Modeling method and device for fractional order chaotic system of doubly-fed induction generator

Abstract

The invention 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 chaotic modeling of a power system. In order to solve the defects that in the prior art, an existing integer order modeling method still has the problems of insufficient modeling precision and difficult parameter solving when describing complex time-varying characteristics and nonlinear coupling behaviors, the technical scheme provided by the invention is as follows: the method comprises the following steps: determining a state variable of a preset model through a fractional order differential operator; constructing a five-dimensional fractional order state differential equation; performing dimension reduction on the five-dimensional equation by using a stator flux linkage orientation condition, and simplifying the five-dimensional equation into a three-dimensional fractional order state differential equation; defining a time fast and slow transformation factor, operating the preset model under a new time scale, and optimizing parameter distribution of state variables; and converting the optimized state variable through a telescopic factor of affine transformation, and optimizing coefficient distribution of the preset model. The method is suitable for depicting the dynamic characteristics of the doubly-fed induction generator.

Description

Technical Field

[0001] The invention relates to the technical field of power system chaos modeling, and in particular to a modeling method of a doubly-fed induction generator fractional-order chaotic system based on affine transformation and time scale transformation. Background Art

[0002] Wind power generation has received extensive attention as a clean energy technology. Doubly-fed induction generators have become the core equipment in modern wind power systems due to their variable speed constant frequency operation, high efficiency, and flexible power regulation capabilities. However, with the expansion of wind power generation and the increasing proportion of grid access, the nonlinear dynamic behavior of the doubly-fed induction generator system has posed new challenges to system stability, especially the study of chaotic behavior in the complex environment of the power system has become a hot topic. Chaotic phenomena are sensitive to initial conditions and have long-term prediction uncertainties, which puts higher requirements on the modeling, analysis, and control of doubly-fed induction generators.

[0003] Traditional nonlinear modeling methods for doubly-fed induction generators are mostly based on integer-order differential equations. Although they can describe basic dynamic characteristics, they have limitations in dealing with nonlinear and complex dynamic behaviors. Fractional calculus, as a mathematical tool that generalizes integer-order calculus, shows unique advantages in dealing with nonlinear systems with memory and heredity. By introducing fractional-order modeling methods, the nonlinear dynamic characteristics of doubly-fed induction generators can be captured more accurately. However, the existing fractional-order modeling methods still have problems such as insufficient modeling accuracy and difficulty in parameter solution when describing complex time-varying characteristics and nonlinear coupling behaviors, which limits their practical application effects.

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

[0005] In order to solve the defects in the prior art that the existing fractional-order modeling method still has the problems of insufficient modeling accuracy and difficulty in parameter solution when describing complex time-varying characteristics and nonlinear coupling behaviors, the technical solution provided by the present invention is: A modeling method for a doubly-fed induction generator fractional-order chaotic system, comprising: The steps of determining the state variables of the preset model through fractional-order differential operators and constructing a five-dimensional fractional-order state differential equation; The five-dimensional equation is reduced in dimension by using the stator flux orientation condition to simplify it into a three-dimensional fractional-order state differential equation; A step of defining a time speed conversion factor and running the preset model at a new time scale to optimize the parameter distribution of state variables; The step of converting the optimized state variables through the scaling factor of the affine transformation to optimize the coefficient distribution of the preset model.

[0006] Furthermore, the fractional-order differential operator is defined according to the fractional-order calculus formula and is used to describe the memory and hereditary characteristics of the state variable.

[0007] Furthermore, the stator flux orientation condition includes: setting the stator d-axis voltage of the target generator to zero and setting the q-axis voltage to the grid voltage.

[0008] Furthermore, the time fast-slow conversion factor is defined according to the relationship between the rotor resistance and the time scale of the target generator, and the original time scale is connected with the new time scale to unify the dynamic change range of the state variable.

[0009] Furthermore, the five-dimensional fractional-order state differential equation is specifically: The d-axis and q-axis currents of the stator and rotor of the target generator and the electrical angular velocity of the rotor are taken as state variables, and the fractional-order differential operator is used to perform fractional derivatives on each state variable to obtain the formula: Among them, the system state variables are the generator stator d-axis current , stator q-axis current , rotor d-axis current , rotor q-axis current , rotor electrical angular velocity , is the fractional differential operator of the generator stator current, is the fractional differential operator of the rotor current, is the fractional differential operator of the rotor electrical angular velocity, is the stator winding inductance, is the rotor winding inductance, is the mutual inductance between the stator winding and the rotor winding, is the stator resistance, is the rotor resistance, is the magnetic flux leakage coefficient, is the stator d-axis voltage, is the stator q-axis voltage, is the rotor d-axis voltage, is the rotor q-axis voltage, For synchronous speed, is the number of generator pole pairs, is the generator moment of inertia, is the generator damping viscosity coefficient, is the driving torque that drives the generator to rotate, is the grid voltage.

[0010] Based on the same inventive concept, the present invention also provides a modeling device for a doubly-fed induction generator fractional-order chaotic system, comprising: Through fractional-order differential operators, the state variables of the preset model are determined, and a module for five-dimensional fractional-order state differential equations is constructed; The five-dimensional equation is reduced in dimension by using the stator flux orientation condition to simplify it into a module of a three-dimensional fractional-order state differential equation; A module for defining a time speed conversion factor and running the preset model at a new time scale to optimize the parameter distribution of state variables; The optimized state variables are transformed by using a scaling factor of an affine transformation to optimize the coefficient distribution module of the preset model.

[0011] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program. When the computer program is read by a computer, the computer executes the described method.

[0012] Based on the same inventive concept, the present invention also provides a computer, including a processor and a storage medium. When the processor reads a computer program stored in the storage medium, the computer executes the method described.

[0013] Based on the same inventive concept, the present invention also provides a computer program product, which is a computer program. When the computer program is executed, the method described above is implemented.

[0014] Compared with the prior art, the technical solution provided by the present invention is beneficial in that: This scheme introduces fractional-order differential operators to accurately characterize the dynamic characteristics of the doubly-fed induction generator, allowing the model to capture the memory and heritability characteristics that traditional integer-order models cannot express. This approach significantly enhances the ability to describe complex nonlinear behaviors, making the prediction of chaotic behaviors more accurate, and has higher dynamic analysis accuracy than existing studies based on integer-order modeling.

[0015] Through time scale transformation, the scheme optimizes the time evolution process of state variables, allowing short-term but critical dynamic behaviors to be extended and observed. This approach not only reduces the demand for computing resources, but also makes the model more intuitive and controllable when analyzing complex time-varying characteristics. Compared with the existing fractional-order modeling under a fixed time scale, it significantly improves the efficiency of dynamic characteristic analysis of the system.

[0016] The introduction of affine transformation to scale the state variables effectively regulates the dynamic range of the state variables and significantly optimizes the distribution of model parameters. This method centralizes the scattered and complex parameters in traditional fractional-order modeling, simplifies the model solution process, and is easier to achieve fast calculation and system optimization in practical applications compared to existing models.

[0017] The solution simplifies the five-dimensional fractional-order state differential equation into a three-dimensional model through dimensionality reduction, which not only retains the core dynamic characteristics but also reduces the complexity of the model. This method greatly saves computing resources while ensuring the consistency of the analysis results. Compared with the existing high-dimensional modeling methods, it is more suitable for the study of real-time dynamic behavior and the design of control strategies.

[0018] It is suitable for characterizing the dynamic characteristics of doubly-fed induction generators. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Flowchart of a modeling method for a doubly-fed induction generator fractional-order chaotic system.

[0020] Figure 2 Comparison diagram of chaotic attractor trajectories of the five-dimensional state differential equation of the doubly fed induction generator under parameter 1.

[0021] Figure 3 Comparison diagram of chaotic attractor trajectories of the three-dimensional state differential equation of the doubly fed induction generator under parameter 1.

[0022] Figure 4 Comparison diagram of chaotic attractor trajectories of the five-dimensional state differential equation of the doubly fed induction generator under parameter 2.

[0023] Figure 5 Comparison diagram of chaotic attractor trajectories of the three-dimensional state differential equation of the doubly-fed induction generator under parameter 2.

[0024] Figure 6 Comparison diagram of chaotic attractor trajectories of the five-dimensional state differential equation of the doubly fed induction generator under parameter 3.

[0025] Figure 7 Comparison diagram of chaotic attractor trajectories of the three-dimensional state differential equation of the doubly fed induction generator under parameter 3.

[0026] Figure 8 Comparison diagram of chaotic attractor trajectories of the five-dimensional state differential equation of the doubly-fed induction generator under parameter 4.

[0027] Fig. 9 Comparison diagram of chaotic attractor trajectories of the three-dimensional state differential equation of the doubly fed induction generator under parameter 4. DETAILED DESCRIPTION

[0028] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically: Embodiment 1: This embodiment provides a modeling method for a doubly-fed induction generator fractional-order chaotic system, comprising: The steps of determining the state variables of the preset model through fractional-order differential operators and constructing a five-dimensional fractional-order state differential equation; The five-dimensional equation is reduced in dimension by using the stator flux orientation condition to simplify it into a three-dimensional fractional-order state differential equation; A step of defining a time speed conversion factor and running the preset model at a new time scale to optimize the parameter distribution of state variables; The step of converting the optimized state variables through the scaling factor of the affine transformation to optimize the coefficient distribution of the preset model.

[0029] The fractional differential operator is defined according to the fractional calculus formula and is used to describe the memory and hereditary characteristics of the state variable.

[0030] The stator flux orientation condition includes: setting the stator d-axis voltage of the target generator to zero and setting the q-axis voltage to the grid voltage.

[0031] The time fast-slow conversion factor is defined according to the relationship between the rotor resistance and the time scale of the target generator, and the original time scale is connected with the new time scale to unify the dynamic change range of the state variable.

[0032] The five-dimensional fractional-order state differential equation is specifically: The d-axis and q-axis currents of the stator and rotor of the target generator and the electrical angular velocity of the rotor are taken as state variables, and the fractional-order differential operator is used to perform fractional derivatives on each state variable to obtain the formula: Among them, the system state variables are the generator stator d-axis current , stator q-axis current , rotor d-axis current , rotor q-axis current , rotor electrical angular velocity , is the fractional differential operator of the generator stator current, is the fractional differential operator of the rotor current, is the fractional differential operator of the rotor electrical angular velocity, is the stator winding inductance, is the rotor winding inductance, is the mutual inductance between the stator winding and the rotor winding, is the stator resistance, is the rotor resistance, is the magnetic flux leakage coefficient, is the stator d-axis voltage, is the stator q-axis voltage, is the rotor d-axis voltage, is the rotor q-axis voltage, For synchronous speed, is the number of generator pole pairs, is the generator moment of inertia, is the generator damping viscosity coefficient, is the driving torque that drives the generator to rotate, is the grid voltage.

[0033] Implementation method 2: This implementation method is a further description of the technical solution provided in implementation method 1, specifically: In the prior art, the modeling of doubly-fed induction generators is usually based on integer-order differential equations, such as using the classic dq-axis mathematical model to describe its electromagnetic and mechanical characteristics. This method has achieved good results in describing basic dynamic characteristics, but its ability to capture complex nonlinear behaviors (such as chaotic phenomena) is relatively limited.

[0034] However, the following technical problems still exist: Insufficient modeling accuracy: Existing integer-order modeling methods have limited accuracy in describing complex time-varying characteristics and nonlinear coupling behaviors, and cannot fully reveal the chaotic characteristics of doubly-fed induction generators.

[0035] 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, which limits the practical application of modeling.

[0036] 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.

[0037] In order to solve the above problems, this embodiment proposes a doubly fed induction generator fractional order chaotic system modeling method based on affine transformation and time scale transformation. This method not only optimizes the parameter distribution of the model and significantly improves the modeling accuracy, but also deeply 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.

[0038] Specific: Step 1: Determine the state variables and establish the five-dimensional fractional-order state differential equation In this step, a five-dimensional fractional-order state differential equation describing the dynamic behavior of the doubly-fed induction generator is constructed by selecting state variables and introducing fractional-order differential operators. Detailed description: State variable selection: The d-axis currents and q-axis currents of the generator stator and rotor and the rotor electrical angular velocity are selected as state variables, which are expressed as stator d-axis current, stator q-axis current, rotor d-axis current, rotor q-axis current and rotor electrical angular velocity respectively.

[0039] Introduction of fractional-order differential operators: The traditional integer-order differential operators used to describe the changes in state variables are replaced by fractional-order differential operators to more accurately describe the dynamic change characteristics of state variables over time. Fractional-order operators can reflect the memory and hereditary characteristics of variables, enabling the model to capture the nonlinear dynamic behavior of the doubly-fed induction generator.

[0040] Establishing equations: Based on the electromagnetic coupling relationship, there are nonlinear relationships between state variables. For example, the changes in stator and rotor currents are affected by the winding resistance, inductance, and mutual inductance, while the changes in the rotor electrical angular velocity are related to torque and inertia. The five-dimensional state differential equation expresses these relationships as dynamic coupling between variables.

[0041] Parameter definition: The key parameters in the equation include the resistance of the stator and rotor, the inductance, the mutual inductance between the windings, the moment of inertia of the generator and the grid voltage. These parameters have a direct impact on the change of the state variables.

[0042] Output: A fractional-order state differential equation containing five variables is established, providing a basic model for subsequent steps.

[0043] Step 2: Reduce the model dimension using the stator flux orientation condition By introducing the stator flux orientation condition, the five-dimensional fractional-order state differential equation is simplified to a three-dimensional fractional-order state differential equation. Stator flux orientation condition is introduced: Assuming that the stator flux direction is aligned with the stator d-axis, this makes the stator d-axis voltage constant to zero, and the stator q-axis voltage equal to the grid voltage. Through this constraint, the dynamic characteristics of the stator variables can be replaced by the rotor variables.

[0044] Ignore the effect of stator resistance: Since the effect of stator resistance on the dynamic behavior is small, its effect is ignored in the model, which further simplifies the equation.

[0045] State variable substitution: Through the above constraints, the stator d-axis and q-axis currents are replaced by the rotor d-axis and q-axis currents. At the same time, the slip electrical angular frequency is defined, which is the difference between the rotor electrical angular velocity and the synchronous electrical angular velocity, and is used to replace the rotor electrical angular velocity as the new state variable.

[0046] Re-derive the equation: Combined with the simplified conditions, the variables in the five-dimensional state equation are reduced to three variables: rotor d-axis current, rotor q-axis current, and slip electrical angular frequency, and the state equation is re-expressed to form a three-dimensional fractional-order state differential equation.

[0047] Output: A three-dimensional fractional-order state differential equation is established, which provides a simplified model for subsequent optimization steps.

[0048] Step 3: Design time scale transformation to optimize parameter distribution By designing the time speed conversion factor, the time scale is transformed from the original time to a new time scale to optimize the dynamic range of state variables and parameter distribution. Detailed description: Time scale transformation factor design: Based on system parameters such as rotor resistance, a time fast and slow transformation factor is defined to adjust the dynamic evolution process of time so that the variables change more uniformly under the new time scale.

[0049] Equation updating: The three-dimensional fractional-order state differential equation is re-expressed at a new time scale, and the parameters in the equation are adjusted to better describe the coupling relationship between variables at the new time scale.

[0050] Parameter optimization: Through time transformation, the dynamic range of state variables is unified and the distribution of parameters in the equation is centralized, thereby reducing computational complexity.

[0051] Verify consistency: Ensure that the model at the new time scale is consistent with the original time scale in terms of dynamic characteristics, and only the parameter distribution is optimized.

[0052] Output: A three-dimensional fractional-order state differential equation for optimizing parameter distribution is established, laying the foundation for the next affine transformation.

[0053] Step 4: Introduce affine transformation to further optimize the equation coefficients By designing the scaling factor of the affine transformation, the state variables are scaled and adjusted, thereby further optimizing the parameter distribution of the three-dimensional fractional-order state differential equation. Affine transformation factor design: Design different scaling factors for each state variable, for example, the scaling factors are designed for the rotor d-axis current, the rotor q-axis current, and the slip electrical angular frequency. The selection of the scaling factor is determined based on the dynamic range of the variable and its contribution to the equation.

[0054] State variable update: Use affine transformation to replace the original state variables with new variables after scaling, thereby standardizing the dynamic range of the variables and reflecting the scale consistency between variables in the equation.

[0055] Equation coefficient optimization: Re-express the three-dimensional fractional state differential equation based on the new variables. The model after scaling is more concentrated in parameter distribution and the nonlinear structure of the equation is clearer.

[0056] Final model construction: After the optimization is completed, 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 the doubly-fed induction generator.

[0057] Output: The final three-dimensional fractional-order mechanism model was constructed, with a clear nonlinear structure and centralized parameter distribution.

[0058] Implementation Method 3: Combination Figure 1-9 This embodiment is described in detail by using specific examples to further describe the technical solution provided above. Specifically: This embodiment provides a modeling method for a doubly-fed induction generator fractional-order chaotic system based on affine transformation and time scale transformation, comprising the following steps: Step 1: Taking the d-axis and q-axis currents of the stator and rotor and the electrical angular velocity of the rotor as state variables, and using the fractional-order differential operator to perform fractional-order derivatives on each state variable, the fractional-order state differential equation of the doubly-fed induction generator can be obtained: Among them, the system state variables are the generator stator d-axis current , stator q-axis current , rotor d-axis current , rotor q-axis current , rotor electrical angular velocity , is the fractional differential operator of the generator stator current, is the fractional differential operator of the rotor current, is the fractional differential operator of the rotor electrical angular velocity, is the stator winding inductance, is the rotor winding inductance, is the mutual inductance between the stator winding and the rotor winding, is the stator resistance, is the rotor resistance, is the magnetic flux leakage coefficient, is the stator d-axis voltage, is the stator q-axis voltage, is the rotor d-axis voltage, is the rotor q-axis voltage, For synchronous speed, is the number of generator pole pairs, is the generator moment of inertia, is the generator damping viscosity coefficient, is the driving torque that drives the generator to rotate, is the grid voltage.

[0059] Step 2: Introduce the stator flux orientation simplification condition into the state differential equation containing fractional-order differential operators. This condition constrains the stator d-axis and q-axis voltages and currents respectively, ignoring the small effect of the stator resistance on the system dynamics. The fractional-order form of the stator d-axis and q-axis currents is replaced by the rotor d-axis and q-axis currents, which can be described as: ; ; ; ; Then the five-dimensional fractional-order state differential equation in step 1 can be simplified to a three-dimensional fractional-order state differential equation: Definition of slip electrical angle frequency ,use replace As a state variable, keep the other two state variables and , then the above three-dimensional fractional-order state differential equation can be rewritten as: Step 3: Design time speed conversion factor and its transformed time scale With the original time scale The relationships between them are: Then the three-dimensional fractional-order state differential equation in step 2.2 can be expressed in the new time scale as: in, For time scale The rotor current fractional differential operator under Time scale The fractional-order differential operator of the rotor electrical angular velocity under .

[0060] Step 4: Design the scaling factor required for the affine transformation , , And the state variables after scaling , and With the original state variable , and The relationships between them are: Further, according to the above designed expansion factor, let , , , , , , then the three-dimensional fractional-order state differential equation in step 3 can be further transformed into: The above formula is the fractional-order three-dimensional mechanism model of the doubly-fed induction generator, which is equivalent to the three-dimensional fractional-order equation obtained in step 2. At the same time, under the condition of stator flux orientation, the main dynamics of the five-dimensional state differential equation in step 1 are also consistent. Further, taking a 15kW doubly-fed induction generator as an example, the benchmark values ​​of the analysis parameters are calculated as follows: , , , = 1.01, and on a uniform time scale and the state variable affine transformation , and The main dynamic consistency of the above mechanism model and the five-dimensional state differential equation is verified by changing the following four parameters: Parameter 1 ( ): When parameter 1 is 0.7 and other parameters keep the reference value, the comparison of chaotic attractor trajectories of the three-dimensional and five-dimensional state differential equations of the doubly fed induction generator is shown in Figure 2 and 3 shown.

[0061] Parameter 2 ( ): When parameter 2 is 0.033 and other parameters remain at the reference value, the comparison of chaotic attractor trajectories of the three-dimensional and five-dimensional state differential equations of the doubly fed induction generator is shown in Figure 4 and 5 shown.

[0062] Parameter 3 ( ): When parameter 3 is 0.05 and other parameters keep the reference value, the comparison of chaotic attractor trajectories of the three-dimensional and five-dimensional state differential equations of the doubly fed induction generator is shown in Figure 6 and 7 shown.

[0063] Parameter 4 ( ): When parameter 4 is 1.13 and other parameters remain at the reference value, the comparison of chaotic attractor trajectories of the three-dimensional and five-dimensional state differential equations of the doubly fed induction generator is shown in Figure 8 and 9 shown.

[0064] 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 trajectories are highly consistent. Therefore, using the three-dimensional fractional-order mechanism model to analyze the chaotic characteristics of the doubly-fed induction generator not only reduces the analysis parameters but also saves computing resources.

[0065] The technical solution provided by the present invention is further described in detail above through several specific implementation modes in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific implementation modes described above are not intended to be used as limitations on the present invention. Any reasonable modification and improvement of the present invention, combination of implementation modes and equivalent substitution within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A modeling method for a doubly-fed induction generator fractional-order chaotic system, characterized in that: include: The steps of determining the state variables of the preset model through fractional-order differential operators and constructing a five-dimensional fractional-order state differential equation; The five-dimensional equation is reduced in dimension by using the stator flux orientation condition to simplify it into a three-dimensional fractional-order state differential equation; A step of defining a time speed conversion factor and running the preset model at a new time scale to optimize the parameter distribution of state variables; The step of converting the optimized state variables through the scaling factor of the affine transformation to optimize the coefficient distribution of the preset model.

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

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

4. The modeling method of a doubly-fed induction generator fractional-order chaotic system according to claim 1, characterized in that: The time fast-slow conversion factor is defined according to the relationship between the rotor resistance and the time scale of the target generator, and the original time scale is connected with the new time scale to unify the dynamic change range of the state variable.

5. The modeling method of a doubly-fed induction generator fractional-order chaotic system according to claim 1, characterized in that: The five-dimensional fractional-order state differential equation is specifically: The d-axis and q-axis currents of the stator and rotor of the target generator and the electrical angular velocity of the rotor are taken as state variables, and the fractional-order differential operator is used to perform fractional derivatives on each state variable to obtain the formula: Among them, the system state variables are the generator stator d-axis current , stator q-axis current , rotor d-axis current , rotor q-axis current , rotor electrical angular velocity , is the fractional differential operator of the generator stator current, is the fractional differential operator of the rotor current, is the fractional differential operator of the rotor electrical angular velocity, is the stator winding inductance, is the rotor winding inductance, is the mutual inductance between the stator winding and the rotor winding, is the stator resistance, is the rotor resistance, is the magnetic flux leakage coefficient, is the stator d-axis voltage, is the stator q-axis voltage, is the rotor d-axis voltage, is the rotor q-axis voltage, For synchronous speed, is the number of generator pole pairs, is the generator moment of inertia, is the generator damping viscosity coefficient, is the driving torque that drives the generator to rotate, is the grid voltage.

6. A modeling device for a doubly-fed induction generator fractional-order chaotic system, characterized in that: include: Through fractional-order differential operators, the state variables of the preset model are determined, and a module for five-dimensional fractional-order state differential equations is constructed; The five-dimensional equation is reduced in dimension by using the stator flux orientation condition to simplify it into a module of a three-dimensional fractional-order state differential equation; A module for defining a time speed conversion factor and running the preset model at a new time scale to optimize the parameter distribution of state variables; The optimized state variables are transformed by using a scaling factor of an affine transformation to optimize the coefficient distribution module of the preset model.

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

8. 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 .

9. A computer program product, being a computer program, characterized in that When the computer program is executed, the method of claim 1 is implemented.

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