Improved super capacitor energy storage frequency modulation grid connection method and system

Abstract

The invention discloses an improved super-capacitor energy storage frequency modulation grid connection method and system, and belongs to the technical field of wind speed prediction, and the method comprises the steps: carrying out the decomposition of an original wind speed sequence of a wind power plant through variational mode decomposition according to a preset decomposition layer number, calculating the sequence correlation degree of all subsequences in an original wind speed subsequence set, taking the reference sub-sequence as a reference, and adjusting the original sub-sequence set according to the sequence relevancy to obtain an adjusted sub-sequence set; fusing the adjusted sub-sequence set with the residual sequence to obtain a corrected sub-sequence set; calculating the sequence correlation of the corrected sub-sequence set to obtain the correlation under the current decomposition layer number; the number of decomposition layers of variational mode decomposition is increased, the steps are repeated, the corrected sub-sequence set under the number of decomposition layers corresponding to the minimum correlation value serves as a modified sub-sequence, the maximum correlation degree of residual sequences in the modified sub-sequence is compared with a threshold value, and a wind speed optimal sub-sequence is obtained; and performing wind speed prediction by using the optimal wind speed sub-sequence.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind speed prediction, and in particular relates to an improved supercapacitor energy storage frequency modulation grid-connected method and system. Background Art

[0002] Due to the randomness of wind speed changes, large-scale wind farms are difficult to connect to the grid, which in turn affects the stability of the system. In order to improve the dispatchability of wind farms, the general method uses a neural network method to predict the wind speed for the next day, calculates the power sent by the wind farm to the grid through the predicted wind speed value, and submits the predicted value to the grid dispatching agency. At the same time, an ultra-capacity 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 emitted by the wind farm and the predicted power, thereby improving the credibility of the grid in dispatching wind farm power generation based on wind power forecasts and improving the coordinated operation capabilities between wind farms and power systems.

[0003] However, the traditional prediction method generally uses VMD (Variational Mode Decomposition) to decompose the original data, and then puts it into the neural network for prediction. However, the existing variational mode decomposition has the problem that the decomposed subsequences are still correlated with each other, that is, the subsequences after VMD decomposition are highly correlated with each other, and the subsequences are not independent of each other. Putting the subsequences that are not independent of each other into the neural network prediction will lead to inaccurate prediction results. Summary of the invention

[0004] The purpose of the present invention is to overcome the problem of high correlation of decomposed subsequences in the existing variational mode decomposition, and propose an improved supercapacitor energy storage frequency modulation grid-connected method and system.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme: In a first aspect, the present invention provides an improved supercapacitor energy storage frequency modulation grid-connected method, comprising the following steps: S1. Obtain the original wind speed sequence of the wind farm; S2. performing variational modal decomposition on the original wind speed sequence of the wind farm based on a preset number of decomposition layers to obtain a set of original wind speed subsequences; S3, calculating the sequence correlation of all subsequences in the original wind speed subsequence set, and comparing the sequence correlations of all subsequences to obtain the minimum correlation; S4, taking the subsequence corresponding to the minimum correlation as a reference subsequence, and adjusting the original subsequence set according to the sequence correlation to obtain an adjusted subsequence set based on the reference subsequence; S5, comparing the original subsequence set with the adjusted subsequence set to obtain a residual sequence, and fusing the adjusted subsequence set with the residual sequence to obtain a modified subsequence set; S6. Calculate the sequence correlation of the modified subsequence set to obtain the correlation at the current decomposition level; S7, return to S2, and increase the number of decomposition levels of variational mode decomposition, repeat steps S2-S6 until the number of decomposition levels of variational mode decomposition reaches the maximum, obtain the correlations under several decomposition levels, and compare the correlations under several decomposition levels to obtain the minimum correlation value; S8, taking the modified subsequence set under the decomposition level corresponding to the minimum correlation value as the transformed subsequence, comparing the maximum correlation of the residual sequence in the transformed subsequence with the threshold value, and if the maximum correlation of the residual sequence in the transformed subsequence is less than the threshold value, retaining the residual sequence in the transformed subsequence as the optimal wind speed subsequence; S9. Use the optimal wind speed subsequence to predict the wind speed to obtain a wind speed prediction sequence, and add the wind speed prediction sequences to obtain the wind speed prediction result of the super-capacity energy storage grid connection.

[0006] Furthermore, the original wind speed sequence of the wind farm includes historical wind speed data of the wind farm, and the number of subsequences in the original subsequence set in S2 is the number of decomposition levels of variational mode decomposition.

[0007] Furthermore, the sequence correlation is calculated using the following formula:

[0008] in, D is the sequence correlation, For the i -1 sequence and i The sequence correlation of the sequences, j For the i -1 is the number of elements in the sequence that satisfy the correlation relationship, N is the i -1 sequence with the i The number of elements in the sequence, i is a positive integer, 1< i ≤30; The correlation relationship is b m =α a m ,in, a m For the i -1 element in the sequence, b m For the i elements in a sequence, α is the correlation function, 0.5<α<1.5.

[0009] Furthermore, the reference subsequence is used as a reference, and the original subsequence set is adjusted according to the sequence correlation to obtain the adjusted subsequence set, and the following adjustment formula is used:

[0010]

[0011] in, is the adjusted element in the subsequence that needs to be adjusted, sigmoid( ) represents the activation function, is the element in the subsequence that needs to be adjusted, represents the supplementary coefficient, K is the number of decomposition layers; D min is the sequence correlation of the benchmark subsequence, D x is the sequence correlation of the subsequence that needs to be adjusted, max( D 1 ,D 2 ,…,D K ) represents the maximum value of the sequence correlation of the original subsequence set, min( s 1 ,s 2 ,..,s i ,...,s N ) represents the minimum element in the base subsequence, max( r 1 ,r 2 ,.., r i ,...,r N ) represents the maximum element in the subsequence that needs to be adjusted. N Represents the total number of elements in the sequence, avg( r 1 ,r 2 ,.., r i ,...,r N ) represents the average value of the elements in the subsequence that needs to be adjusted. e is a natural constant, π is a constant and i is an intermediate variable.

[0012] Furthermore, the comparison of the original subsequence set with the adjusted subsequence set to obtain the residual sequence is performed using the following comparison formula:

[0013] in, is the residual sequence, is the original subsequence set, is the adjusted subsequence set, K is the number of decomposition levels, i is the intermediate variable; The sequence correlation of the modified subsequence set in S6 is the average value of the correlation between the adjusted subsequence set and the residual sequence.

[0014] Furthermore, the maximum correlation of the residual sequence in the transformed subsequence is compared with a threshold. If the maximum correlation of the residual sequence in the transformed subsequence is greater than the threshold, the residual sequence in the transformed subsequence is decomposed using variational mode decomposition until the maximum correlation of the residual sequence in the transformed subsequence is less than the threshold, thereby obtaining the optimal wind speed subsequence.

[0015] Furthermore, the wind speed prediction in S9 adopts the IPSO-SVR model, and the optimal wind speed subsequences are sequentially input into the IPSO-SVR model to obtain the wind speed prediction sequence.

[0016] In a second aspect, the present invention provides an improved supercapacitor energy storage frequency modulation grid-connected system, based on the improved supercapacitor energy storage frequency modulation grid-connected method, comprising: The module for obtaining the original wind speed sequence is used to obtain the original wind speed sequence of the wind farm; A variational mode decomposition module is used to perform variational mode decomposition on the original wind speed sequence of the wind farm based on a preset number of decomposition layers to obtain a set of original wind speed subsequences; The sequence correlation calculation module is used to calculate the sequence correlation of all subsequences in the original wind speed subsequence set, and compare the sequence correlations of all subsequences to obtain the minimum correlation; An original subsequence set adjustment module is used to take the subsequence corresponding to the minimum correlation as a reference subsequence, and adjust the original subsequence set according to the sequence correlation to obtain an adjusted subsequence set based on the reference subsequence; A fusion residual sequence module is used to compare the original subsequence set with the adjusted subsequence set to obtain a residual sequence, and fuse the adjusted subsequence set with the residual sequence to obtain a modified subsequence set; A module for calculating sequence correlation is used to calculate the sequence correlation of the modified subsequence set to obtain the correlation at the current decomposition level; Repeating the decomposition module, used to increase the number of decomposition layers of the variational mode decomposition until the number of decomposition layers of the variational mode decomposition reaches a maximum, obtaining correlations under several decomposition layers, and comparing the correlations under several decomposition layers to obtain the minimum correlation value; The wind speed optimal subsequence module is used to compare and obtain the modified subsequence set under the decomposition layer number corresponding to the minimum correlation value as the transformed subsequence, and compare the maximum correlation of the residual sequence in the transformed subsequence with the threshold value. If the maximum correlation of the residual sequence in the transformed subsequence is less than the threshold value, the residual sequence in the transformed subsequence is retained as the wind speed optimal subsequence; The wind speed prediction result obtaining module is used to use the wind speed optimal subsequence to perform wind speed prediction to obtain a wind speed prediction sequence, and the wind speed prediction sequence is added to obtain the wind speed prediction result of the super-capacity energy storage grid connection.

[0017] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the improved supercapacitor energy storage frequency modulation and grid-connected method when executing the computer program.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the improved supercapacitor energy storage frequency modulation and grid-connected method is implemented.

[0019] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention proposes an improved supercapacitor energy storage frequency modulation grid-connected method, which provides an improved VMD decomposition method, which can make the decomposed subsequences independent of each other, thereby improving the accuracy of prediction, and more effectively decompose the original wind speed sequence to obtain a series of relatively stable intrinsic mode components (Intrinsic Mode Function, IMF) with different characteristic information. Compared with the original sequence, these components can better reflect the inherent laws and characteristics of wind speed changes. By calculating the sequence correlation of the subsequences and adjusting the original subsequence set to reduce the correlation between the subsequences, the subsequences are made more independent, and the neural network can more accurately capture the characteristics of each subsequence, thereby improving the accuracy of wind speed prediction. By continuously increasing the number of decomposition layers of variational mode decomposition, and comparing the correlation under different decomposition layers, the number of decomposition layers with the smallest correlation is found, and the optimal number of decomposition layers can be determined, further improving the accuracy of prediction. By comparing the maximum correlation of the residual sequence with the threshold and deciding whether to retain the residual sequence, the residual information that has an important impact on the prediction result can be retained, while avoiding the introduction of irrelevant noise, and being able to fuse multi-scale wind speed information. The neural network can more comprehensively understand the characteristics of wind speed data and improve the robustness and accuracy of the prediction. The wind speed prediction results obtained by the method of the present invention can more effectively guide the charging and discharging strategies of the super-capacity energy storage system, thereby smoothing out power fluctuations in wind power generation and improving the overall performance and stability of super-capacity energy storage grid connection. The present invention improves the accuracy and robustness of wind speed prediction by optimizing the correlation problem between subsequences, providing strong support for the optimized operation of the super-capacity energy storage system.

[0020] Furthermore, the optimal subsequence obtained by decomposition is put into the IPSO-SVR (Improved Particle Swarm Optimization -Support Vector Regression, a data regression prediction method based on adaptive chaotic particle swarm algorithm to optimize support vector machine) model for prediction. The IPSO-SVR model combines the advantages of particle swarm optimization and support vector regression, which can further improve the accuracy and generalization ability of the prediction model. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are for explanation purposes only and are not intended to limit the scope of the present invention in any way. In addition, the shapes and proportional dimensions of the components in the drawings are only for illustration purposes to help understand the present invention, and are not intended to specifically limit the shapes and proportional dimensions of the components of the present invention. In the drawings: Figure 1 The present invention is a flow chart of an improved supercapacitor energy storage frequency modulation grid-connected method.

[0022] Figure 2This is a structural diagram of an improved supercapacitor energy storage frequency modulation grid-connected system of the present invention.

[0023] Figure 3 This is an electronic device diagram of an improved supercapacitor energy storage frequency modulation grid-connected method of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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 only 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 should fall within the scope of protection of the present invention.

[0025] Embodiment 1 See also Figure 1 , an improved supercapacitor energy storage frequency modulation grid-connected method, comprising the following steps: S1. Obtain the original wind speed sequence of the wind farm; The original wind speed series of the wind farm in S1 includes the historical wind speed data of the wind farm; S2. Decompose the original wind speed sequence of the wind farm using variational mode decomposition according to a preset number of decomposition layers to obtain a set of original wind speed subsequences; The number of subsequences in the original subsequence set in S2 is the number of decomposition levels of variational mode decomposition; S3, calculating the sequence correlation of all subsequences in the original wind speed subsequence set, and comparing the sequence correlations of all subsequences to obtain the minimum correlation; The calculation of sequence correlation in S3 is performed using the following formula:

[0026] in, D is the sequence correlation, For the i -1 sequence and i The sequence correlation of the sequences, j For the i -1 is the number of elements in the sequence that satisfy the correlation relationship, N is the i -1 sequence with the i The number of elements in the sequence, i is a positive integer, 1< i ≤30; The correlation relationship is b m =α a m ,in, am For the i -1 element in the sequence, b m For the i elements in the sequence, α is the correlation function, 0.5<α<1.5; The correlation calculation formula provided in this embodiment can quantify the similarity or correlation between two sequences, and provides a reliable basis for subsequent sequence correction and optimal subsequence selection.

[0027] S4, taking the subsequence corresponding to the minimum correlation as a reference subsequence, and adjusting the original subsequence set according to the sequence correlation to obtain an adjusted subsequence set based on the reference subsequence; In this embodiment, the selection of the reference subsequence is based on the minimum correlation, which means that the difference between the subsequence and other subsequences is the largest, and therefore contains the most unique information or noise. By correcting other subsequences based on this reference subsequence, the noise and redundant information in these subsequences can be removed or reduced, thereby improving the quality; the corrected subsequences are closer to the actual wind speed change characteristics, so the correlation between them will be enhanced. By calculating the correlation between the residual sequence of the corrected subsequence and the corresponding decomposition level, the quality of the subsequence under different decomposition levels can be evaluated, and the optimal decomposition level can be selected to balance the decomposition accuracy and calculation efficiency. In S4, the reference subsequence is used as the reference, and the original subsequence set is adjusted according to the sequence correlation to obtain the adjusted subsequence set, using the following adjustment formula:

[0028]

[0029] in, is the adjusted element in the subsequence that needs to be adjusted, sigmoid( ) represents the activation function, is the element in the subsequence that needs to be adjusted, represents the supplementary coefficient, K is the number of decomposition layers; D min is the sequence correlation of the benchmark subsequence, D x is the sequence correlation of the subsequence that needs to be adjusted, max( D 1 ,D 2 ,…,D K ) represents the maximum value of the sequence correlation of the original subsequence set, min( s 1 ,s 2 ,..,s i ,...,s N) represents the minimum element in the base subsequence, max( r 1 ,r 2 ,.., r i ,...,r N ) represents the maximum element in the subsequence that needs to be adjusted. N Represents the total number of elements in the sequence, avg( r 1 ,r 2 ,.., r i ,...,r N ) represents the average value of the elements in the subsequence that needs to be adjusted. e is a natural constant, π is a constant, i is an intermediate variable; The adjustment formula provided in this embodiment uses the sigmoid activation function and the supplementary coefficient to accurately adjust the subsequence that needs to be adjusted, and can optimize other subsequences based on the subsequence with the minimum correlation, that is, the benchmark subsequence, thereby reducing the impact of noise and outliers and improving the overall quality of the subsequence.

[0030] S5, comparing the original subsequence set with the adjusted subsequence set to obtain a residual sequence, and fusing the adjusted subsequence set with the residual sequence to obtain a modified subsequence set; In S5, the comparison between the original subsequence set and the adjusted subsequence set to obtain the residual sequence is performed using the following comparison formula:

[0031] in, is the residual sequence, is the original subsequence set, is the adjusted subsequence set, K is the number of decomposition layers, i is an intermediate variable; S6. Calculate the sequence correlation of the modified subsequence set to obtain the correlation at the current decomposition level; The sequence correlation of the modified subsequence set in S6 is the average of the correlations of the adjusted subsequence set and the residual sequence; The residual sequence calculation formula provided in this embodiment can quantify the difference between the adjusted subsequence and the original subsequence, which is helpful to evaluate the adjustment effect. At the same time, by calculating the average correlation between the adjusted subsequence and the residual sequence, the quality of the subsequences at different decomposition levels can be further evaluated, providing a basis for selecting the optimal subsequence.

[0032] S7, increasing the number of decomposition levels of variational mode decomposition, repeating steps S2-S6 until the number of decomposition levels of variational mode decomposition reaches a maximum, obtaining correlations under several decomposition levels, and comparing the correlations under several decomposition levels to obtain a minimum correlation value; S8, taking the modified subsequence set under the decomposition level corresponding to the minimum correlation value as the transformed subsequence, comparing the maximum correlation of the residual sequence in the transformed subsequence with the threshold value, and if the maximum correlation of the residual sequence in the transformed subsequence is less than the threshold value, retaining the residual sequence in the transformed subsequence to obtain the optimal wind speed subsequence; The selection criteria of the optimal subsequence in this embodiment not only considers the correlation of the adjusted subsequence, but also considers the correlation between its residual sequence and the remaining sequences. This selection method can ensure that the selected subsequence is both representative and independent to a certain extent, providing more accurate and stable input for subsequent wind speed prediction. Through the above processing and the selection of the optimal subsequence, the noise and outlier interference in the wind speed prediction can be significantly reduced, and the accuracy and stability of the prediction can be improved, which is of great significance for the optimization of the charging and discharging strategy of the ultra-capacity energy storage system and the improvement of the stability and reliability of the grid-connected system.

[0033] In S8, the maximum correlation of the residual sequence in the transformed subsequence is compared with the threshold. If the maximum correlation of the residual sequence in the transformed subsequence is greater than the threshold, the residual sequence in the transformed subsequence is decomposed using variational mode decomposition until the maximum correlation of the residual sequence in the transformed subsequence is less than the threshold, thereby obtaining the optimal wind speed subsequence. This embodiment can more effectively decompose the original wind speed sequence through the improved VMD and extract components of different frequencies and amplitudes, which reflect the inherent laws and characteristics of wind speed changes. By presetting the number of decomposition layers, a series of original subsequences can be obtained, which are more stable in frequency and amplitude, which is conducive to subsequent prediction analysis. By adjusting the original subsequence through the benchmark subsequence, the influence of noise and outliers can be further reduced, and the quality of the subsequence can be improved. By calculating the correlation of the corrected subsequence and the correlation of the corresponding decomposition layer, the similarities and differences between different subsequences can be evaluated, so as to select the optimal subsequence for prediction.

[0034] S9, using the wind speed optimal subsequence to predict the wind speed to obtain a wind speed prediction sequence, and adding the wind speed prediction sequences to obtain a wind speed prediction result for super-capacity energy storage grid connection; Taking the optimal subsequence of wind speed as the input of the prediction model can reduce the sensitivity of the model to noise and outliers, and improve the generalization and robustness of the model; based on the prediction results of the optimal subsequence of wind speed, the ultra-capacity energy storage system can more accurately determine when to charge and when to discharge, so as to maximize energy storage efficiency and reduce energy waste; accurate wind speed prediction helps the grid dispatching department to better plan power production and distribution, and reduce the imbalance between power supply and demand caused by wind speed changes.

[0035] The IPSO-SVR model is used for wind speed prediction in S9, and the optimal subsequences of wind speed are sequentially input into the IPSO-SVR model to obtain the wind speed prediction sequence.

[0036] Putting the optimal subsequence into IPSO-SVR for prediction can make full use of the advantages of IPSO-SVR in nonlinear fitting and generalization ability to improve the accuracy and stability of the prediction model. Since the optimal subsequence has removed some noise and outliers, the prediction model is less sensitive to input data, which improves the robustness of the model.

[0037] The method of using the improved VMD to decompose the original wind speed sequence to obtain the optimal subsequence, and sequentially putting the optimal subsequence into IPSO-SVR for prediction in this embodiment has significant benefits in improving the accuracy and stability of wind speed prediction, improving the reliability and economy of wind power grid-connected systems, and promoting the innovation and development of wind power technology. Through the improved VMD algorithm, the original wind speed sequence is more effectively decomposed into components of different frequencies and amplitudes, which reflect the inherent laws and characteristics of wind speed changes. This decomposition helps to remove noise and outliers, improve the quality of subsequences, and thus improve the accuracy of wind speed prediction.

[0038] Embodiment 2 See also Figure 2 , an improved supercapacitor energy storage frequency modulation grid-connected system, using an improved supercapacitor energy storage frequency modulation grid-connected method described in embodiment 1, comprising: The module for obtaining the original wind speed sequence is used to obtain the original wind speed sequence of the wind farm; A variational mode decomposition module is used to decompose the original wind speed sequence of the wind farm using variational mode decomposition according to a preset number of decomposition layers to obtain an original wind speed subsequence set; The sequence correlation calculation module is used to calculate the sequence correlation of all subsequences in the original wind speed subsequence set, and compare the sequence correlations of all subsequences to obtain the minimum correlation; An original subsequence set adjustment module is used to take the subsequence corresponding to the minimum correlation as a reference subsequence, and adjust the original subsequence set according to the sequence correlation to obtain an adjusted subsequence set based on the reference subsequence; A fusion residual sequence module is used to compare the original subsequence set with the adjusted subsequence set to obtain a residual sequence, and fuse the adjusted subsequence set with the residual sequence to obtain a modified subsequence set; A module for calculating sequence correlation is used to calculate the sequence correlation of the modified subsequence set to obtain the correlation at the current decomposition level; Repeating the decomposition module, used to increase the number of decomposition layers of the variational mode decomposition until the number of decomposition layers of the variational mode decomposition reaches a maximum, obtaining correlations under several decomposition layers, and comparing the correlations under several decomposition layers to obtain the minimum correlation value; The wind speed optimal subsequence module is used to compare and obtain the modified subsequence set under the decomposition layer number corresponding to the minimum correlation value as the transformed subsequence, and compare the maximum correlation of the residual sequence in the transformed subsequence with the threshold value. If the maximum correlation of the residual sequence in the transformed subsequence is less than the threshold value, the residual sequence in the transformed subsequence is retained to obtain the wind speed optimal subsequence; The wind speed prediction result obtaining module is used to use the wind speed optimal subsequence to perform wind speed prediction to obtain a wind speed prediction sequence, and the wind speed prediction sequence is added to obtain the wind speed prediction result of the super-capacity energy storage grid connection.

[0039] This embodiment provides an improved supercapacitor energy storage frequency modulation grid-connected system, which decomposes the original wind speed sequence through improved VMD variational mode decomposition to obtain more accurate and stable optimal subsequences. These subsequences can better reflect the volatility and periodicity of wind speed, thereby improving the accuracy of wind speed prediction; based on the high-precision wind speed prediction results, the supercapacitor energy storage system can more accurately determine when to charge and when to discharge to maximize energy storage efficiency and reduce energy waste; accurate wind speed prediction helps the power grid dispatching department to better plan power production and distribution, and reduce the imbalance between power supply and demand caused by wind speed changes; by optimizing the charging and discharging strategy of the supercapacitor energy storage system, the loss and maintenance cost of the energy storage equipment can be reduced, and the combination of high-precision wind speed prediction and optimized supercapacitor energy storage system can improve the reliability and economy of new energy power generation.

[0040] Embodiment 3 See also Figure 3 , an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, an improved supercapacitor energy storage frequency modulation and grid-connected method as described in Example 1 is implemented.

[0041] Embodiment 4 A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the improved supercapacitor energy storage frequency modulation and grid-connected method described in Example 1 is implemented.

[0042] Embodiment 5 This embodiment takes the preset decomposition level K=6 as an example to provide an improved supercapacitor energy storage frequency modulation grid-connected method, which specifically includes the following steps: Step 1: Decompose the original wind speed time series using the improved VMD proposed in this embodiment to obtain the optimal wind speed subsequence. Specifically, the detailed process steps of step 1 are as follows: This embodiment proposes the concept of relevance: Suppose there are two sequences: IMF a = and IMF b = , if there exists a function α(0.5<α<1.5) at this time, for IMF a Determine how many values ​​satisfy the relationship b m =α a m , assuming there are j values ​​that satisfy this relationship, then IMF a and IMF b The correlation is , , N is the number of elements in the sequence. m is the intermediate variable, and N is the number of elements in the sequence.

[0043] by =[1,2,3,4,5,6], =[2,1.1,7,0,2,8] is used as an example: ; It can be seen that among the six pairs of elements in the two sequences, two pairs of elements a2, b2 and a6, b6 satisfy the relationship b m =α a m , then IMF a and IMF b The correlation is D aVb =2 / 6=1 / 3.

[0044] VMD is used to decompose the original wind speed time series. The initial decomposition layer number K is set to 6, that is, the original wind speed time series x(t) is decomposed into 6 subsequences, IMF1, IMF2, IMF3, ..., IMF6; where IMF is the intrinsic mode component, IMF1 represents the first sequence, IMF2 represents the second sequence, IMF3 represents the second sequence, and IMF6 represents the sixth sequence; The correlation between these 6 subsequences and other sequences is calculated in turn, and then an average is calculated to obtain the sequence correlation; For example, to obtain the correlation between IMF1 and the other five subsequences: The correlation between IMF1 and IMF2 D 1V2 , the correlation between IMF1 and IMF3 1V3 , the correlation between IMF1 and IMF4 D 1V4 , the correlation between IMF1 and IMF5 1V5 , the correlation between IMF1 and IMF6 1V6 , then the serial correlation of IMF1 is D1=(D 1V2 +D 1V3 +D 1V4 +D 1V5 +D 1V6 ) / 5; Find the minimum value of sequence correlation D min The corresponding subsequence IMF min , the remaining 5 subsequences are adjusted in this direction as follows: Assume the subsequence with the smallest correlation IMF min =[s1,s2,..,s i ,...,s N ]The correlation is D min , The subsequence that needs to be adjusted is IMF x =[r1,r2,..,r i ,...,r N ]The correlation is D x ;

[0045] in, is the adjusted element in the subsequence that needs to be adjusted, r i is the element in the subsequence that needs to be adjusted, sigmoid( ) represents the activation function; D min is the sequence correlation of the benchmark subsequence, D x is the sequence correlation of the subsequence that needs to be adjusted; Represents the supplementary coefficient. The specific expression is:

[0046] max( D 1 ,D 2 ,…,D 6) represents the maximum value of the sequence correlation of the original subsequence set, min(s 1 ,s 2 ,.., s i ,...,s N ) represents the minimum element in the base subsequence, max( r 1 ,r 2 ,..,r i ,...,r N ) represents the maximum element in the subsequence that needs to be adjusted. N Indicates the number of elements in a sequence; i is an intermediate variable.

[0047] In addition to the IMF min The remaining subsequences are adjusted according to the above rules, and the new subsequence IMF1 is formed after adjustment. 、 ,IMF2 、 ,IMF3 、 ,...,IMF6 、 .

[0048] Next, solve the residual sequence R s :

[0049] Calculating IMF1 、 ,IMF2 、 ,IMF3 、 ,...,IMF6 、 , R s The correlation between the two, and then solve the average value to get the correlation D under K=6 k=6 . i is an intermediate variable.

[0050] Let K = 7 and repeat the above steps to get D k=7 , follow this logic until you get D k=30 , output D k=6 , D k=7 , …, D k=30 The minimum value D min The corresponding transformed subsequences are IMF1, IMF2, IMF3, .., IMF min ,R S ; For the minimum value D min The corresponding transformed subsequence is calculated as R S and [IMF1,IMF2,IMF3,..,IMF min ]; If the maximum correlation is less than 0.6, then R S reserve; Otherwise, for R S Continue to use VMD decomposition, initial K1=5, iterate K1=K1+1, until R S Every subsequence pair [IMF1,IMF2,IMF3,..,IMF min ] are all less than 0.6.

[0051] Step 2: Put the subsequences into the GRU (Gated Recurrent Unit) network IPSO-SVR model for prediction, using the advantages of the GRU network in processing time series data, and using the IPSO-SVR model to improve the prediction accuracy, and get the prediction result sequence; Step 3: Add the prediction results to get the final wind speed prediction result.

[0052] In order to further verify the advantages of this embodiment, the method of this embodiment and the general prediction method are used to predict the wind speed of the super-capacity energy storage grid using the test data set, and the MAPE (Mean Absolute Percentage Error) is compared. The comparison results are shown in Table 1: Table 1 Comparison results of MAPE of wind speed series predicted by the method of this embodiment and general prediction methods

[0053] It can be seen that the MAPE value generated by the wind speed prediction method provided in this embodiment to obtain the optimal wind speed sequence for super-capacity energy storage grid-connected system is significantly lower than that of the traditional wind speed prediction method. The lower MAPE value means that the wind speed prediction result is more accurate, thereby better guiding the charging and discharging strategy of the super-capacity energy storage grid-connected system.

[0054] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, 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. Furthermore, 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, read-only optical disk, optical storage, etc.) containing computer-usable program code.

[0055] 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 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.

[0056] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture 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.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention is described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not deviate from the spirit and scope of the present invention should be included in the protection scope of the present invention.

Claims

1. An improved supercapacitor energy storage frequency modulation grid-connected method, characterized in that: The following steps are involved: S1. Obtain the original wind speed sequence of the wind farm; S2. performing variational modal decomposition on the original wind speed sequence of the wind farm based on a preset number of decomposition layers to obtain a set of original wind speed subsequences; S3, calculating the sequence correlation of all subsequences in the original wind speed subsequence set, and comparing the sequence correlations of all subsequences to obtain the minimum correlation; S4, taking the subsequence corresponding to the minimum correlation as a reference subsequence, and adjusting the original subsequence set according to the sequence correlation to obtain an adjusted subsequence set based on the reference subsequence; S5, comparing the original subsequence set with the adjusted subsequence set to obtain a residual sequence, and fusing the adjusted subsequence set with the residual sequence to obtain a modified subsequence set; S6. Calculate the sequence correlation of the modified subsequence set to obtain the correlation at the current decomposition level; S7, return to S2, and increase the number of decomposition levels of variational mode decomposition, repeat steps S2-S6 until the number of decomposition levels of variational mode decomposition reaches the maximum, obtain the correlations under several decomposition levels, and compare the correlations under several decomposition levels to obtain the minimum correlation value; S8, taking the modified subsequence set under the decomposition level corresponding to the minimum correlation value as the transformed subsequence, comparing the maximum correlation of the residual sequence in the transformed subsequence with the threshold value, and if the maximum correlation of the residual sequence in the transformed subsequence is less than the threshold value, retaining the residual sequence in the transformed subsequence as the optimal wind speed subsequence; S9. Use the optimal wind speed subsequence to predict the wind speed to obtain a wind speed prediction sequence, and add the wind speed prediction sequences to obtain the wind speed prediction result of the super-capacity energy storage grid connection.

2. The improved supercapacitor energy storage frequency modulation grid-connected method according to claim 1, characterized in that: The original wind speed sequence of the wind farm includes historical wind speed data of the wind farm, and the number of subsequences in the original subsequence set in S2 is the number of decomposition levels of variational mode decomposition.

3. The improved supercapacitor energy storage frequency modulation grid-connected method according to claim 2, characterized in that: The calculation of the sequence correlation is performed using the following formula: in, D is the sequence correlation, For the i -1 sequence and i The sequence correlation of the sequences, j For the i -1 is the number of elements in the sequence that satisfy the correlation relationship, N is the i -1 sequence with the i The number of elements in the sequence, i is a positive integer, 1< i ≤30; The correlation relationship is b m =α a m ,in, a m For the i -1 element in the sequence, b m For the i elements in a sequence, α is the correlation function, 0.5<α<1.

5.

4. The improved supercapacitor energy storage frequency modulation grid-connected method according to claim 1, characterized in that: The reference subsequence is used as a reference, and the original subsequence set is adjusted according to the sequence correlation to obtain the adjusted subsequence set, using the following adjustment formula: in, is the adjusted element in the subsequence that needs to be adjusted, sigmoid( ) represents the activation function, is the element in the subsequence that needs to be adjusted, represents the supplementary coefficient; D min is the sequence correlation of the benchmark subsequence, D x is the sequence correlation of the subsequence that needs to be adjusted, max( D 1 ,D 2 ,…,D K ) represents the maximum value of the sequence correlation of the original subsequence set, K is the number of decomposition levels, min( s 1 ,s 2 ,..,s i ,...,s N ) represents the minimum element in the base subsequence, max( r 1 ,r 2 ,..,r i ,...,r N ) represents the maximum element in the subsequence that needs to be adjusted. N Represents the total number of elements in the sequence, avg( r 1 ,r 2 ,..,r i ,...,r N ) represents the average value of the elements in the subsequence that needs to be adjusted. e is a natural constant, π is a constant, i is an intermediate variable.

5. The improved supercapacitor energy storage frequency modulation grid-connected method according to claim 4, characterized in that: The comparison between the original subsequence set and the adjusted subsequence set to obtain a residual sequence is performed using the following comparison formula: in, is the residual sequence, is the original subsequence set, is the adjusted subsequence set, K is the number of decomposition layers, i is an intermediate variable; The sequence correlation of the modified subsequence set in S6 is the average value of the correlation between the adjusted subsequence set and the residual sequence.

6. The improved supercapacitor energy storage frequency modulation grid-connected method according to claim 1, characterized in that: The maximum correlation of the residual sequence in the transformed subsequence is compared with a threshold. If the maximum correlation of the residual sequence in the transformed subsequence is greater than the threshold, the residual sequence in the transformed subsequence is decomposed using variational mode decomposition until the maximum correlation of the residual sequence in the transformed subsequence is less than the threshold, thereby obtaining the optimal wind speed subsequence.

7. The improved supercapacitor energy storage frequency modulation grid-connected method according to claim 1, characterized in that: The wind speed prediction described in S9 adopts the IPSO-SVR model, and the optimal wind speed subsequences are sequentially input into the IPSO-SVR model to obtain the wind speed prediction sequence.

8. An improved supercapacitor energy storage frequency modulation grid-connected system, based on an improved supercapacitor energy storage frequency modulation grid-connected method as claimed in any one of claims 1 to 7, characterized in that: include: The module for obtaining the original wind speed sequence is used to obtain the original wind speed sequence of the wind farm; A variational mode decomposition module is used to perform variational mode decomposition on the original wind speed sequence of the wind farm based on a preset number of decomposition layers to obtain a set of original wind speed subsequences; The sequence correlation calculation module is used to calculate the sequence correlation of all subsequences in the original wind speed subsequence set, and compare the sequence correlations of all subsequences to obtain the minimum correlation; An original subsequence set adjustment module is used to take the subsequence corresponding to the minimum correlation as a reference subsequence, and adjust the original subsequence set according to the sequence correlation to obtain an adjusted subsequence set based on the reference subsequence; A fusion residual sequence module is used to compare the original subsequence set with the adjusted subsequence set to obtain a residual sequence, and fuse the adjusted subsequence set with the residual sequence to obtain a modified subsequence set; A module for calculating sequence correlation is used to calculate the sequence correlation of the modified subsequence set to obtain the correlation at the current decomposition level; A repeated decomposition module is used to increase the number of decomposition layers of the variational mode decomposition, repeat the variational mode decomposition until the number of decomposition layers of the variational mode decomposition reaches a maximum, obtain the correlations under several decomposition layers, and compare the correlations under several decomposition layers to obtain the minimum value of the correlation; The wind speed optimal subsequence module is used to compare and obtain the modified subsequence set under the decomposition layer number corresponding to the minimum correlation value as the transformed subsequence, and compare the maximum correlation of the residual sequence in the transformed subsequence with the threshold value. If the maximum correlation of the residual sequence in the transformed subsequence is less than the threshold value, the residual sequence in the transformed subsequence is retained as the wind speed optimal subsequence; The wind speed prediction result obtaining module is used to use the wind speed optimal subsequence to perform wind speed prediction to obtain a wind speed prediction sequence, and the wind speed prediction sequence is added to obtain the wind speed prediction result of the super-capacity energy storage grid connection.

9. 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, wherein when the processor executes the computer program, an improved supercapacitor energy storage frequency modulation and grid-connected method as described in any one of claims 1 to 7 is implemented.

10. 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, an improved supercapacitor energy storage frequency modulation and grid-connected method as described in any one of claims 1 to 7 is implemented.

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