A supercapacitor energy storage frequency modulation method, device, medium, and program product

Abstract

The present invention discloses a supercapacitor energy storage frequency modulation method, device, medium, and program product, belonging to the technical field of wind power generation. The supercapacitor energy storage frequency modulation method provided by the present invention collects wind speed data and establishes an original wind speed sequence, divides the original wind speed sequence into a first prediction set and a first verification set, predicts and calculates a reference error by using the first prediction set; then performs position pseudo-random swapping on subsequences in the first prediction set to generate multiple transformed wind speed sequences; divides the transformed wind speed sequences into a second prediction set and a second verification set, predicts and calculates prediction error parameters by using the second prediction set; selects the wind speed sequence with the smallest prediction error parameter to construct a final wind speed sequence. This method can make full use of the internal laws and characteristics of wind speed data, improves the accuracy and reliability of wind speed prediction through the position pseudo-random swapping technology, and provides more accurate data support for the grid connection dispatching of wind farms.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and particularly relates to a supercapacitor energy storage frequency modulation method, device, medium, and program product. Background Art

[0002] Wind power generation, as a clean, environmentally friendly, and sustainable renewable energy technology, has attracted much attention and has developed vigorously globally 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 severe challenges to the grid connection of large-scale wind farms. This randomness causes the output power of the wind farm to exhibit volatility and uncertainty, bringing great difficulties 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. Wind speed prediction, as one of the core technologies in the field of wind power generation, its accuracy and reliability are crucial. Wind farm dispatching is the key to ensuring the coordinated operation between the wind farm and the grid. It involves the intelligent control and optimization of the output power and operating status of the wind farm to balance the output power of the wind farm and the actual demand of the grid, and plays an important role in wind farm dispatching.

[0004] Traditional wind speed prediction methods generally directly put the original wind speed sequence into a neural network for prediction, and then calculate the power sent by the wind farm to the grid based on the predicted wind speed value. However, due to the failure to fully utilize the internal laws and characteristics of wind speed data, this method results in large prediction errors. This not only increases the operating burden of the grid but also may pose a potential threat to the stability of the system.

[0005] Therefore, how to more effectively utilize the internal laws and characteristics of wind speed data to improve the accuracy and reliability of prediction has become a key technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a supercapacitor energy storage frequency modulation method, device, medium, and program product, aiming to fundamentally solve the problem of insufficient accuracy and reliability in wind speed prediction in traditional methods.

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

[0008] A supercapacitor energy storage frequency modulation method includes the following steps:

[0009] Step 1: Collect wind speed data, establish the original wind speed sequence, and divide the original wind speed sequence into a first prediction set and a first verification set according to the chronological order and the first preset ratio; input the first prediction set into the trained neural network model in sequence for prediction to obtain the first prediction value, and calculate the baseline prediction error W based on the first prediction value and the actual values in the first verification set.

[0010] Step 2: Let the first prediction set be X 0 (t)=[X 01 ,X 02 ,X 03 ,...,X 0N , where N is the number of subsequences in X 0 (t), and X 01 ,X 02 ,X 03 ,...,X 0N are the 1st, 2nd, 3rd, …, Nth subsequences in the first prediction set respectively; perform pseudo-random swapping of the input quantity positions for the subsequences in X 0 (t) to obtain N! wind speed sequences after pseudo-random swapping of the input quantity positions. Let X i (t) be the ith wind speed sequence after pseudo-random swapping of the input quantity positions among the N! sequences, where i ∈ {1~N!}, and let i = 1, then execute Step 3.

[0011] Step 3: Divide the wind speed sequence X i (t) into a second prediction set and a second verification set according to the chronological order and the second preset ratio, input the second prediction set into the trained neural network model in sequence for prediction to obtain the second prediction value, and calculate the prediction error parameter A i ;

[0012] Step 4: Determine whether i is equal to N!. If the judgment is no, then let i = i + 1 and execute Step 3. If the judgment is yes, then execute Step 5.

[0013] Step 5: Obtain the minimum value A i in the prediction error parameter A min , and get the corresponding wind speed sequence X min (t). Based on the baseline prediction error W, the minimum value A min , the wind speed sequence X min (t), and the first prediction set X 0 (t), construct the final wind speed sequence, and input the final wind speed sequence into the trained neural network model in sequence for prediction to obtain the final wind speed prediction result.

[0014] Step 6: Calculate the predicted power sent by the wind farm to the power grid based on the final wind speed prediction result, obtain the actual power sent by the wind farm to the power grid, calculate the difference between the predicted power and the actual power. When the difference is positive, adjust the supercapacitor energy storage system connected to the outlet of the wind farm to the charging state; when the difference is 0, the supercapacitor energy storage system connected to the outlet of the wind farm remains in the current state unchanged; when the difference is negative, adjust the supercapacitor energy storage system connected to the outlet of the wind farm to the discharging state.

[0015] A further improvement of the present invention is that: the first preset ratio is 8:2 or 9:1.

[0016] A further improvement of the present invention is that: the second preset ratio is equal to the first preset ratio.

[0017] A further improvement of the present invention is that: the reference prediction error W is specifically:

[0018]

[0019] where, y i is the i-th first prediction value, i is the i-th actual value in the first verification set, and n is the number of subsequences in the first verification set.

[0020] A further improvement of the present invention is that: based on the reference prediction error W, the minimum value A min , the wind speed sequence X min (t) and the first prediction set X 0 (t), the construction of the final wind speed sequence X f (t) is specifically:

[0021] Let the wind speed sequence X min (t) be [X 1 , X 2 , X 3 ,..., X N ,

[0022] The final wind speed sequence X f (t)=[X 01 + sigmoid(X 1 )×X 1 × , X 02 + sigmoid(X 02 )×X 2 × , X 03 + sigmoid(X 3 )×X 3 × ,..., X 0N + sigmoid(XN )×X N × ;

[0023] wherein, X 1 , X 2 , X 3 ,..., X N is the 1st, 2nd, 3rd,..., Nth subsequence in the wind speed sequence X min (t); sigmoid() is an activation function; tanh() is an activation function.

[0024] A further improvement of the present invention lies in that: after collecting wind speed data, the following steps are further included: deleting and supplementing abnormal data.

[0025] Further, the neural network model is LSTM (Long Short-Term Memory).

[0026] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0027] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of the above method are implemented.

[0028] A computer program product includes a computer program. When the computer program is executed by a processor, the steps of the above method are implemented.

[0029] Compared with the prior art, the positive and progressive effects of the present invention are as follows:

[0030] The supercapacitor energy storage frequency modulation method provided by the present invention collects wind speed data and establishes an original wind speed sequence, divides the original wind speed sequence into a first prediction set and a first verification set, predicts and calculates a reference error using the first prediction set; then performs a position pseudo-random swap on the subsequences in the first prediction set to generate multiple transformed wind speed sequences; divides the transformed wind speed sequences into a second prediction set and a second verification set, predicts and calculates prediction error parameters using the second prediction set; selects the wind speed sequence with the smallest prediction error parameter to construct a final wind speed sequence. This method can make full use of the internal laws and characteristics of wind speed data, improves the accuracy and reliability of wind speed prediction through the position pseudo-random swap technology, and provides more accurate data support for the grid connection scheduling of wind farms.

[0031] Further, a reasonable ratio division ensures the sufficiency of training data (the first prediction set), and at the same time, the verification data (the first verification set) can also provide sufficient samples for error evaluation, which helps to improve the generalization ability of the prediction model.

[0032] Furthermore, keeping the ratio of the two datasets consistent helps maintain the consistency of the model's prediction performance at different stages and avoid prediction biases caused by uneven data partitioning.

[0033] Furthermore, by comprehensively considering multiple factors, including the first prediction set, the benchmark prediction error, the minimum prediction error parameter, and the corresponding wind speed sequence, the constructed final wind speed sequence can more accurately reflect the changing trend of the wind speed, improving the accuracy of the prediction; using sigmoid and tanh activation functions for non-linear transformation helps further enhance the accuracy of the prediction model.

[0034] Furthermore, by deleting abnormal data, error data points caused by equipment failures, measurement errors, or data transmission errors can be eliminated, thereby improving the overall quality of the wind speed dataset and the accuracy and reliability of the prediction results; at the same time, reducing the model's dependence on abnormal data, thereby enhancing the generalization ability of the model and enabling it to better adapt to wind speed prediction tasks in different scenarios.

[0035] Furthermore, using an LSTM neural network can effectively process the noise and outliers in the input data, thereby improving the robustness of the prediction model, adapting to complex and variable wind speed changes, and optimizing the model training process. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings in the specification are used to provide a 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 an improper limitation of the present invention.

[0037] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] In the description of the present invention, it should be understood that the terms "include" and "comprise" indicate the presence of the 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 their combinations.

[0040] It should also be understood that the terms used in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0041] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present invention to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the 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.

[0042] Depending on the context, the word "if" as used herein can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detected (stated condition or event)" or "in response to detecting (stated condition or event)".

[0043] Structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the drawings. These figures are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary, and may actually deviate due to manufacturing tolerances or technical limitations, and those skilled in the art can design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0044] The following further describes the present invention in detail with reference to the drawings and specific embodiments, which is an explanation of the present invention rather than a limitation.

[0045] See Figure 1 , the present invention provides a supercapacitor energy storage frequency modulation method, including the following steps:

[0046] Step 1: Collect wind speed data, establish an original wind speed sequence, and divide the original wind speed sequence into a first prediction set and a first verification set according to the order and the first preset ratio; input the first prediction set into the trained neural network model in sequence for prediction to obtain a first prediction value, and calculate a reference prediction error W based on the first prediction value and the actual value in the first verification set;

[0047] Step 2: Let the first prediction set be X 0 (t)=[X 01 ,X02 , X 03 ,..., X 0N , where N is the number of subsequences of X 0 (t), and X 01 , X 02 , X 03 ,..., X 0N are the 1st, 2nd, 3rd,..., Nth subsequences in the first prediction set respectively; for the subsequences in X 0 (t), perform a pseudo-random swap of the input quantity positions to obtain N! wind speed sequences after the pseudo-random swap of the input quantity positions. Let X i (t) be the ith wind speed sequence after the pseudo-random swap of the input quantity positions, where i ∈ {1~N!}. Let i = 1 and execute Step Three;

[0048] Step Three: Divide the wind speed sequence X i (t) into a second prediction set and a second verification set according to the order and the second preset ratio. Input the second prediction set into the trained neural network model in sequence for prediction to obtain a second predicted value. Based on the second predicted value and the actual value in the second verification set, calculate the prediction error parameter A i ;

[0049] Step Four: Determine whether i is equal to N!. If the judgment is no, then let i = i + 1 and execute Step Three. If the judgment is yes, then execute Step Five;

[0050] Step Five: Obtain the minimum value A i in the prediction error parameter A min , and obtain the corresponding wind speed sequence X min (t). Based on the benchmark prediction error W, the minimum value A min , the wind speed sequence X min (t), and the first prediction set X 0 (t), construct the final wind speed sequence. Input the final wind speed sequence into the trained neural network model in sequence for prediction to obtain the final wind speed prediction result;

[0051] Step Six: Calculate the predicted power sent by the wind farm to the power grid according to the final wind speed prediction result, obtain the actual power sent by the wind farm to the power grid, and calculate the difference between the predicted power and the actual power. When the difference is positive, adjust the super-capacitor energy storage system connected at the outlet of the wind farm to the charging state; when the difference is 0, the super-capacitor energy storage system connected at the outlet of the wind farm remains in the current state unchanged; when the difference is negative, adjust the super-capacitor energy storage system connected at the outlet of the wind farm to the discharging state.

[0052] By collecting wind speed data and establishing the original wind speed sequence, the original wind speed sequence is divided into the first prediction set and the first verification set, and the reference error is predicted and calculated using the first prediction set; then, the subsequences in the first prediction set are randomly swapped in position to generate multiple transformed wind speed sequences; the transformed wind speed sequences are divided into the second prediction set and the second verification set, and the prediction error parameters are predicted and calculated using the second prediction set; the wind speed sequence with the smallest prediction error parameter is selected to construct the final wind speed sequence. This method can make full use of the internal laws and characteristics of wind speed data, improve the accuracy and reliability of wind speed prediction through the position pseudo-random swapping technology, and provide more accurate data support for the grid connection scheduling of wind farms.

[0053] Specifically, the first preset ratio is 8:2 or 9:1; a reasonable ratio division ensures the sufficiency of the training data (the first prediction set), and at the same time, the verification data (the first verification set) can also provide enough samples for error evaluation, which helps to improve the generalization ability of the prediction model.

[0054] Specifically, the second preset ratio is equal to the first preset ratio; keeping the ratios of the two data sets consistent helps to maintain the consistency of the prediction performance of the model at different stages and avoid prediction deviations caused by uneven data division.

[0055] Specifically, the reference prediction error W is specifically:

[0056]

[0057] where y i is the i-th first predicted value, i is the i-th actual value in the first verification set, and n is the number of subsequences in the first verification set.

[0058] Specifically, based on the reference prediction error W, the minimum value A min , the wind speed sequence X min (t) and the first prediction set X 0 (t), the final wind speed sequence X f (t) is specifically:

[0059] Let the wind speed sequence X min (t) be [X 1 , X 2 , X 3 ,..., X N , and the final wind speed sequence X f (t)=[X 01 +sigmoid(X 1 )×X 1 × , X 02 +sigmoid(X02 ) × X 2 × , X 03 + sigmoid(X 3 ) × X 3 × ,..., X 0N + sigmoid(X N ) × X N × ;

[0060] Wherein, X 1 , X 2 , X 3 ,..., X N is the 1st, 2nd, 3rd,..., Nth subsequence in the wind speed sequence X min (t); sigmoid() is an activation function; tanh() is an activation function.

[0061] By comprehensively considering multiple factors, including the first prediction set, the benchmark prediction error, the minimum prediction error parameter, and the corresponding wind speed sequence, the constructed final wind speed sequence can more accurately reflect the changing trend of the wind speed, improving the accuracy of the prediction; using the sigmoid and tanh activation functions for non-linear transformation helps to further enhance the accuracy of the prediction model.

[0062] Specifically, after collecting the wind speed data, the following steps are further included: deleting and supplementing abnormal data; by deleting abnormal data, the error data points caused by equipment failures, measurement errors, or data transmission errors can be eliminated, thereby improving the overall quality of the wind speed data set, enhancing the accuracy and credibility of the prediction results; at the same time, reducing the model's dependence on abnormal data, thus enhancing the generalization ability of the model, enabling it to better adapt to the wind speed prediction tasks in different scenarios.

[0063] Specifically, the neural network model is LSTM; using the LSTM neural network can effectively process the noise and outliers in the input data, thereby improving the robustness of the prediction model, adapting to the complex and variable wind speed changes, and optimizing the model training process.

[0064] In a specific embodiment of the present invention, the pseudo-random swapping of the input quantity positions specifically includes the following steps. Let the first prediction set X 0 (t) = [a, b, c, d, e], where a, b, c, d, e are the 5 subsequences of the first prediction set; perform pseudo-random swapping of the input quantity positions on the subsequences in X 0 (t) to obtain 5! wind speed sequences after pseudo-random swapping of the input quantity positions,

[0065] The 5! wind speed sequences after pseudo-random swapping of the input quantity positions are in turn:

[0066] The wind speed sequence after pseudo-random swapping of the positions of 24 input quantities starting with "a":

[0067] abcde, abced, abdce, abdec, abecd, abedc, acbde, acbed, acdbe, acdeb, acebd, acedb, adbce, adbec, adcbe, adceb, adebc, adecb, aebcd, aebdc, aecbd, aecdb, aedbc, aedcb;

[0068] The wind speed sequence after pseudo-random swapping of the positions of 24 input quantities starting with "b":

[0069] bacde, baced, badce, badec, baecd, baedc, bcade, bcaed, bcdae, bcdea, bcead, bceda, bdace, bdaec, bdcae, bdcea, bdeac, bdeca, beacd, beadc, becad, becda, bedac, bedca;

[0070] The wind speed sequence after pseudo-random swapping of the positions of 24 input quantities starting with "c":

[0071] cabde, cabed, cadbe, cadeb, caebd, caedb, cbade, cbaed, cbdae, cbdea, cbead, cbeda, cdabe, cdaeb, cdbae, cdbea, cdeab, cdeba, ceabd, ceadb, cebad, cebda, cedab, cedba;

[0072] The wind speed sequence after pseudo-random swapping of the positions of 24 input quantities starting with "d":

[0073] dabce, dabec, dacbe, daceb, daebc, daecb, dbace, dbaec, dbcae, dbcea, dbeac, dbeca, dcabe, dcaeb, dcbae, dcbea, dceab, dceba, deabc, deacb, debac, debc a, decab, decba;

[0074] The wind speed sequence after pseudo-random swapping of the positions of 24 input quantities starting with "e":

[0075] eabcd, eabdc, eacbd, eacdb, eadbc, eadcb, ebacd, ebadc, ebcad, ebcda, ebdac, ebdca, ecabd, ecadb, ecbad, ecbda, ecdab, ecdba, edabc, edacb, edbac, edbca, edcab, edcba。

[0076] The first preset ratio is 8:2. Let i = 1 and perform the following steps;

[0077] Divide the wind speed sequence X i (t) into a second prediction set and a second verification set in sequence and according to the same preset ratio. Among them, the second prediction set is the first 4 subsequences, and the second verification set is the last 1 subsequence; sequentially input the above second prediction set into the trained neural network model for prediction to obtain a second prediction value. Based on the second prediction value and the actual value in the second verification set, calculate the prediction error parameter A i .

[0078] Judge whether i is equal to 5!. If the judgment is no, then let i = i + 1 and continue to execute the above steps. If the judgment is yes, obtain the minimum value A of the prediction error parameter A i in min , assuming that the minimum value of the above prediction error parameter A i is A 106 , obtain the corresponding wind speed sequence X min (t) = [e, b, c, d, a]. Among them, the second prediction set is [e, b, c, d], and the second verification set is [a]. Based on the benchmark prediction error W, the minimum value A min= A 106 , the wind speed sequence X min (t) = [e, b, c, d, a] and the first prediction set X 0 (t) = [a, b, c, d, e], construct the final wind speed sequence,

[0079] The final wind speed sequence X f (t) = [a + sigmoid(e)×e× , b + sigmoid(b)×b× , c + sigmoid(c)×c× , d + sigmoid(d)×d× , e + sigmoid(a)×a× .

[0080] To further verify the advantages of this method, the method of this application and the traditional prediction method VMD (Variational Mode Decomposition) are respectively used to predict the wind speed. In the experiment of this application, MAPE (Mean Absolute Percentage Error) is selected as the evaluation criterion for each model, and the prediction results (i.e., MAPE values) are as follows:

[0081]

[0082] It can be seen that the method of this application has higher accuracy and reliability in wind speed prediction.

[0083] Based on the same inventive concept, an embodiment of this 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, it implements the steps of the supercapacitor energy storage frequency modulation method. Among them, the memory may include internal memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk memory, etc.; the processor, network interface, and memory are interconnected through an internal bus, and this internal bus can be an Industry Standard Architecture bus, a Peripheral Component Interconnect standard bus, an Extended Industry Standard Architecture 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 program code, and the program code includes computer operation instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0084] Based on the same inventive concept, an embodiment of this application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the supercapacitor energy storage frequency modulation method. 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, etc. The non-volatile memory may include ROM (Read-Only Memory), hard disk, flash memory, optical disc, magnetic disk, etc.

[0085] Based on the same inventive concept, an embodiment of this application provides a computer program product. The computer program product includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer device, the computer device is enabled to execute the steps of the above-mentioned supercapacitor energy storage frequency modulation method.

[0086] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can 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 memory, etc.) that contain computer-usable program code.

[0087] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer device or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0088] These computer program instructions can also be stored in a computer-readable memory that can direct a computer device or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0089] These computer program instructions can also be loaded onto a computer device or other programmable data processing devices, such that a series of operation steps are executed on the computer device or other programmable devices to generate a process implemented by the computer device. Thus, the instructions executed on the computer device or other programmable devices provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0090] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0091] Obviously, those skilled in the art can make various modifications and variations 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 equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A supercapacitor energy storage frequency modulation method, characterized in that: The following steps are involved: Step 1: Collect wind speed data, establish an original wind speed sequence, and divide the original wind speed sequence into a first prediction set and a first verification set according to a sequence and a first preset ratio; input the first prediction set into the trained neural network model in sequence for prediction to obtain a first prediction value, and calculate a benchmark prediction error W based on the first prediction value and the actual value in the first verification set; Step 2: Let the first prediction set be X0(t)=[X 01 ,X 02 ,X 03 ,...,X 0N ], N is the number of subsequences in X0(t), X 01 ,X 02 ,X 03 ,...,X 0N are the 1st, 2nd, 3rd, …, Nth subsequences in the first prediction set respectively; the subsequences in X0(t) are pseudo-randomly swapped for input positions to obtain N wind speed sequences after pseudo-randomly swapping input positions. Let X i (t) is the i-th wind speed sequence after the positions of N! input quantities are pseudo-randomly swapped, i∈{1~N!}, let i=1, and execute step 3; Step 3: Convert wind speed sequence X i (t) Dividing the set into a second prediction set and a second verification set according to the order of precedence and the second preset ratio, inputting the second prediction set into the trained neural network model in order for prediction, obtaining a second prediction value, and calculating the prediction error parameter A based on the second prediction value and the actual value in the second verification set. i ; Step 4: Determine whether i is equal to N! If not, set i=i+1 and execute step 3; if yes, execute step 5; Step 5: Obtain prediction error parameter A i The minimum value A in min , and get the corresponding wind speed sequence X min (t), based on the baseline prediction error W, the minimum value A min , wind speed sequence X min (t) and the first prediction set X0(t), construct the final wind speed sequence, input the final wind speed sequence into the trained neural network model in sequence for prediction, and obtain the final wind speed prediction result; Step 6. Calculate the predicted power sent by the wind farm to the grid based on the final wind speed prediction result, 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 the 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 the discharging state.

2. A supercapacitor energy storage frequency modulation method according to claim 1, characterized in that: The first preset ratio is 8:2 or 9:

1.

3. A supercapacitor energy storage frequency modulation method according to claim 1, characterized in that: The second preset ratio is equal to the first preset ratio.

4. A supercapacitor energy storage frequency modulation method according to claim 1, characterized in that: The benchmark prediction error W is specifically: Among them, y i is the i-th first prediction value, i is the ith actual value in the first validation set, and n is the number of subsequences in the first validation set.

5. A supercapacitor energy storage frequency modulation method according to claim 1, characterized in that: Based on the benchmark prediction error W, the minimum value A min , wind speed sequence X min (t) and the first prediction set X0(t), construct the final wind speed sequence X f (t) Specifically: Assume that the wind speed sequence X min (t) is [X1,X2,X3,...,X N ], Final wind speed sequence X f (t)=[X 01 +sigmoid(X1)×X1× , X 02 +sigmoid(X 02 )×X2× , X 03 +sigmoid(X3)×X3× , ..., X 0N +sigmoid(X N )×X N × ]; Among them, X1,X2,X3,...,X N is the wind speed sequence X min The 1st, 2nd, 3rd, …, Nth subsequence in (t); sigmoid() is the activation function; tanh() is the activation function.

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

7. A supercapacitor energy storage frequency modulation method according to claim 1, characterized in that: The neural network model is LSTM.

8. 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 method according to any one of claims 1 to 7 are implemented.

9. 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 method according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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