reduce control quantity fluctuations. The control weighting function is designed based on the physical limitations and performance requirements of the actuator, limiting the amplitude and rate of change of the control quantity to prevent actuator overload and oscillation. The cross-weighting function is designed by analyzing the coupling relationships between multiple control variables to coordinate the interactions between them and optimize the overall system performance.

[0022] Preferably, the robustness verification specifically includes:

[0023] Step 1: Define the nominal model and the set of uncertainties;

[0024] (1) Determine the nominal model W o First, a "nominal" or "ideal" mathematical model W needs to be established for the entire turbine speed control system. o (s), the mathematical model includes a turbine model, a generator model, a guide vane servo motor model, and a disturbance model;

[0025] (2) Modeling uncertainty Δ: Since there are differences between the actual physical system and the nominal model, these differences are abstracted into a diagonal uncertainty Δ k Δ k ∈Δ, which represents the uncertainty related to the parameters or components of the device, and is expressed by the following formula:

[0026] Its diagonal element Δ i Satisfy |Δ i (jω)|<1 for all frequencies ω, which means that the “magnitude” of the uncertainty is limited to 1 at all frequencies;

[0027] Constructing a set of perturbation models: The real physical system W is considered as the nominal model W o The result under the influence of uncertainty Δ, i.e., W=f(W o ,Δ), indicating that the real system W is derived from a system with respect to W. o Extracted from the set of models centered on Δ and defined by its boundary;

[0028] Step 2: Construct the closed-loop system and calculate the nominal closed-loop transfer function;

[0029] (1) Design controller C o Using polynomial H ∞ The optimization method is the nominal model W. o Design a controller C o Ensure controller C o Able to stabilize nominal system Wo And meet the expected performance indicators;

[0030] (2) Calculate the nominal closed-loop system: The designed controller C o With nominal model W o Connect the loops to form a nominal closed-loop system, and calculate the transfer function matrix T of this nominal closed-loop system. o (s):

[0031] Where I represents the identity matrix.

[0032] Preferably, the robustness verification further includes:

[0033] Step 3: Construct an "uncertainty-closed-loop" circuit and apply the small gain theorem;

[0034] (1) Reconfigure the system: replace the actual closed-loop system containing uncertainty Δ with an equivalent one, where uncertainty Δ is regarded as acting on the nominal closed-loop system T. o Feedback between the output and input; To form a new loop, construct a new feedback loop whose forward path is the nominal closed-loop system and whose feedback path is uncertainty. The open-loop transfer function of this new loop is...

[0035] (3) Application of stability criterion: According to the small gain theorem, if the open-loop transfer function T of this new loop is... o Δ of H ∞ If the norm is less than 1, then the entire closed-loop system containing uncertainty is asymptotically stable; the criterion is as follows: The inequality means that for all possible frequencies ω and all conditions satisfying ||△|| ∞ Uncertainty Δ < 1, product T o The maximum modulus of (jω)△(jω) must be less than 1; where T o (jω) represents the complex gain of the nominal closed-loop system at frequency ω, and Δ(jω) represents the complex gain of this uncertainty Δ at frequency ω;

[0036] Step 4: Perform robustness verification; select the "worst-case" or representative model of uncertainty Δ, and calculate the transfer function T. o The magnitude of (jω)Δ(jω) at different frequencies ω |T o Find the maximum value of this modulus over the entire frequency range using (jω)Δ(jω)∣; compare the calculated maximum value with 1. If the maximum value is less than 1, then pass the robustness test to prove that the designed controller C... oThe closed-loop system is guaranteed to be stable for all uncertainties Δ within this range; if the maximum value is greater than or equal to 1, the test fails, which means that the system may be unstable under certain uncertainties and the controller needs to be redesigned or the uncertainty boundary needs to be adjusted.

[0037] Secondly, embodiments of this application provide a single-input multi-output robust speed controller design system, comprising: a system modeling unit, a controlled object construction unit, a speed controller design unit, a verification unit, and an instruction execution unit; wherein...

[0038] The system modeling unit is used to establish nonlinear turbine models, generator models, guide vane servo motor models, and disturbance models that include water hammer and traveling wave effects;

[0039] The controlled object construction unit is used to reconstruct the system into a single-input multiple-output form: the output vector Y(s)=[ΔG(s),ΔPm(s),Δω(s),Δδ(s)]^T, and the input is the control signal U(s);

[0040] The speed controller design unit is used to employ H ∞ A robust optimization method is used to design a single-input multi-output speed controller. The frequency response characteristics of the speed controller are adjusted through a dynamic weighting function to ensure the stability and performance of the system under uncertain conditions. The specific design steps are as follows:

[0041] Parameter range determination: Determine the range of variation of system parameters based on the actual operating parameters of the turbine;

[0042] Nominal parameter selection: Select a set of nominal parameters within the parameter range as the design basis;

[0043] Uncertainty boundary setting: Define the uncertainty boundary of system parameters and construct a generalized controlled object model that includes uncertainty;

[0044] Dynamic weighting function design: Design error weighting function, control weighting function and cross weighting function to optimize the frequency response characteristics of the speed governor;

[0045] Optimal speed governor calculation: using H ∞ The optimization algorithm solves for the optimal governor parameters to ensure the robust stability of the system under uncertain conditions;

[0046] The verification unit is used to construct a closed-loop system containing a nominal model Wo and an uncertainty Δ, and to verify the robust stability condition: ||ToΔ|| ∞ <1, where To is the nominal closed-loop system transfer function matrix. By calculating the frequency response of this norm, we ensure that its maximum value is less than 1, so as to guarantee that the system is asymptotically stable under all norm bounded uncertainties.

[0047] The command execution unit is used to put the designed governor into operation. The system collects the speed setpoint and actual speed in real time, calculates the deviation, and the governor outputs a unified control signal based on the deviation and auxiliary feedback signal. The governor adjusts the turbine guide vane opening and generator excitation current through the actuator to regulate the speed of the hydro-generator unit.

[0048] Thirdly, embodiments of this application provide a computer-readable storage medium storing program code, which, when executed by a processor, implements the steps of a single-input multiple-output robust speed controller design method as described above.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] 1. Excellent robustness: Utilizing H ∞ Robust control theory, through norm-bounded uncertainty modeling and dynamic weighted function design, ensures that the closed-loop system of the speed governor can still maintain asymptotic stability when the system parameters change over a wide range and unmodeled dynamics exist, thus solving the problem of traditional controllers being sensitive to operating conditions.

[0051] 2. Excellent dynamic performance: Compared with traditional PI / PID controllers, the present invention has extremely small overshoot, short settling time, wider bandwidth, fast and stable system response, and significantly improves power quality and system stability.

[0052] 3. Strong anti-disturbance capability: It exhibits excellent suppression capability against step load disturbances and random load changes, with small speed fluctuations and rapid recovery, ensuring the safe and reliable operation of the power grid.

[0053] 4. Systematic Multivariable Design: Employing a single-input multi-output structure, it fully utilizes multi-dimensional state information such as guide vane position, mechanical power, and load angle as auxiliary feedback, through systematic H... ∞ The optimized design achieves coordinated optimization of each control channel, resulting in overall performance far exceeding that of a single-variable design. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a flowchart of the method of the present invention.

[0057] Figure 2This is a flowchart illustrating the design of the single-input multi-output speed controller of the present invention.

[0058] Figure 3 This is a system flowchart of the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0061] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0062] The terms “first,” “second,” etc., are used only to distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance, nor as requiring or implying any such actual relationship or order between these entities or operations.

[0063] like Figure 1 The single-input multi-output robust speed controller design method shown includes:

[0064] Step 1: Establish system model; establish a nonlinear turbine model, generator model, guide vane servo motor model, and disturbance model that include water hammer effect and traveling wave effect;

[0065] Step 2: Construct a single-input multiple-output controlled object; reconstruct the system into a single-input multiple-output form: output vector Y(s)=[ΔG(s),ΔPm(s),Δω(s),Δδ(s)]^T, input is the control signal U(s);

[0066] Step 3: Robust speed controller design; using H ∞ A robust optimization method is used to design a single-input multi-output speed controller. The frequency response characteristics of the speed controller are adjusted by a dynamic weighting function to ensure the stability and performance of the system under uncertain conditions.

[0067] Step 4: Robustness Verification; Construct a closed-loop system containing the nominal model Wo and the uncertainty Δ, and verify the robust stability condition: ||ToΔ|| ∞<1, where To is the nominal closed-loop system transfer function matrix. By calculating the frequency response of this norm, we ensure that its maximum value is less than 1, so as to guarantee that the system is asymptotically stable under all norm bounded uncertainties.

[0068] Step 5: Execution of control commands; The designed governor is put into operation. The system collects the set speed and actual speed in real time, calculates the deviation, and the governor outputs a unified control signal based on the deviation and auxiliary feedback signal. The governor adjusts the turbine guide vane opening and generator excitation current through the actuator to regulate the speed of the hydro-generator unit.

[0069] Preferably, the single-input multi-output robust speed controller simultaneously generates four parallel control channels:

[0070] Main control channel: used to generate speed regulation signals, including an integral element to ensure that the steady-state speed error is zero;

[0071] Auxiliary control channel 1: Used to generate guide vane position adjustment signals;

[0072] Auxiliary control channel two: used to generate mechanical power adjustment signals;

[0073] Auxiliary control channel three: used to generate load angle adjustment signals.

[0074] Preferably, the error weighting function adopts a variable parameter design, adjusting the error weight in real time according to the system operating status. The error weight is increased during unit startup and shutdown to improve tracking accuracy, and decreased during steady-state operation to reduce control quantity fluctuations. The control weighting function is designed based on the physical limitations and performance requirements of the actuator, limiting the amplitude and rate of change of the control quantity to prevent actuator overload and oscillation. The cross-weighting function is designed by analyzing the coupling relationships between multiple control variables to coordinate the interactions between them and optimize the overall system performance.

[0075] Preferably, the robustness verification specifically includes:

[0076] Step 1: Define the nominal model and the set of uncertainties;

[0077] (1) Determine the nominal model W o First, a "nominal" or "ideal" mathematical model W needs to be established for the entire turbine speed control system. o (s), the mathematical model includes a turbine model, a generator model, a guide vane servo motor model, and a disturbance model;

[0078] (2) Modeling uncertainty Δ: Since there are differences between the actual physical system and the nominal model, these differences are abstracted into a diagonal uncertainty Δ k Δ k∈Δ, which represents the uncertainty related to the parameters or components of the device, and is expressed by the following formula:

[0079] Its diagonal element Δ i Satisfy |Δ i (jω)|<1 for all frequencies ω, which means that the “magnitude” of the uncertainty is limited to 1 at all frequencies;

[0080] Constructing a set of perturbation models: The real physical system W is considered as the nominal model W o The result under the influence of uncertainty Δ, i.e., W=f(W o ,Δ), indicating that the real system W is derived from a system with respect to W. o Extracted from the set of models centered on Δ and defined by its boundary;

[0081] Step 2: Construct the closed-loop system and calculate the nominal closed-loop transfer function;

[0082] (1) Design controller C o Using polynomial H ∞ The optimization method is the nominal model W. o Design a controller C o Ensure controller C o Able to stabilize nominal system W o And meet the expected performance indicators;

[0083] (2) Calculate the nominal closed-loop system: The designed controller C o With nominal model W o Connect the loops to form a nominal closed-loop system, and calculate the transfer function matrix T of this nominal closed-loop system. o (s):

[0084]

[0085] Where I represents the identity matrix.

[0086] Preferably, the robustness verification further includes:

[0087] Step 3: Construct an "uncertainty-closed-loop" circuit and apply the small gain theorem;

[0088] (1) Reconfigure the system: replace the actual closed-loop system containing uncertainty Δ with an equivalent one, where uncertainty Δ is regarded as acting on the nominal closed-loop system T. o Feedback between the output and input;

[0089] (2) Forming a new loop: Construct a new feedback loop whose forward path is the nominal closed-loop system T oThe feedback path is uncertain (Δ), and the open-loop transfer function of this new loop is T. o Δ;

[0090] (3) Application of stability criterion: According to the small gain theorem, if the open-loop transfer function T of this new loop is... o Δ of H ∞ If the norm is less than 1, then the entire closed-loop system containing uncertainty is asymptotically stable; the criterion is as follows: The inequality means that for all possible frequencies ω and all conditions satisfying ||△|| ∞ Uncertainty Δ < 1, product T o The maximum modulus of (jω)△(jω) must be less than 1; where T o (jω) represents the complex gain of the nominal closed-loop system at frequency ω, and Δ(jω) represents the complex gain of this uncertainty Δ at frequency ω;

[0091] Step 4: Perform robustness verification; select the "worst-case" or representative model of uncertainty Δ, and calculate the transfer function T. o The magnitude of (jω)Δ(jω) at different frequencies ω |T o Find the maximum value of this modulus over the entire frequency range using (jω)Δ(jω)∣; compare the calculated maximum value with 1. If the maximum value is less than 1, then pass the robustness test to prove that the designed controller C... o The closed-loop system is guaranteed to be stable for all uncertainties Δ within this range; if the maximum value is greater than or equal to 1, the test fails, which means that the system may be unstable under certain uncertainties and the controller needs to be redesigned or the uncertainty boundary needs to be adjusted.

[0092] like Figure 2 The specific design steps for the single-input multi-output speed controller shown are as follows:

[0093] Parameter range determination: Determine the range of variation of system parameters based on the actual operating parameters of the turbine;

[0094] Nominal parameter selection: Select a set of nominal parameters within the parameter range as the design basis;

[0095] Uncertainty boundary setting: Define the uncertainty boundary of system parameters and construct a generalized controlled object model that includes uncertainty;

[0096] Dynamic weighting function design: Design error weighting function, control weighting function and cross weighting function to optimize the frequency response characteristics of the speed governor;

[0097] Optimal speed governor calculation: using H ∞An optimization algorithm is used to solve for the optimal governor parameters, ensuring the robust stability of the system under uncertain conditions.

[0098] like Figure 3 Furthermore, the present invention also provides a single-input multi-output robust speed controller design system, comprising: a system modeling unit, a controlled object construction unit, a speed controller design unit, a verification unit, and an instruction execution unit; wherein,

[0099] The system modeling unit is used to establish nonlinear turbine models, generator models, guide vane servo motor models, and disturbance models that include water hammer and traveling wave effects;

[0100] The controlled object construction unit is used to reconstruct the system into a single-input multiple-output form: the output vector Y(s)=[ΔG(s),ΔPm(s),Δω(s),Δδ(s)]^T, and the input is the control signal U(s);

[0101] The speed controller design unit is used to employ H ∞ A robust optimization method is used to design a single-input multi-output speed controller. The frequency response characteristics of the speed controller are adjusted through a dynamic weighting function to ensure the stability and performance of the system under uncertain conditions. The specific design steps are as follows:

[0102] Parameter range determination: Determine the range of variation of system parameters based on the actual operating parameters of the turbine;

[0103] Nominal parameter selection: Select a set of nominal parameters within the parameter range as the design basis;

[0104] Uncertainty boundary setting: Define the uncertainty boundary of system parameters and construct a generalized controlled object model that includes uncertainty;

[0105] Dynamic weighting function design: Design error weighting function, control weighting function and cross weighting function to optimize the frequency response characteristics of the speed governor;

[0106] Optimal speed governor calculation: using H ∞ The optimization algorithm solves for the optimal governor parameters to ensure the robust stability of the system under uncertain conditions;

[0107] The verification unit is used to construct a closed-loop system containing a nominal model Wo and an uncertainty Δ, and to verify the robust stability condition: ||ToΔ|| ∞ <1, where To is the nominal closed-loop system transfer function matrix. By calculating the frequency response of this norm, we ensure that its maximum value is less than 1, so as to guarantee that the system is asymptotically stable under all norm bounded uncertainties.

[0108] The command execution unit is used to put the designed governor into operation. The system collects the speed setpoint and actual speed in real time, calculates the deviation, and the governor outputs a unified control signal based on the deviation and auxiliary feedback signal. The governor adjusts the turbine guide vane opening and generator excitation current through the actuator to regulate the speed of the hydro-generator unit.

[0109] This application provides a computer-readable storage medium storing program code. When the program code is executed by a processor, it implements the steps of a single-input multiple-output robust speed controller design method as described above.

[0110] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0112] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0114] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0115] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0116] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0117] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A design method for a single-input multi-output robust speed controller, characterized in that, include: Step 1: Establish system model; establish a nonlinear turbine model, generator model, guide vane servo motor model, and disturbance model that include water hammer effect and traveling wave effect; Step 2: Construct a single-input multiple-output controlled object; reconstruct the system into a single-input multiple-output form: output vector Y(s)=[ΔG(s),ΔPm(s),Δω(s),Δδ(s)]^T, input is the control signal U(s); Step 3: Robust speed controller design; using H ∞ A robust optimization method is used to design a single-input multi-output speed controller. The frequency response characteristics of the speed controller are adjusted through a dynamic weighting function to ensure the stability and performance of the system under uncertain conditions. The specific design steps are as follows: Parameter range determination: Determine the range of variation of system parameters based on the actual operating parameters of the turbine; Nominal parameter selection: Select a set of nominal parameters within the parameter range as the design basis; Uncertainty boundary setting: Define the uncertainty boundary of system parameters and construct a generalized controlled object model that includes uncertainty; Dynamic weighting function design: Design error weighting function, control weighting function and cross weighting function to optimize the frequency response characteristics of the speed governor; Optimal speed governor calculation: using H ∞ The optimization algorithm solves for the optimal governor parameters to ensure the robust stability of the system under uncertain conditions; Step 4: Robustness Verification; Construct a closed-loop system containing the nominal model Wo and the uncertainty Δ, and verify the robust stability condition: ||ToΔ|| ∞ <1, where To is the nominal closed-loop system transfer function matrix. By calculating the frequency response of this norm, we ensure that its maximum value is less than 1, so as to guarantee that the system is asymptotically stable under all norm bounded uncertainties; Step 5: Execution of control commands; The designed governor is put into operation. The system collects the set speed and actual speed in real time, calculates the deviation, and the governor outputs a unified control signal based on the deviation and auxiliary feedback signal. The governor adjusts the turbine guide vane opening and generator excitation current through the actuator to regulate the speed of the hydro-generator unit.

2. The design method for a single-input multi-output robust speed controller according to claim 1, characterized in that, The single-input multi-output robust speed controller simultaneously generates four parallel control channels: Main control channel: used to generate speed regulation signals, including an integral element to ensure that the steady-state speed error is zero; Auxiliary control channel 1: Used to generate guide vane position adjustment signals; Auxiliary control channel two: used to generate mechanical power adjustment signals; Auxiliary control channel three: used to generate load angle adjustment signals.

3. The design method for a single-input multi-output robust speed controller according to claim 1, characterized in that, The error weighting function employs a variable parameter design, adjusting the error weights in real time according to the system's operating status. During unit startup and shutdown, the error weights are increased to improve tracking accuracy, while during steady-state operation, the error weights are decreased to reduce control quantity fluctuations. The control weighting function is designed based on the physical limitations and performance requirements of the actuator, limiting the amplitude and rate of change of the control quantity to prevent actuator overload and oscillation. The cross-weighting function is designed by analyzing the coupling relationships between multiple control variables, coordinating their interactions to optimize overall system performance.

4. The design method for a single-input multi-output robust speed controller according to claim 1, characterized in that, The robustness verification specifically includes: Step 1: Define the nominal model and the set of uncertainties; (1) Determine the nominal model W o First, a "nominal" or "ideal" mathematical model W needs to be established for the entire turbine speed control system. o (s), the mathematical model includes a turbine model, a generator model, a guide vane servo motor model, and a disturbance model; (2) Modeling uncertainty Δ: Since there are differences between the actual physical system and the nominal model, these differences are abstracted into a diagonal uncertainty Δ k Δ k ∈Δ, which represents the uncertainty related to the parameters or components of the device, and is expressed by the following formula: Its diagonal element Δ i Satisfy |Δ i (jω)|<1 for all frequencies ω, which means that the “magnitude” of the uncertainty is limited to 1 at all frequencies; Constructing a set of perturbation models: The real physical system W is considered as the nominal model W o The result under the influence of uncertainty Δ, i.e., W=f(W o ,Δ), indicating that the real system W is derived from a system with respect to W. o Extracted from the set of models centered on Δ and defined by its boundary; Step 2: Construct the closed-loop system and calculate the nominal closed-loop transfer function; (1) Design controller C o Using polynomial H ∞ The optimization method is the nominal model W. o Design a controller C o Ensure controller C o Able to stabilize nominal system W o And meet the expected performance indicators; (2) Calculate the nominal closed-loop system: The designed controller C o With nominal model W o Connect the loops to form a nominal closed-loop system, and calculate the transfer function matrix T of this nominal closed-loop system. o (s): ， Where I represents the identity matrix.

5. The design method for a single-input multi-output robust speed controller according to claim 4, characterized in that, The robustness verification also includes: Step 3: Construct an "uncertainty-closed-loop" circuit and apply the small gain theorem; (1) Reconfigure the system: replace the actual closed-loop system containing uncertainty Δ with an equivalent one, where uncertainty Δ is regarded as acting on the nominal closed-loop system T. o Feedback between the output and input; (2) Forming a new loop: Construct a new feedback loop whose forward path is the nominal closed-loop system T o The feedback path is uncertain (Δ), and the open-loop transfer function of this new loop is T. o Δ; (3) Application of stability criterion: According to the small gain theorem, if the open-loop transfer function T of this new loop is... o Δ of H ∞ If the norm is less than 1, then the entire closed-loop system containing uncertainty is asymptotically stable; the criterion is as follows: The inequality means that for all possible frequencies ω and all conditions satisfying ||△|| ∞ Uncertainty Δ < 1, product T o The maximum modulus of (jω)△(jω) must be less than 1; where T o (jω) represents the complex gain of the nominal closed-loop system at frequency ω, and Δ(jω) represents the complex gain of this uncertainty Δ at frequency ω; Step 4: Perform robustness verification; select the "worst-case" or representative model of uncertainty Δ, and calculate the transfer function T. o The magnitude of (jω)Δ(jω) at different frequencies ω |T o Find the maximum value of this modulus over the entire frequency range using (jω)Δ(jω)∣; compare the calculated maximum value with 1. If the maximum value is less than 1, then pass the robustness test to prove that the designed controller C... o The closed-loop system is guaranteed to be stable for all uncertainties Δ within this range; if the maximum value is greater than or equal to 1, the test fails, which means that the system may be unstable under certain uncertainties and the controller needs to be redesigned or the uncertainty boundary needs to be adjusted.

6. A design system for a single-input multi-output robust speed controller, characterized in that, include: The system comprises a modeling unit, a controlled object construction unit, a speed governor design unit, a verification unit, and an instruction execution unit; among which, The system modeling unit is used to establish nonlinear turbine models, generator models, guide vane servo motor models, and disturbance models that include water hammer and traveling wave effects; The controlled object construction unit is used to reconstruct the system into a single-input multiple-output form: the output vector Y(s)=[ΔG(s),ΔPm(s),Δω(s),Δδ(s)]^T, and the input is the control signal U(s); The speed controller design unit is used to employ H ∞ A robust optimization method is used to design a single-input multi-output speed controller. The frequency response characteristics of the speed controller are adjusted through a dynamic weighting function to ensure the stability and performance of the system under uncertain conditions. The specific design steps are as follows: Parameter range determination: Determine the range of variation of system parameters based on the actual operating parameters of the turbine; Nominal parameter selection: Select a set of nominal parameters within the parameter range as the design basis; Uncertainty boundary setting: Define the uncertainty boundary of system parameters and construct a generalized controlled object model that includes uncertainty; Dynamic weighting function design: Design error weighting function, control weighting function and cross weighting function to optimize the frequency response characteristics of the speed governor; Optimal speed governor calculation: using H ∞ The optimization algorithm solves for the optimal governor parameters to ensure the robust stability of the system under uncertain conditions; The verification unit is used to construct a closed-loop system containing a nominal model Wo and an uncertainty Δ, and to verify the robust stability condition: ||ToΔ|| ∞ <1, where To is the nominal closed-loop system transfer function matrix. By calculating the frequency response of this norm, we ensure that its maximum value is less than 1, so as to guarantee that the system is asymptotically stable under all norm bounded uncertainties; The command execution unit is used to put the designed governor into operation. The system collects the speed setpoint and actual speed in real time, calculates the deviation, and the governor outputs a unified control signal based on the deviation and auxiliary feedback signal. The governor adjusts the turbine guide vane opening and generator excitation current through the actuator to regulate the speed of the hydro-generator unit.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code, which, when executed by a processor, implements the steps of a single-input multiple-output robust speed controller design method as described in any one of claims 1 to 5.

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