Supercapacitor energy storage frequency modulation and grid connection method and system based on operation-improved VMD

Abstract

The present invention discloses a method and system for frequency modulation and grid connection of supercapacitor energy storage based on an operation-improved VMD, belonging to the technical field of wind speed prediction, and comprising the following steps: decomposing an original wind speed time series by using a variational mode decomposition method to obtain a plurality of subsequences; performing amplitude angle conversion on each subsequence to obtain an amplitude representation form of each subsequence; calculating independent values through independent operations on the amplitude representation forms of all subsequences; increasing the decomposition layer number of the variational mode decomposition, repeating the above steps, and recording the independent values obtained in each cycle; comparing to obtain the maximum independent value among all the independent values, and taking the subsequence corresponding to the maximum independent value as the optimal subsequence; inputting the optimal subsequence into a prediction neural network for prediction to obtain a prediction result, and adding the prediction results to obtain a wind speed prediction result for supercapacitor energy storage grid connection. The present invention can improve the prediction accuracy, enhance the model robustness, promote the rational utilization of supercapacitor energy storage, and improve the grid connection stability.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind speed prediction, and particularly relates to a supercapacitor energy storage frequency modulation grid connection method and system based on an operation-improved VMD. Background Technique

[0002] Due to the randomness of wind speed changes, the grid connection of large-scale wind farms brings difficulties to power grid dispatching, thereby affecting the stability of the system. In order to improve the dispatchability of wind farms, the general method is to use the neural network method to predict the wind speed for the next day, calculate the power sent by the wind farm to the power grid through the predicted wind speed value, and submit the predicted value to the power grid dispatching agency. At the same time, a supercapacitor energy storage system is connected at the outlet of the wind farm to quickly respond to make up for the error between the actual power generated by the wind farm and the predicted power, thereby improving the credibility of the power grid's wind farm power generation dispatch based on wind power prediction and improving the coordinated operation ability between the wind farm and the power system.

[0003] However, traditional prediction methods generally use VMD (Variational Mode Decomposition) to decompose the original data and then put it into the neural network for prediction. However, there is a problem that the subsequences after the existing variational mode decomposition are still correlated with each other, that is, the correlation between the subsequences after VMD decomposition is relatively high, and the subsequences are not independent enough from each other. Putting the non-independent subsequences into the neural network for prediction will lead to inaccurate prediction results. Summary of the Invention

[0004] The purpose of the present invention is to overcome the problem that the subsequences after the existing variational mode decomposition are still correlated with each other, and a supercapacitor energy storage frequency modulation grid connection method and system based on an operation-improved VMD are proposed.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In the first aspect, the present invention provides a supercapacitor energy storage frequency modulation grid connection method based on an operation-improved VMD, including the following steps:

[0007] S1. Decompose the original wind speed time series by using the variational mode decomposition method to obtain a plurality of subsequences;

[0008] S2. Perform amplitude angle conversion on each subsequence to obtain the amplitude representation form of each subsequence;

[0009] S3. Calculate independent values through independent operations on the amplitude representation forms of all subsequences;

[0010] S4. Return to S1, increment the decomposition level of variational mode decomposition, repeat the steps of S1 - S3, record the independent values obtained in each loop until the decomposition level of variational mode decomposition reaches the maximum;

[0011] S5. Compare to obtain the maximum independent value among all independent values, and take the subsequence corresponding to the maximum independent value as the optimal subsequence;

[0012] S6. Input the optimal subsequence into the prediction neural network for prediction to obtain the prediction result, and add up the prediction results to obtain the wind speed prediction result for the super - capacity energy storage grid connection.

[0013] Further, the number of the several subsequences is an even number greater than or equal to 4.

[0014] Further, the method of amplitude angle conversion is as follows:

[0015]

[0016]

[0017]

[0018] where are the amplitude representation forms of the 1st, 2nd, 3rd, …, k sub - sequences respectively, is the magnitude of the amplitude representation form of the i sub - sequence, is an element in the sub - sequence, is the angle of the amplitude representation form of the i sub - sequence, k is the number of sub - sequences, N is the number of elements in the sub - sequence, i is an intermediate variable, arccos() is the inverse cosine function, and ∠ represents the measure of an angle.

[0019] Further, the independent operation is:

[0020]

[0021]

[0022] where is the independent value, is the i - th independent sequence, is the independent rule, is the amplitude representation of the (2i - 1) - th sub - sequence, is the amplitude representation of the 2i - th sub - sequence, K is the number of sub - sequences, i is an intermediate variable.

[0023] Furthermore, the specific independent operation rule is as follows:

[0024]

[0025]

[0026] | |=

[0027] Wherein, is the i th independent sequence, | | is the size of the i th independent sequence, is the 2 i -1st subsequence, is the amplitude representation of the 2 i -1st subsequence, is the element in the 2 i -1st subsequence, is the angle of the amplitude representation of the 2 i -1st subsequence, is the 2 i th subsequence, The amplitude representation of the 2 i th subsequence, is the element in the 2 i th subsequence, is the angle of the amplitude representation of the 2 i th subsequence.

[0028] Furthermore, the specific direction representation of the independent sequence is as follows:

[0029] is perpendicular to the plane formed by and ;

[0030] When A 2i-1 > A 2i ), is perpendicular to the plane formed by and and faces upward;

[0031] When A 2i-1 < A 2i ), is perpendicular to the plane formed by and and faces downward.

[0032] Furthermore, the decomposition layer number of the incremental variational mode decomposition is the decomposition layer number plus 2.

[0033] In a second aspect, the present invention provides a supercapacitor energy storage frequency modulation and grid connection system based on an operation-improved VMD, which uses the supercapacitor energy storage frequency modulation and grid connection method based on the operation-improved VMD, and includes:

[0034] A variational mode decomposition sequence module, which is used to decompose the original wind speed time series by using the variational mode decomposition method to obtain a plurality of subsequences;

[0035] An amplitude angle conversion processing module, which is used to perform amplitude angle conversion processing on each subsequence to obtain the amplitude representation form of each subsequence;

[0036] An independent operation module, which is used to calculate independent values for the amplitude representation forms of all subsequences through independent operation rules;

[0037] An increasing loop module, which is used to increase the decomposition layer number of the variational mode decomposition, record the independent values obtained in each loop until the decomposition layer number of the variational mode decomposition reaches the maximum;

[0038] An optimal subsequence obtaining module, which is used to compare and obtain the maximum independent value among all independent values, and use the subsequence corresponding to the maximum independent value as the optimal subsequence;

[0039] A wind speed prediction result obtaining module, which is used to input the optimal subsequence into a prediction neural network for prediction to obtain a prediction result, and add the prediction results to obtain the wind speed prediction result for supercapacitor energy storage grid connection.

[0040] In a third aspect, the present invention provides an electronic 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 supercapacitor energy storage frequency modulation and grid connection method based on the operation-improved VMD is implemented.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the supercapacitor energy storage frequency modulation and grid connection method based on the operation-improved VMD is implemented.

[0042] Compared with the prior art, the present invention has the following beneficial technical effects:

[0043] The supercapacitor energy storage frequency modulation and grid connection method based on operation-improved VMD proposed by the present invention decomposes the original wind speed time series into multiple modal components with different frequencies and amplitudes, namely subsequences, which reflect the characteristics of wind speed data on different time scales, capture the volatility and trend of wind speed more precisely, have good adaptability to different types of wind speed data, and can maintain a high prediction accuracy even in the face of a complex and changeable wind speed environment. By decomposing the wind speed sequence into multiple subsequences for prediction, even if a certain subsequence is affected by external interference or outliers, it will not have too much impact on the entire prediction result, thereby enhancing the anti-interference ability of the model. By selecting the layer with the highest degree of independence for decomposition, it can be ensured that the subsequences used for prediction contain as much useful information as possible, thereby reducing the prediction error. Subsequences with high independence are less sensitive to noise and outliers, so the prediction based on these subsequences is more robust. Compared with directly predicting the original signal, first selecting subsequences with high independence through signal decomposition can significantly reduce the computational complexity. This method can handle non-linear and non-stationary wind speed time series and has good adaptability to different types of wind speed data.

[0044] Furthermore, the method proposed by the present invention decomposes the original wind speed time series through operation-improved VMD, decomposes the complex wind speed changes into multiple simple subsequences, and extracts key information through amplitude angle transformation and specific operation rules. Through iterative iteration and neural network prediction, an accurate prediction of the original wind speed time series is finally obtained. This method combines signal processing, mathematical operations, and machine learning techniques, providing a new and effective approach for wind speed prediction.

[0045] Furthermore, the supercapacitor energy storage grid connection wind speed prediction method based on operation-improved VMD has significant benefits in improving prediction accuracy, enhancing the robustness of the model, promoting the rational utilization of supercapacitors, and improving grid connection stability. Brief Description of the Drawings

[0046] The drawings described herein are for illustrative purposes only and are not intended to limit the scope of the present disclosure in any way. Additionally, the shapes and proportional dimensions of the components in the figures are only schematic and are used to assist in understanding the present invention, rather than specifically defining the shapes and proportional dimensions of the components of the present invention. In the drawings:

[0047] Figure 1 is a flowchart of the supercapacitor energy storage frequency modulation and grid connection method based on operation-improved VMD of the present invention.

[0048] Figure 2 is a structural diagram of the supercapacitor energy storage frequency modulation and grid connection system based on operation-improved VMD of the present invention.

[0049] Figure 3It is a diagram of an electronic device for the method of supercapacitor energy storage frequency modulation and grid connection based on operation-improved VMD of the present invention.

[0050] Figure 4 For the method of supercapacitor energy storage frequency modulation and grid connection based on operation-improved VMD in the embodiment of the present invention Direction schematic diagram.

[0051] Figure 5 For the method of supercapacitor energy storage frequency modulation and grid connection based on operation-improved VMD in the embodiment of the present invention Another direction schematic diagram. Specific implementation manner

[0052] In order to enable those skilled in the art to better understand the solution of the present invention, 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 only a part of the embodiments of the present invention, rather than all the embodiments. 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.

[0053] Embodiment 1

[0054] See Figure 1 , the method of supercapacitor energy storage frequency modulation and grid connection based on operation-improved VMD includes the following steps:

[0055] S1. Decompose the original wind speed time series by using the variational mode decomposition method to obtain a plurality of subsequences;

[0056] The number of the plurality of subsequences in S1 is an even number greater than or equal to 4.

[0057] This step is the basic operation of the VMD method. By iteratively optimizing the bandwidth and center frequency of each mode, the original signal is decomposed into multiple mode components, which helps to capture the wind speed changes of different frequency components.

[0058] S2. Perform amplitude angle conversion on each subsequence to obtain the amplitude representation form of each subsequence;

[0059] That is, convert the subsequence from the time domain to the frequency domain or the complex plane to obtain its amplitude and phase information.

[0060] The amplitude angle conversion in S2 is:

[0061]

[0062]

[0063]

[0064] Among them, are respectively the amplitude representation forms of the 1st, 2nd, 3rd, …, k sub - sequences, is the i size of the amplitude representation form of the i - th sub - sequence, is the i angle of the amplitude representation form of the k i - th sub - sequence, N is the number of elements in the sub - sequence, i is an intermediate variable, arccos() is the inverse cosine function, and ∠ represents the measure of an angle.

[0065] S3. Calculate independent values through independent operations on the amplitude representation forms of all sub - sequences; the independent values can evaluate the potential contribution of each sub - sequence to the prediction result and can further extract the most critical information for wind speed prediction.

[0066] The independent operation described in S3 is:

[0067]

[0068]

[0069] Among them, is the independent value, is the i - th independent sequence, is the independent rule, is the amplitude representation of the (2i - 1) - th sub - sequence, is the amplitude representation of the 2i - th sub - sequence, K is the number of sub - sequences, i is an intermediate variable.

[0070] The specific independent operation rule is:

[0071]

[0072]

[0073] | | =

[0074] Among them, is the i i - th independent sequence, | | is the size of the i i - th independent sequence, is the (2 i i - 1) - th sub - sequence, is the 2 i- The amplitude representation of a subsequence, is the second i - An element in the subsequence, is the second i - The angle of the amplitude representation of the subsequence, is the second i subsequence, The second i amplitude representation of the subsequence, is the second i element in the subsequence, is the second i angle of the amplitude representation of the subsequence.

[0075] The direction representation of the independent sequence is specifically:

[0076] Perpendicular to the plane formed by and ;

[0077] When A 2i-1 > A 2i , Perpendicular to the plane formed by and faces upward;

[0078] When A 2i-1 < A 2i , Perpendicular to the plane formed by and faces downward.

[0079] S4. Increase the decomposition level of the variational mode decomposition, repeat steps S1 - S3, record the independent values obtained in each loop until the decomposition level of the variational mode decomposition reaches the maximum;

[0080] As a preferred solution, the increase in S4 is the decomposition level plus 2.

[0081] S5. Compare to obtain the maximum independent value among all independent values, and use the subsequence corresponding to the maximum independent value as the optimal subsequence;

[0082] It is possible to automatically select the subsequence that contributes the most to the prediction result by incrementally cycling in S4 and comparing in S5, thereby improving the overall prediction accuracy.

[0083] The operation-improved variational mode decomposition method improves the prediction accuracy by judging the overall independence degree of subsequences at the current decomposition layer and selecting the layer with the highest independence degree for decomposition. By adjusting the decomposition parameters of VMD, different numbers of subsequences are generated, and the decomposition layer of variational mode decomposition is incremented to explore the optimal subsequences under different decomposition schemes, and this optimal subsequence will be used for subsequent neural network prediction.

[0084] S6. Input the optimal subsequence into the prediction neural network for prediction to obtain the prediction result, and add up the prediction results to obtain the wind speed prediction result for the super-capacitor energy storage grid connection.

[0085] By optimizing the selection of subsequences and combining the prediction ability of the neural network, the accuracy and reliability of wind speed prediction are improved, and then the operation efficiency of the super-capacitor energy storage grid connection system is optimized.

[0086] In this embodiment, since the VMD method can decompose various frequency components, the model can learn the characteristics of wind speed data more comprehensively, thereby enhancing the generalization ability of the model. By selecting the optimal subsequence for prediction, the dependence of the model on a specific data set can be further reduced, and the applicability of the model under different wind speed conditions can be improved. The design of the incremental loop allows the system to gradually optimize and find the optimal decomposition layer during the process of gradually increasing the decomposition layer, so as to optimize the calculation efficiency while ensuring the prediction accuracy. The processes of amplitude cornering processing and independent operation are relatively simple and efficient, which helps to reduce the computational complexity and improve the calculation speed.

[0087] In this embodiment, by optimizing the selection of subsequences and combining the prediction ability of the neural network, the accuracy and reliability of wind speed prediction can be improved, and then the operation efficiency of the super-capacitor energy storage grid connection system can be optimized. Through the improved variational mode decomposition method, the original wind speed time series is decomposed into multiple subsequences, and the optimal subsequence is selected from them for prediction. This method can more effectively capture the non-linear and non-stationary characteristics of wind speed data, thereby improving the prediction accuracy. Combining the powerful prediction ability of the neural network can further refine the prediction result, reduce the error, and make the prediction result closer to the actual wind speed change.

[0088] Accurate wind speed prediction results help the over-capacity energy storage system to arrange the charge and discharge plan more reasonably. When the predicted wind speed is high, the system can generate more electricity using wind energy and reduce the discharge of the energy storage system; when the predicted wind speed is low, the power supply can be supplemented through the energy storage system to ensure the stable operation of the power grid. By optimizing the use of the energy storage system, the operating cost of the system can be reduced, the energy utilization efficiency can be improved, while reducing the dependence on traditional energy sources, promoting sustainable development, reducing carbon emissions, and achieving the goals of environmental protection and sustainable development. Accurate wind speed prediction helps the power grid dispatching department to better plan power production and distribution, and reduce the power supply and demand imbalance problem caused by wind speed fluctuations. Under extreme weather conditions, such as a sudden increase or decrease in wind speed, accurate wind speed prediction can provide timely warning information for the power grid, helping the dispatching department to take necessary measures to ensure the safe and stable operation of the power grid.

[0089] The operation-improved VMD effectively extracts the key information in the wind speed time series by optimizing the decomposition process, reduces the error in the decomposition process, thereby improving the accuracy of wind speed prediction. Based on the wind speed prediction results of the operation-improved VMD, the over-capacity energy storage system can more accurately judge the change trend of the wind speed, and thus formulate a more reasonable charge and discharge strategy. The combination of accurate wind speed prediction and the optimized charge and discharge strategy can enable the over-capacity energy storage grid-connected system to better adapt to wind speed fluctuations and reduce the power supply and demand imbalance problem caused by wind speed changes.

[0090] Embodiment 2

[0091] See Figure 2 , for the supercapacitor energy storage frequency modulation grid-connected system based on the operation-improved VMD, using the supercapacitor energy storage frequency modulation grid-connected method based on the operation-improved VMD described in Embodiment 1, including:

[0092] The variational mode decomposition sequence module is used to decompose the original wind speed time series into several subsequences by using the variational mode decomposition method;

[0093] The amplitude angularization processing module is used to perform amplitude angularization processing on each subsequence to obtain the amplitude representation form of each subsequence;

[0094] The independent operation module is used to calculate the independent value through the independent operation rule for the amplitude representation forms of all subsequences;

[0095] The increasing loop module is used to increase the decomposition layer number of the variational mode decomposition, record the independent value obtained in each loop until the decomposition layer number of the variational mode decomposition reaches the maximum;

[0096] The optimal subsequence obtaining module is used to compare and obtain the maximum independent value among all independent values, and use the subsequence corresponding to the maximum independent value as the optimal subsequence;

[0097] The wind speed prediction result obtaining module is used to input the optimal subsequence into the prediction neural network for prediction to obtain the prediction result, and add the prediction results to obtain the wind speed prediction result for the over-capacity energy storage grid connection.

[0098] In this embodiment, by optimizing the selection of subsequences and combining the prediction ability of the neural network, according to the predicted wind speed result, the charge and discharge strategy of the over-capacity energy storage system is adjusted, thereby optimizing the operation efficiency of the over-capacity energy storage grid connection system. The traditional VMD method decomposes the signal into multiple modal sub-sequences by iteratively searching for the central frequency and bandwidth of each mode. The optimized VMD for operation improves the decomposition effect and obtains accurate wind speed prediction results.

[0099] Embodiment III

[0100] Refer to Figure 3 , an electronic 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 method for supercapacitor energy storage frequency modulation grid connection based on the optimized VMD in Embodiment I.

[0101] Embodiment IV

[0102] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, it implements the method for supercapacitor energy storage frequency modulation grid connection based on the optimized VMD in Embodiment I.

[0103] Embodiment V

[0104] This embodiment provides a method for supercapacitor energy storage frequency modulation grid connection based on the optimized VMD. Taking 4 sub-sequences as an example, the specific process is as follows:

[0105] Step 1: Decompose the original wind speed time series x(t) using VMD to obtain a series of sub-sequences IMF1, IMF2, IMF3,... IMF K , IMF (Intrinsic Mode Function, intrinsic modal component), where K is the decomposition layer number of VMD, that is, the number of sub-sequences, and it must be an even number greater than or equal to 4. In this example, K = 4.

[0106] Step 2: Amplitude-corner the sub-sequences in sequence;

[0107] Specifically, taking the 1st sub-sequence as an example, the amplitude-cornering process is as follows:

[0108]

[0109]

[0110] where arccos() is the inverse cosine function;

[0111] According to this process, we obtain successively:

[0112] .

[0113] is the amplitude representation form of the first subsequence, is the magnitude of the amplitude representation form of the first subsequence, is the angle of the amplitude representation of the first subsequence;

[0114] is the amplitude representation form of the second subsequence, is the magnitude of the amplitude representation form of the second subsequence, is the angle of the amplitude representation of the second subsequence;

[0115] is the amplitude representation form of the third subsequence, is the magnitude of the amplitude representation form of the third subsequence, is the angle of the amplitude representation of the third subsequence;

[0116] is the amplitude representation form of the fourth subsequence, is the magnitude of the amplitude representation form of the fourth subsequence, is the angle of the amplitude representation of the fourth subsequence.

[0117] where ∠ represents the measure of an angle.

[0118] Step 3: Use operation rules to calculate . Since there are issues with positive and negative signs during addition, take the positive value for the vertical plane upward, and vice versa for the negative value. is an independent value, is the i-th independent sequence, is the independent operation rule, is the amplitude representation of the (2i - 1)-th subsequence, is the amplitude representation of the 2i-th subsequence, i is an intermediate variable.

[0119] Specifically, for obtained in Step 2, use operation rules to obtain the first independent sequence and the second independent sequence ;

[0120] Next, take The generation process is illustrated as follows:

[0121]

[0122] The direction of Figure 4 is as shown: perpendicular to the plane formed by and ;

[0123] When , is perpendicular to the plane formed by and , and the plane faces upward.

[0124] When , is perpendicular to the plane formed by and , and the plane faces downward. As shown in Figure 5 .

[0125] The size of

[0126] is determined as follows:

[0127]

[0128] is the element in the first subsequence IMF1 , is the element in the second subsequence ; is the angle represented by the amplitude of the first subsequence, is the angle represented by the amplitude of the second subsequence;

[0129] Step 4: K = K + 2, repeat Steps 1 to 3, continue the loop, and obtain the corresponding in each loop. Output the K corresponding to the maximum , and the subsequences corresponding to the K. Put these subsequences into the neural network for prediction, and then add the prediction results to obtain the final prediction result of the supercapacitor energy storage grid-connected wind speed.

[0130] Specifically: Iterate according to the logic in Step 3 until K = 32, find the subsequence corresponding to the K value with the maximum , and put it into the neural network for prediction to obtain the final result.

[0131] To further verify the advantages of this embodiment, the method of this embodiment and a general prediction method are respectively used to predict the wind speed of the super-capacitor energy storage grid connection using a test data set, and the MAPE (Mean Absolute Percentage Error) is compared. The results are shown in Table 1 below:

[0132] Table 1 Comparison of the Mean Absolute Percentage Error of the Wind Speed Prediction Results between this Embodiment and the General Prediction Method

[0133]

[0134] It can be seen that the MAPE value generated by the wind speed prediction method provided in this embodiment for the optimal wind speed sequence of the super-capacitor energy storage grid connection is significantly lower than that of the traditional wind speed prediction method. A lower MAPE value means that the wind speed prediction result is more accurate, thus being able to better guide the charge and discharge strategies of the super-capacitor energy storage grid connection system.

[0135] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete 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, optical storage, etc.) containing computer-usable program code.

[0136] 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 process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be realized 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, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0137] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby the instructions executed on the computer or other programmable apparatus provide steps for realizing the functions specified in one process or a plurality of processes and / or boxes Figure 1 one process or a plurality of processes and / or boxes Figure 1 steps of the functions specified in one box or a plurality of boxes. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the present invention.

Claims

1. A supercapacitor energy storage frequency modulation and grid-connected method based on computationally improved VMD, characterized in that: The following steps are involved: S1. Decompose the original wind speed time series using variational mode decomposition method to obtain several subsequences; S2, performing amplitude-angle processing on each subsequence to obtain an amplitude representation of each subsequence; S3, calculating the amplitude representation of all subsequences through independent operations to obtain independent values; The independent operations are: in, is an independent value, is the ith independent sequence, is an independent operation rule. is the amplitude representation of the 2i-1th subsequence, is the amplitude representation of the 2i-th subsequence, K is the number of subsequences, i is an intermediate variable; The independent operation rules are specifically: | |= in, For the i independent sequences, | |For the i The size of the independent sequence, For the 2nd i -1 subsequence, For the 2nd i -1 subsequence amplitude representation, For the 2nd i -1 element in the subsequence, For the 2nd i -1 subsequence of amplitudes representing angles, For the 2nd i subsequences, 2nd i The amplitude of the subsequence is represented by For the 2nd i elements in a subsequence, For the 2nd i The amplitude of each subsequence represents the angle; The direction of the independent sequence is specifically represented as: Perpendicular to and The plane formed; When A 2i-1 >A 2i hour, Perpendicular to and The flat surface formed faces upward; When A 2i-1 <A 2i hour, Perpendicular to and The flat surface formed faces downward; S4, return to S1, increase the number of decomposition levels of variational mode decomposition, repeat steps S1-S3, and record the independent values ​​obtained in each cycle until the number of decomposition levels of variational mode decomposition reaches the maximum; S5. Compare and obtain the maximum independent value among all independent values, and take the subsequence corresponding to the maximum independent value as the optimal subsequence; S6. Input the optimal subsequence into the prediction neural network to obtain the prediction result, and add the prediction results to obtain the wind speed prediction result of the super-capacity energy storage grid-connected.

2. The supercapacitor energy storage frequency modulation and grid-connected method based on the operation-modified VMD according to claim 1 is characterized in that: The number of the plurality of subsequences is an even number greater than or equal to 4.

3. The supercapacitor energy storage frequency modulation and grid-connected method based on the operation-modified VMD according to claim 1 is characterized in that: The method of the amplitude processing is: in, They are 1st, 2nd, 3rd, ..., k The amplitude representation of the subsequences, For the i The amplitude representation of the subsequences is is an element in the subsequence, For the i The amplitude of the subsequences is expressed in terms of angle, k is the number of subsequences, N is the number of elements in the subsequence, i is the intermediate variable, arccos() is the inverse cosine function, and ∠ represents the measure of the angle.

4. The supercapacitor energy storage frequency modulation and grid-connected method based on the operation-modified VMD according to claim 1 is characterized in that: The decomposition level of the incremental variational mode decomposition refers to the decomposition level plus 2.

5. A supercapacitor energy storage frequency modulation grid-connected system based on an operation-improved VMD, using the supercapacitor energy storage frequency modulation grid-connected method based on an operation-improved VMD as described in any one of claims 1 to 4, characterized in that: include: The variational mode decomposition sequence module is used to decompose the original wind speed time series into several subsequences using the variational mode decomposition method; An amplitude processing module is used to perform amplitude processing on each subsequence to obtain an amplitude representation of each subsequence; An independent operation module, used for calculating the amplitude representation forms of all subsequences through independent operation rules to obtain independent values; An increasing loop module is used to increase the number of decomposition layers of the variational mode decomposition and record the independent values ​​obtained in each loop until the number of decomposition layers of the variational mode decomposition reaches the maximum; The optimal subsequence obtaining module is used to compare and obtain the maximum independent value among all independent values, and take the subsequence corresponding to the maximum independent value as the optimal subsequence; The wind speed prediction result obtaining module is used to input the optimal subsequence into the prediction neural network for prediction to obtain the prediction result, and add the prediction results to obtain the wind speed prediction result of the super-capacity energy storage grid connection.

6. An electronic device, characterized in that: The invention comprises 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 supercapacitor energy storage frequency modulation and grid-connected method based on the operation-improved VMD described in any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the supercapacitor energy storage frequency modulation and grid-connected method based on the operation-improved VMD described in any one of claims 1 to 4 is implemented.

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