Coupling optimization method, device and storage medium for supercapacitor energy storage frequency modulation

Abstract

The present invention discloses a coupled optimization method, device and storage medium for supercapacitor energy storage and frequency modulation, and belongs to the field of wind power generation technology. The method provided by the present invention integrates polynomial coupling technology, aiming to gradually approach the dynamic changes of the real wind speed, thereby improving the accuracy of wind speed prediction. By generating random sequences through multiple iterations, the original wind speed sequence can be well fitted, and the closest wind speed data can be screened out, laying a solid data foundation for subsequent wind speed prediction; the introduction of activation processing enhances the nonlinear expression ability of the predicted data and further improves the accuracy of the prediction; at the same time, by activating the first prediction value sequence and the second prediction value sequence, the advantages of polynomial prediction and neural network prediction are combined to improve the comprehensiveness and accuracy of wind speed prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a coupled optimization method, device and storage medium for supercapacitor energy storage and frequency modulation. Background Art

[0002] Wind power generation, as a clean, environmentally friendly and sustainable renewable energy technology, has attracted much attention and has developed rapidly in recent years. With the rapid development of technology and the continuous reduction of costs, wind power generation has gradually become an indispensable part of the energy structure of many countries.

[0003] However, the natural randomness of wind speed poses a severe challenge to the grid connection of large-scale wind farms. This randomness causes the output power of wind farms to show volatility and uncertainty, which brings great difficulty to the grid dispatching agency in prediction and arrangement. This not only increases the operating burden of the grid, but also may pose a potential threat to the stability of the system. As one of the core technologies in the field of wind power generation, the accuracy and reliability of wind speed prediction are crucial. Wind farm scheduling is the key to ensuring the coordinated operation between wind farms and power grids. It involves intelligent control and optimization of the output power and operating status of wind farms to balance the output power of wind farms and the actual needs of the power grid, and plays an important role in wind farm scheduling.

[0004] Although wind power forecasting and scheduling technology has made significant progress and has been widely used, it still faces many challenges. In the field of wind speed forecasting in wind farms, traditional forecasting methods mostly rely on decomposition technology, such as wind speed forecasting methods based on variational mode decomposition (VMD). Although traditional VMD technology can decompose wind speed signals to a certain extent and extract the characteristics of different frequency components, its forecasting ability is often limited when facing complex and changeable wind speed data. Specifically, when dealing with nonlinear and non-stationary wind speed sequences, the VMD method may not be able to accurately capture all key feature information, especially those instantaneous changes and high-frequency components, which directly leads to a decrease in wind speed forecasting accuracy.

[0005] Therefore, how to break through the limitations of the traditional VMD prediction method and improve the accuracy of wind speed prediction has become a key technical problem that needs to be solved urgently by technical personnel in this field. Summary of the invention

[0006] The purpose of the present invention is to provide a coupled optimization method, device and storage medium for supercapacitor energy storage frequency modulation, aiming to solve the problem of insufficient prediction accuracy when using the traditional VMD method to predict wind speed.

[0007] The present invention solves the above technical problems through the following technical solutions:

[0008] A coupled optimization method for supercapacitor energy storage frequency modulation comprises the following steps:

[0009] Step 1: Collect wind speed data and establish the original wind speed sequence X=[X 1 ,X 2 ,X 3 ,...,X N ], N is the number of subsequences in the original wind speed sequence, X 1 ,X 2 ,X 3 ,...,X N They are the 1st, 2nd, 3rd, …, Nth subsequences in the original wind speed sequence respectively;

[0010] Step 2: preset the total number of cycles R, and set the current number of cycles i=0;

[0011] Step 3: Let i=i+1 and generate a random sequence A i =[α 1 ,α 2 ,α 3 ,...,α s ], S is the number of subsequences in the random sequence, α 1 ,α 2 ,α 3 ,...,α s The random sequence A i The 1st, 2nd, 3rd, ..., Sth subsequence in; based on the random sequence A i , generate the polynomial parameter F about the unknown number t i (t); Based on the original wind speed sequence X and the polynomial parameter F i (t), calculate the first difference sequence C i ;

[0012] Step 4: Based on the first difference sequence C i , calculate the polynomial parameters F i (t) The close value M to the original wind speed sequence X i , determine whether i is equal to R, if it is, execute step 5; if it is not, return to step 3;

[0013] Step 5: Get all the approximate values ​​M obtained in the above steps i The minimum value M in min , based on the minimum value M min The corresponding number of cycles i obtains the polynomial parameter F that is closest to the original wind speed sequence X. min (t), for F min (t) Activate and obtain the final wind speed sequence F(t);

[0014] Step 6: Let t = N+1, N+2, ..., N+0.1N, and substitute them into F(t) to obtain the first prediction value sequence f = [F(N+1), F(N+2), F(N+3), ..., F(N+0.1N)], where F(N+1), F(N+2), F(N+3), ..., F(N+0.1N) are the 1st, 2nd, 3rd, ..., 0.1Nth subsequences in the first prediction value sequence, respectively, and 0.1N is the number of first prediction values; based on the original wind speed sequence X and the final wind speed sequence F(t), the residual wind speed sequence Q = XF(t) = [X 1 -F(1),X 2 -F(2),X 3 -F(3),...,X N -F(N)], input the residual wind speed sequence Q into the trained neural network model for prediction, and obtain the second prediction value sequence q=[q N+1 ,q N+2 ,q N+3 ,…,q N+0.1N ], q N+1 ,q N+2 ,q N+3 ,…,q N+0.1N are the 1st, 2nd, 3rd, …, 0.1Nth subsequences in the second prediction value sequence respectively;

[0015] Step 7: Activate the first prediction value sequence f and the second prediction value sequence q to obtain the final wind speed prediction result Z;

[0016] Step 8. Calculate the predicted power sent by the wind farm to the grid based on the final wind speed prediction result Z, obtain the actual power sent by the wind farm to the grid, and calculate the difference between the predicted power and the actual power. When the difference is positive, adjust the super-capacity energy storage system connected to the wind farm exit to a charging state; when the difference is 0, the super-capacity energy storage system connected to the wind farm exit maintains the current state unchanged; when the difference is negative, adjust the super-capacity energy storage system connected to the wind farm exit to a discharging state.

[0017] The present invention is further improved in that: the number S of subsequences in the random sequence is greater than 40; and the total number of cycles R is greater than or equal to 100.

[0018] A further improvement of the present invention is that: the polynomial parameter F about the unknown number t i (t) Specifically:

[0019] F i (t) = α 1 t+α 2 t 2 +α 3 t 3 +.....+αs t s .

[0020] The present invention is further improved in that: min (t) is activated to obtain the final wind speed sequence F(t), which is:

[0021] F(t)=α 1 t+α 2 t 2 +α 3 t 3 +.....+α s t s +De Et

[0022] in,

[0023]

[0024]

[0025] e is a natural constant; sin() is a sine function; sigmoid() is an activation function; tanh() is an activation function; gelu() is a Gaussian activation function; rand() is a random number generation function.

[0026] A further improvement of the present invention is that the first difference sequence is specifically:

[0027] C i =[ , , ,..., ];

[0028] Where, t=N; , , ,..., They are respectively the 1st, 2nd, 3rd, …, Nth subsequences in the first difference sequence.

[0029] The present invention is further improved in that: i (t) The close value M to the original wind speed sequence X i Specifically:

[0030] .

[0031] A further improvement of the present invention is that the first prediction value sequence f and the second prediction value sequence q are activated to obtain the final wind speed prediction result Z, which is specifically:

[0032] Z=[H 1 F(N+1)+H 2 q N+1 , H 1 F(N+2)+H 2 q N+2 , H 1 F(N+3)+H 2 q N+3 , …, H 1 F(N+0.1N)+H 2 q N+0.1N ];

[0033] in:

[0034]

[0035]

[0036] e is a natural constant; max() is the maximum value function; min() is the minimum value function; sin() is the sine function; cos() is the cosine function; sigmoid() is the activation function; tanh() is the activation function; gelu() is the Gaussian activation function; Swish() is the activation function; q N+0.1N ,q N+0.1N-1 ,q N+0.1N-2 ,…,q N+1 They are respectively the 0.1N, 0.1N-1, 0.1N-2, …, 1th subsequences of the second prediction value sequence q arranged in reverse order.

[0037] A further improvement of the present invention is that after collecting the wind speed data, the method further includes the following steps: deleting and supplementing abnormal data.

[0038] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the coupled optimization method for supercapacitor energy storage frequency modulation are implemented.

[0039] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the coupled optimization method for supercapacitor energy storage frequency modulation are implemented.

[0040] Compared with the prior art, the positive and progressive effects of the present invention are:

[0041] The coupled optimization method of supercapacitor energy storage frequency modulation provided by the present invention integrates polynomial coupling technology, aiming to gradually approach the dynamic changes of the real wind speed, thereby improving the accuracy of wind speed prediction. By generating random sequences through multiple iterations, it is possible to better fit the original wind speed sequence, screen out the closest wind speed data, and lay a solid data foundation for subsequent wind speed prediction; the introduction of activation processing enhances the nonlinear expression ability of the predicted data and further improves the accuracy of the prediction; at the same time, by activating the first predicted value sequence and the second predicted value sequence, the advantages of polynomial prediction and neural network prediction are combined to improve the comprehensiveness and accuracy of wind speed prediction; according to the difference between the predicted power and the actual power, the state of the supercapacitor energy storage system is adjusted to achieve power balance between the wind farm and the power grid, reduce power grid fluctuations, and improve the stability and reliability of the power system.

[0042] Furthermore, by deleting abnormal data, erroneous data points caused by equipment failure, measurement errors, or data transmission errors can be eliminated, thereby improving the overall quality of the wind speed dataset and improving the accuracy and credibility of the prediction results; at the same time, reducing the model's dependence on abnormal data, thereby enhancing the model's generalization ability and enabling it to better adapt to wind speed prediction tasks in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0044] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0047] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0048] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, W and / or P can represent: W exists alone, W and P exist at the same time, and P exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0049] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0050] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0051] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0052] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments, which are intended to explain the present invention rather than to limit it.

[0053] The present invention provides a coupled optimization method for supercapacitor energy storage frequency modulation, comprising the following steps:

[0054] A coupled optimization method for supercapacitor energy storage frequency modulation comprises the following steps:

[0055] Step 1: Collect wind speed data and establish the original wind speed sequence X=[X 1 ,X 2 ,X 3 ,...,X N ], N is the number of subsequences in the original wind speed sequence, X 1 ,X 2 ,X 3 ,...,X N They are the 1st, 2nd, 3rd, …, Nth subsequences in the original wind speed sequence respectively;

[0056] Step 2: preset the total number of cycles R, and set the current number of cycles i=0;

[0057] Step 3: Let i=i+1 and generate a random sequence A i =[α 1 ,α 2 ,α 3 ,...,α s ], S is the number of subsequences in the random sequence, α 1 ,α 2 ,α 3 ,...,α s The random sequence A i The 1st, 2nd, 3rd, ..., Sth subsequence in; based on the random sequence A i , generate the polynomial parameter F about the unknown number t i (t); Based on the original wind speed sequence X and the polynomial parameter F i (t), calculate the first difference sequence C i ;

[0058] Step 4: Based on the first difference sequence C i , calculate the polynomial parameters F i (t) The close value M to the original wind speed sequence X i , determine whether i is equal to R, if it is, execute step 5; if it is not, return to step 3;

[0059] Step 5: Get all the approximate values ​​M obtained in the above steps i The minimum value M in min , based on the minimum value M min The corresponding number of cycles i obtains the polynomial parameter F that is closest to the original wind speed sequence X. min (t), for F min (t) Activate and obtain the final wind speed sequence F(t);

[0060] Step 6: Let t = N+1, N+2, ..., N+0.1N, and substitute them into F(t) to obtain the first prediction value sequence f = [F(N+1), F(N+2), F(N+3), ..., F(N+0.1N)], where F(N+1), F(N+2), F(N+3), ..., F(N+0.1N) are the 1st, 2nd, 3rd, ..., 0.1Nth subsequences in the first prediction value sequence, respectively, and 0.1N is the number of first prediction values; based on the original wind speed sequence X and the final wind speed sequence F(t), the residual wind speed sequence Q = XF(t) = [X 1 -F(1),X 2 -F(2),X 3 -F(3),...,X N -F(N)], input the residual wind speed sequence Q into the trained neural network model for prediction, and obtain the second prediction value sequence q=[q N+1 ,q N+2 ,q N+3 ,…,q N+0.1N ], q N+1 ,q N+2 ,q N+3 ,…,q N+0.1N are the 1st, 2nd, 3rd, …, 0.1Nth subsequences in the second prediction value sequence respectively;

[0061] Step 7: Activate the first prediction value sequence f and the second prediction value sequence q to obtain the final wind speed prediction result Z;

[0062] Step 8. Calculate the predicted power sent by the wind farm to the grid based on the final wind speed prediction result Z, obtain the actual power sent by the wind farm to the grid, and calculate the difference between the predicted power and the actual power. When the difference is positive, adjust the super-capacity energy storage system connected to the wind farm exit to a charging state; when the difference is 0, the super-capacity energy storage system connected to the wind farm exit maintains the current state unchanged; when the difference is negative, adjust the super-capacity energy storage system connected to the wind farm exit to a discharging state.

[0063] The coupled optimization method of supercapacitor energy storage frequency modulation provided by the present invention integrates polynomial coupling technology, aiming to gradually approach the dynamic changes of the real wind speed, thereby improving the accuracy of wind speed prediction. By generating random sequences through multiple iterations, it is possible to better fit the original wind speed sequence, screen out the closest wind speed data, and lay a solid data foundation for subsequent wind speed prediction; the introduction of activation processing enhances the nonlinear expression ability of the predicted data and further improves the accuracy of the prediction; at the same time, by activating the first predicted value sequence and the second predicted value sequence, the advantages of polynomial prediction and neural network prediction are combined to improve the comprehensiveness and accuracy of wind speed prediction; according to the difference between the predicted power and the actual power, the state of the supercapacitor energy storage system is adjusted to achieve power balance between the wind farm and the power grid, reduce power grid fluctuations, and improve the stability and reliability of the power system.

[0064] Specifically, the number of subsequences S in the random sequence is greater than 40; and the total number of cycles R is greater than or equal to 100.

[0065] Specifically, the polynomial parameter F about the unknown number t i (t) Specifically:

[0066] F i (t) = α 1 t+α 2 t 2 +α 3 t 3 +.....+α s t s .

[0067] Specifically, for F min (t) is activated to obtain the final wind speed sequence F(t), which is:

[0068] F(t)=α 1 t+α 2 t 2 +α 3 t 3 +.....+α s t s +De Et

[0069] in,

[0070]

[0071]

[0072] e is a natural constant; sin() is a sine function; sigmoid() is an activation function; tanh() is an activation function; gelu() is a Gaussian activation function; rand() is a random number generation function.

[0073] Specifically, the first difference sequence is:

[0074] C i =[ , , ,..., ];

[0075] Where, t=N; , , ,..., They are respectively the 1st, 2nd, 3rd, …, Nth subsequences in the first difference sequence.

[0076] Specifically, F i (t) The close value M to the original wind speed sequence X i Specifically:

[0077] .

[0078] Specifically, the first prediction value sequence f and the second prediction value sequence q are activated to obtain the final wind speed prediction result Z, which is specifically:

[0079] Z=[H 1 F(N+1)+H 2 q N+1 , H 1 F(N+2)+H 2 q N+2 , H 1 F(N+3)+H 2 q N+3 , …, H 1 F(N+0.1N)+H 2 q N+0.1N ];

[0080] in:

[0081]

[0082]

[0083] e is a natural constant; max() is the maximum value function; min() is the minimum value function; sin() is the sine function; cos() is the cosine function; sigmoid() is the activation function; tanh() is the activation function; gelu() is the Gaussian activation function; Swish() is the activation function; q N+0.1N ,q N+0.1N-1 ,q N+0.1N-2 ,…,q N+1 They are respectively the 0.1N, 0.1N-1, 0.1N-2, …, 1th subsequences of the second prediction value sequence q arranged in reverse order.

[0084] Specifically, after collecting the wind speed data, the following steps are also included: deleting and supplementing abnormal data.

[0085] By deleting abnormal data, erroneous data points caused by equipment failure, measurement errors, or data transmission errors can be eliminated, thereby improving the overall quality of the wind speed dataset and improving the accuracy and credibility of the prediction results; at the same time, the model's dependence on abnormal data is reduced, thereby enhancing the model's generalization ability and enabling it to better adapt to wind speed prediction tasks in different scenarios.

[0086] In order to further verify the advantages of this method, the wind speed is predicted by using the method of this application and the traditional prediction method VMD (Variational Mode Decomposition). In the experiment of this application, MAPE (Mean Absolute Percentage Error) is selected as the evaluation standard of each model. The prediction results (i.e., MAPE values) are as follows:

[0087]

[0088] It can be seen that the wind speed prediction using this method is more accurate and reliable.

[0089] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the coupled optimization method of supercapacitor energy storage frequency modulation are implemented, wherein the memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc.; the processor, the network interface, and the memory are interconnected through an internal bus, and the internal bus may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard structure bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0090] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the coupled optimization method of supercapacitor energy storage frequency modulation are implemented. Specifically, the computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include RAM (Random Access Memory) and / or cache memory (cache), etc. The non-volatile memory may include ROM (Read-Only Memory), hard disk, flash memory, optical disk, magnetic disk, etc.

[0091] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM (Compact Disc Read-Only Memory), optical storage, etc.) containing computer-usable program codes.

[0092] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer device or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer readable memory capable of directing a computer device or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer device or other programmable data processing device so that a series of operation steps are executed on the computer device or other programmable device to produce a process implemented by the computer device, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0095] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0096] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A coupled optimization method for supercapacitor energy storage frequency modulation, characterized in that: The following steps are involved: Step 1: Collect wind speed data and establish the original wind speed sequence X=[X1,X2,X3,...,X N ], N is the number of subsequences in the original wind speed sequence, X1, X2, X3, ..., X N They are the 1st, 2nd, 3rd, …, Nth subsequences in the original wind speed sequence respectively; Step 2: preset the total number of cycles R, and set the current number of cycles i=0; Step 3: Let i=i+1 and generate a random sequence A i =[α1,α2,α3,...,α s ], S is the number of subsequences in the random sequence, α1, α2, α3, ..., α s The random sequence A i The 1st, 2nd, 3rd, ..., Sth subsequence in; based on the random sequence A i , generate the polynomial parameter F about the unknown number t i (t); Based on the original wind speed sequence X and the polynomial parameter F i (t), calculate the first difference sequence C i ; Step 4: Based on the first difference sequence C i , calculate the polynomial parameters F i (t) The close value M to the original wind speed sequence X i , determine whether i is equal to R, if it is, execute step 5; if it is not, return to step 3; Step 5: Get all the approximate values ​​M obtained in the above steps i The minimum value M in min , based on the minimum value M min The corresponding number of cycles i obtains the polynomial parameter F that is closest to the original wind speed sequence X. min (t), for F min (t) Activate and obtain the final wind speed sequence F(t); Step 6: Let t = N+1, N+2, ..., N+0.1N, and substitute them into F(t) to obtain the first prediction value sequence f = [F(N+1), F(N+2), F(N+3), ..., F(N+0.1N)], where F(N+1), F(N+2), F(N+3), ..., F(N+0.1N) are the 1st, 2nd, 3rd, ..., 0.1Nth subsequences in the first prediction value sequence, respectively, and 0.1N is the number of first prediction values; based on the original wind speed sequence X and the final wind speed sequence F(t), the residual wind speed sequence Q = XF(t) = [X1-F(1), X2-F(2), X3-F(3), ..., X N -F(N)], input the residual wind speed sequence Q into the trained neural network model for prediction, and obtain the second prediction value sequence q=[q N+1 ,q N+2 ,q N+3 ,…,q N+0.1N ], q N+1 ,q N+2 ,q N+3 ,…,q N+0.1N are the 1st, 2nd, 3rd, …, 0.1Nth subsequences in the second prediction value sequence respectively; Step 7: Activate the first prediction value sequence f and the second prediction value sequence q to obtain the final wind speed prediction result Z; Step 8. Calculate the predicted power sent by the wind farm to the grid based on the final wind speed prediction result Z, obtain the actual power sent by the wind farm to the grid, and calculate the difference between the predicted power and the actual power. When the difference is positive, adjust the super-capacity energy storage system connected to the wind farm exit to a charging state; when the difference is 0, the super-capacity energy storage system connected to the wind farm exit maintains the current state unchanged; when the difference is negative, adjust the super-capacity energy storage system connected to the wind farm exit to a discharging state.

2. A coupled optimization method for supercapacitor energy storage frequency modulation according to claim 1, characterized in that: The number of subsequences in the random sequence S>40; the total number of cycles R≥100.

3. A coupled optimization method for supercapacitor energy storage frequency modulation according to claim 1, characterized in that: Polynomial Parameter F on Unknown t i (t) Specifically: F i (t)=α1t+α2t 2 +α3t 3 +.....+a s t s 。 4. The coupled optimization method for supercapacitor energy storage frequency modulation according to claim 3, characterized in that: F min (t) is activated to obtain the final wind speed sequence F(t), which is: F(t)=α1t+α2t 2 +α3t 3 +.....+a s t s +De Et in, e is a natural constant; sin() is a sine function; sigmoid() is an activation function; tanh() is an activation function; gelu() is a Gaussian activation function; rand() is a random number generation function.

5. The coupled optimization method for supercapacitor energy storage frequency modulation according to claim 1, characterized in that: The first difference sequence is specifically: C i =[ , , ,..., ]； Where, t=N; , , ,..., They are respectively the 1st, 2nd, 3rd, …, Nth subsequences in the first difference sequence.

6. A coupled optimization method for supercapacitor energy storage frequency modulation according to claim 5, characterized in that: F i (t) The close value M to the original wind speed sequence X i Specifically: 。 7. A coupled optimization method for supercapacitor energy storage frequency modulation according to claim 1, characterized in that: The first prediction value sequence f and the second prediction value sequence q are activated to obtain the final wind speed prediction result Z, which is specifically: Z=[H1 F(N+1)+H2 q N+1 ,H1 F(N+2)+H2 q N+2 ,H1 F(N+3)+H2 q N+3 ,…,H1 F(N+0.1N)+H2 q N+0.1N ]; in: e is a natural constant; max() is the maximum value function; min() is the minimum value function; sin() is the sine function; cos() is the cosine function; sigmoid() is the activation function; tanh() is the activation function; gelu() is the Gaussian activation function; Swish() is the activation function; q N+0.1N ,q N+0.1N-1 ,q N+0.1N-2 ,…,q N+1 They are respectively the 0.1N, 0.1N-1, 0.1N-2, …, 1th subsequences of the second prediction value sequence q arranged in reverse order.

8. The coupled optimization method for supercapacitor energy storage frequency modulation according to claim 1, characterized in that: After collecting wind speed data, the following steps are also included: deleting and supplementing abnormal data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the coupled optimization method for supercapacitor energy storage frequency modulation described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the coupled optimization method for supercapacitor energy storage frequency modulation described in any one of claims 1 to 8 are implemented.

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