A method and device for modeling a fractional order chaotic system of a doubly-fed induction generator

Abstract

The application discloses a modeling method and device for a fractional order chaotic system of a doubly-fed induction generator, and relates to the technical field of power system chaos modeling. In order to solve the defects that the existing integer order modeling method still has the problems of insufficient modeling precision and difficult parameter solving in describing complex time-varying characteristics and nonlinear coupling behaviors, the technical scheme provided by the application is as follows: including: determining state variables of a preset model through a fractional order differential operator, and constructing a five-dimensional fractional order state differential equation; dimension reduction is performed on the five-dimensional equation by using a stator flux linkage orientation condition, and the five-dimensional fractional order state differential equation is simplified into a three-dimensional fractional order state differential equation; time fast and slow transformation factors are defined, and the preset model is run in a new time scale to optimize the parameter distribution of the state variables; the optimized state variables are converted through a scaling factor of affine transformation to optimize the coefficient distribution of the preset model. The application is suitable for application in the depiction work of the dynamic characteristics of the doubly-fed induction generator.

Description

Technical Field

[0001] This invention relates to the field of power system chaos modeling technology, specifically to a modeling method for a fractional-order chaotic system of a doubly-fed induction generator based on affine transformation and time-scale transformation. Background Technology

[0002] Wind power generation, as a clean energy technology, has received widespread attention. Among them, the doubly-fed induction generator (DFIG) has become a core component of modern wind power systems due to its variable-speed constant-frequency operation, high efficiency, and flexible power regulation capabilities. However, with the expansion of wind power generation scale and the increasing proportion of grid connection, the nonlinear dynamic behavior of DFIG systems poses new challenges to system stability, especially the study of chaotic behavior in complex power system environments. Chaotic phenomena, characterized by sensitivity to initial conditions and long-term prediction uncertainties, place higher demands on the modeling, analysis, and control of DFIGs.

[0003] Traditional nonlinear modeling methods for doubly-fed induction generators (DFIGs) are mostly based on integer-order differential equations. While these methods can describe basic dynamic characteristics, they have limitations in handling nonlinear and complex dynamic behaviors. Fractional calculus, as a mathematical tool that extends integer-order calculus, exhibits unique advantages in dealing with nonlinear systems exhibiting memory and heredity. By introducing fractional-order modeling methods, the nonlinear dynamic characteristics of DFIGs can be captured more accurately. However, existing fractional-order modeling methods still suffer from insufficient modeling accuracy and difficulties in parameter solving when describing complex time-varying characteristics and nonlinear coupling behaviors, limiting their practical application.

[0004] Affine transformations and time-scale transformations, as two mathematical tools, can transform and adjust the state variables and time characteristics of a system, thereby simplifying the modeling of complex nonlinear systems. This method can reduce modeling complexity and improve solution efficiency while preserving the essential characteristics of the system's dynamic behavior. Therefore, developing a modeling method for fractional-order chaotic systems of doubly-fed induction generators based on affine transformations and time-scale transformations is of great significance for improving the safety and stability of wind power generation systems. Summary of the Invention

[0005] To address the shortcomings of existing fractional-order modeling methods in describing complex time-varying characteristics and nonlinear coupling behaviors, such as insufficient modeling accuracy and difficulty in parameter solving, the present invention provides the following technical solution:

[0006] A modeling method for a doubly-fed induction generator fractional-order chaotic system includes:

[0007] The steps are as follows: determine the state variables of the preset model and construct the five-dimensional fractional-order state differential equation using fractional differential operators;

[0008] The steps for reducing the dimensionality of the five-dimensional fractional-order state differential equation to a three-dimensional fractional-order state differential equation by utilizing the stator flux orientation condition;

[0009] The steps include defining a time-rate transformation factor and running the preset model on the new time scale to optimize the parameter distribution of the state variables;

[0010] The step of transforming the optimized state variables using the scaling factor of affine transformation to optimize the coefficient distribution of the preset model.

[0011] Furthermore, the fractional differential operator is defined according to the fractional calculus formula and is used to describe the memory and heritability characteristics of state variables.

[0012] Furthermore, the stator flux orientation condition includes setting the stator d-axis voltage of the target generator to zero and the q-axis voltage to the grid voltage.

[0013] Furthermore, the time-slow transformation factor is defined based on the rotor resistance of the target generator and the relationship between the time scale, connecting the original time scale with the new time scale, and unifying the dynamic change range of the state variables.

[0014] Furthermore, the five-dimensional fractional-order state differential equation is specifically as follows:

[0015] Using the stator and rotor d-axis and q-axis currents and the rotor electric angular velocity of the target generator as state variables, and obtaining the formula by fractional derivative of each state variable using a fractional-order differential operator:

[0016]

[0017] Among them, the system state variables are the generator stator d-axis current i ds Stator q-axis current i qs Rotor d-axis current i dr Rotor q-axis current i qr Rotor electric angular velocity ω g , For the fractional differential operator of generator stator current, For the fractional differential operator of the rotor current, For the fractional differential operator of the rotor's electric angular velocity, L s For stator winding inductance, L r For rotor winding inductance, L m For the mutual inductance between the stator winding and the rotor winding, R s For stator resistance, R r For rotor resistance, Leakage coefficient, uds For stator d-axis voltage, u qs For stator q-axis voltage, u dr For rotor d-axis voltage, u qr For the rotor q-axis voltage, ω s For synchronous speed, n p Where J is the number of pole pairs of the generator, K is the moment of inertia of the generator, and T is the damping viscosity coefficient of the generator. hs The driving torque that drives the generator to rotate, U g This is the grid voltage.

[0018] Based on the same inventive concept, this invention also provides a modeling device for a fractional-order chaotic system of a doubly-fed induction generator, comprising:

[0019] The state variables of the preset model are determined by fractional differential operators, and a module for constructing five-dimensional fractional state differential equations is built.

[0020] The five-dimensional fractional state differential equation is reduced in dimensionality by using the stator flux orientation condition, and simplified into a three-dimensional fractional state differential equation module.

[0021] A module that defines a time-rate transformation factor and runs the preset model at a new time scale to optimize the parameter distribution of state variables;

[0022] The module that transforms the optimized state variables using the scaling factor of affine transformation to optimize the coefficient distribution of the preset model.

[0023] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computing program, wherein when the computer program is read by a computer, the computer executes the method described thereon.

[0024] Based on the same inventive concept, the present invention also provides a computer, including a processor and a storage medium, wherein when the processor reads a computer program stored in the storage medium, the computer executes the method described thereon.

[0025] Based on the same inventive concept, the present invention also provides a computer program product, which, when executed, implements the method described.

[0026] Compared with the prior art, the advantages of the technical solution provided by the present invention are as follows:

[0027] This scheme introduces fractional-order differential operators to accurately characterize the dynamics of doubly-fed induction generators, enabling the model to capture memory and hereditary characteristics that traditional integer-order models cannot represent. This approach significantly enhances the ability to describe complex nonlinear behavior, making the prediction of chaotic behavior more accurate, and achieving higher dynamic analysis precision compared to existing research based on integer-order modeling.

[0028] By transforming the time scale, the scheme optimizes the temporal evolution of state variables, allowing for the extension and observation of transient but critical dynamic behaviors. This approach not only reduces the demand for computational resources but also makes the model more intuitive and controllable when analyzing complex time-varying characteristics. Compared with existing fractional-order modeling under fixed time scales, it significantly improves the efficiency of dynamic characteristic analysis of the system.

[0029] By introducing affine transformations to scale the state variables, the dynamic range of the state variables is effectively standardized, while the distribution of model parameters is significantly optimized. This method centralizes the scattered and complex parameters in traditional fractional-order modeling, simplifies the solution process, and makes it easier to achieve rapid computation and system optimization in practical applications compared to existing models.

[0030] The proposed solution simplifies the five-dimensional fractional-order state differential equations into a three-dimensional model through dimensionality reduction, preserving the core dynamic characteristics while reducing model complexity. This approach ensures consistency in analysis results while significantly saving computational resources, making it more suitable for studying real-time dynamic behavior and designing control strategies compared to existing high-dimensional modeling methods.

[0031] It is suitable for characterizing the dynamic characteristics of doubly-fed induction generators. Attached Figure Description

[0032] Figure 1 A flowchart of a modeling method for a fractional-order chaotic system of a doubly-fed induction generator;

[0033] Figure 2 Comparison of chaotic attractor trajectories for the five-dimensional state differential equation of a doubly fed induction generator under parameter 1.

[0034] Figure 3 Comparison of chaotic attractor trajectories in the three-dimensional state differential equation of a doubly fed induction generator under parameter 1.

[0035] Figure 4 Comparison of chaotic attractor trajectories in the five-dimensional state differential equation of a doubly fed induction generator under parameter 2.

[0036] Figure 5 Comparison of chaotic attractor trajectories in the three-dimensional state differential equation of a doubly fed induction generator under parameter 2.

[0037] Figure 6 Comparison of chaotic attractor trajectories in the five-dimensional state differential equation of a doubly fed induction generator with parameter 3.

[0038] Figure 7 Comparison of chaotic attractor trajectories in the three-dimensional state differential equation of a doubly fed induction generator with parameter 3.

[0039] Figure 8 Comparison of chaotic attractor trajectories in the five-dimensional state differential equation of a doubly fed induction generator with parameter 4.

[0040] Figure 9 Comparison of chaotic attractor trajectories in the three-dimensional state differential equation of a doubly fed induction generator with parameter 4. Detailed Implementation

[0041] To make the advantages and benefits of the technical solution provided by the present invention clearer, the technical solution provided by the present invention will now be described in further detail with reference to the accompanying drawings, specifically:

[0042] Implementation Method 1: This implementation method provides a modeling method for a fractional-order chaotic system of a doubly-fed induction generator, characterized by including:

[0043] The steps are as follows: determine the state variables of the preset model and construct the five-dimensional fractional-order state differential equation using fractional differential operators;

[0044] The steps for reducing the dimensionality of the five-dimensional fractional-order state differential equation to a three-dimensional fractional-order state differential equation by utilizing the stator flux orientation condition;

[0045] The steps include defining a time-rate transformation factor and running the preset model on the new time scale to optimize the parameter distribution of the state variables;

[0046] The step of transforming the optimized state variables using the scaling factor of affine transformation to optimize the coefficient distribution of the preset model.

[0047] The fractional differential operator, defined according to the fractional calculus formula, is used to describe the memory and heritability characteristics of state variables.

[0048] The stator flux linkage orientation conditions include setting the stator d-axis voltage of the target generator to zero and the q-axis voltage to the grid voltage.

[0049] The time-rate conversion factor is defined based on the rotor resistance of the target generator and the relationship between the time scale, connecting the original time scale with the new time scale, and unifying the dynamic change range of the state variables.

[0050] The five-dimensional fractional-order state differential equation is specifically as follows:

[0051] Using the stator and rotor d-axis and q-axis currents and the rotor electric angular velocity of the target generator as state variables, and obtaining the formula by fractional derivative of each state variable using a fractional-order differential operator:

[0052]

[0053] Among them, the system state variables are the generator stator d-axis current i ds Stator q-axis current i qs Rotor d-axis current i dr Rotor q-axis current i qr Rotor electric angular velocity ω g , For the fractional differential operator of generator stator current, For the fractional differential operator of the rotor current, For the fractional differential operator of the rotor's electric angular velocity, L s For stator winding inductance, L r For rotor winding inductance, L m For the mutual inductance between the stator winding and the rotor winding, R s For stator resistance, R r For rotor resistance, Leakage coefficient, u ds For stator d-axis voltage, u qs For stator q-axis voltage, u dr For rotor d-axis voltage, u qr For the rotor q-axis voltage, ω s For synchronous speed, n p Where J is the number of pole pairs of the generator, K is the moment of inertia of the generator, and T is the damping viscosity coefficient of the generator. hs The driving torque that drives the generator to rotate, U g This is the grid voltage.

[0054] Implementation Method Two: This implementation method is a further explanation of the technical solution provided in Implementation Method One, specifically:

[0055] In existing technologies, doubly-fed induction generator (DFIG) modeling is typically based on integer-order differential equations, such as using the classic dq-axis mathematical model to describe its electromagnetic and mechanical characteristics. This method achieves good results in describing basic dynamic characteristics, but its ability to capture complex nonlinear behaviors (such as chaotic phenomena) is relatively limited.

[0056] However, the following technical problems still exist:

[0057] Insufficient modeling accuracy: Existing integer-order modeling methods have limited accuracy in describing complex time-varying characteristics and nonlinear coupling behavior, and cannot fully reveal the chaotic characteristics of doubly-fed induction generators.

[0058] Complex parameter distribution: Whether it is an integer-order model or a fractional-order model, the complex distribution of parameters often leads to difficulties in solving the problem and limits the practical application of the model.

[0059] Insufficient research on chaotic behavior: Traditional modeling methods lack in-depth research on chaotic behavior in doubly-fed induction generators, especially in dynamic analysis and control in complex application scenarios such as wind power grid connection.

[0060] To address the aforementioned issues, this implementation proposes a modeling method for fractional-order chaotic systems of doubly-fed induction generators based on affine transformation and time-scale transformation. This method not only optimizes the parameter distribution of the model and significantly improves modeling accuracy, but also reveals the chaotic behavior and nonlinear dynamic characteristics of the doubly-fed induction generator by introducing fractional-order differential operators, thus providing theoretical support for improving the safety and stability of wind power systems.

[0061] Specifically:

[0062] Step 1: Determine the state variables and establish a five-dimensional fractional-order state differential equation.

[0063] This step involves selecting state variables and introducing fractional differential operators to construct a five-dimensional fractional state differential equation describing the dynamic behavior of a doubly-fed induction generator.

[0064] Detailed description:

[0065] State variable selection: The d-axis current and q-axis current of the generator stator and rotor and the rotor electric angular velocity are selected as state variables, which are represented as stator d-axis current, stator q-axis current, rotor d-axis current, rotor q-axis current and rotor electric angular velocity, respectively.

[0066] The introduction of fractional differential operators: Traditional integer differential operators used to describe changes in state variables are replaced with fractional differential operators to more accurately describe the dynamic changes of state variables over time. Fractional operators can reflect the memory and heredity properties of variables, enabling the model to capture the nonlinear dynamic behavior in doubly-fed induction generators.

[0067] Equations are established based on electromagnetic coupling, which establishes nonlinear relationships between state variables. For example, changes in stator and rotor currents are influenced by winding resistance, inductance, and mutual inductance, while changes in rotor electric angular velocity are related to torque and inertia. Five-dimensional state differential equations express these relationships as dynamic couplings between variables.

[0068] Parameter definitions: Key parameters in the equations include the stator and rotor resistances and inductances, mutual inductance between windings, generator moment of inertia, and grid voltage. These parameters have a direct impact on changes in state variables.

[0069] Output: A fractional-order state differential equation with five variables was established, providing a basic model for subsequent steps.

[0070] Step 2: Dimensionality reduction of the model using stator flux orientation conditions

[0071] By introducing stator flux linkage orientation conditions, the five-dimensional fractional-order state differential equation is simplified into a three-dimensional fractional-order state differential equation.

[0072] Detailed description:

[0073] The stator flux linkage orientation condition is introduced: It is assumed that the stator flux linkage direction is aligned with the stator d-axis, which ensures that the stator d-axis voltage is always zero, while the stator q-axis voltage equals the grid voltage. Through this constraint, the dynamic characteristics of stator variables can be replaced by rotor variables.

[0074] Ignoring the effect of stator resistance: Since the stator resistance has a small impact on dynamic behavior, its effect is ignored in the model, which further simplifies the equations.

[0075] State variable substitution: Based on the above constraints, the stator d-axis and q-axis currents are replaced by the rotor d-axis and q-axis currents. Simultaneously, a slip electrical angular frequency is defined, which is the difference between the rotor electrical angular velocity and the synchronous electrical angular velocity; this frequency is used to replace the rotor electrical angular velocity as a new state variable.

[0076] The equations are re-derived: by combining the simplified conditions, the variables in the five-dimensional state equations are reduced to three variables: rotor d-axis current, rotor q-axis current and slip electric angular frequency. The state equations are then re-expressed to form three-dimensional fractional-order state differential equations.

[0077] Output: A three-dimensional fractional-order state differential equation was established, providing a simplified model for subsequent optimization steps.

[0078] Step 3: Design time-scale transformation to optimize parameter distribution

[0079] By designing a time-rate transformation factor, the time scale is transformed from the original time to a new time scale, thereby optimizing the dynamic range of state variables and parameter distribution.

[0080] Detailed description:

[0081] Time scale transformation factor design: Based on system parameters such as rotor resistance, a time speed transformation factor is defined to adjust the dynamic evolution of time, so that the variables change more uniformly under the new time scale.

[0082] Equation update: Under the new time scale, the three-dimensional fractional-order state differential equation is re-expressed, and the parameters in the equation are adjusted to better describe the coupling relationship between variables under the new time scale.

[0083] Parameter optimization: By transforming the time, the dynamic range of the state variables is unified, and the distribution of parameters in the equation is centralized, thereby reducing computational complexity.

[0084] Validation of consistency: Ensure that the model at the new time scale is consistent with the original time scale in terms of dynamic characteristics, with optimizations only in parameter distribution.

[0085] Output: A three-dimensional fractional-order state differential equation for optimizing parameter distribution was established, laying the foundation for the next step of affine transformation.

[0086] Step 4: Introduce affine transformation to further optimize the equation coefficients.

[0087] By designing a scaling factor for affine transformation, the state variables are scaled and adjusted, thereby further optimizing the parameter distribution of the three-dimensional fractional state differential equation.

[0088] Detailed description:

[0089] Affine transformation factor design: Different scaling factors are designed for each state variable. For example, scaling factors are designed for the rotor d-axis current, rotor q-axis current, and slip electrical angular frequency, respectively. The selection of the scaling factor is determined based on the dynamic range of the variable and its contribution to the equation.

[0090] State variable update: Using affine transformation, the original state variables are replaced with new variables after scaling, thereby standardizing the dynamic range of the variables and reflecting the scale consistency between variables in the equation.

[0091] Equation coefficient optimization: The three-dimensional fractional-order state differential equations are re-expressed based on the new variables. The scaled model has a more concentrated parameter distribution and a clearer nonlinear structure of the equations.

[0092] Final model construction: After optimization, a three-dimensional fractional-order mechanism model with a clear structure and optimized parameter distribution is formed, which is used for the chaotic behavior analysis of doubly-fed induction generators.

[0093] Output: The final three-dimensional fractional-order mechanistic model was constructed, with a clear nonlinear structure and a centralized parameter distribution.

[0094] Implementation Method 3: Combination Figure 1-9 This embodiment describes the technical solution provided above in further detail through specific examples. Specifically:

[0095] This embodiment provides a modeling method for a fractional-order chaotic system of a doubly-fed induction generator based on affine transformation and time-scale transformation, including the following steps:

[0096] Step 1: Using the d-axis and q-axis currents of the stator and rotor, as well as the rotor's electric angular velocity, as state variables, and by performing fractional derivatives on each state variable using fractional-order differential operators, the fractional-order state differential equations of the doubly-fed induction generator can be obtained:

[0097]

[0098] Among them, the system state variables are the generator stator d-axis current i ds Stator q-axis current i qs Rotor d-axis current i dr Rotor q-axis current i qr Rotor electric angular velocity ω g , For the fractional differential operator of generator stator current, For the fractional differential operator of the rotor current, For the fractional differential operator of the rotor's electric angular velocity, L s For stator winding inductance, L r For rotor winding inductance, L m For the mutual inductance between the stator winding and the rotor winding, R s For stator resistance, R r For rotor resistance, Leakage coefficient, u ds For stator d-axis voltage, u qs For stator q-axis voltage, u dr For rotor d-axis voltage, u qr For the rotor q-axis voltage, ω s For synchronous speed, n p Where J is the number of pole pairs of the generator, K is the moment of inertia of the generator, and T is the damping viscosity coefficient of the generator. hs The driving torque that drives the generator to rotate, U g This is the grid voltage.

[0099] Step 2: Introduce stator flux orientation simplification conditions into the state differential equations containing fractional-order differential operators. These conditions constrain the stator's d-axis and q-axis voltages and currents, respectively, neglecting the minor influence of stator resistance on system dynamics. The fractional-order forms of the stator d-axis and q-axis currents are replaced by the rotor d-axis and q-axis currents, which can be described as:

[0100] u ds =0; u qs =U g ;R s ≈0;

[0101] Then the five-dimensional fractional state differential equation in step 1 can be simplified to a three-dimensional fractional state differential equation:

[0102]

[0103] Define the slip electric angular frequency ω e =ω s -ω g , using ω e Replace ω g As one state variable, retain the other two state variables i. dr and i qr Then the above three-dimensional fractional-order state differential equation can be rewritten as:

[0104]

[0105] Step 3: Design the time rate transformation factor τ and its transformed time scale. The relationships with the original time scale t are as follows:

[0106]

[0107] Then, the three-dimensional fractional-order state differential equation in step 2.2 can be expressed as follows under the new time scale:

[0108]

[0109] in, Time scale The fractional-order differential operator of the rotor current under the following conditions Time scale The fractional-order differential operator of the rotor electric angular velocity.

[0110] Step 4: Design the scaling factors a1, a2, a3 required for the affine transformation and the state variables after scaling. and With the original state variable i dr i qr and ω e The relationships between them are as follows:

[0111]

[0112] Furthermore, based on the scaling factor of the above design, let μ=τω s Given ε = Kτ / J, the three-dimensional fractional-order state differential equation in step 3 can be further transformed into:

[0113]

[0114] The above equation represents the fractional-order three-dimensional mechanism model of a doubly-fed induction generator, which is equivalent to the three-dimensional fractional-order equation obtained in step 2. Furthermore, under the condition of stator flux orientation, its main dynamics are consistent with the five-dimensional state differential equation in step 1. Further, using a 15kW doubly-fed induction generator as an example, the baseline values ​​of the calculated analysis parameters are as follows: α r =1.01, and on a uniform time scale Affine transformation of state variables and The main dynamic consistency of the above mechanism model and the five-dimensional state differential equation is verified by changing four parameters:

[0115] Parameter 1 When parameter 1 is 0.7 and other parameters remain at their baseline values, the comparison diagram of the chaotic attractor trajector trajectories of the three-dimensional and five-dimensional state differential equations of the doubly-fed induction generator is as follows: Figure 2 and 3 As shown.

[0116] Parameter 2 When parameter 2 is 0.033 and other parameters remain at their baseline values, the comparison diagram of the chaotic attractor trajector trajectories of the three-dimensional and five-dimensional state differential equations of the doubly-fed induction generator is as follows: Figure 4 and 5 As shown.

[0117] Parameter 3 When parameter 3 is 0.05 and other parameters remain at their baseline values, the comparison diagram of the chaotic attractor trajector trajectories of the three-dimensional and five-dimensional state differential equations of the doubly-fed induction generator is as follows: Figure 6 and 7 As shown.

[0118] Parameter 4 (α) r When parameter 4 is 1.13 and other parameters remain at their baseline values, the comparison diagram of the chaotic attractor trajector trajectories of the three-dimensional and five-dimensional state differential equations of the doubly-fed induction generator is as follows: Figure 8 and 9 As shown.

[0119] according to Figures 2 to 9 It can be seen that the three-dimensional and five-dimensional fractional-order state differential equations have similar dynamic characteristics under the same parameter conditions, and their respective attractor trajector trajectories have a high degree of consistency. Therefore, using the three-dimensional fractional-order mechanism model to analyze the chaotic characteristics of the doubly-fed induction generator not only reduces the number of analysis parameters but also saves computational resources.

[0120] The above description of several specific embodiments further details the technical solution provided by the present invention in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the above-described specific embodiments are not intended to limit the present invention. Any reasonable modifications and improvements to the present invention, combinations of embodiments, and equivalent substitutions based on the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A modeling method for a fractional-order chaotic system of a doubly-fed induction generator, characterized in that, include: The steps are as follows: determine the state variables of the preset model and construct the five-dimensional fractional-order state differential equation using fractional differential operators; The steps for reducing the dimensionality of the five-dimensional fractional-order state differential equation to a three-dimensional fractional-order state differential equation by utilizing the stator flux orientation condition; The steps include defining a time-rate transformation factor and running the preset model on the new time scale to optimize the parameter distribution of the state variables; The step of transforming the optimized state variables by using the scaling factor of the affine transformation to optimize the coefficient distribution of the preset model; The five-dimensional fractional-order state differential equation is specifically as follows: Using the stator and rotor d-axis and q-axis currents and the rotor electric angular velocity of the target generator as state variables, and obtaining the formula by fractional derivative of each state variable using a fractional-order differential operator: Among them, the system state variables are the generator stator d-axis current i ds Stator q-axis current i qs Rotor d-axis current i dr Rotor q-axis current i qr Rotor electric angular velocity ω g , For the fractional differential operator of generator stator current, For the fractional differential operator of the rotor current, For the fractional differential operator of the rotor's electric angular velocity, L s For stator winding inductance, L r For rotor winding inductance, L m For the mutual inductance between the stator winding and the rotor winding, R s For stator resistance, R r For rotor resistance, Leakage coefficient, u ds For stator d-axis voltage, u qs For stator q-axis voltage, u dr For rotor d-axis voltage, u qr For the rotor q-axis voltage, ω s For synchronous speed, n p Where J is the number of pole pairs of the generator, K is the moment of inertia of the generator, and T is the damping viscosity coefficient of the generator. hs The driving torque that drives the generator to rotate, U g This is the grid voltage.

2. The modeling method for a fractional-order chaotic system of a doubly-fed induction generator according to claim 1, characterized in that, The fractional differential operator, defined according to the fractional calculus formula, is used to describe the memory and heritability characteristics of state variables.

3. The modeling method for a fractional-order chaotic system of a doubly-fed induction generator according to claim 1, characterized in that, The stator flux linkage orientation conditions include setting the stator d-axis voltage of the target generator to zero and the q-axis voltage to the grid voltage.

4. The modeling method for a fractional-order chaotic system of a doubly-fed induction generator according to claim 1, characterized in that, The time-rate conversion factor is defined based on the rotor resistance of the target generator and the relationship between the time scale, connecting the original time scale with the new time scale, and unifying the dynamic change range of the state variables.

5. A modeling device for a fractional-order chaotic system of a doubly-fed induction generator, characterized in that, include: The state variables of the preset model are determined by fractional differential operators, and a module for constructing five-dimensional fractional state differential equations is built. The five-dimensional fractional state differential equation is reduced in dimensionality by using the stator flux orientation condition, and simplified into a three-dimensional fractional state differential equation module. A module that defines a time-rate transformation factor and runs the preset model at a new time scale to optimize the parameter distribution of state variables; The module that transforms the optimized state variables using the scaling factor of affine transformation to optimize the coefficient distribution of the preset model; The five-dimensional fractional-order state differential equation is specifically as follows: Using the stator and rotor d-axis and q-axis currents and the rotor electric angular velocity of the target generator as state variables, and obtaining the formula by fractional derivative of each state variable using a fractional-order differential operator: Among them, the system state variables are the generator stator d-axis current i ds Stator q-axis current i qs Rotor d-axis current i dr Rotor q-axis current i qr Rotor electric angular velocity ω g , For the fractional differential operator of generator stator current, For the fractional differential operator of the rotor current, For the fractional differential operator of the rotor's electric angular velocity, L s For stator winding inductance, L r For rotor winding inductance, L m For the mutual inductance between the stator winding and the rotor winding, R s For stator resistance, R r For rotor resistance, Leakage coefficient, u ds For stator d-axis voltage, u qs For stator q-axis voltage, u dr For rotor d-axis voltage, u qr For the rotor q-axis voltage, ω s For synchronous speed, n p Where J is the number of pole pairs of the generator, K is the moment of inertia of the generator, and T is the damping viscosity coefficient of the generator. hs The driving torque that drives the generator to rotate, U g This is the grid voltage.

6. A computer storage medium for storing computer programs, characterized in that, When the computer program is read by the computer, the computer executes the method of claim 1.

7. A computer, comprising a processor and a storage medium, characterized in that, When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 1.

8. A computer program product, as a computer program, is characterized by: When the computer program is executed, it implements the method of claim 1.

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