Super capacitor energy storage frequency modulation grid connection method and system

Abstract

The invention discloses a super-capacitor energy storage frequency modulation grid connection method and system, and belongs to the technical field of wind speed prediction.The method comprises the steps that an original wind speed time sequence is decomposed according to the number of decomposition layers, corresponding items of similarity sequences of all subsequence pairs are added, a linearity sequence is obtained, regular sequence values of all the subsequences are added, and the linearity sequence is obtained; obtaining a general regularity sequence; using the linearity sequence and the general regularity sequence to calculate and obtain a fitness function; starting from the initial decomposition layer number, continuously increasing the value of the decomposition layer number to re-decompose and calculate fitness functions until the decomposition layer number reaches the maximum, and comparing the fitness functions to obtain an optimal sub-sequence set; and inputting the optimal sub-sequence set into a prediction network for prediction to obtain a prediction result of each sub-sequence in the optimal sub-sequence set, and adding the prediction results of all the sub-sequences in the optimal sub-sequence set to obtain the predicted wind speed of the super-capacity energy storage grid connection.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind speed prediction, and in particular relates to a 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 a decomposition algorithm to decompose the original wind speed sequence, puts the decomposed sequence into a neural network for prediction, and then superimposes the prediction results to obtain the final result. However, the existing decomposition methods have problems such as modal aliasing and a large degree of linear correlation between subsequences. These problems will lead to poor prediction accuracy, so a new method is urgently needed. Summary of the invention

[0004] The purpose of the present invention is to overcome the problems of modal aliasing and high linear correlation between subsequences in existing decomposition methods, and propose a 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 a supercapacitor energy storage frequency modulation grid-connected method, comprising the following steps: S1, decompose the original wind speed time series according to the number of decomposition layers to obtain several subsequences; S2. Select any two subsequences from a number of subsequences as a subsequence pair, compare the similarity between any two subsequences to obtain the similarity sequence of all subsequence pairs, add the corresponding items of the similarity sequence of all subsequence pairs to obtain a linear sequence; S3, calculating the regular sequence of each subsequence by a regular sequence calculation method, adding the regular sequence values ​​of all subsequences to obtain a total regular sequence; S4, using the linearity sequence and the total regularity sequence to calculate the fitness function; S5, starting from the initial decomposition level, continuously increase the decomposition level to re-decompose and calculate the fitness function until the decomposition level reaches the maximum, and compare the fitness function to obtain the optimal subsequence set; S6. Input the optimal subsequence set into the prediction network for prediction to obtain the prediction result of each subsequence in the optimal subsequence set, and add the prediction results of all subsequences in the optimal subsequence set to obtain the predicted wind speed of the super-capacity energy storage grid-connected.

[0006] Furthermore, the original wind speed time series is decomposed according to the number of decomposition layers in the following manner:

[0007] in, is the i-th subsequence, i is the intermediate variable, is the K-th subsequence, K is the number of decomposition layers, rand() is a random function, x(t)= is the original wind speed time series, t is the time, is the historical wind speed data, N is the total number of data points in the sequence, softplus(), sigmoid(), and tanh() are all activation functions; The softplus() activation function is:

[0008] The sigmoid() activation function is:

[0009] The tanh() activation function is:

[0010] Among them, x is the input of the activation function, e is a natural constant, It is an exponential function with e as base and x as exponent, ln(1+ ) is 1+ The natural logarithm of It is an exponential function with e as base and x as exponent; The sigmoid() activation function maps any real number to the interval (0,1), and the tanh() activation function maps any real number to the interval (-1,1) and is symmetric about the origin.

[0011] Further, each element in the similarity sequence of the subsequence pair is a ratio of corresponding elements in the subsequence pair; The comparison method for comparing the similarity between any two subsequences to obtain the similarity sequence of all subsequence pairs is: P 1 =a 1 ×q 1 ,P 2 =a2 ×q 2 ,P 3 =a 3 ×q 3 ,...,P N =a N ×q N Among them, S i-1 is the i-1th subsequence, q 1 ,q 2 ,q 3 ,..,q N is the element in the i-1th subsequence, S i is the ith subsequence, P 1 ,P 2 ,P 3 ,...,P N is the element in the i-th subsequence, S i-1 and S i is a subsequence pair, a 1 ,a 2 ,a 3 ,...,a N is the similarity sequence between the i-1th subsequence and the i-th subsequence, and N is the total number of data points in the sequence.

[0012] Furthermore, the regular sequence calculation method is:

[0013] Among them, rand() is a random function, N is the total number of data points in the sequence, K is the number of decomposition levels, S i is the ith subsequence, P 1 ,P 2 ,P 3 ,P N-1 ,...,P N is the element in the i-th subsequence, sigmoid() and tanh() are both activation functions; The sigmoid() activation function is:

[0014] The tanh() activation function is:

[0015] Among them, x is the input of the activation function, e is a natural constant, is an exponential function with e as base and x as exponent. It is an exponential function with e as base and x as exponent; The sigmoid() activation function maps any real number to the interval (0,1), and the tanh() activation function maps any real number to the interval (-1,1) and is symmetric about the origin.

[0016] Furthermore, the fitness function is calculated by using the linearity sequence and the total regularity sequence as follows:

[0017] in, is the fitness function, β(x) is the linear sequence, c 1 ,c 2 ,c 3 ,..,c N is an element in the linear sequence, α(x) is the total regularity sequence, d 1 ,d 2 ,d 3 ,..,d N is the element in the total regularity sequence, and N is the total number of data points in the sequence.

[0018] Furthermore, the comparison fitness function obtains the optimal subsequence set, specifically: In each iteration, the fitness functions at different decomposition levels are compared; The decomposition layer value with the largest fitness function is the optimal decomposition layer value, and the subsequence set of the optimal decomposition layer value is the optimal subsequence set.

[0019] Furthermore, the prediction network adopts a GRU network.

[0020] In a second aspect, the present invention provides a supercapacitor energy storage frequency modulation grid-connected system, based on the supercapacitor energy storage frequency modulation grid-connected method, comprising: The decomposition module is used to decompose the original wind speed time series according to the number of decomposition layers to obtain several subsequences; A linearity sequence obtaining module is used to select any two subsequences from a number of subsequences as a subsequence pair, compare the similarity between any two subsequences to obtain similarity sequences of all subsequence pairs, and add corresponding items of the similarity sequences of all subsequence pairs to obtain a linearity sequence; A total regularity sequence obtaining module is used to calculate the regularity sequence of each subsequence by a regularity sequence calculation method, and add the regularity sequence values ​​of all subsequences to obtain the total regularity sequence; A fitness function obtaining module is used to calculate the fitness function using the linearity sequence obtained in the linearity sequence obtaining module and the total regularity sequence obtained in the total regularity sequence obtaining module; The module for obtaining the optimal subsequence set is used to start from the initial decomposition layer number, continuously increase the value of the decomposition layer number, re-decompose and calculate the fitness function until the decomposition layer number reaches the maximum, and compare the fitness function to obtain the optimal subsequence set; The predicted wind speed obtaining module is used to input the optimal subsequence set into the prediction network for prediction to obtain the prediction result of each subsequence in the optimal subsequence set, and add the prediction results of all subsequences in the optimal subsequence set to obtain the predicted wind speed of super-capacity energy storage grid connection.

[0021] 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 supercapacitor energy storage frequency modulation and grid-connected method when executing the computer program.

[0022] 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 supercapacitor energy storage frequency modulation and grid-connected method is implemented.

[0023] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention proposes a supercapacitor energy storage frequency modulation grid-connected method, which decomposes the original wind speed sequence into multiple subsequences and further optimizes these subsequences to capture the changing characteristics of different frequencies and scales in the wind speed data. This multi-scale analysis method helps to more accurately simulate the complex dynamics of wind speed and effectively avoid modal aliasing. The optimal decomposition can reduce the linear correlation between subsequences, improve the accuracy of prediction, and jointly improve the accuracy and reliability of wind speed prediction, providing more accurate wind speed prediction data for supercapacitor energy storage grid connection. Through the decomposition and reconstruction process, the impact of adverse factors such as noise and non-stationarity on the prediction results can be reduced, making the model more robust. Even in the case of missing or outliers in the wind speed data, the good prediction performance is maintained. The present invention can not only improve the accuracy and reliability of wind speed prediction, but also optimize the operation strategy of the supercapacitor energy storage system, reduce operating costs, and promote the utilization of renewable energy and the construction of smart grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] 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 a supercapacitor energy storage frequency modulation grid-connected method.

[0025] Figure 2This is a structural diagram of a supercapacitor energy storage frequency modulation grid-connected system of the present invention.

[0026] Figure 3 This is an electronic device diagram of a supercapacitor energy storage frequency modulation grid-connected method of the present invention. DETAILED DESCRIPTION

[0027] 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.

[0028] Embodiment 1 See also Figure 1 , a supercapacitor energy storage frequency modulation grid-connected method, comprising the following steps: S1, decompose the original wind speed time series according to the number of decomposition layers to obtain several subsequences; As described in S1, the original wind speed time series is decomposed according to the number of decomposition layers in the following way:

[0029] in, is the i-th subsequence, i is the intermediate variable, is the K-th subsequence, K is the number of decomposition layers, rand() is a random function, x(t)= is the original wind speed time series, t is the time, is the historical wind speed data, N is the total number of data points in the sequence, softplus(), sigmoid(), and tanh() are all activation functions; The softplus() activation function is:

[0030] The sigmoid() activation function is:

[0031] The tanh() activation function is:

[0032] Among them, x is the input of the activation function, e is a natural constant, It is an exponential function with e as base and x as exponent, ln(1+ ) is 1+ The natural logarithm of It is an exponential function with e as base and x as exponent; The sigmoid() activation function maps any real number to the interval (0,1), and the tanh() activation function maps any real number to the interval (-1,1) and is symmetric about the origin.

[0033] In this embodiment, the original wind speed sequence is obtained. The wind speed data is obtained from a meteorological station, a wind power plant or a public data set. The wind speed data is cleaned, such as checking and processing missing values, outliers, etc. Decomposition decomposes the wind speed sequence into equal subsequences. The purpose of decomposition is to better understand the composition of the wind speed, and different parts may be processed or modeled differently. Optimal decomposition further analyzes or processes the decomposed subsequences to select or generate a set of subsequences that are most useful for prediction. It includes feature selection techniques such as screening based on statistical characteristics and principal component analysis. The optimal one depends on the specific requirements of the prediction task and the performance evaluation criteria.

[0034] S2. Any two subsequences in a number of subsequences are a subsequence pair. The similarity between any two subsequences is compared to obtain a similarity sequence of the subsequence pair. The corresponding items of the similarity sequences of all subsequence pairs are added together to obtain a linear sequence. Each element in the similarity sequence of the subsequence pair in S2 is the ratio of the corresponding element in the subsequence pair; The comparison method for comparing the similarity between any two subsequences in S2 to obtain the similarity sequence of all subsequence pairs is: P 1 =a 1 ×q 1 ,P 2 =a 2 ×q 2 ,P 3 =a 3 ×q 3 ,...,P N =a N ×q N Among them, S i-1 is the i-1th subsequence, q 1 ,q 2 ,q 3 ,..,q N is the element in the i-1th subsequence, S i is the ith subsequence, P 1 ,P 2 ,P 3 ,...,P N is the element in the i-th subsequence, S i-1 and S iis a subsequence pair, a 1 ,a 2 ,a 3 ,...,a N is the similarity sequence between the i-1th subsequence and the i-th subsequence, and N is the total number of data points in the sequence.

[0035] S3, calculating the regular sequence of each subsequence by a regular sequence calculation method, adding the regular sequence values ​​of all subsequences to obtain a total regular sequence; The calculation method of the regular sequence described in S3 is:

[0036] Among them, rand() is a random function, N is the total number of data points in the sequence, K is the number of decomposition levels, S i is the ith subsequence, P 1 ,P 2 ,P 3 ,P N-1 ,...,P N is the element in the i-th subsequence, sigmoid() and tanh() are both activation functions; The sigmoid() activation function is:

[0037] The tanh() activation function is:

[0038] Among them, x is the input of the activation function, e is a natural constant, is an exponential function with e as base and x as exponent. It is an exponential function with e as base and x as exponent; The sigmoid() activation function maps any real number to the interval (0,1), and the tanh() activation function maps any real number to the interval (-1,1) and is symmetric about the origin.

[0039] S4, using the linearity sequence obtained in S2 and the total regularity sequence obtained in S3 to calculate the fitness function; The fitness function calculated by using the linearity sequence and the total regularity sequence described in S4 is specifically:

[0040] in, is the fitness function, β(x) is the linear sequence, c 1 ,c 2 ,c 3 ,..,c Nis an element in the linear sequence, α(x) is the total regularity sequence, d 1 ,d 2 ,d 3 ,..,d N is the element in the total regularity sequence, and N is the total number of data points in the sequence.

[0041] S5, starting from the initial decomposition level, continuously increase the decomposition level to re-decompose and calculate the fitness function until the decomposition level reaches the maximum, and compare the fitness function to obtain the optimal subsequence set; In S5, the fitness function is compared to obtain the optimal subsequence set, which is: In each iteration, the fitness functions at different decomposition levels are compared; The decomposition layer value with the largest fitness function is the optimal decomposition layer value, and the subsequence set of the optimal decomposition layer value is the optimal subsequence set.

[0042] The calculation process of the fitness function in this embodiment helps to evaluate the advantages and disadvantages of subsequence sets under different decomposition levels. By continuously optimizing the decomposition level, the system can find the decomposition method that best suits the current wind speed data, thereby improving the adaptability and flexibility of the system.

[0043] S6. Input the optimal subsequence set into the prediction network for prediction to obtain the prediction result of each subsequence in the optimal subsequence set, and add the prediction results of all subsequences in the optimal subsequence set to obtain the predicted wind speed of the super-capacity energy storage grid-connected.

[0044] The prediction network described in S6 adopts a GRU network.

[0045] In this embodiment, the GRU (Gated Recurrent Unit) network is a variant of a recurrent neural network suitable for processing sequence data. The optimal subsequence set is used as the input feature and the corresponding part of the original wind speed sequence is used as the target output to train the GRU network. The network structure, learning rate, optimizer and other hyperparameters such as the number of layers and hidden units are adjusted by cross-validation and other methods to improve the model performance.

[0046] Use the trained GRU model to predict new wind speed data and obtain a predicted wind speed sequence. Based on the predicted wind speed sequence, its impact on the power grid can be further analyzed, especially the synergy with the ultra-capacity energy storage system. The predicted wind speed can help determine the charging and discharging strategy of the energy storage system to optimize energy utilization and grid stability. Using the GRU network for prediction can make full use of the long-term dependencies in time series data and further improve the prediction accuracy.

[0047] The high-precision predicted wind speed obtained by the method of this embodiment can provide strong support for the charging and discharging strategy, power allocation and scheduling of the super-capacity energy storage system, thereby optimizing the operating efficiency and economic benefits of the system. The real-time and accuracy of the predicted wind speed can help the super-capacity energy storage system better respond to grid demand and improve the stability and reliability of the grid. Accurate wind speed prediction can reduce the spare capacity demand of the super-capacity energy storage system and reduce the operating cost of the system. By optimizing the charging and discharging strategy of the energy storage system, the energy utilization efficiency can be further improved and energy waste can be reduced.

[0048] Embodiment 2 See also Figure 2 A supercapacitor energy storage frequency modulation grid-connected system, using a supercapacitor energy storage frequency modulation grid-connected method described in Example 1, comprising: The decomposition module is used to decompose the original wind speed time series according to the number of decomposition layers to obtain several subsequences; A linearity sequence obtaining module is used to select any two subsequences from a number of subsequences as a subsequence pair, compare the similarity between any two subsequences to obtain similarity sequences of all subsequence pairs, and add corresponding items of the similarity sequences of all subsequence pairs to obtain a linearity sequence; A total regularity sequence obtaining module is used to calculate the regularity sequence of each subsequence by a regularity sequence calculation method, and add the regularity sequence values ​​of all subsequences to obtain the total regularity sequence; A fitness function obtaining module is used to calculate the fitness function using the linearity sequence obtained in the linearity sequence obtaining module and the total regularity sequence obtained in the total regularity sequence obtaining module; The module for obtaining the optimal subsequence set is used to start from the initial decomposition layer number, continuously increase the value of the decomposition layer number, re-decompose and calculate the fitness function until the decomposition layer number reaches the maximum, and compare the fitness function to obtain the optimal subsequence set; The predicted wind speed obtaining module is used to input the optimal subsequence set into the prediction network for prediction to obtain the prediction result of each subsequence in the optimal subsequence set, and add the prediction results of all subsequences in the optimal subsequence set to obtain the predicted wind speed of super-capacity energy storage grid connection.

[0049] The super-capacity energy storage grid-connected system of this embodiment decomposes the original wind speed time series into multiple sub-sequences, and considers the similarities and regularities between these sub-sequences. The system can capture the characteristics of wind speed changes more carefully, allowing the system to analyze wind speed data from multiple angles and levels, thereby improving the accuracy and reliability of wind speed prediction, and can improve the accuracy of wind speed prediction and enhance the adaptability of the system. By providing accurate wind speed prediction results, it helps the super-capacity energy storage system to better cooperate with the power grid, thereby improving the stability and efficiency of the entire power system. By predicting wind speed changes in advance, the system can dispatch wind power generation equipment more effectively, reduce energy waste, and improve resource utilization efficiency. It can reduce power grid failures and downtime caused by wind speed fluctuations, reduce operation and maintenance costs, and improve the overall reliability and safety of the power grid.

[0050] 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, a supercapacitor energy storage frequency modulation and grid-connected method as described in Example 1 is implemented.

[0051] Embodiment 4 A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a supercapacitor energy storage frequency modulation and grid-connected method as described in Example 1 is implemented.

[0052] Embodiment 5 This embodiment takes the decomposition number K=5 as an example to implement a supercapacitor energy storage frequency modulation grid-connected method, and the specific steps are as follows: Step 1: First, decompose the original wind speed time series x(t) to obtain the subsequence S 1 ,S 2 ,..,S K .

[0053] Assume that the original wind speed time series x(t)=[x 1 ,x 2 ,x 3 ,...,x N ], the number of decomposition layers is K, and the specific decomposition method is:

[0054] in, is the i-th subsequence, i is the intermediate variable, is the K-th subsequence, K is the number of decomposition layers, rand() is a random function, x(t)= is the original wind speed time series, t is the time, is the historical wind speed data, N is the total number of data points in the sequence, softplus(), sigmoid(), and tanh() are all activation functions; The softplus() activation function is:

[0055] The sigmoid() activation function is:

[0056] The tanh() activation function is:

[0057] Among them, x is the input of the activation function, e is a natural constant, It is an exponential function with e as base and x as exponent, ln(1+ ) is 1+ The natural logarithm of It is an exponential function with e as base and x as exponent; The sigmoid() activation function maps any real number to the interval (0,1), and the tanh() activation function maps any real number to the interval (-1,1) and is symmetric about the origin.

[0058] Step 2: Based on subsequence Generates a linear sequence β(x).

[0059] Let the i-1th subsequence S i-1 =[q 1 ,q 2 ,q 3 ,..,q N ] and the i-th subsequence S i =[P 1 ,P 2 ,P 3 ,...,P N ]; If the sequence [a 1 ,a 2 ,a 3 ,...,a N ]Satisfies the relation P 1 =a 1 ×q 1 ,P 2 =a 2 ×q 2 ,P 3 =a 3 ×q 3 ,...,PN =a N ×q N , Then the sequence [a 1 ,a 2 ,a 3 ,...,a N ] is the i-1th subsequence S i-1 and the i-th subsequence S i similarity sequence.

[0060] Subsequence S 1 ,S 2 ,..,S K There are K(K-1) / 2 similarity sequences between each pair, where K is the number of decomposition levels. Adding up each of these similarity sequences gives the linear sequence β(x).

[0061] Step 3: Based on the subsequence S 1 ,S 2 ,..,S K Calculate the total regularity sequence α(x) Let the ith subsequence S i =[P 1 ,P 2 ,P 3 ,...,P N ], the regular sequence of this sequence is:

[0062] Among them, rand() is a random function, N is the total number of data points in the sequence, K is the number of decomposition levels, S i is the ith subsequence, P 1 ,P 2 ,P 3 ,P N-1 ,...,P N is the element in the i-th subsequence, sigmoid() and tanh() are both activation functions; Calculate the regular sequence of each subsequence and then sum them up to get the total regular sequence α(x).

[0063] Step 4: Get the fitness function F(x).

[0064] Assume the linear sequence β(x)=[c 1 ,c 2 ,c 3 ,..,c N ], the total regularity sequence α(x)=[d 1 ,d 2 ,d 3 ,..,d N], then the fitness function:

[0065] in, is the fitness function, β(x) is the linear sequence, c 1 ,c 2 ,c 3 ,..,c N is an element in the linear sequence, α(x) is the total regularity sequence, d 1 ,d 2 ,d 3 ,..,d N is the element in the total regularity sequence, and N is the total number of data points in the sequence.

[0066] Step 5: Continuously iterate and update the decomposition, and output the subsequence S corresponding to the maximum value of the fitness function 1 ,S 2 ,..,S K .

[0067] Initially generate the decomposition level K = 5, then generate a subsequence to obtain a fitness function F(x), then decompose the level K = 6, and generate its corresponding fitness function F(x), until the maximum decomposition level K = 30 is reached. Output the subsequence corresponding to the largest fitness function among all fitness functions, which is the optimal subsequence used to predict wind speed.

[0068] Step 6: Put the optimal subsequence obtained in step 5 into the GRU network for prediction, and finally add up the prediction results to obtain the predicted wind speed of the super-capacity energy storage grid-connected.

[0069] 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 results are shown in Table 1 below: Table 1 Comparison of MAPE of wind speed prediction by the method of this embodiment and general prediction method

[0070] 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.

[0071] 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, optical storage, etc.) containing computer-usable program code.

[0072] 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.

[0073] 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 comprising 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.

[0074] 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. A supercapacitor energy storage frequency modulation grid-connected method, characterized in that: The following steps are involved: S1, decompose the original wind speed time series according to the number of decomposition layers to obtain several subsequences; S2. Select any two subsequences from a number of subsequences as a subsequence pair, compare the similarity between any two subsequences to obtain the similarity sequence of all subsequence pairs, add the corresponding items of the similarity sequence of all subsequence pairs to obtain a linear sequence; S3, calculating the regular sequence of each subsequence by a regular sequence calculation method, adding the regular sequence values ​​of all subsequences to obtain a total regular sequence; S4, using the linearity sequence and the total regularity sequence to calculate the fitness function; S5, starting from the initial decomposition level, continuously increase the decomposition level to re-decompose and calculate the fitness function until the decomposition level reaches the maximum, and compare the fitness function to obtain the optimal subsequence set; S6. Input the optimal subsequence set into the prediction network for prediction to obtain the prediction result of each subsequence in the optimal subsequence set, and add the prediction results of all subsequences in the optimal subsequence set to obtain the predicted wind speed of the super-capacity energy storage grid-connected.

2. A supercapacitor energy storage frequency modulation grid-connected method according to claim 1, characterized in that: The original wind speed time series is decomposed according to the number of decomposition layers in the following manner: in, is the i-th subsequence, i is the intermediate variable, is the K-th subsequence, K is the number of decomposition layers, rand() is a random function, x(t)= is the original wind speed time series, t is the time, is the historical wind speed data, N is the total number of data points in the sequence, softplus(), sigmoid(), and tanh() are all activation functions; The softplus() activation function is: The sigmoid() activation function is: The tanh() activation function is: Among them, x is the input of the activation function, e is a natural constant, It is an exponential function with e as base and x as exponent, ln(1+ ) is 1+ The natural logarithm of It is an exponential function with e as base and x as exponent; The sigmoid() activation function maps any real number to the interval (0,1), and the tanh() activation function maps any real number to the interval (-1,1) and is symmetric about the origin.

3. A supercapacitor energy storage frequency modulation grid-connected method according to claim 1, characterized in that: Each element in the similarity sequence of the subsequence pair is the ratio of the corresponding elements in the subsequence pair; The comparison method for comparing the similarity between any two subsequences to obtain the similarity sequence of all subsequence pairs is: P1=a1×q1,P2=a2×q2,P3=a3×q3,...,P N =a N ×q N Among them, S i-1 is the i-1th subsequence, q1,q2,q3,..,q N is the element in the i-1th subsequence, S i is the i-th subsequence, P1, P2, P3, ..., P N is the element in the i-th subsequence, S i-1 and S i is a subsequence pair, a1,a2,a3,...,a N is the similarity sequence between the i-1th subsequence and the i-th subsequence, and N is the total number of data points in the sequence.

4. A supercapacitor energy storage frequency modulation grid-connected method according to claim 1, characterized in that: The regular sequence calculation method is: Among them, rand() is a random function, N is the total number of data points in the sequence, K is the number of decomposition levels, S i is the i-th subsequence, P1, P2, P3, P N-1 ,...,P N is the element in the i-th subsequence, sigmoid() and tanh() are both activation functions; The sigmoid() activation function is: The tanh() activation function is: Among them, x is the input of the activation function, e is a natural constant, It is an exponential function with e as base and x as exponent. It is an exponential function with e as base and x as exponent; The sigmoid() activation function maps any real number to the interval (0,1), and the tanh activation function maps any real number to the interval (-1,1) and is symmetric about the origin.

5. A supercapacitor energy storage frequency modulation grid-connected method according to claim 1, characterized in that: The fitness function calculated by using the linearity sequence and the total regularity sequence is specifically: in, is the fitness function, β(x) is the linear sequence, c1,c2,c3,..,c N is an element in the linear sequence, α(x) is the total regularity sequence, d1,d2,d3,..,d N is the element in the total regularity sequence, and N is the total number of data points in the sequence.

6. A supercapacitor energy storage frequency modulation grid-connected method according to claim 1, characterized in that: The comparison fitness function obtains the optimal subsequence set, specifically: In each iteration, the fitness functions at different decomposition levels are compared; The decomposition layer value with the largest fitness function is the optimal decomposition layer value, and the subsequence set of the optimal decomposition layer value is the optimal subsequence set.

7. A supercapacitor energy storage frequency modulation grid-connected method according to claim 1, characterized in that: The prediction network adopts the GRU network.

8. A supercapacitor energy storage frequency modulation grid-connected system, based on a supercapacitor energy storage frequency modulation grid-connected method as claimed in any one of claims 1 to 7, characterized in that: include: The decomposition module is used to decompose the original wind speed time series according to the number of decomposition layers to obtain several subsequences; A linearity sequence obtaining module is used to select any two subsequences from a number of subsequences as a subsequence pair, compare the similarity between any two subsequences to obtain similarity sequences of all subsequence pairs, and add corresponding items of the similarity sequences of all subsequence pairs to obtain a linearity sequence; A total regularity sequence obtaining module is used to calculate the regularity sequence of each subsequence by a regularity sequence calculation method, and add the regularity sequence values ​​of all subsequences to obtain the total regularity sequence; A fitness function obtaining module is used to calculate the fitness function using the linearity sequence obtained in the linearity sequence obtaining module and the total regularity sequence obtained in the total regularity sequence obtaining module; The module for obtaining the optimal subsequence set is used to start from the initial decomposition layer number, continuously increase the value of the decomposition layer number, re-decompose and calculate the fitness function until the decomposition layer number reaches the maximum, and compare the fitness function to obtain the optimal subsequence set; The predicted wind speed obtaining module is used to input the optimal subsequence set into the prediction network for prediction to obtain the prediction result of each subsequence in the optimal subsequence set, and add the prediction results of all subsequences in the optimal subsequence set to obtain the predicted wind speed of 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, a 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, a 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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