Energy storage frequency modulation instruction prediction method and system based on two-stage method

Abstract

The invention relates to the field of energy storage frequency modulation prediction, and discloses an energy storage frequency modulation instruction prediction method and system based on a two-stage method, and the method comprises the steps: obtaining an energy storage frequency modulation instruction, and carrying out the variational mode decomposition; judging mutual attraction or mutual repulsion of the subsequences according to a variation mode decomposition result; if it is judged that the two subsequences attract each other, sequence fusion operation is carried out, and if it is judged that the two subsequences repel each other, sequence generation operation is carried out; obtaining a new sub-sequence set through sequence fusion operation and sequence generation operation; and respectively inputting the sequences in the new sub-sequence set into the recurrent neural network to obtain a final prediction result of the energy storage frequency modulation instruction. By optimizing the frequency modulation instruction in advance, the energy storage system can respond to the frequency change requirement more quickly and accurately, the frequency modulation efficiency and economic benefits of the power system are remarkably improved, and meanwhile the stability of the system and the operation efficiency of the power plant are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage frequency modulation prediction, and in particular to an energy storage frequency modulation instruction prediction method and system based on a two-level method. Background Art

[0002] Frequency regulation in the power system is a key link to ensure the stable operation of the power grid. Traditional thermal power units have a slow response speed and are difficult to quickly adapt to load changes and maintain the stability of the power grid frequency. In order to improve this situation, a hybrid energy storage system (such as a combination of supercapacitors and lithium batteries) is usually used to assist thermal power units in frequency regulation. In this mode, the hybrid energy storage system is responsible for processing the difference between the frequency modulation command and the output of the thermal power unit, where the high-frequency component is borne by the supercapacitor with a fast response speed, and the low-frequency component is handled by the lithium battery with a higher energy density. However, in actual operation, the transmission of the signal (frequency modulation command is transmitted to the supercapacitor / lithium battery) takes time, and the supercapacitor or lithium battery itself responds. This will lead to inevitable time delays, which not only affects the system's immediate response capability, but also has a negative impact on the economic benefits of the power plant.

[0003] In order to solve this problem, researchers have developed a prediction algorithm to predict the changing trend of the frequency modulation instructions in advance, so that the energy storage system can act in advance, thereby improving the effect of frequency regulation and economic benefits. One of the traditional prediction methods is to use variational mode decomposition (VMD) to decompose the original frequency modulation instruction sequence into multiple subsequences, predict these subsequences separately, and finally synthesize the prediction results. Although this method helps to improve the prediction accuracy, it also has some limitations: for example, similar subsequences may lead to repeated calculations; at the same time, there may be mutual exclusion between some subsequences, resulting in false components, and new subsequences need to be generated to compensate. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for predicting energy storage frequency modulation instructions based on a two-level method to solve the problem that the traditional decomposition and prediction process fails to effectively deal with the similarities and exclusivity between subsequences, resulting in possible repeated calculations and false components, affecting the prediction accuracy and timeliness.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for predicting energy storage frequency modulation instructions based on a two-level method, comprising: Obtain energy storage frequency modulation instructions and perform variational mode decomposition; Determining whether subsequences attract or repel each other based on the result of the variational mode decomposition; If it is determined that the two subsequences attract each other, a sequence fusion operation is performed to merge the two subsequences into a new sequence. If it is determined that the two subsequences repel each other, a sequence generation operation is performed to generate a new sequence based on the two mutually repelling subsequences. A new subsequence set is obtained through the sequence fusion operation and the sequence generation operation; The sequences in the new subsequence set are respectively input into the recursive neural network to obtain the final prediction result of the energy storage frequency modulation instruction.

[0007] As a preferred solution of the energy storage frequency modulation instruction prediction method based on the two-level method described in the present invention, the judgment of whether the subsequences attract or repel each other includes: If the subsequence IMF r =[X r1 ,X r2 ,X r3 ,..X ri ..,X rN ] and subsequence IMF j =[X j1 ,X j2 ,X j3 ,..X ji ..,X jN ] If the sequence attracts each other, the subsequence IMF is determined. r and IMF j Mutually attract each other; if the judgment condition of mutual repulsion of sequences is met, the subsequence IMF is determined r and IMF j mutually exclusive; The judgment condition for the sequences to attract each other is: and , Where N represents the total length of the energy storage frequency modulation sequence, Represents the subsequence IMF j The experimental group error, Represents the subsequence IMF r The experimental group error of The conditions for determining whether the sequences are mutually exclusive are: and .

[0008] As a preferred solution of the energy storage frequency modulation instruction prediction method based on the two-level method described in the present invention, the judgment of whether the subsequences attract or repel each other also includes: If a subsequence IMF q Both with subsequence IMFj Mutually attract each other and subsequence IMF r Mutually exclusive, then compare the subsequences IMF q With subsequence IMF j The attraction between the subsequences and IMF q With subsequence IMF r The magnitude of the repulsive force between them; If attraction Greater than repulsion , then the subsequence IMF is identified q With subsequence IMF j Mutually attract each other, otherwise the subsequence IMF is recognized q With subsequence IMF r Mutually exclusive.

[0009] As a preferred solution of the energy storage frequency modulation instruction prediction method based on the two-level method described in the present invention, the sequence fusion operation includes: If we identify the subsequence IMF r and IMF j attract each other, then perform sequence fusion operation to merge the subsequences IMF r and IMF j Fusion into a new sequence IMF rj , where the new sequence IMF rj The value of the element at position i in It is expressed as: , in, represents the coefficient of the preceding term, Represents the coefficient of the latter term.

[0010] As a preferred solution of the energy storage frequency modulation instruction prediction method based on the two-level method described in the present invention, the sequence generation operation includes: If we identify the subsequence IMF r and IMF j mutually exclusive, then a sequence generation operation is performed, according to the subsequence IMF r and IMF j Generate a new sequence IMF r-j , where the new sequence IMF r-j The value of the element at position i in It is expressed as: .

[0011] As a preferred solution of the energy storage frequency modulation instruction prediction method based on the two-level method described in the present invention, the acquisition process of the new subsequence set includes: The IMF sequence after the variational mode decomposition is traversed using the two-level method 1 ,IMF 2 ,IMF 3 ,...,IMF K , and judge whether the subsequences attract or repel each other. If the subsequences attract each other, perform sequence fusion operation; if the subsequences repel each other, perform sequence generation operation; keep repeating the judgment until the final sequence IMF 1 、 ,IMF 2 、 ,IMF 3 、 ,...,IMF S 、 There are neither mutually attractive sequences nor mutually repelling sequences.

[0012] As a preferred solution of the energy storage frequency modulation instruction prediction method based on the two-level method described in the present invention, the final prediction result of the energy storage frequency modulation instruction includes: Each sequence in the new subsequence set is input into the recursive neural network respectively, the value of the corresponding unknown group is output, and then the value of the unknown group predicted by each subsequence is added to obtain the final prediction result of the energy storage frequency modulation instruction.

[0013] In a second aspect, the present invention provides a system for predicting energy storage frequency modulation instructions based on a two-level method, comprising: The instruction decomposition module is used to obtain the energy storage frequency modulation instruction and perform variational mode decomposition; A sequence processing module, used for judging whether the subsequences are mutually attractive or mutually exclusive according to the result of the variational mode decomposition, and performing a sequence fusion operation if the subsequences are mutually attractive, and performing a sequence generation operation if the subsequences are mutually exclusive; a new subsequence set is obtained through the sequence fusion operation and the sequence generation operation; The instruction prediction module is used to input the sequences in the new subsequence set into the recursive neural network respectively to obtain the final prediction result of the energy storage frequency modulation instruction.

[0014] In a third aspect, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the energy storage frequency modulation instruction prediction method based on the two-level method are implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the energy storage frequency modulation instruction prediction method based on the two-level method.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a method and system for predicting energy storage frequency modulation instructions based on the two-level method, which intelligently pre-processes the energy storage frequency modulation instructions by introducing variational mode decomposition combined with an innovative processing method of the two-level rule, implements a fusion strategy for mutually attractive subsequences to reduce redundancy, and creates new independent sequences for mutually exclusive subsequences to eliminate the influence of false components, and finally uses a recursive neural network to achieve accurate prediction; the present invention can not only effectively identify and process the similarity and repulsion between subsequences, reduce redundant calculations and eliminate false components, thereby improving the accuracy and stability of the prediction model; and by optimizing the frequency modulation instructions in advance, the energy storage system can respond to frequency change requirements more quickly and accurately, significantly improving the frequency modulation efficiency and economic benefits of the power system, while enhancing the stability of the system and the operating efficiency of the power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 It is a schematic diagram of the overall process logic of the energy storage frequency modulation instruction prediction method based on the two-stage method according to an embodiment of the present invention; Figure 2 A schematic diagram of the prediction flow of a method for predicting energy storage frequency modulation instructions based on a two-stage method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0020] Example 1 Reference Figure 1-Figure 2As an embodiment of the present invention, a method for predicting energy storage frequency regulation instructions based on a two-level method is provided, aiming to more effectively utilize the energy storage system, improve the accuracy of frequency regulation instruction prediction, and thus enhance the stability of the power system and the operating efficiency of the power plant. Figure 1 The specific steps shown include: S100: Obtain energy storage frequency modulation instructions and perform variational mode decomposition; S200: judging whether the subsequences are mutually attractive or mutually repulsive according to the result of variational mode decomposition, performing a sequence fusion operation if the subsequences are mutually attractive, and performing a sequence generation operation if the subsequences are mutually repulsive; obtaining a new subsequence set through the sequence fusion operation and the sequence generation operation; S300: Input the sequences in the new subsequence set into the recursive neural network respectively to obtain the final prediction result of the energy storage frequency modulation instruction.

[0021] It should be noted that the present invention provides a method and system for predicting energy storage frequency modulation instructions based on a two-level method. By introducing an innovative processing method of variational mode decomposition combined with a two-level rule, the energy storage frequency modulation instructions are intelligently preprocessed, and a fusion strategy is implemented for mutually attractive subsequences to reduce redundancy. For mutually exclusive subsequences, new independent sequences are created to eliminate the influence of false components, and finally a recursive neural network is used to achieve accurate prediction. The present invention can not only effectively identify and process the similarity and repulsion between subsequences, reduce redundant calculations and eliminate false components, thereby improving the accuracy and stability of the prediction model; but also by optimizing the frequency modulation instructions in advance, the energy storage system can respond to frequency change requirements more quickly and accurately, significantly improving the frequency modulation efficiency and economic benefits of the power system, while enhancing the stability of the system and the operating efficiency of the power plant.

[0022] In the embodiment of the present application, the above step S100 of obtaining the energy storage frequency modulation instruction and performing variational mode decomposition includes: The energy storage frequency modulation instruction is set as Pt=[X 1 ,X 2 ,X 3 ,.X i ..,X N ]; Set the energy storage frequency modulation instruction to a length of 0.9N[X 1 ,X 2 ,X 3 .....,X 0.9N ] as input to predict the experimental group [X 0.9N+1 ,X 0.9N+2 ,X 0.9N+3 ,...,X N ] and the unknown group [X N+1 ,X N+2 ,X N+3,...,X N+0.1N ]; Perform variational mode decomposition on the energy storage frequency modulation instruction Pt to obtain the sequence IMF after variational mode decomposition 1 ,IMF 2 ,IMF 3 ,...,IMF K ; It should be noted that the actual values ​​and predicted values ​​of the test group are used to generate all errors (MAPE) in the following examples, and what the present invention actually predicts is the value of the unknown group.

[0023] It should be noted that the above step S100 can effectively decompose the complex original frequency modulation instruction sequence into multiple subsequences with different characteristic frequencies by obtaining the energy storage frequency modulation instruction and performing variational mode decomposition. Variational mode decomposition, as an adaptive signal processing technology, can more accurately capture the essential characteristics of the signal than the traditional decomposition method, ensuring that each subsequence carries the effective information of the original signal, providing a high-quality data basis for the subsequent two-level rule judgment, sequence fusion or generation operation, thereby improving the performance and reliability of the entire prediction system.

[0024] In the embodiment of the present application, the above step S200 determines whether the subsequences are mutually attractive or mutually exclusive based on the result of variational mode decomposition. If the subsequences are mutually attractive, a sequence fusion operation is performed; if the subsequences are mutually exclusive, a sequence generation operation is performed; a new subsequence set is obtained through the sequence fusion operation and the sequence generation operation, including: Specifically, the judgment of whether subsequences attract or repel each other includes: If the subsequence IMF r =[X r1 ,X r2 ,X r3 ,..X ri ..,X rN ] and subsequence IMF j =[X j1 ,X j2 ,X j3 ,..X ji ..,X jN ] If the sequence attracts each other, the subsequence IMF is determined. r and IMF j Mutually attract; if the subsequence IMF r =[X r1 ,X r2 ,X r3 ,..X ri ..,X rN ] and subsequence IMF j =[X j1 ,Xj2 ,X j3 ,..X ji ..,X jN ] satisfies the judgment condition of sequence mutual exclusion, then the subsequence IMF is determined r and IMF j mutually exclusive; Among them, the judgment condition for the mutual attraction of sequences is: and , Where N represents the total length of the energy storage frequency modulation sequence, Represents the subsequence IMF j The experimental group error, Represents the subsequence IMF r The experimental group error of The conditions for determining whether the sequences are mutually exclusive are: and , Specifically, the judgment of whether subsequences attract or repel each other also includes: If a subsequence IMF q Both with subsequence IMF j Mutually attract each other and subsequence IMF r Mutually exclusive, then compare the subsequences IMF q With subsequence IMF j The attraction between the subsequences and IMF q With subsequence IMF r The magnitude of the repulsive force between them; If attraction Greater than repulsion , then the subsequence IMF is identified q With subsequence IMF j Mutual attraction; If attraction Less than repulsive force , then the subsequence IMF is identified q With subsequence IMF r Mutually exclusive.

[0025] Specifically, the sequence fusion operation includes: If we identify the subsequence IMF r and IMF j attract each other, then perform sequence fusion operation and merge the subsequences IMF r and IMF j Fusion into a new sequence IMF rj ; Among them, the new sequence IMF rjThe value of the element at position i in It is expressed as: , in, represents the coefficient of the preceding term, Represents the coefficient of the latter term.

[0026] Specifically, the sequence generation operation includes: If we identify the subsequence IMF r and IMF j mutually exclusive, then a sequence generation operation is performed, according to the subsequence IMF r and IMF j Generate a new sequence IMF r-j ; Among them, the new sequence IMF r-j The value of the element at position i in It is expressed as: , Specifically, the two-level method is used to traverse the sequence IMF after variational mode decomposition 1 ,IMF 2 ,IMF 3 ,...,IMF K , and judge whether the subsequences attract or repel each other. If the subsequences attract each other, perform sequence fusion operation; if the subsequences repel each other, perform sequence generation operation; keep repeating the judgment until the final sequence IMF 1 、 ,IMF 2 、 ,IMF 3 、 ,...,IMF S 、 There are neither mutually attractive sequences nor mutually repelling sequences.

[0027] It should be noted that the above step S200 introduces a two-level rule to make an intelligent judgment on the mutual attraction or mutual repulsion of the subsequences after the variational mode decomposition, and implements sequence fusion or generation operations accordingly, which not only effectively reduces the redundant calculations caused by similar subsequences and avoids resource waste, but also eliminates the influence of false components by creating new independent sequences, thereby improving the data input quality of the prediction model. This step enhances the accuracy and stability of subsequent recursive neural network predictions, allowing the energy storage system to respond to frequency change requirements more accurately, thereby improving the frequency regulation efficiency and economic benefits of the power system, while ensuring the stability and reliability of power grid operation.

[0028] In the embodiment of the present application, the above step S300 inputs the sequences in the new subsequence set into the recursive neural network respectively, and obtains the final prediction result of the energy storage frequency modulation instruction including: like Figure 2 As shown, each sequence in the new subsequence set is input into the recursive neural network, the corresponding unknown group value is output, and then the unknown group value predicted by each subsequence is added to obtain the final prediction result of the energy storage frequency modulation instruction.

[0029] It should be noted that the above step S300 can fully utilize the powerful nonlinear modeling ability of the neural network to accurately predict the energy storage frequency modulation instructions by inputting the optimized new subsequence set into the recursive neural network. Recursive neural networks are particularly good at processing data with time series characteristics, and can capture long-term dependencies in subsequences, ensuring that the prediction results are not only accurate but also stable and reliable, greatly improving the operating efficiency and economic benefits of power plants, and also enhancing the overall stability and flexibility of the power system.

[0030] Example 2 Based on the previous embodiment, this embodiment provides an application example of a method and system for predicting energy storage frequency modulation instructions based on a two-level method, in order to verify and illustrate the technical effects adopted in this method.

[0031] In order to further verify the advantages of the present invention, this embodiment uses the energy storage frequency modulation instruction prediction method provided by the present invention and the VMD-GRU prediction method to predict the frequency modulation sequence. The prediction results are shown in Table 1, and the definitions of the four evaluation indicators are shown in Table 2.

[0032] Table 1: Prediction results of the frequency modulation sequence by the prediction method of the present invention and VMD-GRU.

[0033] , Table 2: Four evaluation indicators.

[0034] , Where N represents the sample size, and Represent the actual value and predicted value at time n respectively.

[0035] It can be seen from the above comparative prediction results that the energy storage frequency modulation instruction prediction method provided by the present invention performs well in all four evaluation indicators, and its performance is better than that of the VMD-GRU prediction method.

[0036] Therefore, the present invention introduces an innovative processing method of variational mode decomposition combined with a two-level rule to perform intelligent preprocessing on energy storage frequency modulation instructions, implements a fusion strategy for mutually attractive subsequences to reduce redundancy, and creates new independent sequences for mutually exclusive subsequences to eliminate the influence of false components, and finally uses a recursive neural network to achieve accurate prediction; the present invention can not only effectively identify and process the similarities and repulsions between subsequences, reduce redundant calculations and eliminate false components, thereby improving the accuracy and stability of the prediction model; but also optimize the frequency modulation instructions in advance, so that the energy storage system can respond to frequency change requirements more quickly and accurately, significantly improve the frequency modulation efficiency and economic benefits of the power system, and at the same time enhance the stability of the system and the operating efficiency of the power plant.

[0037] Example 3 In this embodiment, a system for predicting energy storage frequency modulation instructions based on a two-stage method is provided, comprising an instruction decomposition module, a sequence processing module, and an instruction prediction module; Specifically, the instruction decomposition module is used to obtain energy storage frequency modulation instructions and perform variational mode decomposition; Specifically, the sequence processing module is used to judge whether the subsequences are mutually attractive or mutually repulsive according to the result of variational mode decomposition. If the subsequences are mutually attractive, a sequence fusion operation is performed; if the subsequences are mutually repulsive, a sequence generation operation is performed; a new subsequence set is obtained through the sequence fusion operation and the sequence generation operation; Specifically, the instruction prediction module is used to input the sequences in the new subsequence set into the recursive neural network respectively to obtain the final prediction result of the energy storage frequency modulation instruction.

[0038] It should be noted that the technical solution of the system for predicting energy storage frequency modulation instructions based on the two-stage method and the technical solution of the above-mentioned method for predicting energy storage frequency modulation instructions based on the two-stage method belong to the same concept. For the details not described in detail in the technical solution of the system for predicting energy storage frequency modulation instructions based on the two-stage method in this embodiment, please refer to the description of the technical solution of the above-mentioned method for predicting energy storage frequency modulation instructions based on the two-stage method.

[0039] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0040] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for predicting energy storage frequency modulation instructions based on a two-level method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0041] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method proposed in the above embodiment is implemented.

[0042] The storage medium proposed in this embodiment and the method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0043] Through the above description of the implementation mode, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation mode. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform the method of the embodiment of the present invention.

[0044] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0045] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The scheme in the embodiments of the present application may be implemented in various computer languages.

[0046] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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.

[0047] 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.

[0048] 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 The steps for the functions specified in one or more boxes.

[0049] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0050] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for predicting energy storage frequency modulation instructions based on a two-level method, characterized in that: include: Obtain energy storage frequency modulation instructions and perform variational mode decomposition; Determining whether subsequences attract or repel each other based on the result of the variational mode decomposition; If it is determined that the two subsequences attract each other, a sequence fusion operation is performed to merge the two subsequences into a new sequence. If it is determined that the two subsequences repel each other, a sequence generation operation is performed to generate a new sequence based on the two mutually repelling subsequences. A new subsequence set is obtained through the sequence fusion operation and the sequence generation operation; The sequences in the new subsequence set are respectively input into the recursive neural network to obtain the final prediction result of the energy storage frequency modulation instruction.

2. The energy storage frequency modulation instruction prediction method based on the two-stage method as claimed in claim 1 is characterized in that: The determination of whether the subsequences attract or repel each other includes: If the subsequence IMF r =[X r1 ,X r2 ,X r3 ,..X ri ..,X rN ] and subsequence IMF j =[X j1 ,X j2 ,X j3 ,..X ji ..,X jN ] If the sequence attracts each other, the subsequence IMF is determined. r and IMF j Mutually attract each other; if the judgment condition of mutual repulsion of sequences is met, the subsequence IMF is determined r and IMF j mutually exclusive; The judgment condition for the sequences to attract each other is: and ， Where N represents the total length of the energy storage frequency modulation sequence, Represents the subsequence IMF j The experimental group error, Represents the subsequence IMF r The experimental group error of The conditions for determining whether the sequences are mutually exclusive are: and 。 3. The energy storage frequency modulation instruction prediction method based on the two-stage method as claimed in claim 2 is characterized in that: The determination of whether the subsequences attract or repel each other further includes: If a subsequence IMF q Both with subsequence IMF j Mutually attract each other and subsequence IMF r Mutually exclusive, then compare the subsequences IMF q With subsequence IMF j The attraction between the subsequences and IMF q With subsequence IMF r The magnitude of the repulsive force between them; If attraction Greater than repulsion , then the subsequence IMF is identified q With subsequence IMF j attract each other, otherwise the subsequence IMF is recognized q With subsequence IMF r Mutually exclusive.

4. The energy storage frequency modulation instruction prediction method based on the two-stage method as claimed in claim 3 is characterized in that: The sequence fusion operation includes: If we identify the subsequence IMF r and IMF j attract each other, then perform sequence fusion operation to merge the subsequences IMF r and IMF j Fusion into a new sequence IMF rj , where the new sequence IMF rj The value of the element at position i in It is expressed as: ， in, represents the coefficient of the preceding term, Represents the coefficient of the latter term.

5. The energy storage frequency modulation instruction prediction method based on the two-stage method as claimed in claim 4 is characterized in that: The sequence generation operation includes: If we identify the subsequence IMF r and IMF j mutually exclusive, then a sequence generation operation is performed, according to the subsequence IMF r and IMF j Generate a new sequence IMF r-j , where the new sequence IMF r-j The value of the element at position i in It is expressed as: 。 6. The energy storage frequency modulation instruction prediction method based on the two-stage method as claimed in claim 5 is characterized in that: The process of acquiring the new subsequence set includes: The sequence IMF1, IMF2, IMF3, ..., IMF after the variational mode decomposition is traversed using the two-level method K , and judge whether the subsequences attract or repel each other. If the subsequences attract each other, perform sequence fusion operation; if the subsequences repel each other, perform sequence generation operation; keep repeating the judgment until the final sequence IMF1 、 ,IMF2 、 ,IMF3 、 ,...,IMF S 、 There are neither mutually attractive sequences nor mutually repelling sequences.

7. The energy storage frequency modulation instruction prediction method based on the two-stage method as claimed in claim 6 is characterized in that: The final prediction result of the energy storage frequency modulation instruction includes: Each sequence in the new subsequence set is input into the recursive neural network respectively, the value of the corresponding unknown group is output, and then the value of the unknown group predicted by each subsequence is added to obtain the final prediction result of the energy storage frequency modulation instruction.

8. A storage frequency modulation instruction prediction system based on the two-pole method, using the method described in any one of claims 1 to 7, characterized in that: include: The instruction decomposition module is used to obtain the energy storage frequency modulation instruction and perform variational mode decomposition; A sequence processing module, used to judge whether the subsequences attract or repel each other according to the result of the variational mode decomposition, and to perform a sequence fusion operation if the subsequences attract each other, and to perform a sequence generation operation if the subsequences repel each other; A new subsequence set is obtained through the sequence fusion operation and the sequence generation operation; The instruction prediction module is used to input the sequences in the new subsequence set into the recursive neural network respectively to obtain the final prediction result of the energy storage frequency modulation instruction.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the computer executable instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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