Method for mechanical-electrical coupling characteristic analysis applicable to large wind turbine

Abstract

This application belongs to the field of wind power generation technology, and specifically discloses a method for analyzing the mechanical-electrical coupling characteristics of large wind turbine units. The method includes: constructing a nonlinear time-varying model of the wind turbine unit's machine-side system, which includes mechanical and electrical subsystems, based on the wind turbine unit's mechanical parameters, electrical parameters, control parameters, operating data, and periodic aerodynamic loads caused by wind shear and tower shadow effects; performing time-domain simulation on the nonlinear time-varying model to obtain the periodic steady-state trajectory of the machine-side system under set operating conditions; linearizing the nonlinear time-varying model based on the periodic steady-state trajectory to establish a linear periodic time-varying state-space model, and calculating the state transition matrix within one fundamental period; calculating the Floquet characteristic index of the wind turbine unit using Floquet-Lyapunov theory based on the state transition matrix, and determining the small-disturbance stability of the machine-side system based on the real part of the Floquet characteristic index.

Description

Technical Field

[0001] This application belongs to the field of wind power generation technology, and more specifically, relates to a method for analyzing the mechanical-electrical coupling characteristics of large wind turbine units. Background Technology

[0002] As wind turbines rapidly develop towards larger and more flexible designs, blade sizes are constantly increasing, with rotor diameters now exceeding 200 meters. Against this backdrop, the amplitude of periodic aerodynamic loads caused by natural factors such as wind shear and tower shadow effects has increased significantly, and their fluctuation frequency is closely related to the rotor's rotational frequency and its harmonics. These periodic loads are transmitted to the generator and control system via the transmission chain, continuously exciting the dynamic behaviors of the unit, including shaft torsional vibration, electromagnetic torque, and DC bus voltage, thus posing a challenge to the stable operation of the unit.

[0003] Due to the tight coupling between the mechanical and electrical systems, periodic mechanical excitation causes the operating point of the turbine-side system to converge to a steady-state periodic trajectory rather than a constant equilibrium point, exhibiting typical periodic time-varying characteristics. Traditional small-disturbance modeling and stability analysis based on linear time-invariant (LTI) systems relies on time-invariant processing of the periodic time-varying nonlinear system to adapt it to small-disturbance modeling at a constant equilibrium point. The periodic characteristics of the wind turbine-side system under periodic aerodynamic loads are influenced by various factors such as wind speed. Applying linear time-invariant system modeling methods cannot accurately characterize the periodic event-varying characteristics. Even using the frozen coefficient method to approximate a single moment, it is difficult to accurately characterize the dynamic behavior of the system throughout the entire cycle, and it is even more difficult to capture the coupling effect between periodic excitation and the system's inherent modes, resulting in low accuracy in determining the stability of large wind turbine-side systems. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this application is to provide a mechanical-electrical coupling characteristic analysis method applicable to large wind turbine units. This method aims to solve the problem that traditional small-disturbance modeling and stability analysis methods based on LTI systems cannot accurately characterize the characteristics of periodic events, resulting in low accuracy in determining the stability of the turbine-side system of large wind turbine units.

[0005] To achieve the above objectives, in a first aspect, this application provides a method for analyzing the mechanical-electrical coupling characteristics of large wind turbine units, comprising: Step S1: Based on the mechanical parameters, electrical parameters, control parameters, and operating data of the wind turbine, as well as the periodic aerodynamic loads caused by wind shear and tower shadow effects, construct a nonlinear time-varying model of the wind turbine side system, which includes mechanical and electrical subsystems. Step S2: Perform time-domain simulation on the nonlinear time-varying model to obtain the periodic steady-state trajectory of the machine-side system under the set operating conditions; Step S3: Linearize the nonlinear time-varying model based on the periodic steady-state trajectory, establish a linear periodic time-varying state-space model, and calculate the state transition matrix within one fundamental period. Step S4: Based on the state transition matrix, calculate the Floquet characteristic index of the wind turbine using Floquet-Lyapunov theory, and determine the small disturbance stability of the turbine-side system based on the real part of the Floquet characteristic index.

[0006] This application constructs a nonlinear time-varying model considering periodic aerodynamic loads caused by wind shear and tower shadow effects. A linear time-periodic (LTP) state-space model is established by linearizing the model at the periodic steady-state trajectory. This model accurately preserves the influence of periodic aerodynamic loads caused by wind shear and tower shadow effects on the system's dynamic behavior. The characteristic index is calculated using Floquet-Lyapunov theory to determine the small-disturbance stability of the wind turbine. This approach allows the stability analysis of the turbine-side system to be based on the periodic steady-state trajectory rather than a constant equilibrium point, fundamentally overcoming the limitation of traditional analysis methods based on linear time-invariant (LTI) models in accurately characterizing periodic time-varying characteristics. Furthermore, by including coupled modeling of both mechanical and electrical subsystems, the model comprehensively reflects the impact of mechanical-electrical coupling characteristics on system stability, enabling accurate determination of the small-disturbance stability of large wind turbine-side systems and improving the accuracy of stability assessment for large wind turbine-side systems.

[0007] According to the mechanical-electrical coupling characteristic analysis method for large wind turbine units provided in this application, the mechanical subsystem includes a wind turbine aerodynamic model and a transmission shaft system model, and the electrical subsystem includes a generator model, a converter model and a DC link model.

[0008] This application clearly divides the turbine-side system into a mechanical subsystem, which includes a wind turbine aerodynamic model and a transmission shaft model, and an electrical subsystem, which includes a permanent magnet synchronous generator, a turbine-side voltage source converter, and a DC link model. This enables the model framework to fully cover the entire dynamic process from aerodynamic energy input to electrical energy output, ensuring a comprehensive characterization of mechanical-electrical coupling characteristics and avoiding modeling accuracy loss due to missing factor systems.

[0009] According to the mechanical-electrical coupling characteristic analysis method for large wind turbine units provided in this application, the step of performing time-domain simulation on the nonlinear time-varying model to obtain the periodic steady-state trajectory of the turbine-side system under a set operating condition includes: Run the nonlinear time-varying model until all state variables reach a periodic steady state; Fourier series decomposition is performed on the steady-state time-domain waveform data to extract the amplitude and phase of each harmonic. The periodic steady-state trajectory of the machine-side system under the set operating conditions is reconstructed by the Fourier series synthesis formula.

[0010] This application performs Fourier series decomposition on the periodic steady-state waveform obtained from time-domain simulation, extracts the amplitude and phase of each harmonic, and then reconstructs the periodic steady-state trajectory through the Fourier series synthesis formula. This allows for an analytical and accurate characterization of the steady-state behavior of the system under periodic aerodynamic load excitation, providing a high-precision benchmark for subsequent linearization at this trajectory. Furthermore, the modeling accuracy can be flexibly controlled by adjusting the truncation order N.

[0011] According to the mechanical-electrical coupling characteristic analysis method for large wind turbine units provided in this application, the method involves linearizing the nonlinear time-varying model based on the periodic steady-state trajectory to establish a linear periodic time-varying state-space model and calculating the state transition matrix within one fundamental period, including: Solving the differential-algebraic equations of the nonlinear time-varying model at the periodic steady-state trajectory yields the Jacobian matrix of the periodic system. The state transition matrix is ​​obtained by performing an integral operation on the periodic system matrix over one fundamental period.

[0012] This application obtains the periodic system matrix by solving the Jacobian matrix of the nonlinear differential-algebraic equation at the periodic steady-state trajectory, and then obtains the state transition matrix by performing integration within the fundamental period. This allows the linearization process to retain the characteristic of the system matrix changing periodically with time. The established LTP model can accurately reflect the dynamic characteristics of the periodic time-varying system, overcoming the shortcomings of the traditional frozen coefficient method, which only approximates at a single moment and cannot characterize the dynamic behavior of the system throughout the entire period.

[0013] According to the mechanical-electrical coupling characteristic analysis method for large wind turbines provided in this application, the method involves calculating the Floquet characteristic index of the wind turbine based on the state transition matrix using Floquet-Lyapunov theory, and determining the small-disturbance stability of the turbine-side system based on the real part of the Floquet characteristic index. The eigenvalues ​​of the state transition matrix are calculated, and the natural logarithm of the eigenvalues ​​is divided by the fundamental period to obtain the Floquet characteristic index. The dominant eigenvalue with the largest real part among the Floquet eigenvalues ​​is used as a stability index. If the real part of the dominant eigenvalue is less than zero, the system is determined to be stable. If the real part of the dominant eigenvalue is greater than zero, the system is determined to be unstable.

[0014] This application calculates the Floquet eigenindex by taking the eigenvalues ​​of the state transition matrix, dividing the natural logarithm by the fundamental period, and using the dominant eigenindex with the largest real part as the stability index. This enables a quantitative assessment of the stability of periodic time-varying systems under small disturbances. The system is considered stable when the real part of the dominant eigenindex is less than zero, and unstable when it is greater than zero. The criteria are clear and explicit, accurately determining the stable state of the machine-side system under periodic aerodynamic loads, overcoming the problem that traditional LTI methods cannot capture the coupling effect between periodic excitation and the system's inherent modes.

[0015] According to the mechanical-electrical coupling characteristic analysis method for large wind turbine units provided in this application, the method further includes: Change the values ​​of key parameters affecting system stability within a preset range, and repeat steps S3 and S4. Plot the trajectory of the Floquet characteristic index as a function of the key parameters to identify the critical point of instability and the dominant instability mode of the machine-side system.

[0016] This application, by changing the values ​​of key parameters and repeating LTP modeling and Floquet characteristic index calculation, plots the trajectory of characteristic index changes with parameters, which can quantitatively analyze the influence of key control parameters on system stability, identify the system instability critical point and dominant instability mode, and provide clear stability boundary guidance for the parameter optimization design of wind turbine generator side system, which has important engineering application value.

[0017] Secondly, this application provides a mechanical-electrical coupling characteristic analysis device suitable for large wind turbine units, comprising: The module is used to construct a nonlinear time-varying model of the wind turbine side system, which includes mechanical and electrical subsystems, based on the mechanical parameters, electrical parameters, control parameters, operating data of the wind turbine, as well as the periodic aerodynamic loads caused by wind shear and tower shadow effects. The simulation module is used to perform time-domain simulation of the nonlinear time-varying model and obtain the periodic steady-state trajectory of the machine-side system under set operating conditions. The calculation module is used to linearize the nonlinear time-varying model based on the periodic steady-state trajectory, establish a linear periodic time-varying state-space model, and calculate the state transition matrix within a fundamental period. The decision module is used to determine the small-disturbance stability of the computer-side system based on the state transition matrix, the Floquet characteristic index of the computer-side system using Floquet-Lyapunov theory, and the real part of the Floquet characteristic index.

[0018] Thirdly, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the mechanical-electrical coupling characteristic analysis method for large wind turbine units described in the first aspect or any possible implementation of the first aspect.

[0019] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the mechanical-electrical coupling characteristic analysis method for large wind turbine units described in the first aspect or any possible implementation of the first aspect.

[0020] Fifthly, this application provides a computer program product that, when run on a processor, causes the processor to execute the mechanical-electrical coupling characteristic analysis method for large wind turbine units described in the first aspect or any possible implementation of the first aspect.

[0021] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0022] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art: This application constructs a nonlinear time-varying model considering periodic aerodynamic loads caused by wind shear and tower shadow effects. A linear periodic time-varying state-space model is established by linearizing the model at the periodic steady-state trajectory. This accurately preserves the influence of periodic aerodynamic loads caused by wind shear and tower shadow effects on the system's dynamic behavior. The Floquet-Lyapunov theory is used to calculate characteristic indices to determine the small-disturbance stability of the wind turbine. This approach allows the stability analysis of the turbine-side system to be based on the periodic steady-state trajectory rather than a constant equilibrium point, fundamentally overcoming the limitation of traditional analysis methods based on linear time-invariant models in accurately characterizing periodic time-varying characteristics. Simultaneously, by including coupled modeling of both mechanical and electrical subsystems, the impact of mechanical-electrical coupling characteristics on system stability can be comprehensively reflected, enabling accurate determination of the small-disturbance stability of large wind turbine-side systems and improving the accuracy of stability assessment for large wind turbine-side systems. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating the mechanical-electrical coupling characteristic analysis method for large wind turbine units provided in the embodiments of this application; Figure 2 This is a schematic diagram illustrating the trajectory of the characteristic index as a function of the rotational speed feedback delay time constant, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the generator speed response provided in an embodiment of this application; Figure 4 This is a schematic diagram of the mechanical-electrical coupling characteristic analysis device for large wind turbine units provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0026] In this article, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The symbol " / " in this article indicates that the related objects are in an "or" relationship; for example, A / B means A or B.

[0027] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0028] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.

[0029] Next, combined Figures 1-3The mechanical-electrical coupling characteristic analysis method for large wind turbine units provided in the embodiments of this application is introduced.

[0030] Figure 1 This is a flowchart illustrating the mechanical-electrical coupling characteristic analysis method for large wind turbine units provided in this application embodiment, as shown below. Figure 1 As shown, the method includes the following steps: Step S1: Based on the mechanical parameters, electrical parameters, control parameters, and operating data of the wind turbine, as well as the periodic aerodynamic loads caused by wind shear and tower shadow effects, construct a nonlinear time-varying model of the wind turbine side system, which includes mechanical and electrical subsystems. Optionally, the mechanical structural parameters of the wind turbine may include the rotational inertia of the wind turbine, gearbox, and generator rotor, as well as the stiffness coefficient and damping coefficient of the low-speed shaft and high-speed shaft.

[0031] Optionally, electrical parameters may include the number of pole pairs of the motor, the flux linkage of the permanent magnet, the stator resistance and stator inductance of the motor, and the DC bus capacitance.

[0032] Optionally, the control parameters may include the proportional and integral coefficients of the speed loop and current loop.

[0033] Optionally, the rated operating data may include the rated wind speed, the rated speed of the low and high speed shafts, etc.

[0034] Optionally, the nonlinear time-varying model of the wind turbine generator side system is used to comprehensively describe the complete dynamic process of the wind turbine generator side system from aerodynamic energy input to electrical energy output. It serves as a bridge between the physical system and the LTP stability analysis framework. By constructing a nonlinear time-varying model that considers the periodic aerodynamic loads caused by wind shear and tower shadow effects, it can accurately retain the influence of the periodic aerodynamic loads caused by wind shear and tower shadow effects on the dynamic behavior of the generator side system.

[0035] Step S2: Perform time-domain simulation on the nonlinear time-varying model to obtain the periodic steady-state trajectory of the machine-side system under the set operating conditions; A time-domain simulation of the nonlinear time-varying model is performed until the state variables of the machine-side system reach a periodic steady state, thereby obtaining the periodic steady-state trajectory of the machine-side system under the set operating conditions. The periodic steady-state trajectory is the linearization benchmark for the subsequent establishment of the LTP model.

[0036] Step S3: Linearize the nonlinear time-varying model based on the periodic steady-state trajectory, establish a linear periodic time-varying state-space model, and calculate the state transition matrix within one fundamental period. Step S4: Based on the state transition matrix, calculate the Floquet characteristic index of the wind turbine using Floquet-Lyapunov theory, and determine the small-disturbance stability of the turbine-side system based on the real part of the Floquet characteristic index.

[0037] The mechanical-electrical coupling characteristic analysis method for large wind turbines provided in this application constructs a nonlinear time-varying model considering periodic aerodynamic loads caused by wind shear and tower shadow effects. Linearization is performed at the periodic steady-state trajectory to establish a linear periodic time-varying state-space model, accurately preserving the influence of periodic aerodynamic loads caused by wind shear and tower shadow effects on the system's dynamic behavior. Characteristic indices are calculated using Floquet-Lyapunov theory to determine the small-disturbance stability of the wind turbine. This approach allows the stability analysis of the turbine-side system to be based on a periodic steady-state trajectory rather than a constant equilibrium point, fundamentally overcoming the limitation of traditional analysis methods based on linear time-invariant models that cannot accurately characterize periodic time-varying characteristics. Simultaneously, by including coupled modeling of both mechanical and electrical subsystems, it comprehensively reflects the impact of mechanical-electrical coupling characteristics on system stability, enabling accurate determination of the small-disturbance stability of the turbine-side system of large wind turbines and improving the accuracy of stability determination for large wind turbine-side systems.

[0038] In some embodiments, the mechanical subsystem in step S1 includes a wind turbine aerodynamic model and a transmission shaft system model, and the electrical subsystem includes a permanent magnet synchronous generator, a machine-side voltage source converter, and a DC link model.

[0039] Optionally, the detailed nonlinear time-varying model of the machine-side system includes: The aerodynamic torque equations for wind turbines, established based on blade element-momentum theory, consider the periodic aerodynamic loads caused by wind shear and tower shadow effects, describing the nonlinear mapping relationship between aerodynamic torque and wind speed, turbine speed, and blade pitch angle. The dynamic equations for a three-mass transmission shaft system, where the turbine, gearbox, and generator rotor are respectively represented as concentrated mass blocks, are constructed. The relative angular displacement and angular velocity relationships between the mass blocks are characterized by torsional springs and damping elements, reflecting the torsional vibration dynamics of the transmission chain. The voltage and electromagnetic torque equations for a permanent magnet synchronous generator (PMSG), established in a two-phase rotating coordinate system, provide the linear relationship between electromagnetic torque and stator current, describing the electromechanical energy conversion process. A generator-side voltage source converter (Voltage) is also constructed, neglecting high-frequency switching details. The SourceConverter (VSC) average value model equations are used to establish the instantaneous power balance relationship between the AC and DC sides by equating the AC side to a controlled voltage source and the DC side to a controlled current source. The DC side RC parallel equivalent circuit equations, consisting of the DC bus capacitor and the grid-side equivalent resistance, are used to establish the dynamic differential equation of the DC bus voltage based on Kirchhoff's current law. The speed loop and current loop control equations of the dual closed-loop vector control strategy are also used. The speed outer loop generates the q-axis current reference command based on the speed deviation, and the current inner loop generates the modulated voltage signal through the PI controller. The speed feedback delay is considered to be equivalent to a first-order inertial element.

[0040] In some embodiments, step S2 specifically includes: Step S21: Run the nonlinear time-varying model until each state variable reaches a periodic steady state; Step S22: Perform Fourier series decomposition on the steady-state time-domain waveform data, extract the amplitude and phase of each harmonic, and reconstruct the periodic steady-state trajectory of the machine-side system under the set operating conditions using the Fourier series synthesis formula.

[0041] Optionally, the specific steps for obtaining the system's periodic steady-state trajectory are as follows: First, run the nonlinear mathematical model until all state variables of the system reach a periodic steady state; second, select the time-domain waveform data after steady state and perform Fourier series decomposition, setting the truncation order of the Fourier series according to the required modeling accuracy. N Extract state variables x In the DC component to N Amplitude and phase under the second harmonic, the third k The amplitude of the second harmonic is denoted as Phase is denoted as Finally, based on the extracted amplitude and phase parameters, the periodic steady-state trajectory of the state variable is reconstructed using the Fourier series synthesis formula:

[0042] In some embodiments, step S3 specifically includes: Step S31: Solve the Jacobian matrix of the nonlinear time-varying model with respect to the state variables at the periodic steady-state trajectory to obtain the periodic system matrix; Step S32: Perform an integral operation on the periodic system matrix over one fundamental period to obtain the state transition matrix.

[0043] Optionally, the specific steps for building the LTP model and obtaining the state transition matrix are as follows: First, based on the nonlinear differential-algebraic equation established in step S2, solve for its state variables at the periodic steady-state trajectory. x The Jacobian matrix is ​​used to obtain the periodic system matrix. ,Right now:

[0044] in, For a detailed nonlinear time-varying model of the machine-side system, For input variables.

[0045] Secondly, according to linear system theory, the state transition matrix satisfies the evolution equation. The initial time value is set to the identity matrix, in [0, T Perform integration within the period to obtain... T Matrix values ​​at time points .

[0046] In some embodiments, step S4 specifically includes: Step S41: Calculate the eigenvalues ​​of the state transition matrix, take the natural logarithm of the eigenvalues ​​and divide it by the fundamental period to obtain the Floquet eigenindex. Step S42: The dominant eigenvalue with the largest real part among the Floquet eigenvalues ​​is used as the stability index. If the real part of the dominant eigenvalue is less than zero, the machine-side system is determined to be stable. If the real part of the dominant eigenvalue is greater than zero, the machine-side system is determined to be unstable.

[0047] Optionally, the Floquet characteristic index of the system is calculated using Floquet-Lyapunov theory, and the distribution position of the characteristic index in the complex plane is used as a quantitative criterion for the small-disturbance stability of the mechanical-electrical coupling system. The criteria for determining the small-disturbance stability of the mechanical-electrical coupling system based on the Floquet characteristic index include: Based on the state transition matrix obtained in step S4 Calculate the Floquet characteristic index , and The relationship satisfies:

[0048] The dominant eigenvalue with the largest real part among the Floquet eigenvalues ​​of the system. As a stability indicator, if Re( If Re() < 0, then the system is determined to be in a stable state under the current operating parameters; if Re() < 0, then the system is determined to be in a stable state under the current operating parameters; If )>0, the system is considered unstable.

[0049] In some embodiments, the method further includes: Change the values ​​of key parameters affecting system stability within the preset range, and repeat steps S3 and S4. Plot the trajectory of the Floquet characteristic index as a function of key parameters to identify the critical point of instability and the dominant instability mode of the machine-side system.

[0050] Analyze the impact of key parameters on system stability. Select key parameters that affect system stability, change the parameter values ​​within a preset range, repeat steps S3 to S4, plot the trajectory of the system's Floquet characteristic index as a function of the parameter, and identify the system's instability critical point and dominant instability mode.

[0051] In one embodiment of this application, taking a typical 5 MW offshore semi-direct drive permanent magnet wind turbine as an example, the method of this application is used to perform small-disturbance stability analysis of the wind turbine side system considering mechanical-electrical coupling characteristics. The specific steps are as follows: The first step is to construct a simulation model of the turbine-side system in MATLAB / Simulink based on the parameters of the typical semi-direct drive permanent magnet wind turbine mentioned above. This model includes the wind turbine aerodynamic model, the three-mass drive shaft system, PMSG, turbine-side VSC, and DC link.

[0052] The second step involves substituting the steady-state operating parameters into the machine-side system simulation model for time-domain simulation. Fourier series decomposition is used to extract the amplitude and phase of each harmonic, and the periodic steady-state trajectory of the system under rated operating conditions is reconstructed. Subsequently, the Jacobian matrix is ​​solved based on this trajectory to establish the LTP model of the machine-side system, and the state transition matrix within one fundamental period is calculated.

[0053] The third step is to calculate the Floquet characteristic index using Floquet-Lyapunov theory. With the rotational speed feedback delay time constant Taking this as an example, we analyze the impact of key system parameters on system stability.

[0054] Figure 2 This is a schematic diagram illustrating the trajectory of the characteristic index as a function of the rotational speed feedback delay time constant, as provided in the embodiments of this application. Figure 2 As shown, during the test in this embodiment, simulation was performed. The process gradually increases from 0.1 s to 2 s. The results show that as... As the value increases, seven characteristic indices of the system show a significant tendency to migrate to the right side of the complex plane, reflecting a continuous weakening of the damping level of the corresponding modes and a gradual decrease in system stability. Meanwhile, when... When it increases to 1.453 s, The first mode to cross the imaginary axis and enter the right half of the complex plane becomes the dominant unstable mode, marking the transition of the system from a stable state to an unstable state.

[0055] Figure 3 This is a schematic diagram of the generator speed response provided in an embodiment of this application, such as... Figure 3 As shown, after the system reaches steady state, it will When the time was switched from 0.1 s to 1.6 s, the image showed that the generator rotor speed oscillated and became unstable, which verified the correctness of the mechanical-electrical coupling characteristic analysis method for large wind turbine units provided in this application.

[0056] This application introduces LTP theory to linearize the nonlinear model near the periodic steady-state trajectory, which can accurately retain the influence of periodic aerodynamic loads caused by wind shear and tower shadow effect on the dynamic behavior of the system, overcoming the limitations of the traditional LTI model. At the same time, this application takes into account the mechanical dynamics of the three-mass drive shaft system and the electrical dynamics of PMSG and VSC, which can comprehensively reflect the mechanical-electric coupling characteristics and is suitable for the stability analysis of the turbine-side system of large wind turbine units.

[0057] The mechanical-electrical coupling characteristic analysis device for large wind turbines provided in this application is described below. The mechanical-electrical coupling characteristic analysis device for large wind turbines described below can be referred to in correspondence with the mechanical-electrical coupling characteristic analysis method for large wind turbines described above.

[0058] Figure 4 This is a schematic diagram of a mechanical-electrical coupling characteristic analysis device for large wind turbine units provided in an embodiment of this application, as shown below. Figure 4 As shown, the device 400 includes: Module 410 is used to construct a nonlinear time-varying model of the wind turbine side system, which includes mechanical and electrical subsystems, based on the mechanical parameters, electrical parameters, control parameters, operating data of the wind turbine, as well as the periodic aerodynamic loads caused by wind shear and tower shadow effects. Simulation module 420 is used to perform time-domain simulation of nonlinear time-varying models and obtain the periodic steady-state trajectory of the machine-side system under set operating conditions. The calculation module 430 is used to linearize the nonlinear time-varying model based on the periodic steady-state trajectory, establish a linear periodic time-varying state-space model, and calculate the state transition matrix within a fundamental period. The decision module 440 is used to determine the small-disturbance stability of the computer-side system based on the state transition matrix, the Floquet eigenindex of the computer-side system using Floquet-Lyapunov theory, and the real part of the Floquet eigenindex.

[0059] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0060] Based on the methods in the above embodiments, Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown in the illustration, this application provides an electronic device that may include a processor 510, a communication interface 520, a memory 530, and a communication bus 540. The processor 510, communication interface 520, and memory 530 communicate with each other via the communication bus 540. The processor 510 can call logical instructions from the memory 530 to execute the mechanical-electrical coupling characteristic analysis method for large wind turbine units described in the above embodiment.

[0061] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the mechanical-electrical coupling characteristic analysis method for large wind turbine units described in the various embodiments of this application.

[0062] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program. When the computer program is run on a processor, it causes the processor to execute the mechanical-electrical coupling characteristic analysis method applicable to large wind turbine units in the above embodiments.

[0063] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the mechanical-electrical coupling characteristic analysis method applicable to large wind turbine units in the above embodiments.

[0064] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0065] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0066] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0067] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0068] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for mechanical-electrical coupling characteristic analysis suitable for large wind turbine generators, characterized in that, include: Step S1: Based on the mechanical parameters, electrical parameters, control parameters, and operating data of the wind turbine, as well as the periodic aerodynamic loads caused by wind shear and tower shadow effects, construct a nonlinear time-varying model of the wind turbine side system, which includes mechanical and electrical subsystems. Step S2: Perform time-domain simulation on the nonlinear time-varying model to obtain the periodic steady-state trajectory of the machine-side system under the set operating conditions; Step S3: Linearize the nonlinear time-varying model based on the periodic steady-state trajectory, establish a linear periodic time-varying state-space model, and calculate the state transition matrix within one fundamental period. Step S4: Based on the state transition matrix, calculate the Floquet characteristic index of the wind turbine using Floquet-Lyapunov theory, and determine the small disturbance stability of the turbine-side system based on the real part of the Floquet characteristic index.

2. The method for mechanical-electrical coupling characteristic analysis applicable to large wind turbine generators according to claim 1, wherein, The mechanical subsystem includes a wind turbine aerodynamic model and a transmission shaft system model, while the electrical subsystem includes a permanent magnet synchronous generator, a generator-side voltage source converter, and a DC link model.

3. The method for mechanical-electrical coupling characteristic analysis applicable to large wind turbine generators according to claim 1, wherein, The step of performing time-domain simulation on the nonlinear time-varying model to obtain the periodic steady-state trajectory of the machine-side system under set operating conditions includes: Run the nonlinear time-varying model until all state variables reach a periodic steady state; Fourier series decomposition is performed on the steady-state time-domain waveform data to extract the amplitude and phase of each harmonic. The periodic steady-state trajectory of the machine-side system under the set operating conditions is reconstructed by the Fourier series synthesis formula.

4. The method for mechanical-electrical coupling characteristic analysis applicable to large wind turbine generators according to claim 1, wherein, The linearization of the nonlinear time-varying model based on the periodic steady-state trajectory, establishing a linear periodic time-varying state-space model, and calculating the state transition matrix within one fundamental period includes: Solving the differential-algebraic equations of the nonlinear time-varying model at the periodic steady-state trajectory yields the Jacobian matrix of the periodic system. The state transition matrix is ​​obtained by performing an integral operation on the periodic system matrix over one fundamental period.

5. The method for mechanical-electrical coupling characteristic analysis applicable to large wind turbine generators according to claim 1, wherein, The process of calculating the Floquet characteristic index of the wind turbine based on the state transition matrix using Floquet-Lyapunov theory, and determining the small-disturbance stability of the turbine-side system based on the real part of the Floquet characteristic index, includes: The eigenvalues ​​of the state transition matrix are calculated, and the natural logarithm of the eigenvalues ​​is divided by the fundamental period to obtain the Floquet characteristic index. The dominant eigenvalue with the largest real part among the Floquet eigenvalues ​​is used as a stability index. If the real part of the dominant eigenvalue is less than zero, the system is determined to be stable. If the real part of the dominant eigenvalue is greater than zero, the system is determined to be unstable.

6. The method for mechanical-electrical coupling characteristic analysis applicable to large wind turbine generators according to claim 1, wherein, The method further includes: Change the values ​​of key parameters affecting system stability within a preset range, and repeat steps S3 and S4. Plot the trajectory of the Floquet characteristic index as a function of the key parameters to identify the critical point of instability and the dominant instability mode of the machine-side system.

7. A mechanical-electrical coupling characteristic analysis device suitable for a large wind turbine generator, characterized by, include: The module is used to construct a nonlinear time-varying model of the wind turbine side system, which includes mechanical and electrical subsystems, based on the mechanical parameters, electrical parameters, control parameters, operating data of the wind turbine, as well as the periodic aerodynamic loads caused by wind shear and tower shadow effects. The simulation module is used to perform time-domain simulation of the nonlinear time-varying model and obtain the periodic steady-state trajectory of the machine-side system under set operating conditions. The calculation module is used to linearize the nonlinear time-varying model based on the periodic steady-state trajectory, establish a linear periodic time-varying state-space model, and calculate the state transition matrix within a fundamental period. The decision module is used to determine the small-disturbance stability of the computer-side system based on the state transition matrix, the Floquet characteristic index of the computer-side system using Floquet-Lyapunov theory, and the real part of the Floquet characteristic index.

8. An electronic device, comprising: include: At least one memory for storing computer programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the mechanical-electrical coupling characteristic analysis method applicable to large wind turbine units as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. When the computer program is run on the processor, the processor performs the mechanical-electrical coupling characteristic analysis method for large wind turbine units as described in any one of claims 1-6.

10. A computer program product, characterised in that, When the computer program product is run on a processor, the processor executes the mechanical-electrical coupling characteristic analysis method for large wind turbine units as described in any one of claims 1-6.

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