Energy storage frequency modulation instruction prediction method and system based on symmetric adjustment

Abstract

The invention relates to the technical field of power grid frequency modulation, in particular to an energy storage frequency modulation instruction prediction method and system based on symmetric adjustment, and the method comprises the steps: decomposing a frequency modulation instruction signal, obtaining a plurality of component signals, and then carrying out the discrete division of each component signal, and obtaining a subsequence corresponding to each component signal; determining a target symmetric line of each sub-sequence according to the distribution of all original data points of each sub-sequence; performing symmetric mapping on each original data point according to the target symmetric line of each sub-sequence to obtain symmetric data points of each original data point; the original data points and the symmetrical data points are fused, and finally adjusted data points after adjustment are obtained; performing prediction through the adjustment data point corresponding to each sub-sequence to obtain each prediction sub-sequence; and superposing all the prediction subsequences to obtain prediction signal data of the final frequency modulation instruction. According to the invention, the influence of nonlinearity is reduced, and the prediction accuracy is improved.

Description

Technical Field

[0001] The present invention 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 symmetrical adjustment. Background Art

[0002] Energy storage frequency regulation command prediction refers to predicting frequency regulation command changes when energy storage is used to implement frequency regulation (frequency regulation) tasks in power systems, thereby optimizing the energy storage system's scheduling and response. Frequency regulation in power systems is crucial, especially as the proportion of renewable energy (such as wind and solar power) increases, increasing frequency instability. Energy storage systems play an increasingly important role in this context.

[0003] Frequency regulation commands are signals sent by the power system dispatch center or automated equipment, telling the energy storage system how to adjust charging and discharging power to maintain grid frequency stability. The energy storage system needs to respond quickly to these frequency regulation commands to ensure frequency stability.

[0004] The traditional method for predicting energy storage frequency modulation commands involves decomposing the original signal corresponding to the frequency modulation command using the VMD (Variational Mode Decomposition) algorithm to obtain multiple component signals. Each component is then predicted separately, and the predicted results for all components are superimposed to obtain the final frequency modulation command prediction. However, because some component signals are highly nonlinear, even more so than the original signal, using these highly nonlinear components increases the difficulty of prediction and reduces the accuracy of the prediction results. Summary of the Invention

[0005] The present invention provides a method and system for predicting energy storage frequency modulation instructions based on symmetrical adjustment, which are used to solve existing problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The first aspect of the present invention is to provide a method for predicting energy storage frequency modulation instructions based on symmetric adjustment, comprising: Obtaining frequency modulation command signal; Decompose the frequency modulation command signal to obtain several component signals, then discretely divide each component signal to obtain a subsequence corresponding to each component signal; construct a coordinate system with time as the horizontal axis and the data in each subsequence as the vertical axis, and map all data in each subsequence into the coordinate system to obtain several original data points; determine the target symmetry line of each subsequence based on the distribution of all original data points in each subsequence; and perform symmetric mapping on each original data point based on the target symmetry line of each subsequence to obtain a symmetric data point for each original data point; Fuse the original data points and the symmetric data points to obtain the adjusted data points after final adjustment; predict through the adjusted data points corresponding to each subsequence to obtain each predicted subsequence. Superimpose all the predicted subsequences to obtain the predicted signal data of the final frequency modulation command.

[0007] Further, the decomposition of the frequency modulation command signal to obtain a plurality of component signals, and then the discrete division of each component signal to obtain the subsequence corresponding to each component signal includes: Decompose the frequency modulation command signal through the VMD decomposition algorithm to obtain a plurality of component signals; at a preset time interval To divide each component signal to obtain a plurality of data corresponding to each divided component signal, and in chronological order, form a set of sequences from the plurality of data, denoted as the subsequence corresponding to each component signal.

[0008] Further, determining the target symmetry line of each subsequence according to the distribution of all the original data points of each subsequence includes: Determine the angular range of the symmetry line through the distribution of all the original data points in each subsequence; the angular range of the symmetry line is specifically expressed by the formula:

[0009] Among them, the lower limit value of the angular range of the symmetry line is specifically expressed by the formula:

[0010] Among them, the upper limit value of the angular range of the symmetry line is specifically expressed by the formula:

[0011] In the formula, represents the set of all data in each subsequence, represents the maximum value of the absolute values of all data in each subsequence, represents the minimum value of the absolute values of all data in each subsequence, represents the minimum value of all data in each subsequence, represents the maximum value of all data in each subsequence, is the absolute value symbol, represents the arctangent function, represents function, used for data normalization; represents the minimum angle of the angular range of the symmetry line, represents the maximum angle of the angular range of the symmetry line; represents the angle between the symmetry line and the horizontal line; From the minimum angle of the symmetric line angle range to the maximum angle of the symmetric line angle range , each time increasing and iterating by a preset angle degree; during the iteration process, a new iteration reference angle is determined each time a change occurs; through each iteration reference angle, each reference symmetric line corresponding to each iteration reference angle is determined, and the number of original data points existing on each reference symmetric line is obtained, denoted as the reference data volume of each reference symmetric line; The reference symmetric line with the largest reference data volume is used as the target symmetric line for each subsequence.

[0012] Furthermore, the symmetric mapping of each original data point according to the target symmetric line of each subsequence to obtain the symmetric data point of each original data point includes:

[0013] In the formula, represents the minimum value of all data in each subsequence, represents the maximum value of the absolute values of all data in each subsequence, is the absolute value symbol, represents the arctangent function, represents function, used for data normalization, represents the th data value in each subsequence, represents the th target angle of the original data point; where the target angle is the angle between the extension line of the original data point and the corresponding target symmetric line; Through the target angle of each original data point, first determine the intersection point of the extension line of the original data point on the target symmetric line, denoted as the target symmetric line intersection point of each original data point; record the direction from each original data point to the corresponding target symmetric line intersection point as the target extension direction of each original data point; Then the original data point is extended along the target extension direction to obtain the symmetric data point; where, during the process of adjusting and obtaining the symmetric data point, it is ensured that the angle between the symmetric data point and the target symmetric line is equal to the target angle of the original data point, and the distance from the symmetric data point to the target symmetric line intersection point is equal to the distance from the original data point to the target symmetric line intersection point.

[0014] Furthermore, the fusion of the original data point and the symmetric data point to obtain the adjusted data point after final adjustment includes: The formula for fusing the original data point and the symmetric data point is specifically expressed as:

[0015] Among them, the adjustment coefficient of the coordinate value of the original data point in the fusion process is specifically expressed by the formula:

[0016] Among them, the adjustment coefficient of the coordinate value of the symmetric data point in the fusion process is specifically expressed by the formula:

[0017] In the formula, represents the coordinate value of the th original data point, represents the coordinate value of the symmetric data point corresponding to the th original data point, represents the adjustment coefficient of the coordinate value of the th original data point, represents the adjustment coefficient of the coordinate value of the symmetric data point corresponding to the th original data point, is the natural constant, is the th data in each subsequence, represents the angle between the target symmetry line and the horizontal line, represents the th target angle of the original data point; represents the coordinate value of the adjusted data point after the final adjustment of the th original data point; is the activation function; is the sine function; Among them, when adjusting the data point through the horizontal and vertical coordinate values are adjusted separately.

[0018] Furthermore, predicting through the adjusted data points corresponding to each subsequence to obtain each prediction subsequence includes: Sorting all the adjusted data points after the final adjustment corresponding to each subsequence in chronological order to form a new sequence, denoted as each reorganized subsequence; predicting according to each reorganized subsequence using the GRU neural network, and forming each prediction subsequence with the predicted data in chronological order.

[0019] Furthermore, superimposing all the prediction subsequences to obtain the predicted signal data of the final frequency modulation command includes: Performing curve fitting on each prediction subsequence through the least squares method to obtain the predicted fitting curve of each prediction subsequence; superimposing the predicted fitting curves of all the prediction subsequences in the frequency domain through Fourier transform to obtain the predicted signal data of the final frequency modulation command.

[0020] The second aspect of the present invention is to provide a prediction system for energy storage frequency modulation commands based on symmetric adjustment, including: Data acquisition module: used to obtain frequency modulation command signals; Symmetric adjustment module: used to decompose the frequency modulation command signal to obtain a number of component signals, then discretely divide each component signal to obtain subsequences corresponding to each component signal; taking time as the horizontal axis and the data in each subsequence as the vertical axis, construct a coordinate system, map all the data in each subsequence into the coordinate system to obtain a number of original data points; determine the target symmetry line of each subsequence according to the distribution of all the original data points in each subsequence; according to the target symmetry line of each subsequence, perform symmetric mapping on each original data point to obtain the symmetric data point of each original data point; Fusion prediction module: used to fuse the original data points and the symmetric data points to obtain the adjusted data points after final adjustment; predict through the adjusted data points corresponding to each subsequence to obtain each predicted subsequence; Superposition module: used to superpose all the predicted subsequences to obtain the predicted signal data of the final frequency modulation command.

[0021] The third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for predicting energy storage frequency modulation commands based on symmetric adjustment.

[0022] The fourth aspect of the present invention is to provide a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method for predicting energy storage frequency modulation commands based on symmetric adjustment.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows: decompose the frequency modulation command signal to obtain a number of component signals, then discretely divide each component signal to obtain subsequences corresponding to each component signal for subsequent analysis of the distribution of each component data; determine the target symmetry line of each subsequence according to the distribution of all the original data points in each subsequence; according to the target symmetry line of each subsequence, perform symmetric mapping on each original data point to obtain the symmetric data point of each original data point, reducing the influence of the non-linear degree in the component; fuse the original data points and the symmetric data points to obtain the adjusted data points after final adjustment; predict through the adjusted data points corresponding to each subsequence to obtain each predicted subsequence, improving the accuracy of predicting the component signal; superpose all the predicted subsequences to obtain the predicted signal data of the final frequency modulation command, improving the response speed to emergencies and enhancing the stability of the system. Description of the Drawings

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0025] Figure 1 It is a schematic flow chart of the steps of a method for predicting energy storage frequency modulation commands based on symmetric adjustment provided by the present invention; Figure 2 It is a schematic module flow chart of a system for predicting energy storage frequency modulation commands based on symmetric adjustment provided by the present invention. Detailed implementation manners

[0026] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] In response to the problems in the background art, a method and system for predicting energy storage frequency modulation commands based on symmetric adjustment are studied and designed, which has important practical significance.

[0029] As Figure 1 shown, the first aspect of the present invention is to provide a method for predicting energy storage frequency modulation commands based on symmetric adjustment, including the following steps: Step S001: Collect frequency modulation command signals.

[0030] It should be noted that when there are deviations in the power supply-demand balance (such as load changes, power generation fluctuations, etc.), the dispatching center or automation system of the power system generates frequency regulation commands according to the frequency fluctuations of the system, load demand, and power generation conditions, and maintains frequency stability through the frequency regulation commands, which can respond to emergencies and reduce the possibility of failures.

[0031] Furthermore, it should be noted that in order to respond to emergencies more quickly, it can be predicted by collecting frequency regulation commands to respond more quickly. Therefore, the frequency regulation commands are collected for prediction to respond to emergencies or failures more quickly.

[0032] Specifically, obtain the frequency regulation command signal of the power system dispatching center for a preset duration hours before the current moment. Among them, in this embodiment, the preset duration Among them, in this embodiment, the preset duration is not specifically limited, and the implementer can determine it according to specific circumstances.

[0033] Thus, the frequency regulation command signal is obtained.

[0034] Step S002: Decompose the frequency regulation command signal to obtain a number of component signals, then discretely divide each component signal to obtain a subsequence corresponding to each component signal; use time as the horizontal axis and the data in each subsequence as the vertical axis to construct a coordinate system, map all the data in each subsequence into the coordinate system to obtain a number of original data points; determine the target symmetry line of each subsequence according to the distribution of all the original data points in each subsequence; perform symmetric mapping on each original data point according to the target symmetry line of each subsequence to obtain the symmetric data point of each original data point.

[0035] It should be noted that since the non-linearity of some component signals is very high, resulting in poor prediction results, in order to solve such problems, a symmetry-based adjustment method is proposed to improve the smoothness of the data and reduce the influence of the non-linearity degree of the component signals on the prediction results.

[0036] Furthermore, it should be noted that for symmetry adjustment, each component signal is discretized to obtain discrete data points, and the symmetry line is determined through the concentrated distribution of the data points, and the discrete data points are symmetrically adjusted through the symmetry line.

[0037] Specifically, decompose the frequency regulation command signal through the VMD decomposition algorithm to obtain a number of component signals; at a preset time interval Each component signal is divided to obtain a number of corresponding data after the division of each component signal. According to the time sequence, the number of data is grouped into a sequence, which is recorded as the subsequence corresponding to each component signal. Among them, the VMD decomposition algorithm is a well-known technology and will not be specifically described here. Among them, in this embodiment, the preset time interval seconds, where the preset time interval is not specifically limited, and the implementer can determine it according to the specific situation.

[0038] So far, the subsequence corresponding to each component signal is obtained.

[0039] It should be noted that for selecting the symmetry line, which is equivalent to a mean line, making more data points on the symmetry line and the remaining data points fluctuate on both sides of the symmetry line, so that the symmetry adjustment can be well carried out.

[0040] Specifically, with time as the horizontal axis and the data in each subsequence as the vertical axis, a coordinate system is constructed, and all the data in each subsequence are mapped into the coordinate system to obtain a number of original data points; through the distribution of all the original data points in each subsequence, the angular range of the symmetry line is determined; the angular range of the symmetry line is specifically expressed by the formula:

[0041] Among them, the lower limit value of the angular range of the symmetry line is specifically expressed by the formula:

[0042] Among them, the upper limit value of the angular range of the symmetry line is specifically expressed by the formula:

[0043] In the formula, represents the set of all data in each subsequence, represents the maximum value of the absolute values of all data in each subsequence, represents the minimum value of the absolute values of all data in each subsequence, represents the minimum value of all data in each subsequence, represents the maximum value of all data in each subsequence, is the absolute value symbol, represents the arctangent function, represents function, which is used for data normalization; represents the minimum angle of the angular range of the symmetry line, represents the maximum angle of the angular range of the symmetry line; represents the angle between the symmetry line and the horizontal line.

[0044] From the minimum angle of the symmetric line angle range start, and go all the way to the maximum angle of the symmetric line angle range , and each time increase and iterate with the preset angle as the increased angle degree; during the iteration process, re-determine an iteration reference angle every time it changes; through each iteration reference angle, determine each reference symmetric line corresponding to each iteration reference angle (the iteration reference angle is the included angle between the reference symmetric line and the horizontal line), and obtain the number of original data points existing on each reference symmetric line, which is recorded as the reference data volume of each reference symmetric line. Among them, in this embodiment, the analysis is carried out with the preset angle as , where the preset angle is not specifically limited in this embodiment, and the implementer can determine it according to the specific situation.

[0045] Take the reference symmetric line with the largest reference data volume as the target symmetric line of each subsequence.

[0046] It should be noted that the symmetric line passes through the origin of the coordinate system.

[0047] Perform symmetric mapping on a number of original data points in the coordinate system through the target symmetric line. During the symmetric mapping process, first determine the included angle between the extension line of each original data point and the corresponding target symmetric line, and record it as the target angle of each original data point; obtain the symmetric data point of each original data point according to the target angle of each original data point; The target angle of each original data point is specifically expressed by the formula as follows:

[0048] In the formula, represents the minimum value of all data in each subsequence, represents the maximum value of the absolute values of all data in each subsequence, is the absolute value symbol, represents the arctangent function, represents function, used for data normalization, represents the th data value in each subsequence, represents the th target angle of the original data point.

[0049] Through the target angle of each original data point, first determine the intersection point of the extension line of the original data point on the target symmetric line, which is recorded as the target symmetric line intersection point of each original data point; record the direction from each original data point to the corresponding target symmetric line intersection point as the target extension direction of each original data point; Then the original data points are extended along the target extension direction to obtain symmetric data points; among them, in the process of adjusting to obtain symmetric data points, the angle between the symmetric data points and the target symmetry line is equal to the target angle of the original data points, and the distance from the symmetric data points to the intersection point of the target symmetry line is equal to the distance from the original data points to the intersection point of the target symmetry line.

[0050] Thus, the symmetric data points after symmetric mapping of each original data point are obtained.

[0051] Step S003: Fuse the original data points and the symmetric data points to obtain the adjusted data points after final adjustment; predict through the adjusted data points corresponding to each subsequence to obtain each predicted subsequence.

[0052] It should be noted that since the symmetric data points have strong symmetry and their symmetric data points are closer to the target symmetry line, the symmetric data points have a strong degree of linearity. Therefore, by fusing the symmetric data points with the previous original data points to adjust the original data points, the adjusted data points after final adjustment are obtained, which can not only retain the information of the original data but also improve the linear degree of the original data.

[0053] Specifically, the formula for fusing the original data points and the symmetric data points is specifically expressed as:

[0054] Among them, the adjustment coefficient of the coordinate value of the original data point in the fusion process is specifically expressed by the formula:

[0055] Among them, the adjustment coefficient of the coordinate value of the symmetric data point in the fusion process is specifically expressed by the formula:

[0056] In the formula, represents the coordinate value of the th original data point, represents the coordinate value of the symmetric data point corresponding to the th original data point, represents the adjustment coefficient of the coordinate value of the th original data point, represents the adjustment coefficient of the coordinate value of the symmetric data point corresponding to the th original data point, is the natural constant, is the th data in each subsequence, represents the angle between the target symmetry line and the horizontal line, represents the th target angle of the original data point; Denote the coordinate value of the adjusted data point after the final adjustment of the th original data point; is the activation function; is the sine function.

[0057] Among them, when adjusting the data points through , the horizontal and vertical coordinate values are adjusted separately.

[0058] Sort all the adjusted data points corresponding to each subsequence after the final adjustment in chronological order to form a new sequence, denoted as each reorganized subsequence; use the GRU neural network to make predictions according to each reorganized subsequence, and form each predicted subsequence with the predicted data in chronological order.

[0059] Among them, when using the GRU neural network for prediction, the prediction error is measured by the metrics MAE (Mean Absolute Error), SSE (Sum of Squared Errors), RMSE (Root Mean Square Error), MAPE (Mean Absolute Percentage Error), that is, used to measure the prediction results before and after the adjustment of the analysis component signal. Among them, the loss function in the GRU neural network is the cross-entropy loss function.

[0060] Among them, the GRU neural network, MAE, SSE, RMSE, MAPE, and the cross-entropy loss function are all well-known technologies, and will not be elaborated specifically here.

[0061] Step S004: Superimpose all the predicted subsequences to obtain the predicted signal data of the final frequency modulation command.

[0062] Through the least squares method, perform curve fitting on each predicted subsequence to obtain the predicted fitting curve of each predicted subsequence. Superimpose the predicted fitting curves of all the predicted subsequences in the frequency domain through Fourier transform to obtain the predicted signal data of the final frequency modulation command.

[0063] Among them, the prediction error analysis is respectively carried out on the sequence after predicting the frequency modulation command signal and the predicted signal data of the final frequency modulation command through the above four indicators; among them, the corresponding error indicators for the sequence after predicting the frequency modulation command signal are MAE of 8.22, SSE of 98.12, RMSE of 0.97, and MAPE of 16.02%; while the corresponding error indicators for each predicted subsequence after adjustment are MAE of 0.56, SSE of 10.01, RMSE of 0.25, and MAPE of 4.13%; judging from the results, the prediction error is significantly reduced, that is, the prediction accuracy is significantly improved.

[0064] Among them, the least squares method and the Fourier transform are both well-known technologies, and no specific elaboration will be carried out here.

[0065] As Figure 2 shown, the second aspect of the present invention is to provide a prediction system for energy storage frequency modulation commands based on symmetric adjustment, including the following modules: Data acquisition module 101: used to obtain the frequency modulation command signal; Symmetric adjustment module 102: used to decompose the frequency modulation command signal to obtain several component signals, then discretely divide each component signal to obtain the subsequence corresponding to each component signal; taking time as the horizontal axis and the data in each subsequence as the vertical axis, construct a coordinate system, map all the data in each subsequence into the coordinate system to obtain several original data points; according to the distribution of all the original data points in each subsequence, determine the target symmetry line of each subsequence; according to the target symmetry line of each subsequence, perform symmetric mapping on each original data point to obtain the symmetric data point of each original data point; Fusion prediction module 103: used to fuse the original data points and the symmetric data points to obtain the adjusted data points after final adjustment; predict through the adjusted data points corresponding to each subsequence to obtain each predicted subsequence; Superposition module 104: used to superpose all the predicted subsequences to obtain the predicted signal data of the final frequency modulation command.

[0066] The third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for predicting energy storage frequency modulation commands based on symmetric adjustment.

[0067] The fourth aspect of the present invention is to provide a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements a method for predicting energy storage frequency modulation commands based on symmetric adjustment.

[0068] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) that contain computer-usable program code.

[0069] The present invention is described with reference to the flowcharts and / or block diagrams of methods, systems, and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0070] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still can modify the specific implementation manners of the present invention or make equivalent replacements, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the present invention.

Claims

1. A method for predicting energy storage frequency modulation commands based on symmetric adjustment, characterized in that Including: Obtain a frequency modulation command signal; Decompose the frequency modulation command signal to obtain a number of component signals, and then perform discrete partitioning on each component signal to obtain a subsequence corresponding to each component signal; With time as the horizontal axis and the data in each subsequence as the vertical axis, construct a coordinate system, map all the data in each subsequence into the coordinate system, and obtain a number of original data points; Determine the target symmetry line of each subsequence according to the distribution of all the original data points in each subsequence; Perform symmetric mapping on each original data point according to the target symmetry line of each subsequence to obtain the symmetric data point of each original data point; Fuse the original data points and the symmetric data points to obtain the adjusted data points after final adjustment; Perform prediction through the adjusted data points corresponding to each subsequence to obtain each predicted subsequence; Superimpose all the predicted subsequences to obtain the predicted signal data of the final frequency modulation command.

2. The energy storage frequency modulation command prediction method based on symmetric adjustment according to claim 1, wherein The decomposition of the frequency modulation command signal to obtain a number of component signals, and then the discrete partitioning of each component signal to obtain a subsequence corresponding to each component signal includes: The FM command signal is decomposed by the VMD decomposition algorithm to obtain a number of component signals; at a preset time interval to divide each component signal to obtain a number of corresponding data after the division of each component signal. According to the time sequence, the number of data is formed into a set of sequences, which is recorded as the subsequence corresponding to each component signal.

3. A method for predicting energy storage frequency modulation commands based on symmetric adjustment according to claim 1, characterized in that The determination of the target symmetry line of each subsequence according to the distribution of all the original data points in each subsequence includes: Determine the angular range of the symmetry line through the distribution of all the original data points in each subsequence; the angular range of the symmetry line is specifically expressed by the formula: Among them, the lower limit value of the angular range of the symmetry line is specifically expressed by the formula: Among them, the upper limit value of the angular range of the symmetry line is specifically expressed by the formula: In the formula, represents the set of all data in each subsequence, represents the maximum value of the absolute values of all data in each subsequence, represents the minimum value of the absolute values of all data in each subsequence, represents the minimum value of all data in each subsequence, represents the maximum value of all data in each subsequence, is the absolute value symbol, represents the arctangent function, represents function, used for data normalization; represents the minimum angle of the symmetric line angle range, represents the maximum angle of the symmetric line angle range; represents the angle between the symmetric line and the horizontal line; From the minimum angle of the symmetric line angle range starting, until the maximum angle of the symmetric line angle range , each time increasing and iterating by a preset angle as the increased angle degree; during the iteration process, each time it changes, a new iteration reference angle is determined; through each iteration reference angle, each reference symmetric line corresponding to each iteration reference angle is determined, the number of original data points existing on each reference symmetric line is obtained, and is recorded as the reference data volume of each reference symmetric line; Take the reference symmetry line with the largest amount of reference data as the target symmetry line of each subsequence.

4. A method for predicting energy storage frequency modulation commands based on symmetric adjustment according to claim 1, characterized in that The symmetric mapping of each original data point according to the target symmetry line of each subsequence to obtain the symmetric data point of each original data point includes: Wherein, represents the minimum value of all data in each subsequence, represents the maximum value of the absolute values of all data in each subsequence, is the absolute value symbol, represents the arctangent function, represents function, used for data normalization, represents the th data value of the data in each subsequence, represents the th target angle of the original data point; wherein, the target angle is the angle between the extension line of the original data point and the corresponding target symmetry line; Through the target angle of each original data point, first determine the intersection point of the extension line of the original data point on the target symmetry line, denoted as the target symmetry line intersection point of each original data point; denote the direction from each original data point to the corresponding target symmetry line intersection point as the target extension direction of each original data point; Then the original data point extends along the target extension direction to obtain the symmetric data point; among them, in the process of adjusting to obtain the symmetric data point, it is satisfied that the angle between the symmetric data point and the target symmetry line is equal to the target angle of the original data point, and the distance from the symmetric data point to the target symmetry line intersection point is equal to the distance from the original data point to the target symmetry line intersection point.

5. A method for predicting energy storage frequency modulation commands based on symmetric adjustment according to claim 4, characterized in that The fusion of the original data points and the symmetric data points to obtain the adjusted data points after final adjustment includes: The formula for fusing the original data points and the symmetric data points is specifically expressed as: Among them, the adjustment coefficient of the coordinate value of the original data point in the fusion process is specifically expressed by the formula: Among them, the adjustment coefficient of the coordinate value of the symmetric data point in the fusion process is specifically expressed by the formula: In the formula, represents the coordinate value of the th original data point, represents the coordinate value of the symmetric data point corresponding to the th original data point, represents the adjustment coefficient of the coordinate value of the th original data point, represents the adjustment coefficient of the coordinate value of the symmetric data point corresponding to the th original data point, is the natural constant, is the th data in each subsequence, represents the angle between the target symmetry line and the horizontal line, represents the th target angle of the original data point; represents the coordinate value of the adjusted data point after the final adjustment of the th original data point; is the activation function; is the sine function; Among them, when adjusting data points through the horizontal and vertical coordinate values are adjusted separately.

6. The method for predicting energy storage frequency modulation instructions based on symmetric adjustment according to claim 1, wherein The prediction through the adjusted data points corresponding to each subsequence to obtain each predicted subsequence includes: For each subsequence corresponding to all the adjusted data points after final adjustment, sort them in chronological order to form a new sequence, denoted as each reorganized subsequence; use a GRU neural network to make predictions based on each reorganized subsequence, and form each predicted subsequence with the predicted data in chronological order.

7. A method for predicting energy storage frequency modulation commands based on symmetric adjustment according to claim 1, characterized in that Superimposing all the predicted subsequences to obtain the predicted signal data of the final frequency modulation command, including: Through the least squares method, perform curve fitting on each predicted subsequence to obtain the predicted fitting curve of each predicted subsequence; perform superposition on the predicted fitting curves of all predicted subsequences in the frequency domain through Fourier transform to obtain the predicted signal data of the final frequency modulation command.

8. A prediction system for energy storage frequency modulation commands based on symmetric adjustment, characterized in that, Including: Data acquisition module: used to obtain the frequency modulation command signal; Symmetric adjustment module: used to decompose the frequency modulation command signal to obtain several component signals, and then perform discrete division on each component signal to obtain the subsequence corresponding to each component signal; Construct a coordinate system with time as the horizontal axis and the data in each subsequence as the vertical axis, map all the data in each subsequence into the coordinate system to obtain several original data points; Determine the target symmetry line of each subsequence according to the distribution of all the original data points of each subsequence; Perform symmetric mapping on each original data point according to the target symmetry line of each subsequence to obtain the symmetric data point of each original data point; Fusion prediction module: used to fuse the original data points and the symmetric data points to obtain the adjusted data points after final adjustment; Make predictions through the adjusted data points corresponding to each subsequence to obtain each predicted subsequence; Superposition module: used to superimpose all the predicted subsequences to obtain the predicted signal data of the final frequency modulation command.

9. An electronic device, characterized in that, Including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, it implements the method for predicting an energy storage frequency modulation command based on symmetric adjustment according to any one of claims 1-7.

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 the processor, it implements the method for predicting an energy storage frequency modulation command based on symmetric adjustment according to any one of claims 1-7.

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