High voltage ride through optimization control method and system for doubly-fed induction wind power generator

Abstract

The application provides a high-voltage ride-through optimization control method and system of a doubly-fed induction wind power generator, relates to the field of the doubly-fed induction wind power generator, and comprises the following steps: obtaining a mathematical model of a rotor-side converter and a mathematical model of a grid-side converter respectively, constructing a target function of high-voltage ride-through optimization control according to the mathematical model of the rotor-side converter and the mathematical model of the grid-side converter; obtaining an improved growth optimization algorithm, solving the target function according to the improved growth optimization algorithm to obtain optimal control parameters, and optimizing high-voltage ride-through of the doubly-fed induction wind power generator according to the optimal control parameters. The application optimizes the high-voltage ride-through control parameters of the doubly-fed induction wind power generator, so that the determination of the controller parameters and other variables for the high-voltage ride-through of the DFIG has a better effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of doubly fed induction wind power generator, and particularly relates to a high voltage ride through optimization control method and system of doubly fed induction wind power generator. BACKGROUND

[0002] Doubly fed induction wind power generator (DFIG) has the characteristics of low price, mature production technology and simple maintenance, and is rapidly becoming the leading model of wind power generation, but the topology structure of DFIG also greatly affects the grid voltage fault.

[0003] In recent years, the low voltage ride through (LVRT) technology of DFIG has made a major breakthrough, and the control technology is relatively mature. At the same time, the high voltage ride through (HVRT) of DFIG has gradually become a research hotspot in recent years.

[0004] At present, in the high voltage ride through technology of DFIG, the controller parameters and other variables are generally set artificially according to experience, which leads to uncertain control effect, so that the controller parameters and other variables of the high voltage ride through of DFIG cannot be well determined, thereby leading to easy large-scale off-grid of the wind turbine, so there is an urgent need for a method for determining the controller parameters and other variables of the high voltage ride through of DFIG. SUMMARY

[0005] Therefore, the present application provides a high voltage ride through optimization control method and system of doubly fed induction wind power generator, which can solve the technical problem that the controller parameters and other variables cannot be well determined in the prior art, thereby leading to easy large-scale off-grid of the wind turbine.

[0006] In one aspect, the present application provides a high voltage ride through optimization control method of doubly fed induction wind power generator, the doubly fed induction wind power generator comprising a rotor side converter and a grid side converter, the method comprising:

[0007] obtaining a mathematical model of the rotor side converter and a mathematical model of the grid side converter respectively, and constructing a target function of high voltage ride through optimization control according to the mathematical model of the rotor side converter and the mathematical model of the grid side converter;

[0008] Obtaining an improved growth optimization algorithm, solving the objective function according to the improved growth optimization algorithm to obtain optimal control parameters, and optimizing the high voltage ride through of the doubly-fed induction wind turbine according to the optimal control parameters;

[0009] The improved growth optimization algorithm includes a learning phase of the improved growth optimization algorithm, and the improved expression is:

[0010]

[0011] in:

[0012]

[0013] Where, Gap 1 represents the gap between the best individual and the better individual; Gap 2 represents the gap between the best individual and the worst individual; Gap 3 represents the gap between the better and worse individuals; Gap 4 means two random individuals X the gap between individuals; X best 、 X better 、 X worse 、 X r1 、 X r2 Individual X The best individual, the better individual, the worse individual and two random individuals selected from the sample; KA k represents the acquisition of knowledge; SF i Indicates the i Individuals' assessment of their own status; f ( X i )for i The individual's objective function value; f ( X worst ) is the objective function value of the worst individual in the population; f ( X best ) is the best objective function value of the worst individual in the population; Gap k ‖ is the Euclidean distance of the k-th group gap; LF k is the normalized result; is the position of the i-th solution in the t+1-th iteration; is the position of the i-th solution in the t-th iteration; ηrand(0,1) represents a random number between 0 and 1.

[0014] The high-voltage ride-through optimization control method of the double-fed induction wind power generator can automatically optimize the high-voltage ride-through control parameters of the double-fed induction wind power generator to determine the optimal control parameters, thereby ensuring the control effect, and making the determination of the controller parameters and other variables for the high-voltage ride-through of the DFIG have a better effect, and avoiding the technical problems that the control effect is uncertain and the wind turbine is easy to be large-scale off-grid due to the manual setting of the controller parameters and other variables according to experience.

[0015] In addition, the high-voltage ride-through optimization control method of the double-fed induction wind power generator according to the present application can have the following additional technical features:

[0016] Further, the improved growth optimization algorithm includes an improved reflection stage of the growth optimization algorithm, and the improved expression is:

[0017]

[0018] wherein, ;

[0019] In the formula, AF represents a constant changing with the number of iterations; MaxFEs represents the maximum number of iterations; FEs represents the current number of iterations; ub and lb are the upper bound of the problem and the lower bound of the problem, respectively; and rand(0,1) represents a random number between 0 and 1. R j represents a random individual; and t represents the current number of iterations. represents the position of the i-th solution in the j-th dimension in the t-th iteration.

[0020] Further, the improved growth optimization algorithm includes an improved late stage of the growth optimization algorithm, and a jump formula is added in the late stage, and the improved expression is:

[0021]

[0022] In the formula, TF t is a constant value, X mean represents the average value of the individual solution, DOF represents the direction factor; and r1 and r2 are both random numbers between 0 and 1.

[0023] Further, the expression of the objective function is:

[0024]

[0025] wherein, ;

[0026] wherein, f represents the target function; T e represents the electromagnetic torque; represents a reference value of the electromagnetic torque; I r represents the rotor current; represents a reference value of the rotor current; U dc represents the DC-side voltage; represents a reference value of the DC-side voltage; K pi and K ii respectively represent a control parameter on the stator side and a control parameter on the rotor side; R represents the resistance.

[0027] Further, the electromagnetic torque is expressed as:

[0028]

[0029] wherein, P n represents the number of pole pairs; L m represents the mutual inductance; ψ qs represents the stator flux on the q-axis; i ds represents the stator current on the d-axis; ψ ds represents the stator flux on the d-axis; i qs represents the stator current on the q-axis.

[0030] Further, the rotor current is expressed as:

[0031]

[0032] wherein, I r represents the rotor current; represents the rotor current under steady-state operation; represents the rotor transient time constant; L s represents the stator self-inductance; represents the rotor transient self-inductance; w s represents the angular frequency of the stator; ω r represents the angular speed of the rotor rotation; ω srepresents the slip angular velocity; β represents time; j represents the imaginary unit of current; represents the stator process transient time constant; represents the rotor process transient time constant; du s represents the differential of the stator voltage.

[0033] Another aspect of the present application provides a high voltage ride through optimization control system of a doubly-fed induction wind power generator, the doubly-fed induction wind power generator comprising a rotor-side converter and a grid-side converter, the system comprising:

[0034] an acquisition module, configured to acquire a mathematical model of the rotor-side converter and a mathematical model of the grid-side converter respectively, and to construct a target function of high voltage ride through optimization control according to the mathematical model of the rotor-side converter and the mathematical model of the grid-side converter;

[0035] an optimization module, configured to acquire an improved growth optimization algorithm, to solve the target function to obtain optimal control parameters according to the improved growth optimization algorithm, and to optimize high voltage ride through of the doubly-fed induction wind power generator according to the optimal control parameters;

[0036] wherein the improved growth optimization algorithm comprises a learning stage of the improved growth optimization algorithm, and the improved expression is:

[0037]

[0038] wherein:

[0039]

[0040] in the formula, Gap 1 represents a gap between the optimal individual and the better individual; Gap 2 represents a gap between the optimal individual and the worse individual; Gap 3 represents a gap between the better individual and the worse individual; Gap 4 represents a gap between two random individuals different from the individual X ; X best , X better , X worse , X r1 , X r2 are respectively an optimal individual, a better individual, a worse individual and two random individuals selected by the individual X sample; KA k represents acquisition of knowledge;SF i represents the evaluation of the individual on the state of itself; i f X i represents the objective function value of the individual; i f X worst represents the objective function value of the worst individual in the population; f X best represents the objective function value of the worst individual in the population; Gap k represents the Euclidean distance of the kth group of gaps; LF k represents the normalized result; represents the position of the ith solution in the t+1th iteration; represents the position of the ith solution in the tth iteration; η represents a random number between 0 and 1.

[0041] Another aspect of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the high voltage ride-through optimization control method of the doubly-fed induction wind power generator as described above.

[0042] Another aspect of the present application also provides a data processing device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the high voltage ride-through optimization control method of the doubly-fed induction wind power generator as described above when executing the program. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is a flow chart of the high voltage ride-through optimization control method of the doubly-fed induction wind power generator in the embodiment of the present application;

[0044] Figure 2 is a comparison diagram of the convergence curves of the improved growth optimization algorithm and the conventional growth optimization algorithm;

[0045] The following detailed description will further illustrate the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0046] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. Several embodiments of the present application are shown in the drawings. However, the present application can be realized in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present application more thorough and comprehensive.

[0047] ​​​​​Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the application. The use herein of the terms "and / or" includes a set of one or more associated listed items.

[0048] In order to solve the technical problem that the controller parameters and other variables of the high voltage ride-through of the DFIG cannot be well determined in the prior art, and the wind turbine is prone to large-scale off-grid, the application provides a high voltage ride-through optimization control method and system of a doubly-fed induction wind turbine. The optimal control parameters are determined by automatically optimizing the high voltage ride-through control parameters of the doubly-fed induction wind turbine, so as to ensure the control effect, thereby making the determination of the controller parameters and other variables of the high voltage ride-through of the DFIG have a better effect, and avoiding the technical problem that the control effect is uncertain and the wind turbine is prone to large-scale off-grid due to the manual setting of the controller parameters and other variables according to experience.

[0049] In order to facilitate the understanding of the present application, several embodiments of the present application will be given below. However, the present application can be realized in many different forms, and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0050] Embodiment one

[0051] Please refer to Figure 1 , which shows a high voltage ride-through optimization control method of a doubly-fed induction wind turbine in the first embodiment of the present application. The doubly-fed induction wind turbine includes a rotor-side converter and a grid-side converter. The method includes steps S101 to S102:

[0052] S101, the mathematical model of the rotor-side converter and the mathematical model of the grid-side converter are obtained respectively, and the objective function of the high voltage ride-through optimization control is constructed according to the mathematical model of the rotor-side converter and the mathematical model of the grid-side converter.

[0053] Specifically, the expression of the objective function is:

[0054]

[0055] Wherein, ;

[0056] In the formula, f represents the objective function; T e represents the electromagnetic torque; represents the reference value of the electromagnetic torque; I rrepresents the rotor current; represents a reference value of the rotor current; U dc represents the DC-side voltage; represents a reference value of the DC-side voltage; K pi and K ii respectively represent a control parameter on the stator side and a control parameter on the rotor side; R represents the resistance.

[0057] Further, the expression of the electromagnetic torque is:

[0058]

[0059] wherein, P n represents the number of pole pairs; L m represents the mutual inductance; ψ qs represents the flux of the stator on the q-axis; i ds represents the current of the stator on the d-axis; ψ ds represents the flux of the stator on the d-axis; i qs represents the current of the stator on the q-axis.

[0060] Further, the expression of the rotor current is:

[0061]

[0062] wherein, I r represents the rotor current; represents the rotor current under steady-state operation; represents the rotor transient time constant; L s represents the stator self-inductance; represents the rotor transient self-inductance; w s represents the angular frequency of the stator; ω r represents the angular speed of rotation of the rotor; ω s represents the slip angular speed; β represents time; j represents the imaginary unit of the current; represents the stator process transient time constant; represents the rotor process transient time constant; du s represents the differential of the stator voltage.

[0063] S102: Obtain an improved growth optimization algorithm, solve the objective function according to the improved growth optimization algorithm to obtain optimal control parameters, and optimize the high voltage ride through of the doubly-fed induction wind turbine according to the optimal control parameters.

[0064] As a specific example, in the step of obtaining the improved growth optimization algorithm and solving the objective function according to the improved growth optimization algorithm to obtain the optimal control parameters, first, the population size and the maximum number of iterations are set; secondly, the population is initialized; then the fitness is calculated; furthermore, the growth optimization algorithm is improved, including the optimization learning phase and the reflection phase, and boundary constraints and update rules are set; the fitness is updated according to the improved growth optimization algorithm; finally, the current number of iterations is judged based on the current number of iterations to see whether it has reached the maximum number of iterations; if the maximum number of iterations has not been reached, the iteration continues; if the maximum number of iterations has been reached, the iteration is stopped and the optimal control parameters are output. The improved growth optimization algorithm is used to optimize the controller parameters to find better control parameters, thereby improving the control effect of the controller; avoiding artificial setting of parameters and uncertain control effects. Figure 2 As shown in FIG, a schematic diagram comparing the convergence curves of the improved growth optimization algorithm and the conventional growth optimization algorithm.

[0065] Specifically, in order to avoid the problem of uneven distribution of the initial population due to the random generation of the initial population in the original algorithm, this embodiment introduces the Henon chaotic map to initialize the population, and the expression is:

[0066]

[0067] in:

[0068]

[0069] Where: X represents the initial population; x i,j Representative i Individuals in j The values ​​in the dimensions, where i ∈1, 2,…, n, j∈1, 2,…, d, n is the population size, d is the dimension of the problem, and each individual represents a set of high voltage ride-through related control parameter solutions for a doubly fed induction wind turbine; ub and lb are the upper bound and the lower bound of the problem respectively, where:

[0070]

[0071] Where, X i represents the corrected position of the initial population; X i,jrepresents the position of the current individual.

[0072] In order to solve the problem that the search range of the exponential growth optimization algorithm is small in the initial iteration, the improved growth optimization algorithm includes an improved learning stage of the growth optimization algorithm, and the improved expression is specifically as follows:

[0073]

[0074] wherein:

[0075]

[0076] In the formula, Gap 1 represents the gap between the optimal individual and the better individual; Gap 2 represents the gap between the optimal individual and the worse individual; Gap 3 represents the gap between the better individual and the worse individual; Gap 4 represents the gap between two random individuals different from the individual X . X best , X better , X worse , X r1 , X r2 are the optimal individual, the better individual, the worse individual and two random individuals selected from the sample by the individual X . KA k represents the acquisition of knowledge; SF i represents the evaluation of the state of the individual i . f ( X i ) is the objective function value of the individual i . f ( X worst ) is the objective function value of the worst individual in the population; f ( X best ) is the objective function value of the worst individual in the population; Gap k ‖ is the Euclidean distance of the kth group of gaps; LF k is the normalized result; is the position of the ith solution in the t+1th iteration; is the position of the ith solution in the tth iteration; η represents a random number between 0 and 1.

[0077] Secondly, in order to avoid the problem that the growth optimization algorithm itself converges too slowly, an adaptive factor is introduced in the reflection stage to improve it, and the improved expression is:

[0078]

[0079] wherein, ;

[0080] In the formula, AF represents a constant changing with the number of iterations; MaxFEs T represents the maximum number of iterations; FEs t represents the current number of iterations; ub and lb are the upper bound of the problem and the lower bound of the problem respectively; and rand(0,1) represents a random number between 0 and 1; R j rand() represents a random individual; and t represents the current number of iterations; xij(t) represents the position of the i-th solution in the j-th dimension in the t-th iteration.

[0081] Furthermore, in order to avoid the problem that the growth optimization algorithm is prone to fall into a local optimal value in the later stage of iteration, a jump formula is added in the later stage of growth optimization, so that it jumps out when falling into a local optimal value; specifically, when t>2 / 3T, it is the later stage of growth optimization, wherein T is the maximum number of iterations, and t is the current number of iterations. The improved expression is:

[0082]

[0083] In the formula, TF t is a constant value, X mean x represents the average value of the individual solution, DOF represents a direction factor; and r1 and r2 are both random numbers between 0 and 1.

[0084] As a specific example, in order to obtain the optimal control parameter, the updated rule is:

[0085]

[0086] In the formula, P2 is 0.001.

[0087] To sum up, the high-voltage ride-through optimization control method of the doubly-fed induction wind power generator in the above embodiment of the application can automatically optimize the high-voltage ride-through control parameters of the doubly-fed induction wind power generator to determine the optimal control parameters, ensure the control effect, and thus make the determination of the controller parameters and other variables for the high-voltage ride-through of the DFIG have a better effect, and avoid the technical problems that the control effect is uncertain and the wind turbine is prone to large-scale off-grid due to the manual setting of the controller parameters and other variables according to experience.

[0088] Example 2

[0089] A second embodiment of the present invention provides a high voltage ride-through optimization control system for a doubly-fed induction wind turbine generator. The doubly-fed induction wind turbine generator includes a rotor-side converter and a grid-side converter. The system includes:

[0090] an acquisition module, configured to respectively acquire a mathematical model of a rotor-side converter and a mathematical model of a grid-side converter, and construct an objective function of high voltage ride-through optimization control according to the mathematical model of the rotor-side converter and the mathematical model of the grid-side converter;

[0091] an optimization module, configured to obtain an improved growth optimization algorithm, solve the objective function according to the improved growth optimization algorithm to obtain optimal control parameters, and optimize the high voltage ride through of the doubly-fed induction wind turbine according to the optimal control parameters;

[0092] The improved growth optimization algorithm includes a learning phase of the improved growth optimization algorithm, and the improved expression is:

[0093]

[0094] in:

[0095]

[0096] Where, Gap 1 represents the gap between the best individual and the better individual; Gap 2 represents the gap between the best individual and the worst individual; Gap 3 represents the gap between the better and worse individuals; Gap 4 means two random individuals X the gap between individuals; X best 、 X better 、 X worse 、 X r1 、 X r2 Individual X The best individual, the better individual, the worse individual and two random individuals selected from the sample; KA k represents the acquisition of knowledge; SF i Indicates the i Individuals' assessment of their own status; f ( X i )for ithe objective function value of the worst individual in the population; f X worst the objective function value of the worst individual in the population; f X best the objective function value of the worst individual in the population; Gap k the Euclidean distance of the kth group of gaps; LF k the normalized result; the position of the ith solution in the t+1th iteration; the position of the ith solution in the tth iteration; η represents a random number between 0 and 1.

[0097] To sum up, the high-voltage ride-through optimization control system of the doubly-fed induction wind power generator in the above-mentioned embodiments of the application can automatically optimize the high-voltage ride-through control parameters of the doubly-fed induction wind power generator to determine the optimal control parameters, thereby ensuring the control effect, so that the determination of the controller parameters and other variables for the high-voltage ride-through of the DFIG has a better effect, and the technical problem of uncertain control effect and easy large-scale disconnection of the wind turbine caused by the manual setting of the controller parameters and other variables according to experience is avoided.

[0098] In addition, an embodiment of the application also provides a computer readable storage medium, which has a computer program stored thereon, and the program is executed by a processor to realize the steps of the method in the above-mentioned embodiment.

[0099] In addition, an embodiment of the application also provides a data processing device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor realizes the steps of the method in the above-mentioned embodiment when executing the program.

[0100] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions to cause a machine to perform the logic function, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination thereof. For the purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be for example but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.

[0101] ​​More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, using suitable methods, before being stored in a computer memory.

[0102] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following techniques, which are well known in the art, can be used to implement the application: a hybrid of the techniques mentioned above; a combination of one or more of the techniques mentioned above; or one or more other techniques suitable for use in the computer-based systems described above.

[0103] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like is intended to mean that a specific feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In the specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in one or more embodiments or examples.

[0104] Although embodiments of the application have been shown and described, it would be recognized by those of ordinary skill in the art that various changes, modifications, substitutions, and alterations can be made hereto without departing from the spirit and scope of the application. The scope of the application is limited only by the claims and the equivalents thereof.

Claims

1. A high voltage ride through optimization control method of a doubly-fed induction wind power generator, characterized in that, The double-fed induction wind power generator comprises a rotor-side converter and a grid-side converter, and the method comprises the following steps: respectively acquiring a mathematical model of the rotor-side converter and a mathematical model of the grid-side converter, and constructing a target function of high-voltage ride-through optimization control according to the mathematical model of the rotor-side converter and the mathematical model of the grid-side converter; acquiring an improved growth optimization algorithm, solving the target function according to the improved growth optimization algorithm to obtain optimal control parameters, and optimizing high-voltage ride-through of the double-fed induction wind power generator according to the optimal control parameters; wherein the improved growth optimization algorithm comprises a learning stage of the improved growth optimization algorithm, and the improved expression is as follows: wherein: Where, Gap 1 represents the gap between the best individual and the better individual; Gap 2 represents the gap between the best individual and the worst individual; Gap 3 represents the gap between the better and worse individuals; Gap 4 means two random individuals X the gap between individuals; X best 、 X better 、 X worse 、 X r1 、 X r2 Individual X The best individual, the better individual, the worse individual and two random individuals selected from the sample; KA k represents the acquisition of knowledge; SF i Indicates the i Individuals' assessment of their own status; f ( X i )for i The individual's objective function value; f ( X worst ) is the objective function value of the worst individual in the population; f ( X best ) is the best objective function value of the worst individual in the population; Gap k ‖ is the Euclidean distance of the k-th group gap; LF k is the normalized result; is the position of the i-th solution in the t+1-th iteration; is the position of the i-th solution in the t-th iteration; η Represents a random number between 0 and 1; i represents the i-th individual; t represents the current number of iterations; The expression of the target function is as follows: ； wherein ; wherein f represents a target function; T e represents an electromagnetic torque; represents a reference value of the electromagnetic torque; I r represents a rotor current; represents a reference value of the rotor current; U dc represents a DC-side voltage; represents a reference value of the DC-side voltage; K pi and K ii respectively represent a control parameter on the stator side and a control parameter on the rotor side; R represents a resistance.

2. The high voltage ride through optimization control method of a doubly-fed induction wind power generator according to claim 1, characterized in that, The improved growth optimization algorithm comprises a reflection stage of the improved growth optimization algorithm, and the improved expression is as follows: wherein ; where AF represents a constant that varies with the iteration number; MaxFEs represents the maximum iteration number; FEs represents the current iteration number; ub and lb are the upper bound of the problem and the lower bound of the problem, respectively; rand(0, 1) represents a random number between 0 and 1; R j represents a random individual; t represents the current iteration number; represents the position of the i-th solution in the j-th dimension in the t-th iteration.

3. The high voltage ride through control method of doubly-fed induction wind generator according to claim 2, wherein, The improved growth optimization algorithm comprises a later stage of the improved growth optimization algorithm, and a jump formula is added in the later stage, and the improved expression is as follows: wherein TF t is a constant value, wherein t represents the current iteration number; X mean represents the average of the individual solutions, DOF represents the direction factor; r1 and r2 are both random numbers between 0 and 1.

4. The high voltage ride through control method of doubly-fed induction wind generator according to claim 1, wherein, The expression of the electromagnetic torque is as follows: wherein P n represents the number of pole pairs; L m represents the mutual inductance; ψ qs represents the stator flux linkage on the q-axis; i ds represents the stator current on the d-axis; ψ ds represents the stator flux linkage on the d-axis; i qs represents the stator current on the q-axis.

5. The high voltage ride through control method of doubly-fed induction wind generator according to claim 1, wherein, The expression of the rotor current is as follows: wherein I r denotes the rotor current; denotes the rotor current in steady state operation; denotes the rotor transient time constant; L s denotes the stator self-inductance; denotes the rotor transient self-inductance; w s denotes the angular frequency of the stator; ω r denotes the angular speed of rotation of the rotor; ω s denotes the slip angular speed; β denotes time; j denotes the imaginary unit of the current; denotes the stator process transient time constant; denotes the rotor process transient time constant; du s denotes the derivative of the stator voltage.

6. A high voltage ride through optimization control system for a doubly-fed induction wind power generator, characterized in that, The double-fed induction wind power generator comprises a rotor-side converter and a grid-side converter, and the system comprises: an acquisition module, configured to respectively acquire a mathematical model of the rotor-side converter and a mathematical model of the grid-side converter, and construct a target function of high-voltage ride-through optimization control according to the mathematical model of the rotor-side converter and the mathematical model of the grid-side converter; an optimization module, configured to acquire an improved growth optimization algorithm, solve the target function according to the improved growth optimization algorithm to obtain optimal control parameters, and optimize high-voltage ride-through of the double-fed induction wind power generator according to the optimal control parameters; wherein the improved growth optimization algorithm comprises a learning stage of the improved growth optimization algorithm, and the improved expression is as follows: wherein: wherein, Gap 1 represents the gap between the best individual and the better individual; Gap 2 represents the gap between the best individual and the worse individual; Gap 3 represents the gap between the better individual and the worse individual; Gap 4 represents the gap between two random individuals different from the individual X ; X best , X better , X worse , X r1 , X r2 are the best individual, the better individual, the worse individual and two random individuals selected from the sample, respectively; X KA k represents the acquisition of knowledge; SF i represents the evaluation of the state of the individual i ; f ( X i ) is the objective function value of the individual i ; f ( X worst ) is the objective function value of the worst individual in the population; f ( X best ) is the objective function value of the worst individual in the population; Gap k ‖ is the Euclidean distance of the kth set of gaps; LF k is the normalized result; is the position of the ith solution in the t+1th iteration; is the position of the ith solution in the tth iteration; η represents a random number between 0 and 1; i represents the ith individual; t represents the current iteration number;​ The expression of the target function is as follows: ； wherein ; In the formula, f represents a target function; T e represents an electromagnetic torque; represents a reference value of the electromagnetic torque; I r represents a rotor current; represents a reference value of the rotor current; U dc represents a DC side voltage; represents a reference value of the DC side voltage; K pi and K ii respectively represent a control parameter on the stator side and a control parameter on the rotor side; R represents a resistance.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the high-voltage ride-through optimization control method of the double-fed induction wind power generator according to any one of claims 1-5.

8. A data processing device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the high-voltage ride-through optimization control method of the double-fed induction wind power generator according to any one of claims 1-5.

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