Hybrid energy storage frequency modulation method and system for calculating VMD modal aliasing amount

Abstract

The invention discloses a hybrid energy storage frequency modulation method for calculating a VMD modal aliasing amount. Acquiring a frequency modulation instruction signal; decomposing the frequency modulation instruction signal into a plurality of modal quantities based on a preset algorithm, and calculating an original average aliasing number; adding a suppression function to the frequency modulation instruction signal according to the average aliasing number; calculating an average aliasing number after suppression; comparing the suppressed average aliasing number with a predetermined threshold condition; when the predetermined threshold condition is not met, further generating a suppression function based on the suppressed average aliasing number; when a preset threshold condition is met, restoring the plurality of modal quantities; applying the reduced modal quantity to power distribution in hybrid energy storage; by calculating the aliasing amount and correcting aliasing, the overlapping degree of adjacent modes can be quantified, then an optimization algorithm is executed to obtain an optimal decomposition subsequence, frequency modulation instruction distribution is more accurate, the problem of inaccurate frequency modulation instruction signal distribution caused by mode aliasing is solved, the cost of an energy storage system is reduced, and the income of a power plant is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power grid frequency regulation, and in particular to a hybrid energy storage frequency regulation method and system for calculating VMD modal aliasing. Background Art

[0002] At present, in major regional power grids in my country, large hydropower and thermal power units (coal-fired units / gas-fired units) are mainly used as power grid frequency regulation power sources. The frequency regulation power output is adjusted to respond to system frequency changes. However, hydropower and thermal power units have certain limitations in frequency regulation. In recent years, the increase in the proportion of new energy power generation such as wind power and solar power has led to a large number of thermal power units bearing heavy AGC regulation tasks for a long time, resulting in a series of negative effects such as increased coal consumption for power generation and serious equipment wear. The existing power frequency regulation resources can no longer meet the demand for renewable energy access to the grid. The hybrid energy storage system with supercapacitors participates in the coordinated control of primary frequency regulation. By dividing the typical working conditions, the frequency deviation is converted to power according to the optimization decision module, and the charge / discharge control of the supercapacitor energy storage system is realized through the PCS system control module, and finally assists the traditional generator set to complete the primary frequency regulation process. Within the rated power range, the specified power output can be completed within 1s with an accuracy of more than 99%. Its comprehensive response capability fully meets the power conversion requirements within the time scale of AGC frequency regulation, which greatly exceeds the regulation capacity of conventional thermal power plants. The more urgent the system's regulation needs, the more obvious the advantages of this technology.

[0003] The hybrid energy storage system based on the above-mentioned supercapacitor energy storage usually uses VMD algorithm to realize the distribution of frequency modulation signals. The traditional hybrid energy storage (supercapacitor + lithium battery) assists the frequency modulation method of thermal power units, which is to pass the difference between the frequency modulation command and the thermal power unit to the hybrid energy storage, and use VMD decomposition technology to decompose the original signal into K subsequences IMF1 to IMF K , where the low frequency part is borne by the battery, and the high frequency part is borne by the overcapacitor. VMD is an adaptive, completely non-recursive modal variation and signal processing method. This technology has the advantage of being able to determine the number of modal decompositions. Its adaptability is reflected in determining the number of modal decompositions of a given sequence according to actual conditions. In the subsequent search and solution process, it can adaptively match the optimal center frequency and limited bandwidth of each mode, and can achieve effective separation of intrinsic modal components (IMFs), frequency domain division of signals, and then obtain effective decomposition components of a given signal, and finally obtain the optimal solution to the variational problem. Decomposition obtains relatively stable subsequences containing multiple different frequency scales, which is suitable for non-stationary sequences.

[0004] However, the traditional VMD decomposition technology has the problem of modal aliasing. For example, a modal component IMFi may contain both high-frequency components and low-frequency components, which is not conducive to the power allocation of batteries and supercapacitors.

[0005] When processing frequency modulation signals, the aliasing degree generated by VMD algorithm decomposition will use historical data to train the selected model, and then complete the prediction of the aliasing degree of the frequency modulation signal during the processing. However, the aliasing degree value predicted based on the frequency modulation instruction is obtained by the historical model prediction, which has errors, resulting in unsatisfactory frequency modulation effect and poor operation economy.

[0006] The existing direct calculation method does not have relevant quantitative parameters for the degree of aliasing of adjacent modes, and cannot accurately quantify the degree of modal aliasing after decomposition, and cannot realize direct calculation of modal aliasing. Summary of the invention

[0007] In view of the shortcomings of the prior art, the purpose of the present invention is to propose a new hybrid energy storage frequency modulation method for calculating the VMD modal aliasing amount. By calculating the aliasing amount and correcting the aliasing, the degree of overlap of adjacent modes can be quantified, and then an optimization algorithm can be executed.

[0008] A hybrid energy storage frequency modulation method for calculating VMD modal aliasing; characterized in that: S101, obtaining a frequency modulation command signal; S102, decomposing the frequency modulation command signal into a plurality of modal subsequences based on a preset algorithm, and calculating the original average aliasing number using an average aliasing number algorithm, wherein the average aliasing number algorithm first calculates the aliasing feature sequence of each subsequence, then calculates the shared aliasing feature sequence of adjacent subsequences, and obtains the aliasing numbers of adjacent subsequences, and sums and averages the aliasing numbers of all two adjacent subsequences to obtain an average aliasing number; S103, adding a suppression function to the frequency modulation instruction signal according to the average aliasing number; and using the average aliasing number algorithm in step S102 to calculate the average aliasing number after suppression; S104, comparing the average aliasing number after suppression with a predetermined threshold condition; When the predetermined threshold condition is not met, the suppression function is generated again based on the average aliasing number after suppression; and steps S103-S104 are repeated; When the predetermined threshold condition is met, proceed to step S105; S105. After the average aliasing number after suppression reaches a predetermined threshold condition, restore a number of modal subsequences; and apply the restored modal subsequences to power allocation in hybrid energy storage.

[0009] Further, In step S102, the frequency modulation instruction signal is decomposed based on a preset algorithm; the decomposition level is determined, and the frequency modulation instruction is decomposed by VMD decomposition; the VMD decomposition level is K, and the frequency modulation instruction is decomposed into K subsequences IMF1, IMF2, IMF3, ..., IMF K .

[0010] Further, In step S102, each subsequence IMF1-IMF K Aliasing characteristic sequence; for subsequence IMF i Derivatives are taken, and an aliasing characteristic sequence is formed by points where the derivative is 0 and points where the derivative does not exist.

[0011] Further, subsequenceIMF i =f(x), there are m points where the derivative is 0 and where the derivative does not exist; The m points are arranged in the order from front to back along the coordinate axis: (t1, f(t1)), (t2, f(t2)), (t3, f(t3)), ..., (t m , f(t m )), then the subsequence IMF i The aliasing characteristic sequence of is expressed as, [(t2-t1),sigmoid(avg(f(t1),f(t2)))],[(t3-t2),sigmoid(avg(f(t2),f(t3)))],...,[(t m -t m-1 ),sigmoid(avg(f(t m ),f(t m-1 )))], where sigmoid() is the activation function and avg() is the mean.

[0012] Further, Calculate the shared aliasing feature sequence of adjacent subsequences, the number of shared aliasing feature sequences of two adjacent subsequences is the adjacent subsequence aliasing number; The aliasing features in the shared aliasing feature sequence must meet the following conditions: The ratio of the maximum horizontal coordinate to the minimum horizontal coordinate is not greater than a first preset value; The ratio of the maximum ordinate to the minimum ordinate is not greater than a second preset value.

[0013] Further, After calculating the aliasing numbers of all two-by-two adjacent subsequences, they are summed and averaged to obtain an average aliasing number; the average aliasing number is used to indicate the aliasing degree of the K sequences decomposed by the current frequency modulation instruction.

[0014] Further, Step S103 includes: According to the average aliasing number, a suppression function r is added to the frequency modulation instruction Pt to suppress the degree of aliasing, and the corrected frequency modulation instruction Pt+r is obtained. ; Where gelu() is the activation function, sin() is the trigonometric function, Q is the average aliasing number, and t is the time parameter; The average aliasing number algorithm in step S102 is used to decompose the corrected frequency modulation instruction Pt+r, and obtain the suppressed average aliasing number Q1.

[0015] Further, Step S104 includes: Determine whether the average aliasing number Q1 after suppression is less than half of the original average aliasing number; If it is not less than half of the original average aliasing number, a new suppression function r1 is generated; , Q1 is the average aliasing number, Decompose the corrected frequency modulation instruction Pt+r1 using the average aliasing number algorithm in step S102, and obtain the suppressed average aliasing number Q2; Determine whether the average aliasing number Q2 after suppression is less than half of the original average aliasing number; If it is not less than half of the original average aliasing number, a new suppression function r2 is generated; , Q2 is the average aliasing number, The cycle continuously generates a new average aliasing number Q j , and the average aliasing number Q j Compare with half the original average aliasing number; Until the predetermined threshold condition is met, that is, the average aliasing number Q j Less than half of the original average aliasing number.

[0016] Further, Step S105 includes: When the predetermined threshold condition is met, the average aliasing number Q j When the calculated Q is less than half of the original average aliasing number, j It is based on the modified frequency modulation instruction Pt+r j Based on the modified frequency modulation instruction Pt+r j The generated subsequence IMF1-IMF K Perform a reduction operation; the calculation formula for the reduction operation is, , where sigmoid() is the activation function and rand() is the random function; Through the restoration operation, the subsequence IMF1-IMF for the frequency modulation instruction Pt is regenerated K ; The restored modal components are applied to power distribution in hybrid energy storage.

[0017] A hybrid energy storage frequency modulation system for calculating VMD modal aliasing is also provided; it is characterized in that: A command acquisition module, which acquires a frequency modulation command signal; An initial decomposition module decomposes the frequency modulation command signal into a plurality of modal subsequences based on a preset algorithm, and calculates the original average aliasing number using an average aliasing number algorithm, wherein the average aliasing number algorithm first calculates the aliasing feature sequence of each subsequence, then calculates the shared aliasing feature sequence of adjacent subsequences, and obtains the aliasing number of adjacent subsequences, and sums and averages the aliasing numbers of all two adjacent subsequences to obtain an average aliasing number; The suppression correction module adds a suppression function to the frequency modulation command signal according to the average aliasing number; and uses the average aliasing number algorithm to calculate the average aliasing number after suppression; A judgment and comparison module compares the average aliasing number after suppression with a predetermined threshold condition; When the predetermined threshold condition is not met, a suppression function is generated again based on the average aliasing number after suppression; modal decomposition and average aliasing number are calculated cyclically; When the predetermined threshold condition is met, the loop is exited; The restoration allocation module restores several modal subsequences after the average aliasing number after suppression reaches a predetermined threshold condition; and applies the restored modal subsequences to power allocation in the hybrid energy storage.

[0018] The beneficial effects of the present invention are: The present disclosure provides a hybrid energy storage frequency modulation method for calculating the VMD modal aliasing amount, which can respond in time when the power plant power grid frequency fluctuates. By calculating the aliasing amount and correcting the aliasing, the overlapping degree of adjacent modes can be quantified, and then an optimization algorithm is executed to obtain the optimal decomposition subsequence, thereby making the frequency modulation instruction allocation more accurate, solving the problem of inaccurate frequency modulation instruction signal allocation caused by modal aliasing, reducing the cost of the energy storage system, and improving the power plant revenue. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A diagram of the method steps.

[0020] Figure 2 It is a processing flow chart. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0023] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0024] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0025] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0026] The present disclosure provides a hybrid energy storage frequency modulation method for calculating VMD modal aliasing, such as Figure 1 As shown, including: S101, obtaining a frequency modulation command signal; S102, decomposing the frequency modulation command signal into a plurality of modal subsequences based on a preset algorithm, and calculating the original average aliasing number using an average aliasing number algorithm, wherein the average aliasing number algorithm first calculates the aliasing feature sequence of each subsequence, then calculates the shared aliasing feature sequence of adjacent subsequences, and obtains the aliasing numbers of adjacent subsequences, and sums and averages the aliasing numbers of all two adjacent subsequences to obtain an average aliasing number; S103, adding a suppression function to the frequency modulation instruction signal according to the average aliasing number; and using the average aliasing number algorithm in step S102 to calculate the average aliasing number after suppression; S104, comparing the average aliasing number after suppression with a predetermined threshold condition; When the predetermined threshold condition is not met, a suppression function is further generated based on the average aliasing number after suppression; and steps S103-S104 are repeated; When the predetermined threshold condition is met, proceed to step S105; S105. After the average aliasing number after suppression reaches a predetermined threshold condition, restore a number of modal subsequences; and apply the restored modal subsequences to power allocation in hybrid energy storage.

[0027] The frequency modulation method based on supercapacitor energy storage provided by the embodiment of the present disclosure adopts new supercapacitors and lithium batteries as energy storage devices and modulates the frequency of the power grid in the power plant when the power supply frequency fluctuates. The state of the power plant power grid is monitored in real time. When the power supply frequency in the power plant power grid fluctuates, a corresponding frequency modulation command signal is generated. After the signal is obtained, the frequency modulation command signal is cyclically suppressed by adding a suppression function, calculating the average aliasing number and other constraints, and the power plant power grid is frequency modulated using the command allocation corresponding to the optimal suppression function.

[0028] In another embodiment provided by the present disclosure, the above step "S102, decomposing the frequency modulation instruction signal into a plurality of modal subsequences based on a preset algorithm, and calculating the original average aliasing number using an average aliasing number algorithm, wherein the average aliasing number algorithm first calculates the aliasing feature sequence of each subsequence, and then calculates the shared aliasing feature sequence of adjacent subsequences, and obtains the aliasing number of adjacent subsequences, and sums and averages the aliasing numbers of all two adjacent subsequences to obtain an average aliasing number" includes: Step 1: Receive a frequency modulation instruction; let the frequency modulation instruction be Pt; Pt is a function of a time parameter t.

[0029] Step 2: Decompose the frequency modulation instruction signal based on a preset algorithm; determine the decomposition level, and decompose the frequency modulation instruction by VMD decomposition; set the VMD decomposition level to K, and decompose the frequency modulation instruction into K subsequences IMF1, IMF2, IMF3, ..., IMF K .

[0030] Step 3: Calculate the aliasing characteristic sequence of each subsequence and find IMF1-IMF in turn K Aliasing characteristic sequence of a sequence.

[0031] Suppose a subsequence IMF i =f(x), find the points where the derivative of the function is 0 and the points where the corresponding derivative does not exist. Suppose there are m points where the derivative is 0 and the points where the derivative does not exist.

[0032] The m points are arranged in the order from front to back along the coordinate axis: (t1, f(t1)), (t2, f(t2)), (t3, f(t3)), ..., (t m , f(t m )), where the horizontal axis is time t and the vertical axis is f(t); then the subsequence IMF i The aliasing characteristic sequence can be expressed as, [(t2-t1),sigmoid(avg(f(t1),f(t2)))],[(t3-t2),sigmoid(avg(f(t2),f(t3)))],...,[(t m -tm-1 ),sigmoid(avg(f(t m ),f(t m-1 )))]. Where sigmoid() is the activation function and avg() is the mean.

[0033] Relative to the subsequence IMF in step 3 i , its adjacent subsequence IMF i-1 , Let the adjacent subsequence IMF i-1 =f(x), find the points where the derivative of the function is 0 and the points where the corresponding derivative does not exist. Suppose there are n points where the derivative is 0 and the points where the derivative does not exist.

[0034] Similarly, the above n points are arranged in the order from front to back according to the coordinate axis as follows (t 12 ,f(t 12 )),(t 22 ,f(t 22 )),(t 32 ,f(t 32 )),…,(t n2 ,f(t n2 )), subsequence IMF i-1 The aliasing characteristic sequence of is expressed as, [(t 22 -t 12 ),sigmoid(avg(f(t 12 ),f(t 22 )))],[(t 32 -t 22 ),sigmoid(avg(f(t 22 ),f(t 32 )))],...,[(t n2 -t (n-1)2 ),sigmoid(avg(f(t n2 ),f(t (n-1)2 )))].

[0035] Step 4: Calculate the shared aliasing feature sequence of adjacent subsequences to obtain the adjacent subsequence aliasing number.

[0036] As the IMF i and IMF i-1 When comparing the aliasing feature quantities of each pair in adjacent subsequences, the following conditions need to be met: Condition 1: The ratio of the maximum horizontal coordinate to the minimum horizontal coordinate is not greater than 1.1; Condition 2: The ratio of the maximum ordinate to the minimum ordinate is not greater than 1.05.

[0037] For example, IMF i The aliasing feature sequence of the subsequence includes three aliasing feature quantities [1,1], [2,3], and [1.8 7.6]; IMF i-1 The aliasing feature sequence of the subsequence includes three aliasing feature quantities [1.09, 1.02], [2.01, 3.11], and [1.8 12]; Among them, after the comparison operation between each pair of aliased features, [1,1] and [1.09,1.02] satisfy the above two ratio conditions, 1.09 / 1<1.1, 1.02 / 1<1.05; [2,3] and [2.01,3.11], 2.01 / 2<1.1, 3.11 / 3<1.05, satisfy the above two ratio conditions; Then the shared aliasing feature sequence of two adjacent subsequences includes the following two pairs of aliasing feature quantities, [1,1] and [1.09,1.02], [2,3] and [2.01,3.11].

[0038] It can be seen that the aliasing number of adjacent subsequences is 2.

[0039] Step 5: Calculate the aliasing numbers of all two adjacent subsequences and then sum and average them to obtain an average aliasing number Q. The average aliasing number Q is used to indicate the aliasing degree of the K sequences decomposed by the current frequency modulation instruction.

[0040] In another embodiment provided by the present disclosure, the above step "S103, adding a suppression function to the frequency modulation instruction signal according to the average aliasing number; and calculating the average aliasing number after suppression" includes: Step 1: Add a suppression function r for suppressing the aliasing degree to the frequency modulation instruction Pt according to the average aliasing number to obtain the corrected frequency modulation instruction Pt+r. , Where gelu() is the activation function, sin() is the trigonometric function, Q is the average aliasing number, and t is the time parameter.

[0041] Step 2: Use the average aliasing number algorithm in step S102 to decompose the corrected frequency modulation instruction Pt+r and obtain the suppressed average aliasing number Q1.

[0042] In another embodiment provided by the present disclosure, the above step “S104, comparing the average aliasing number after suppression with a predetermined threshold condition; when the predetermined threshold condition is not met, further generating a suppression function based on the average aliasing number after suppression; repeating steps S103-S104” includes: Step 1: determine whether the average aliasing number Q1 after suppression is less than half of the original average aliasing number Q; If it is not less than half of Q, a new suppression function r1 is generated; , Where gelu() is the activation function, sin() is the trigonometric function, Q and Q1 are the average aliasing numbers, and t is the time parameter.

[0043] Step 2: Use the average aliasing number algorithm in step S102 to decompose the corrected frequency modulation instruction Pt+r1 and obtain the suppressed average aliasing number Q2.

[0044] Determine whether the average aliasing number Q2 after suppression is less than half of the original average aliasing number Q; If it is not less than half of Q, a new suppression function r2 is generated; , Where gelu() is the activation function, sin() is the trigonometric function, Q, Q1, Q2 are the average aliasing numbers, and t is the time parameter.

[0045] The cycle continuously generates a new average aliasing number Q j , and the average aliasing number Q j Compare with half the original average aliasing number Q.

[0046] Step 3: until the predetermined threshold condition is met, that is, the average aliasing number Q j Less than half of the original average aliasing number Q.

[0047] In another embodiment provided by the present disclosure, the above step "S105, after the average aliasing number after suppression reaches a predetermined threshold condition, restore a plurality of modal subsequences; apply the restored modal subsequences to power allocation in hybrid energy storage" includes: When the predetermined threshold condition is met, the average aliasing number Q j When the calculated Q is less than half of the original average aliasing number Q, j It is based on the modified frequency modulation instruction Pt+r j At this time, it is necessary to correct the frequency modulation instruction Pt+r j The generated subsequence IMF1-IMF K Perform the reduction operation. The calculation formula for the reduction operation is: , Among them, sigmoid() is the activation function and rand() is the random function.

[0048] Through the restoration operation, the subsequence IMF1-IMF for the frequency modulation instruction Pt is regenerated K .

[0049] The restored modal components are applied to power distribution in hybrid energy storage.

[0050] With the above Figure 1 Corresponding to the method shown, the embodiment of the present disclosure also provides a frequency modulation system based on ultra-capacitor energy storage, including: A command acquisition module, which acquires a frequency modulation command signal; An initial decomposition module decomposes the frequency modulation command signal into a plurality of modal subsequences based on a preset algorithm, and calculates the original average aliasing number using an average aliasing number algorithm, wherein the average aliasing number algorithm first calculates the aliasing feature sequence of each subsequence, then calculates the shared aliasing feature sequence of adjacent subsequences, and obtains the aliasing number of adjacent subsequences, and sums and averages the aliasing numbers of all two adjacent subsequences to obtain an average aliasing number; The suppression correction module adds a suppression function to the frequency modulation command signal according to the average aliasing number; and uses the average aliasing number algorithm to calculate the average aliasing number after suppression; A judgment and comparison module compares the average aliasing number after suppression with a predetermined threshold condition; When the predetermined threshold condition is not met, a suppression function is further generated based on the average aliasing number after suppression; modal decomposition and average aliasing number are calculated cyclically; When the predetermined threshold condition is met, the loop is exited; The restoration allocation module restores several modal subsequences after the average aliasing number after suppression reaches a predetermined threshold condition; and applies the restored modal subsequences to power allocation in the hybrid energy storage.

[0051] In order to further verify the advantages of the present invention, the prediction method of optimizing VMD decomposition and VMD direct decomposition in the present invention are respectively used to predict and analyze the sample frequency modulation sequence. The performance evaluation results are as follows. The algorithm of the present invention is better.

[0052]

[0053] The four evaluation indicators in the table are as follows:

[0054] N represents the sample size, and Represent the actual value and predicted value at time n respectively.

[0055] An embodiment of the present disclosure provides a computer device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and 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.

[0056] 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 execution instructions, including internal memory and external memory; the internal memory here is also called internal memory, which is used to temporarily store the operation 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: Obtaining frequency modulation command signal; Based on a preset algorithm, the frequency modulation command signal is decomposed into a plurality of modal subsequences, and an average aliasing number algorithm is used to calculate the original average aliasing number, in which the aliasing feature sequence of each subsequence is first calculated, and then the shared aliasing feature sequence of adjacent subsequences is calculated, and the aliasing numbers of adjacent subsequences are obtained, and the aliasing numbers of all two adjacent subsequences are summed and averaged to obtain an average aliasing number; Adding a suppression function to the frequency modulation command signal according to the average aliasing number; and using the average aliasing number algorithm to calculate the average aliasing number after suppression; comparing the average aliasing number after suppression with a predetermined threshold condition; When the predetermined threshold condition is not met, a suppression function is further generated based on the average aliasing number after suppression; modal decomposition and average aliasing number are calculated cyclically; When the predetermined threshold condition is met, the loop is exited; After the average aliasing number after suppression reaches a predetermined threshold condition, several modal subsequences are restored; and the restored modal subsequences are applied to power allocation in hybrid energy storage.

[0057] The embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps provided in any embodiment of the present disclosure are executed. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0058] Through the description of the above implementation methods, those skilled in the art can clearly understand that the embodiments of the present disclosure can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution 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 (which can be a read-only CD, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0059] Those skilled in the art will appreciate 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.

[0060] Those skilled in the art can understand that the modules in the device in the embodiment can be distributed in the device in the embodiment according to the description of the embodiment, or can be changed accordingly and located in one or more devices different from the present embodiment. The modules in the above embodiment can be combined into one module, or can be further divided into multiple sub-modules.

[0061] The serial numbers of the above-mentioned embodiments of the present disclosure are only for description and do not represent the advantages or disadvantages of the embodiments.

[0062] Obviously, those skilled in the art can 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 also intended to include these modifications and variations.

[0063] Finally, it should be noted that the above description is only 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 can still modify the technical solutions described above or replace some of the technical features with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A hybrid energy storage frequency modulation method for calculating VMD modal aliasing; characterized in that: S101, obtaining a frequency modulation command signal; S102, decomposing the frequency modulation command signal into a plurality of modal subsequences based on a preset algorithm, and calculating the original average aliasing number using an average aliasing number algorithm, wherein the average aliasing number algorithm first calculates the aliasing feature sequence of each subsequence, then calculates the shared aliasing feature sequence of adjacent subsequences, and obtains the aliasing numbers of adjacent subsequences, and sums and averages the aliasing numbers of all two adjacent subsequences to obtain an average aliasing number; S103, adding a suppression function to the frequency modulation instruction signal according to the average aliasing number; and using the average aliasing number algorithm in step S102 to calculate the average aliasing number after suppression; S104, comparing the average aliasing number after suppression with a predetermined threshold condition; When the predetermined threshold condition is not met, the suppression function is generated again based on the average aliasing number after suppression; and steps S103-S104 are repeated; When the predetermined threshold condition is met, proceed to step S105; S105. After the average aliasing number after suppression reaches a predetermined threshold condition, a number of modal subsequences are restored; and the restored modal subsequences are applied to power allocation in hybrid energy storage.

2. The hybrid energy storage frequency modulation method for calculating VMD modal aliasing according to claim 1, characterized in that: In step S102, the frequency modulation instruction signal is decomposed based on a preset algorithm; the decomposition level is determined, and the frequency modulation instruction is decomposed by VMD decomposition; the VMD decomposition level is K, and the frequency modulation instruction is decomposed into K subsequences IMF1, IMF2, IMF3, ..., IMF K .

3. The hybrid energy storage frequency modulation method for calculating VMD modal aliasing according to claim 2 is characterized in that: In step S102, each subsequence IMF1-IMF K Aliasing characteristic sequence; for subsequence IMF i Derivatives are taken, and an aliasing characteristic sequence is formed by points where the derivative is 0 and points where the derivative does not exist.

4. The hybrid energy storage frequency modulation method for calculating VMD modal aliasing according to claim 3 is characterized in that: subsequenceIMF i =f(x), there are m points where the derivative is 0 and where the derivative does not exist; The m points are arranged in the order from the front to the back of the coordinate axis: (t1, f(t1)), (t2, f(t2)), (t3, f(t3)), ..., (t m , f(t m )), then the subsequence IMF i The aliasing characteristic sequence of is expressed as, [(t2-t1),sigmoid(avg(f(t1),f(t2)))],[(t3-t2),sigmoid(avg(f(t2),f(t3)))],...,[(t m -t m-1 ),sigmoid(avg(f(t m ),f(t m-1 )))], where sigmoid() is the activation function and avg() is the mean.

5. The hybrid energy storage frequency modulation method for calculating VMD modal aliasing according to claim 4 is characterized in that: Calculate the shared aliasing feature sequence of adjacent subsequences, the number of shared aliasing feature sequences of two adjacent subsequences is the adjacent subsequence aliasing number; The aliasing features in the shared aliasing feature sequence must meet the following conditions: The ratio of the maximum horizontal coordinate to the minimum horizontal coordinate is not greater than a first preset value; The ratio of the maximum ordinate to the minimum ordinate is not greater than a second preset value.

6. The hybrid energy storage frequency modulation method for calculating VMD modal aliasing according to claim 5, characterized in that: After calculating the aliasing numbers of all two-by-two adjacent subsequences, they are summed and averaged to obtain an average aliasing number; the average aliasing number is used to indicate the aliasing degree of the K sequences decomposed by the current frequency modulation instruction.

7. The hybrid energy storage frequency modulation method for calculating VMD modal aliasing according to claim 6, characterized in that: Step S103 includes: According to the average aliasing number, a suppression function r for suppressing the aliasing degree is added to the frequency modulation instruction Pt to obtain the modified frequency modulation instruction Pt+r. ; Where gelu() is the activation function, sin() is the trigonometric function, Q is the average aliasing number, and t is the time parameter; The average aliasing number algorithm in step S102 is used to decompose the corrected frequency modulation instruction Pt+r, and obtain the suppressed average aliasing number Q1.

8. The hybrid energy storage frequency modulation method for calculating VMD modal aliasing according to claim 7, characterized in that: Step S104 includes: Determine whether the average aliasing number Q1 after suppression is less than half of the original average aliasing number; If it is not less than half of the original average aliasing number, a new suppression function r1 is generated; , Q1 is the average aliasing number, Decompose the corrected frequency modulation instruction Pt+r1 using the average aliasing number algorithm in step S102, and obtain the suppressed average aliasing number Q2; Determine whether the average aliasing number Q2 after suppression is less than half of the original average aliasing number; If it is not less than half of the original average aliasing number, a new suppression function r2 is generated; , Q2 is the average aliasing number, The cycle continuously generates a new average aliasing number Q j , and the average aliasing number Q j Compare with half the original average aliasing number; Until the predetermined threshold condition is met, that is, the average aliasing number Q j Less than half of the original average aliasing number.

9. The hybrid energy storage frequency modulation method for calculating VMD modal aliasing according to claim 8, characterized in that: Step S105 includes: When the predetermined threshold condition is met, the average aliasing number Q j When the calculated Q is less than half of the original average aliasing number, j It is based on the modified frequency modulation instruction Pt+r j Based on the modified frequency modulation instruction Pt+r j The generated subsequence IMF1-IMF K Perform a reduction operation; the calculation formula for the reduction operation is, , where sigmoid() is the activation function and rand() is the random function; Through the restoration operation, the subsequence IMF1-IMF for the frequency modulation instruction Pt is regenerated K ; The restored modal components are applied to power distribution in hybrid energy storage.

10. A hybrid energy storage frequency modulation system for calculating VMD modal aliasing; characterized in that: A command acquisition module, which acquires a frequency modulation command signal; An initial decomposition module decomposes the frequency modulation command signal into a plurality of modal subsequences based on a preset algorithm, and calculates the original average aliasing number using an average aliasing number algorithm, wherein the average aliasing number algorithm first calculates the aliasing feature sequence of each subsequence, then calculates the shared aliasing feature sequence of adjacent subsequences, and obtains the aliasing number of adjacent subsequences, and sums and averages the aliasing numbers of all two adjacent subsequences to obtain an average aliasing number; The suppression correction module adds a suppression function to the frequency modulation command signal according to the average aliasing number; and uses the average aliasing number algorithm to calculate the average aliasing number after suppression; A judgment and comparison module compares the average aliasing number after suppression with a predetermined threshold condition; When the predetermined threshold condition is not met, a suppression function is generated again based on the average aliasing number after suppression; modal decomposition and average aliasing number are calculated cyclically; When the predetermined threshold condition is met, the loop is exited; The restoration allocation module restores several modal subsequences after the average aliasing number after suppression reaches a predetermined threshold condition; and applies the restored modal subsequences to power allocation in the hybrid energy storage.

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