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

Abstract

The invention provides a high-voltage ride-through optimization control method and system for a doubly-fed induction wind generator, and relates to the field of doubly-fed induction wind generators, and the method comprises the steps: respectively obtaining a mathematical model of a rotor side converter and a mathematical model of a power grid side converter; according to the mathematical model of the rotor side converter and the mathematical model of the power grid side converter, constructing an objective function of high voltage ride through optimization control; and obtaining an improved growth optimization algorithm, solving the target function according to the improved growth optimization algorithm to obtain an optimal control parameter, and optimizing the high voltage ride through of the doubly-fed induction wind generator according to the optimal control parameter. According to the method, the high voltage ride through control parameters of the doubly-fed induction generator are optimized, so that a better effect on determining the controller parameters and other variables of the high voltage ride through of the DFIG is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of doubly-fed induction wind generators, and particularly relates to a high-voltage ride-through optimization control method and system for a doubly-fed induction wind generator. Background Art

[0002] Doubly Fed Induction Generator (DFIG) has the characteristics of low cost, mature production technology, simple maintenance, etc., and is rapidly becoming the dominant model of wind power generation. However, the topology of DFIG also makes the impact of grid voltage faults on it quite large.

[0003] In recent years, significant breakthroughs have been made in the research on the Low Voltage Ride Through (LVRT) technology of DFIG, 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] Currently, in the technology of high-voltage ride-through of DFIG, controller parameters and other variables are generally set manually according to experience, resulting in uncertain control effects, so that the controller parameters and other variables of high-voltage ride-through of DFIG cannot be well determined, which leads to large-scale disconnection of wind turbines. Therefore, a method for determining the controller parameters and other variables of high-voltage ride-through of DFIG is urgently needed. Summary of the Invention

[0005] Based on this, the purpose of the present invention is to provide a high-voltage ride-through optimization control method and system for a doubly-fed induction wind generator, which is used to solve the technical problem that in the prior art, controller parameters and other variables are set manually according to experience, and the controller parameters and other variables of high-voltage ride-through of DFIG cannot be well determined, resulting in large-scale disconnection of wind turbines.

[0006] The present invention provides a high-voltage ride-through optimization control method for a doubly-fed induction wind generator. The doubly-fed induction wind generator includes a rotor-side converter and a grid-side converter. The method includes: Respectively obtain the mathematical models of the rotor-side converter and the grid-side converter, and construct an objective function for high-voltage ride-through optimization control according to the mathematical models of the rotor-side converter and the grid-side converter; Obtain an improved growth optimization algorithm, solve the objective function according to the improved growth optimization algorithm to obtain the optimal control parameters, and optimize the high-voltage ride-through of the doubly-fed induction wind generator according to the optimal control parameters; Among them, the improved growth optimization algorithm includes improving the learning stage of the growth optimization algorithm, and the improved expression is:

[0007] Where:

[0008] In the formula, Gap 1 represents the gap between the optimal individual and the relatively better individual; Gap 2 represents the gap between the optimal individual and the relatively worse individual; Gap 3 represents the gap between the relatively better individual and the relatively worse individual; Gap 4 represents the gap between two randomly selected individuals different from individual X ; X best , X better , X worse , X r1 , X r2 are the optimal individual, relatively better individual, relatively worse individual, and two randomly selected individuals respectively selected from the individual X samples; KA k represents the acquisition of knowledge; SF i represents the i th individual's assessment of its own state; f ( X i ) is the i th 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 optimal 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 normalization result; is the position of the ith solution in the (t + 1)th iteration; is the position of the ith solution in the tth iteration; η represents a random number between 0 and 1.

[0009] The above high-voltage ride-through optimization control method for a doubly-fed induction wind generator determines the optimal control parameters by automatically optimizing the high-voltage ride-through control parameters of the doubly-fed induction wind generator, ensuring the control effect. As a result, the determination of the controller parameters and other variables for the high-voltage ride-through of the DFIG has a better effect, avoiding the technical problems of uncertain control effects caused by artificially setting the controller parameters and other variables according to experience, and the large-scale disconnection of the wind turbines from the grid.

[0010] In addition, according to the above high-voltage ride-through optimization control method for a doubly-fed induction wind generator of the present invention, the following additional technical features may also be included: Further, the improved growth optimization algorithm includes the reflection stage of the improved growth optimization algorithm, and the improved expression is:

[0011] Wherein, ; In the formula, AF represents a constant that changes with the number of iterations; MaxFEs represents the maximum number of iterations; FEs represents the current number of iterations; ub and lb are respectively the upper bound and the lower bound of the problem; rand(0,1) represents a random number between 0 and 1; R j represents a randomly selected individual; 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.

[0012] Further, the improved growth optimization algorithm includes the later stage of the improved growth optimization algorithm, and a jump formula is added in the later stage. The improved expression is:

[0013] In the formula, TF t is a constant value, X mean represents the average value of the individual solutions, DOF represents the direction factor; r 1 and r 2 are both random numbers between 0 and 1.

[0014] Further, the expression of the objective function is:

[0015] Wherein, ; In the formula, f represents the objective function; T e represents the electromagnetic torque; represents the reference value of the electromagnetic torque; I r represents the rotor current; represents the reference value of the rotor current; U dc represents the DC - side voltage; represents the reference value of the DC - side voltage; K pi and K ii respectively represent the control parameters on the stator side and the control parameters on the rotor side; R represents resistance.

[0016] Furthermore, the expression of the electromagnetic torque is:

[0017] wherein, P n represents the number of pole pairs; L m represents the mutual inductance; ψ qs represents the magnetic flux of the stator on the q - axis; i ds represents the current of the stator on the d - axis; ψ ds represents the magnetic flux of the stator on the d - axis; i qs represents the current of the stator on the q - axis.

[0018] Furthermore, the expression of the rotor current is:

[0019] 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 self - inductance of the stator; represents the transient self - inductance of the rotor; w s represents the angular frequency of the stator; ω r represents the angular velocity of rotor rotation; ω s represents 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 sRepresents the differential of the stator voltage.

[0020] On the other hand, the present invention provides a high-voltage ride-through optimization control system for a doubly-fed induction wind generator. The doubly-fed induction wind generator includes a rotor-side converter and a grid-side converter. The system includes: An acquisition module, configured to respectively acquire the mathematical models of the rotor-side converter and the grid-side converter, and construct an objective function for high-voltage ride-through optimization control according to the mathematical models of the rotor-side converter and the grid-side converter; 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 generator according to the optimal control parameters; Wherein, the improved growth optimization algorithm includes a learning stage of the improved growth optimization algorithm, and the improved expression is:

[0021] Where:

[0022] In the formula, Gap 1 Represents the gap between the optimal individual and the relatively optimal individual; Gap 2 Represents the gap between the optimal individual and the relatively poor individual; Gap 3 Represents the gap between the relatively optimal individual and the relatively poor individual; Gap 4 Represents the gap between two randomly selected individuals different from individual X ; X best , X better , X worse , X r1 , X r2 Are respectively the optimal individual, the relatively optimal individual, the relatively poor individual, and two randomly selected individuals selected from the individual X samples; KA k Represents the acquisition of knowledge; SF i Represents the evaluation of the i th individual's own state; f ( X i ) is the i objective function value of the individual; f ( Xworst ) is the objective function value of the worst individual in the population; f ( X best ) is the optimal 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 normalization 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.

[0023] On the other hand, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the high-voltage ride-through optimization control method of the doubly-fed induction wind generator as described above.

[0024] On the other hand, the present invention further provides a data processing device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the high-voltage ride-through optimization control method of the doubly-fed induction wind generator as described above. Brief Description of the Drawings

[0025] Figure 1 is a flowchart of the high-voltage ride-through optimization control method of the doubly-fed induction wind generator in an embodiment of the present invention; Figure 2 is a schematic diagram of the comparison of the convergence curves of the improved growth optimization algorithm and the conventional growth optimization algorithm; The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. Specific Embodiments

[0026] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented 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 invention more thorough and comprehensive.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0028] To solve the technical problem in the prior art that the controller parameters and other variables for the high-voltage ride-through of DFIG cannot be well determined, resulting in the easy large-scale disconnection of wind turbines from the grid, the present application provides an optimized control method and system for the high-voltage ride-through of a doubly-fed induction wind generator. By automatically optimizing the high-voltage ride-through control parameters of the doubly-fed induction wind generator to determine the optimal control parameters, the control effect is ensured, so that the determination of the controller parameters and other variables for the high-voltage ride-through of DFIG has a better effect, avoiding the technical problem that the control effect is uncertain and the wind turbines are prone to large-scale disconnection due to the artificial setting of the controller parameters and other variables according to experience.

[0029] To facilitate the understanding of the present invention, several embodiments of the present invention will be given below. However, the present invention can be implemented 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 invention more thorough and comprehensive.

[0030] Embodiment 1 Please refer to Figure 1 , which shows the optimized control method for the high-voltage ride-through of a doubly-fed induction wind generator in the first embodiment of the present invention. The doubly-fed induction wind generator includes a rotor-side converter and a grid-side converter. The method includes steps S101 to S102: S101. Respectively obtain the mathematical models of the rotor-side converter and the grid-side converter, and construct an objective function for the optimized control of the high-voltage ride-through according to the mathematical models of the rotor-side converter and the grid-side converter.

[0031] Specifically, the expression of the objective function is:

[0032] Wherein, ; In the formula, f represents the objective function; T e represents the electromagnetic torque; represents the reference value of the electromagnetic torque; I r represents the rotor current; represents the reference value of the rotor current; U dc represents the DC-side voltage; represents the reference value of the DC-side voltage; K pi and K ii respectively represent the control parameters on the stator side and the control parameters on the rotor side; R represents the resistance.

[0033] Furthermore, the expression of electromagnetic torque is:

[0034] In the formula, 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.

[0035] Furthermore, the expression of rotor current is:

[0036] In the formula, 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 velocity of rotor rotation; ω s represents 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.

[0037] S102. Obtain the improved growth optimization algorithm, solve the objective function according to the improved growth optimization algorithm to obtain the optimal control parameters, and optimize the high-voltage ride-through of the doubly-fed induction wind generator according to the optimal control parameters.

[0038] 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; second, the population is initialized; then, the fitness is calculated; furthermore, the growth optimization algorithm is improved, including the optimization learning stage and the reflection stage, and the boundary constraints and the update rules are set; the fitness is updated according to the improved growth optimization algorithm; finally, it is judged whether the current number of iterations reaches the maximum number of iterations according to the current number of iterations; if the maximum number of iterations is not reached, the iteration continues; if the maximum number of iterations is reached, the iteration stops and the optimal control parameters are output. The improved growth optimization algorithm is used to optimize the controller parameters to find better control parameters, so as to improve the control effect of the controller; avoid manually setting parameters and the uncertain control effect. As Figure 2 shown, it is a schematic diagram of the comparison of the convergence curves between the improved growth optimization algorithm and the conventional growth optimization algorithm.

[0039] Specifically, in order to avoid the problem of uneven distribution of the initial population caused by the random generation of the initial population of the original algorithm, in this embodiment, the Henon chaotic mapping is introduced to initialize the population, and the expression is:

[0040] where:

[0041] In the formula: X represents the initial population; x i,j represents the i th individual at the j th dimension, 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 solutions of the control parameters related to the high-voltage ride-through of the doubly-fed induction wind generator; ub and lb are the upper bound and the lower bound of the problem respectively, where:

[0042] In the formula, X i represents the corrected position of the initial population; X i,j represents the position of the current individual.

[0043] Aiming at the defect that the exponential growth optimization algorithm has a small search range in the initial stage of iteration, the improved growth optimization algorithm includes the learning stage of the improved growth optimization algorithm. Specifically, the improved expression is:

[0044] where:

[0045] In the formula, Gap 1 represents the gap between the optimal individual and the relatively better individual; Gap 2 represents the gap between the optimal individual and the relatively worse individual; Gap 3 represents the gap between the relatively better individual and the relatively worse individual; Gap 4 represents the gap between two randomly selected individuals different from individual X ; X best , X better , X worse , X r1 , X r2 are respectively the optimal individual, relatively better individual, relatively worse individual, and two randomly selected individuals selected from the individual X sample; KA k represents the acquisition of knowledge; SF i represents the i th individual's evaluation of its own state; f ( X i ) is the i th 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 optimal 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 normalization result; is the position of the ith solution in the (t + 1)th iteration; is the position of the ith solution in the tth iteration; η represents a random number between 0 and 1.

[0046] Secondly, in order to avoid the problem that the growth optimization algorithm itself has too slow a convergence speed, in the reflection stage, an adaptive factor is introduced to improve it. Specifically, the improved expression is:

[0047] Where, ; In the formula, AF represents a constant that varies 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 and lower bound of the problem respectively; rand(0,1) represents a random number between 0 and 1; R j represents a randomly selected individual; 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.

[0048] Furthermore, in order to avoid the problem that the growth optimization algorithm is prone to falling into local optimal values in the later stage of iteration, a jump formula is added in the later stage of growth optimization. When it falls into a local optimal value, it can jump out; specifically, when t > 2 / 3T, it is the later stage of growth optimization, where T is the maximum number of iterations and t is the current number of iterations. The improved expression is:

[0049] In the formula, TF t is a constant value, X mean represents the average value of the individual solutions, DOF represents the direction factor; r 1 and r 2 are both random numbers between 0 and 1.

[0050] As a specific example, in order to obtain the optimal control parameters, the updated rule is:

[0051] In the formula, P 2 is 0.001.

[0052] In summary, in the above embodiments of the present invention, the high-voltage ride-through optimization control method for a doubly-fed induction wind generator automatically optimizes the high-voltage ride-through control parameters of the doubly-fed induction wind generator to determine the optimal control parameters, ensuring the control effect. Therefore, it has a better effect on determining the controller parameters and other variables for the high-voltage ride-through of DFIG, avoiding the technical problems that the control effect is uncertain and the wind turbines are prone to large-scale grid disconnection caused by artificially setting the controller parameters and other variables according to experience.

[0053] Embodiment 2 The second embodiment of the present invention provides a high-voltage ride-through optimization control system for a doubly-fed induction wind generator. The doubly-fed induction wind generator includes a rotor-side converter and a grid-side converter. The system includes: An acquisition module is used to respectively acquire the mathematical models of the rotor-side converter and the grid-side converter, and construct an objective function for high-voltage ride-through optimization control according to the mathematical models of the rotor-side converter and the grid-side converter; An optimization module is used to obtain an improved growth optimization algorithm, solve the objective function according to the improved growth optimization algorithm to obtain the optimal control parameters, and optimize the high-voltage ride-through of the doubly-fed induction wind generator according to the optimal control parameters; Among them, the improved growth optimization algorithm includes the learning stage of the improved growth optimization algorithm, and the improved expression is:

[0054] Among them:

[0055] In the formula, Gap 1 represents the gap between the optimal individual and the relatively better individual; Gap 2 represents the gap between the optimal individual and the relatively worse individual; Gap 3 represents the gap between the relatively better individual and the relatively worse individual; Gap 4 represents the gap between two randomly selected individuals different from individual X ; X best , X better , X worse , X r1 , X r2 are respectively the optimal individual, the relatively better individual, the relatively worse individual and two randomly selected individuals selected from the samples of individual X ; KA k represents the acquisition of knowledge; SF i represents the evaluation of the state of the i -th individual by itself; f ( X i ) is the i objective function value of the individual; f ( X worst ) is the objective function value of the worst individual in the population; f ( X best ) is the optimal objective function value of the worst individual in the population; ‖ Gap k‖ is the Euclidean distance of the k-th group of differences; LF k is the normalization 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.

[0056] In summary, the high-voltage ride-through optimization control system of the doubly-fed induction wind generator in the above embodiments of the present invention automatically optimizes the high-voltage ride-through control parameters of the doubly-fed induction wind generator to determine the optimal control parameters, ensuring the control effect, thereby achieving better results in determining the controller parameters and other variables for the high-voltage ride-through of the DFIG, and avoiding the technical problems of uncertain control effects and large-scale disconnection of the wind turbines caused by manually setting the controller parameters and other variables according to experience.

[0057] In addition, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0058] In addition, an embodiment of the present invention also provides a data processing device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the method in the above embodiment are implemented.

[0059] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0060] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0061] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0062] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations 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 one or more embodiments or examples in a suitable manner.

[0063] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A high voltage ride through optimization control method for a doubly fed induction wind turbine generator, characterized in that: The doubly-fed induction wind turbine generator comprises a rotor-side converter and a grid-side converter, and the method comprises: Respectively obtaining a mathematical model of a rotor-side converter and a mathematical model of a grid-side converter, and constructing 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; Acquire 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 generator according to the optimal control parameters; The improved growth optimization algorithm includes a learning phase of the improved growth optimization algorithm, and the improved expression is: in: In the formula, Gap 1 represents the gap between the best individual and the better individuals; 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 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 It represents the acquisition of knowledge; SF i Indicates i An individual's assessment of his or her own status; f ( X i )for i The objective function value of the individual; 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 kth 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 iteration number.

2. The high voltage ride through optimization control method of a doubly fed induction wind generator according to claim 1, characterized in that: The improved growth optimization algorithm includes the reflection phase of the improved growth optimization algorithm. The improved expression is: in, ; In the formula, AF represents a constant that changes with the number of iterations; MaxFEs Indicates the maximum number of iterations; FEs Indicates the current iteration number; ub and lb are the upper bound 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 number of iterations; represents the position of the i-th solution in the j-th dimension at the t-th iteration.

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

4. The high voltage ride through optimization control method of a doubly-fed induction wind turbine according to claim 1, characterized in that: The expression of the objective function is: in, ; In the formula, f represents the objective function; T e represents electromagnetic torque; Indicates the reference value of electromagnetic torque; I r represents the rotor current; Indicates the reference value of the rotor current; U dc Indicates the DC side voltage; Indicates the reference value of DC side voltage; K pi and K ii represent the control parameters of the stator side and the control parameters of the rotor side respectively; R Indicates resistance.

5. The high voltage ride through optimization control method of a doubly-fed induction wind generator according to claim 4, characterized in that: The expression of electromagnetic torque is: In the formula, P n represents the number of pole pairs; L m Indicates mutual induction; ψ qs represents the magnetic flux of the stator on the q-axis; i ds represents the stator current on the d-axis; ψ ds represents the magnetic flux of the stator on the d-axis; i qs represents the stator current on the q axis.

6. The high voltage ride through optimization control method of a doubly fed induction wind generator according to claim 4, characterized in that: The expression of rotor current is: In the formula, I r represents the rotor current; Indicates the rotor current under steady-state operation; represents the rotor transient time constant; L s represents the stator self-inductance; It represents the transient self-inductance of the rotor; w s represents the angular frequency of the stator; ω r represents the rotor rotation angular velocity; ω s Indicates the slip angular velocity; β Indicates time; j An imaginary unit representing electric current; represents the stator process transient time constant; represents the transient time constant of the rotor process; du s Represents the differential of the stator voltage.

7. A high voltage ride through optimization control system for a doubly fed induction wind turbine generator, characterized in that: The double-fed induction wind turbine generator includes a rotor-side converter and a grid-side converter, and the system includes: An acquisition module, used 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; an optimization module, used for 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 generator according to the optimal control parameters; The improved growth optimization algorithm includes a learning phase of the improved growth optimization algorithm, and the improved expression is: in: In the formula, Gap 1 represents the gap between the best individual and the better individuals; 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 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 It represents the acquisition of knowledge; SF i Indicates i An individual's assessment of his or her own status; f ( X i )for i The objective function value of the individual; 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 kth 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.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the high voltage ride through optimization control method of the doubly-fed induction wind generator as claimed in any one of claims 1 to 6 is implemented.

9. A data processing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the high voltage ride through optimization control method of the doubly-fed induction wind generator as claimed in any one of claims 1 to 6 is implemented.

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