A method and system for predicting energy storage frequency modulation instructions based on signal sequence regularization

Abstract

A method for predicting energy storage frequency modulation instructions based on signal sequence regularization, comprising: obtaining an original frequency modulation instruction signal sequence; obtaining regularization characteristic parameters representing the degree of regularity of the original frequency modulation instruction signal sequence, wherein the regularization characteristic parameters include a regularization index P and an average regularization degree value D; adjusting the original frequency modulation instruction signal sequence based on the regularization characteristic parameters to generate a reference frequency modulation instruction signal sequence; using a neural network to perform predictions based on the original frequency modulation instruction signal sequence and the reference frequency modulation instruction signal sequence to obtain intermediate prediction results; performing mathematical operations on the intermediate prediction results to obtain a final prediction result; making the prediction result of the frequency modulation instruction more accurate, predicting the size of the frequency modulation instruction in advance for energy storage regulation, and improving response accuracy and frequency modulation benefits.

Description

Technical Field

[0001] The present application relates to the technical field of power grid frequency regulation, and in particular to a method and system for predicting energy storage frequency regulation instructions based on signal sequence regularization. Background Art

[0002] Currently, large hydropower and thermal power units (coal-fired and gas-fired) are the primary frequency regulation sources in my country's major regional power grids. Frequency regulation power output is adjusted to respond to system frequency changes. Hybrid energy storage frequency regulation command prediction involves analyzing historical frequency regulation commands and related data to develop a prediction model to predict future frequency regulation command requirements. This optimizes the control strategy of the hybrid energy storage system and improves its frequency regulation performance and efficiency. This system can enhance system performance and provide rapid response. Output power can be adjusted quickly to respond to grid frequency changes, effectively balancing supply and demand within the power system and maintaining grid frequency stability. Improved regulation accuracy allows for more precise tracking of frequency regulation commands, reducing regulation errors and improving power quality and stability. Enhanced system stability can be achieved by rationally allocating power and energy across different energy storage devices, reducing overall system risk, improving reliability and stability, and mitigating the risk of system failure due to failure or performance degradation of a single energy storage device. Prediction can also extend device life and optimize charging and discharging strategies. Based on the prediction results, a reasonable charging and discharging strategy can be formulated to avoid excessive or frequent charging and discharging of energy storage devices, thereby extending their service life. Balanced equipment usage: Rationally allocating the usage frequency and load of different energy storage devices ensures relatively even aging across all devices, reducing maintenance costs and replacement frequency. This reduces costs and improves economic efficiency. By improving frequency regulation performance and efficiency, hybrid energy storage systems can achieve greater frequency regulation benefits while reducing power outages and equipment damage costs caused by unstable grid frequency. Investment costs can also be optimized. Based on forecast results, the capacity and quantity of different energy storage devices can be rationally allocated, avoiding overinvestment and waste of resources, and improving return on investment.

[0003] The traditional prediction method is to directly put the original FM sequence into the GRU network for prediction. The original FM sequence has strong nonlinearity, which increases the difficulty of neural network prediction. The reasons are as follows:

[0004] 1. Capturing complex patterns;

[0005] Nonlinear dynamic characteristics: Nonlinear time series may contain complex patterns such as mutations, multimodal fluctuations, chaotic behavior (such as Lorentz systems), etc. Traditional linear models cannot fully model these patterns. Although neural networks can theoretically approximate these patterns, they require higher model complexity.

[0006] 2. Challenges in model training;

[0007] Overfitting risk: Complex nonlinear relationships require deeper networks and more parameters, but are prone to overfitting with small sample sizes. Optimization difficulty: The non-convexity of the loss function can lead to falling into local optima. For example, when training GRUs, careful selection of learning rates and initialization strategies is crucial.

[0008] 3. Data requirements and quality;

[0009] Data requirements: Learning nonlinear patterns often requires large amounts of data. For example, predicting chaotic systems may require tens of thousands of time steps of samples to cover different states.

[0010] 4. Uncertainty in dynamic systems;

[0011] Sensitivity to chaotic behavior: Even if the model fits historical data well, long-term forecasts may still fail due to exponentially amplified errors in initial conditions.

[0012] Directly applying the original sequence to prediction results in inaccurate predictions and large prediction errors. Therefore, the original FM sequence must be preprocessed before prediction. To address this issue, decomposition and denoising are proposed: VMD decomposition or wavelet transforms are used to separate trends, cycles, and residuals, reducing nonlinear complexity.

[0013] like Figure 2 As shown, common methods, such as VMD decomposition, decompose the original FM sequence into a series of low-frequency subsequences, which are then fed into a neural network for prediction. However, while VMD (Variational Mode Decomposition) improves the accuracy of non-stationary signal processing, it also suffers from the following drawbacks: complex parameter tuning and high computational cost. Wavelet transforms are highly dependent on the time basis function and have limited frequency resolution. Summary of the Invention

[0014] In view of the shortcomings of the existing technology, the present invention proposes a prediction method that can reduce the nonlinearity and irregularity of the original sequence without using decomposition and noise reduction.

[0015] A method for predicting energy storage frequency modulation instructions based on signal sequence regularization, characterized by comprising:

[0016] S101, obtaining an original frequency modulation instruction signal sequence;

[0017] S102, obtaining regularity characteristic parameters representing the degree of regularity of the original frequency modulation instruction signal sequence, wherein the regularity characteristic parameters include a regularity index P and an average regularity degree value D;

[0018] S103, adjusting the original frequency modulation instruction signal sequence based on the regularized characteristic parameters to generate a reference frequency modulation instruction signal sequence;

[0019] S104, using a neural network to perform prediction based on the original frequency modulation instruction signal sequence and the reference frequency modulation instruction signal sequence to obtain intermediate prediction results;

[0020] S105: Perform mathematical operations on the intermediate prediction results to obtain the final prediction results.

[0021] A system for predicting energy storage frequency modulation instructions based on signal sequence regularization, characterized by including:

[0022] The command acquisition module acquires the original frequency modulation command signal sequence;

[0023] The regularization characteristic parameter calculation module obtains the regularization characteristic parameters representing the regularity degree of the original frequency modulation instruction signal sequence, and the regularization characteristic parameters include the regularization index P and the average regularity degree value D;

[0024] An intermediate sequence generation module adjusts the original frequency modulation command signal sequence based on regularized characteristic parameters to generate a reference frequency modulation command signal sequence;

[0025] The prediction processing module uses a neural network to predict the original frequency modulation instruction signal sequence and the reference frequency modulation instruction signal sequence to obtain an intermediate prediction result;

[0026] The prediction processing module performs mathematical operations on the intermediate prediction results to obtain the final prediction results.

[0027] The beneficial effects of the present invention are:

[0028] Reducing the nonlinearity of the original signal sequence makes the corrected sequence smoother, greatly reduces the difficulty of prediction, and improves the accuracy of frequency regulation instruction prediction, thereby improving the response accuracy of the power plant and the frequency regulation benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A diagram of the method steps.

[0030] Figure 2 This is an illustration of the traditional algorithm. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0032] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are to be executed.

[0033] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0034] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0035] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0036] The embodiment of the present disclosure provides a method for predicting energy storage frequency modulation instructions based on the regularization of signal sequences, such as Figure 1 As shown, including:

[0037] S101, obtaining an original frequency modulation instruction signal sequence;

[0038] S102, obtaining regularity characteristic parameters representing the degree of regularity of the original frequency modulation instruction signal sequence, wherein the regularity characteristic parameters include a regularity index P and an average regularity degree value D;

[0039] S103, adjusting the original frequency modulation instruction signal sequence based on the regularized characteristic parameters to generate a reference frequency modulation instruction signal sequence;

[0040] S104, using a neural network to perform prediction based on the original frequency modulation instruction signal sequence and the reference frequency modulation instruction signal sequence to obtain intermediate prediction results;

[0041] S105: Perform mathematical operations on the intermediate prediction results to obtain the final prediction results.

[0042] The frequency modulation command prediction method based on supercapacitor energy storage, provided in the embodiments of the present disclosure, uses novel supercapacitors and lithium batteries as energy storage devices and modulates the frequency of a power plant's power grid when its frequency fluctuates. The method monitors the power plant's power grid in real time and generates a corresponding frequency modulation command signal when the power supply frequency fluctuates. After acquiring this original signal, the method then predicts future signals to implement frequency modulation of the power plant's power grid.

[0043] Wherein, step S101, obtaining the original frequency modulation instruction signal sequence, includes:

[0044] The original frequency modulation command signal sequence is Pt=[ X1,X2,X3,…,X i ,…,XN ]. The original frequency modulation instruction signal sequence Pt is a function of time t.

[0045] Project each numerical point in the original frequency modulation command signal sequence Pt onto the plane coordinate system, and the vertical axis represents the magnitude of the numerical value, which is the signal value X1, X2, X3, ..., X i ,…,X N , the horizontal axis is the number of sampling times, one sample per second.

[0046] In another embodiment disclosed in this application, for an original FM command signal sequence, the greater the degree of irregularity, the greater the difficulty of prediction. Generally speaking, if the original FM command signal sequence follows a regular fluctuation similar to that of a sine / cosine function, then the regularity of the sequence is relatively strong and therefore easier to predict. Conversely, the prediction difficulty is greater. The degree of regularity of the original FM command signal sequence can be characterized by a regularity index P and an average degree of regularity D.

[0047] The above S102, obtaining regularity characteristic parameters representing the degree of regularity of the original frequency modulation instruction signal sequence, wherein the regularity characteristic parameters include a regularity index P and an average regularity degree value D; includes:

[0048] Step 1: Determine whether there is a regular sequence in the signal sequence within the unit length.

[0049] A certain number of continuous signals are selected as unit length. In order to reflect the regularity of continuous signals, 6 continuous signals can be selected.

[0050] Select 6 consecutive signals [X q1 ,X q2 ,X q3 ,X q4 ,X q5 ,X q6 ], if X is satisfied q2 is the minimum value of 6 consecutive signals, and X q5 is the maximum value of 6 consecutive signals, then the continuous signal [X q1 ,X q2 ,X q3 ,X q4 ,X q5 ,X q6 ] form a regular number sequence.

[0051] Step 2: Use the traversal algorithm to find all regular number sequences from the original frequency modulation command signal sequence and obtain the regularization index P.

[0052] Traverse and judge [X1,X2,X3,…,X i ,…,X N] to determine whether every 6 consecutive signals form a regular sequence, and to obtain the number Q of all regular sequence by statistical summation.

[0053] For example, Pt=[X1,X2,X3,…,X i ,…,X 12 ], first determine whether [X1, X2, X3, X4, X5, X6] is a regular sequence, then determine whether [X2, X3, X4, X5, X6, X7] is a regular sequence, then determine whether [X3, X4, X5, X6, X7, X8] is a regular sequence, and so on until [X7,…, X 12 ] is a regular sequence.

[0054] Calculate the regularization index P=Q / N, where Q is the number of all regular series and N is the number of signals in the original frequency modulation command signal sequence.

[0055] Step 3: Calculate the regularity value D of each regular series i , and obtain the average regularity degree value D of the original FM command signal sequence.

[0056] Calculate the regularity degree value D of all regular series i .

[0057] Suppose one of the regularized sequences is [X q1 ,X q2 ,X q3 ,X q4 ,X q5 ,X q6 ], calculate the non-extreme point [X q1 ,X q3 ,X q4 ,X q6 The average value X avg , calculate the non-extreme point [X q1 ,X q3 ,X q4 ,X q6 ] the median X Z ,

[0058] The mathematical meaning of the median is the value in the middle of a set of data after it is sorted in order of size. It reflects the "median level" of the data and is insensitive to extreme values. It is an important indicator of the trend in a dataset. Calculation steps: First, sort the data from smallest to largest (or largest to smallest). Calculate the median based on the following scenarios: For an odd number of data points, take the middle number after sorting. For an even number of data points, take the average of the two middle numbers.

[0059] Example: Odd number case: The data is [3, 1, 5, 2, 4], and after sorting it is [1, 2, 3, 4, 5], and the median is the third number 3.

[0060] Even number case: The data is [7, 2, 4, 6], which becomes [2, 4, 6, 7] after sorting. The median is the average of the two middle numbers (4 and 6), which is 5.

[0061] Regularized sequence [X q1 ,X q2 ,X q3 ,X q4 ,X q5 ,X q6 ]’s regularity value D i yes:

[0062] ,

[0063] Sigmoid() is the activation function, and the two vertical line operators represent the absolute value.

[0064] The regularity degree value D of all regular series i Calculate and obtain the average regularization degree value D,

[0065] .

[0066] The above-mentioned S103, adjusting the original frequency modulation instruction signal sequence based on the regularized characteristic parameters to generate a reference frequency modulation instruction signal sequence, includes:

[0067] For the original frequency modulation command signal sequence Pt=[ X1,X2,X3,…,X i ,…,X N ] is adjusted by the irregular number series; the irregular number series includes continuous signals within unit length, and 6 continuous signals can be selected as a group for adjustment.

[0068] Suppose a set of continuous signals [X c1 ,X c2 ,X c3 ,X c4 ,X c5 ,X c6 ] is the irregular sequence of numbers in the original frequency modulation command signal sequence Pt, where X c2 It must not be the minimum value, and X c5 Must not be a maximum value.

[0069] [X c1 ,X c2 ,X c3 ,X c4 ,X c5 ,X c6 ] is the minimum value of X min ,[X c1 ,X c2 ,Xc3 ,X c4 ,X c5 ,X c6 ]The maximum value is X max .

[0070] First, for X c2 Based on the regularization index P and the average regularization degree value D, mathematical transformation is performed to minimize them. The mathematical transformation method is:

[0071] ,

[0072] Where rand() is a random function.

[0073] Similarly, for X c5 Based on the regularization index P and the average regularization degree value D, mathematical transformation is performed to make it the maximum value. The mathematical transformation method is:

[0074] ,

[0075] Where rand() is a random function.

[0076] Secondly, for [X c1 ,X c3 ,X c4 ,X c6 ] generates a random value for each signal,

[0077] X ci = rand ( X ci -exp( D ), X ci +exp( D )),i=1, 3, 4, 6, rand() is a random function.

[0078] Generate random values in a continuous loop, the number of cycles is predefined and can be set to 200 times.

[0079] For each randomly generated [X c1 ,X c3 ,X c4 ,X c6 ] and after the above mathematical transformation [X c2 ,X c5 ] after combining, calculate [X c1 ,X c2 ,X c3 ,X c4 ,X c5 ,X c6 ]’s regularity degree value.

[0080] The calculated regularity value closest to the average regularity value D corresponds to [X c1 ,X c3 ,X c4 ,X c6 ] as the replacement value, and replace the value [X c1 ,X c3 ,X c4 ,X c6 ] and after the above mathematical transformation [X c2 ,X c5 ] After combining, we get [X c1 ,X c2 ,X c3 ,X c4 ,X c5 ,X c6 ].

[0081] Finally, after mathematical transformation of the irregular series in the original FM command signal sequence, the reference FM command signal sequence Pt is obtained. 、 .

[0082] After performing mathematical transformation on an irregular number sequence, mathematical transformation is continued on other subsequent irregular number sequences.

[0083] The above-mentioned S104, using a neural network to predict based on the original frequency modulation instruction signal sequence and the reference frequency modulation instruction signal sequence to obtain an intermediate prediction result, includes:

[0084] The final reference frequency modulation command signal sequence Pt 、 Put it into the GRU network for prediction and get the first intermediate prediction result Y1=[Y1,Y2,Y3,…,Y S ].

[0085] The difference sequence Pt-Pt between the original FM command signal sequence and the reference FM command signal sequence 、 Put it into the GRU network for prediction and get the second intermediate prediction result Y2=[Y1 、 ,Y2 、 ,Y3 、 ,…,Y S 、 ].

[0086] The above S105, performing mathematical operations on the intermediate prediction results to obtain the final prediction results, includes:

[0087] The first intermediate prediction result and the second intermediate prediction result are the prediction results of the reference frequency modulation instruction signal sequence and the difference sequence, but are not the prediction results of the original frequency modulation instruction signal sequence Pt, and mathematical operations are required.

[0088] The final prediction result is Y3=Y1+Y2.

[0089] With the above Figure 1 The method shown corresponds to the embodiment of the present disclosure.

[0090] A system for predicting energy storage frequency modulation instructions based on signal sequence regularization, characterized by including:

[0091] The command acquisition module acquires the original frequency modulation command signal sequence;

[0092] The regularization characteristic parameter calculation module obtains the regularization characteristic parameters representing the regularity degree of the original frequency modulation instruction signal sequence, and the regularization characteristic parameters include the regularization index P and the average regularity degree value D;

[0093] An intermediate sequence generation module adjusts the original frequency modulation command signal sequence based on regularized characteristic parameters to generate a reference frequency modulation command signal sequence;

[0094] The prediction processing module uses a neural network to predict the original frequency modulation instruction signal sequence and the reference frequency modulation instruction signal sequence to obtain an intermediate prediction result;

[0095] The prediction processing module performs mathematical operations on the intermediate prediction results to obtain the final prediction results.

[0096] In order to further verify the advantages of the present invention, the present invention uses the method of the present invention and the GRU direct prediction method to predict the frequency modulation sequence, and the results are as follows.

[0097] Frequency modulation sequence 1 comes from the frequency modulation data of a power plant in Hulunbuir from 0:00 to 20:00 on December 1, 2024.

[0098] Frequency modulation sequence 2 comes from the frequency modulation data of a power plant in Hulunbuir from 2:00 to 22:00 on December 2, 2024.

[0099] The performance comparison is as follows:

[0100] Table 1

[0101]

[0102] The evaluation indicators are defined as follows:

[0103] Table 2

[0104]

[0105] N represents the sample size, and represent the actual value and predicted value at time n respectively.

[0106] The experimental results show that all four evaluation indicators have been reduced, indicating that the decomposition method proposed in this paper can reduce the nonlinearity of the original sequence, which is comparable to the traditional GRU prediction method, further improving the prediction accuracy. This helps power plants improve their frequency modulation response capabilities and further increase their profits.

[0107] An embodiment of the present disclosure provides a computer device, comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps provided in any embodiment of the present disclosure are performed.

[0108] The computer device provided in the embodiment of the present application includes a processor, a memory, and a bus. The memory is used to store and execute instructions, and includes internal memory and external memory. The internal memory here is also called internal memory, which is used to temporarily store the calculation data in the processor and the data exchanged with the external memory such as the hard disk. The processor exchanges data with the external memory through the internal memory. When the electronic device is running, the processor and the memory communicate through the bus, so that the processor executes the following instructions:

[0109] Obtaining the original frequency modulation command signal sequence;

[0110] Obtaining regularity characteristic parameters representing the degree of regularity of the original frequency modulation command signal sequence, wherein the regularity characteristic parameters include a regularity index P and an average regularity degree value D;

[0111] Adjusting the original frequency modulation command signal sequence based on the regularized characteristic parameters to generate a reference frequency modulation command signal sequence;

[0112] Based on the original frequency modulation instruction signal sequence and the reference frequency modulation instruction signal sequence, neural networks are used to predict and obtain intermediate prediction results;

[0113] Perform mathematical operations on the intermediate prediction results to obtain the final prediction results.

[0114] The present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, executes the steps provided in any embodiment of the present disclosure. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0115] Through the above description of the embodiments, those skilled in the art will clearly understand that the embodiments of the present disclosure can be implemented through hardware or through software plus the necessary general hardware platform. Based on this understanding, the technical solutions of the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a read-only optical disk, a USB flash drive, a mobile hard disk, etc.) and includes a number of instructions for causing a computer device (such as a personal computer, a server, or a network device) to execute the methods described in the various embodiments of the present disclosure.

[0116] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present disclosure.

[0117] Those skilled in the art will appreciate that the modules in the devices of the embodiments may be distributed in the devices of the embodiments as described in the embodiments, or may be located in one or more devices different from the embodiments with corresponding changes. The modules of the above embodiments may be combined into one module or further divided into multiple submodules.

[0118] The serial numbers of the above-mentioned embodiments of the present disclosure are for description only and do not represent the advantages or disadvantages of the embodiments.

[0119] Obviously, those skilled in the art may make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is intended to include these modifications and variations.

[0120] Finally, it should be noted that the foregoing description is merely an explanation of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail, those skilled in the art will be able to modify the aforementioned technical solutions or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for predicting energy storage frequency modulation instructions based on signal sequence regularization, characterized in that: include: S101, obtaining an original frequency modulation instruction signal sequence; S102, obtaining regularity characteristic parameters representing the degree of regularity of the original frequency modulation instruction signal sequence, wherein the regularity characteristic parameters include a regularity index P and an average regularity degree value D; S103, adjusting the original frequency modulation instruction signal sequence based on the regularized characteristic parameters to generate a reference frequency modulation instruction signal sequence; S104, using a neural network to perform prediction based on the original frequency modulation instruction signal sequence and the reference frequency modulation instruction signal sequence to obtain intermediate prediction results; S105: Perform mathematical operations on the intermediate prediction results to obtain the final prediction results; Step S102 includes: Step 1: Determine whether there is a regular sequence in the signal sequence within the unit length; Step 2: Use the traversal algorithm to extract the original FM command signal sequence [X1, X2, X3, ..., X i ,…,X N ] to find all regular number sequences, sum up the statistics to get the number Q of all regular number sequences, and calculate the regularization index P; Step 3: Calculate the regularity value D of each regular series i , and obtain the average regularity degree value D of the original FM command signal sequence; In step one, The unit length is 6 consecutive signals; Select 6 consecutive signals [X q1 ,X q2 ,X q3 ,X q4 ,X q5 ,X q6 ], if X is satisfied q2 is the minimum value of 6 consecutive signals, and X q5 is the maximum value of 6 consecutive signals, then the continuous signal [X q1 ,X q2 ,X q3 ,X q4 ,X q5 ,X q6 ] form a regular sequence; In step 2, Traverse and judge the original frequency modulation command signal sequence [X1,X2,X3,…,X i ,…,X N ] whether every 6 consecutive signals form a regular sequence, and the number Q of all regular sequences is obtained by statistical summation; Calculate the regularization index P = Q / N, where Q is the number of all regular series and N is the number of signals in the original frequency modulation command signal sequence; In step three, Calculate the regularity degree value D of all regular series i ; One of the regularized series is [X q1 ,X q2 ,X q3 ,X q4 ,X q5 ,X q6 ], calculate the non-extreme point [X q1 ,X q3 ,X q4 ,X q6 The average value X avg , calculate the non-extreme point [X q1 ,X q3 ,X q4 ,X q6 ] the median X Z , Regularized sequence [X q1 ,X q2 ,X q3 ,X q4 ,X q5 ,X q6 ]’s regularity value D i yes ， Sigmoid() is the activation function, and the two vertical line operators represent the absolute value; The regularity degree value D of all regular series i Calculate and obtain the average regularization degree value D, ； Step S103 includes: Adjust the irregular number series in the original frequency modulation command signal sequence Pt; the irregular number series includes continuous signals within a unit length, and 6 continuous signals are selected as a group for adjustment; Continuous signal [X c1 ,X c2 ,X c3 ,X c4 ,X c5 ,X c6 ] is the irregular sequence of numbers in the original frequency modulation command signal sequence Pt, where X c2 It must not be the minimum value, and X c5 It must not be the maximum value; [X c1 ,X c2 ,X c3 ,X c4 ,X c5 ,X c6 ] is the minimum value of X min ,[X c1 ,X c2 ,X c3 ,X c4 ,X c5 ,X c6 ]The maximum value is X max ; First, for X c2 Based on the regularization index P and the average regularization degree value D, mathematical transformation is performed to minimize them. The mathematical transformation method is: ， Among them, rand() is a random function; Similarly, for X c5 Based on the regularization index P and the average regularization degree value D, mathematical transformation is performed to make it the maximum value. The mathematical transformation method is: ， Among them, rand() is a random function; Step S103 further includes: To [X c1 ,X c3 ,X c4 ,X c6 ] generates a random value for each signal, X ci = rand ( X ci -exp( D ), X ci +exp( D )),i=1,3,4,6,rand() is a random function; Generate random values in a continuous loop for a predetermined number of times; The calculated regularity value closest to the average regularity value D corresponds to [X c1 ,X c3 ,X c4 ,X c6 ] as the replacement value, and replace the value [X c1 ,X c3 ,X c4 ,X c6 ] and after mathematical transformation [X c2 ,X c5 ] After combining, we get [X c1 ,X c2 ,X c3 ,X c4 ,X c5 ,X c6 ]; After mathematically transforming the irregular series in the original FM command signal sequence, the reference FM command signal sequence Pt is obtained. 、 .

2. The energy storage frequency modulation instruction prediction method based on signal sequence regularization according to claim 1 is characterized in that: In step S101, the original frequency modulation instruction signal sequence Pt is [X1, X2, X3, ..., X i ,…,X N ], Project each numerical point in the original frequency modulation command signal sequence Pt onto the plane coordinate system. The vertical axis represents the magnitude of the numerical value, and the horizontal axis represents the number of samplings, with one sampling per second.

3. The energy storage frequency modulation instruction prediction method based on signal sequence regularization according to claim 1 is characterized in that: Step S104 includes: The final reference frequency modulation command signal sequence Pt 、 Put it into the GRU network for prediction and get the first intermediate prediction result Y1; The original frequency modulation instruction signal sequence Pt and the reference frequency modulation instruction signal sequence Pt 、 The difference sequence is put into the GRU network for prediction to obtain the second intermediate prediction result Y2; Step S105 includes: The final prediction result is Y3=Y1+Y2.

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