A Hybrid Energy Storage Frequency Modulation Method and System for Calculating the Mode Aliasing Amount of VMD

Abstract

A hybrid energy storage frequency modulation method for calculating the VMD modal aliasing amount; obtaining a frequency modulation command signal; decomposing the frequency modulation command signal into several modal quantities based on a preset algorithm, and calculating the original average aliasing number; adding an inhibition function to the frequency modulation command signal according to the average aliasing number; and calculating the average aliasing number after inhibition; comparing the average aliasing number after inhibition with a predetermined threshold condition; when the predetermined threshold condition is not met, further generating an inhibition function based on the average aliasing number after inhibition; when the predetermined threshold condition is met, restoring several modal quantities; applying the restored modal quantities to the power distribution in the hybrid energy storage; 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 an optimal decomposition subsequence, thereby making the frequency modulation command allocation more accurate, solving the problem of inaccurate allocation of the frequency modulation command signal caused by modal aliasing, reducing the cost of the energy storage system, and improving the revenue of the power plant.

Description

Technical Field

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

[0002] Currently, in major regional power grids in China, large-scale hydropower and thermal power units (coal-fired units / gas-fired units) are mainly used as power grid frequency modulation power sources. The output of the frequency modulation power source is adjusted to respond to the system frequency change. However, hydropower and thermal power units have certain limitations in frequency modulation. In recent years, the increasing proportion of new energy power generation such as wind power and solar energy has led to a large number of thermal power units undertaking heavy AGC regulation tasks for a long time, resulting in a series of negative impacts such as increased power generation coal consumption and serious equipment wear. The existing power frequency modulation resources are difficult to meet the demand for renewable energy access to the grid. The hybrid energy storage system involving supercapacitors for primary frequency modulation coordinated control divides typical working conditions, realizes the conversion from frequency deviation to power based on the optimization decision module, and realizes the charge / discharge control of the supercapacitor energy storage system through the PCS system control module, and finally assists the traditional generator set to complete the primary frequency modulation process. Within the rated power range, it can complete the output of the specified power with an accuracy of more than 99% within 1 s, and its comprehensive response ability fully meets the power conversion requirements within the time scale of AGC frequency modulation, far exceeding the regulation ability of conventional thermal power plants. The more urgent the regulation demand of the system, the more obvious the advantages of this technology.

[0003] Based on the above hybrid energy storage system with supercapacitor energy storage, the VMD algorithm etc. is usually adopted to realize the distribution of frequency modulation signals. The traditional frequency modulation method of hybrid energy storage (supercapacitor + lithium battery) assisting thermal power units is to transfer the difference between the frequency modulation command and the thermal power unit to the hybrid energy storage, and adopt the VMD decomposition technology to decompose the original signal into K sub-sequences IMF1 to IMF K , where the low-frequency part is borne by the battery and the high-frequency part is borne by the supercapacitor. VMD is an adaptive and completely non-recursive method for modal variational and signal processing. This technology has the advantage of being able to determine the number of modal decompositions. Its adaptability is manifested in determining the number of modal decompositions of the given sequence according to the actual situation, and in the subsequent search and solution process, it can adaptively match the optimal central frequency and finite bandwidth of each mode, and can effectively separate the intrinsic mode components (IMF), divide the frequency domain of the signal, and then obtain the effective decomposition components of the given signal, and finally obtain the optimal solution of the variational problem. The decomposed sequence contains multiple sub-sequences with different frequency scales and relatively stable, which is suitable for non-stationary sequences.

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

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

[0006] In the existing direct calculation method, there are no relevant quantization parameters for the degree of aliasing of adjacent modes, and it is impossible to accurately quantify the degree of modal aliasing after decomposition, and it is impossible to directly calculate the modal aliasing. Summary of the Invention

[0007] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to propose a brand-new hybrid energy storage frequency modulation method for calculating the VMD modal aliasing amount. 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.

[0008] A hybrid energy storage frequency modulation method for calculating the VMD modal aliasing amount; characterized in that,

[0009] S101. Obtain the FM command signal;

[0010] S102. Decompose the FM command signal into several modal subsequences based on a preset algorithm, and calculate the original average aliasing number by using the average aliasing number algorithm. In the average aliasing number algorithm, first calculate the aliasing feature sequence of each subsequence, then calculate the shared aliasing feature sequence of adjacent subsequences, and obtain the aliasing number of adjacent subsequences. Sum and average the aliasing numbers of all pairwise adjacent subsequences to obtain an average aliasing number;

[0011] S103. Add an inhibition function to the FM command signal according to the average aliasing number; and calculate the average aliasing number after inhibition by using the average aliasing number algorithm as in step S102;

[0012] S104. Compare the average aliasing number after inhibition with a predetermined threshold condition;

[0013] When it does not meet the predetermined threshold condition, regenerate the inhibition function based on the average aliasing number after inhibition; repeat steps S103 - S104;

[0014] When it meets the predetermined threshold condition, go to step S105;

[0015] S105. After the average aliasing number after inhibition reaches the predetermined threshold condition, restore several modal subsequences; apply the restored modal subsequences to the power distribution of the hybrid energy storage.

[0016] Further,

[0017] In step S102, the frequency modulation command signal is decomposed based on a preset algorithm; the decomposition layer number is determined, and the frequency modulation command is decomposed by using VMD decomposition; the decomposition layer number of VMD is K, and the frequency modulation command is decomposed into K sub-sequences IMF1, IMF2, IMF3, ..., IMF K .

[0018] Furthermore,

[0019] In step S102, the aliasing feature sequences of each sub-sequence IMF1 - IMF K are calculated; the sub-sequence IMF i is differentiated, and the aliasing feature sequence is formed by the points where the derivative is 0 and the points where the derivative does not exist.

[0020] Furthermore,

[0021] The sub-sequence IMF i = f(x), and there are m points where the derivative is 0 and the derivative does not exist;

[0022] The m points are arranged in the order from front to back along the coordinate axis as (t1, f(t1)), (t2, f(t2)), (t3, f(t3)), …, (t m , f(t m ))), then the aliasing feature sequence of the sub-sequence IMF i is expressed as,

[0023] [(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 function to calculate the mean value.

[0024] Furthermore,

[0025] The shared aliasing feature sequences of adjacent sub-sequences are calculated, and the number of the shared aliasing feature sequences of two adjacent sub-sequences is the aliasing number of the adjacent sub-sequences;

[0026] The following conditions need to be satisfied between the aliasing feature quantities in the shared aliasing feature sequences:

[0027] The ratio of the maximum abscissa to the minimum abscissa is not greater than the first preset value;

[0028] The ratio of the maximum ordinate to the minimum ordinate is not greater than the second preset value.

[0029] Further,

[0030] After calculating the aliasing numbers of all pairwise adjacent subsequences and summing and averaging them, an average aliasing number is obtained; the average aliasing number is used to represent the aliasing degree of the K sequences obtained by decomposing the current frequency modulation command.

[0031] Further,

[0032] In step S103, it includes:

[0033] According to the average aliasing number, a suppression function r for suppressing the aliasing degree is added to the frequency modulation command Pt to obtain a corrected frequency modulation command Pt + r,

[0034] ; where gelu() is an activation function, sin() is a trigonometric function, Q is the average aliasing number, and t is a time parameter;

[0035] Use the average aliasing number algorithm in step S102 to decompose the corrected frequency modulation command Pt + r and obtain a suppressed average aliasing number Q1.

[0036] Further,

[0037] In step S104, it includes:

[0038] Judge whether the suppressed average aliasing number Q1 is less than half of the original average aliasing number;

[0039] If it is not less than half of the original average aliasing number, a new suppression function r1 is generated;

[0040] , Q1 is the average aliasing number,

[0041] Use the average aliasing number algorithm in step S102 to decompose the corrected frequency modulation command Pt + r1 and obtain a suppressed average aliasing number Q2;

[0042] Judge whether the suppressed average aliasing number Q2 is less than half of the original average aliasing number;

[0043] If it is not less than half of the original average aliasing number, a new suppression function r2 is generated;

[0044] , Q2 is the average aliasing number,

[0045] Continuously generate new average aliasing numbers Q j , and compare the average aliasing number Q j with half of the original average aliasing number;

[0046] Until the predetermined threshold condition is met, that is, the average aliasing number Q j is less than half of the original average aliasing number.

[0047] Further,

[0048] In step S105, it includes:

[0049] When meeting the predetermined threshold condition and the average aliasing number Q j is less than half of the original average aliasing number, the calculated Q j is based on the corrected frequency modulation instruction Pt + r j For the subsequence IMF1 - IMF generated based on the corrected frequency modulation instruction Pt + r j perform a reduction operation; the calculation formula for the reduction operation is K , where sigmoid() is the activation function and rand() is the random function;

[0050]

[0051] Through the reduction operation, regenerate the subsequence IMF1 - IMF for the frequency modulation instruction Pt K ;

[0052] Apply the reduced modal components to the power distribution in the hybrid energy storage.

[0053] There is also provided a hybrid energy storage frequency modulation system for calculating the VMD modal aliasing amount; characterized in that

[0054] An instruction acquisition module to acquire a frequency modulation instruction signal;

[0055] An initial decomposition module that decomposes the frequency modulation instruction signal into several modal subsequences based on a preset algorithm, calculates the original average aliasing number using the average aliasing number algorithm. In the average aliasing number algorithm, first calculate the aliasing feature sequence of each subsequence, then calculate the shared aliasing feature sequence of adjacent subsequences, and obtain the aliasing number of adjacent subsequences. Sum and average the aliasing numbers of all pairwise adjacent subsequences to obtain an average aliasing number;

[0056] An inhibition correction module that adds an inhibition function to the frequency modulation instruction signal according to the average aliasing number; and calculates the average aliasing number after inhibition using the average aliasing number algorithm;

[0057] A judgment and comparison module that compares the average aliasing number after inhibition with the predetermined threshold condition;

[0058] When not meeting the predetermined threshold condition, regenerate the inhibition function based on the average aliasing number after inhibition; loop through modal decomposition and calculation of the average aliasing number;

[0059] When meeting the predetermined threshold condition, exit the loop;

[0060] The restoration allocation module restores a number of modal subsequences after the average aliasing number after suppression reaches a predetermined threshold condition, and applies the restored modal subsequences to the power allocation in the hybrid energy storage.

[0061] The beneficial effects of the present invention are:

[0062] The present disclosure provides a hybrid energy storage frequency modulation method for calculating the VMD modal aliasing amount, which can respond in a timely manner when the frequency of the power plant power grid 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, so as to make the frequency modulation command allocation more accurate, solve the problem of inaccurate frequency modulation command signal allocation caused by modal aliasing, reduce the cost of the energy storage system, and improve the benefits of the power plant. Description of the Drawings

[0063] Figure 1 It is a diagram of method steps.

[0064] Figure 2 It is a processing flow chart. Detailed Embodiments

[0065] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0066] It should be understood that the step numbers used in the text are only for convenient description and do not limit the execution order of the steps.

[0067] It should be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, unless otherwise clearly specified in the context, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0068] The terms "include" and "comprise" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0069] The term " / and" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0070] The embodiments of the present disclosure provide a hybrid energy storage frequency modulation method for calculating the VMD modal aliasing amount, such asFigure 1 As shown in the figure, it includes:

[0071] S101. Obtain a frequency modulation command signal;

[0072] S102. Decompose the frequency modulation command signal into several modal subsequences based on a preset algorithm, and calculate the original average aliasing number using the average aliasing number algorithm. In the average aliasing number algorithm, first calculate the aliasing characteristic sequence of each subsequence, then calculate the shared aliasing characteristic sequence of adjacent subsequences, and obtain the aliasing number of adjacent subsequences. Sum and average the aliasing numbers of all pairwise adjacent subsequences to obtain an average aliasing number;

[0073] S103. Add an inhibition function to the frequency modulation command signal according to the average aliasing number; and calculate the average aliasing number after inhibition using the average aliasing number algorithm as in step S102;

[0074] S104. Compare the average aliasing number after inhibition with a predetermined threshold condition;

[0075] When it does not meet the predetermined threshold condition, further generate an inhibition function based on the average aliasing number after inhibition; repeat steps S103 - S104;

[0076] When it meets the predetermined threshold condition, enter step S105;

[0077] S105. After the average aliasing number after inhibition reaches the predetermined threshold condition, restore several modal subsequences; apply the restored modal subsequences to power distribution in the hybrid energy storage.

[0078] The frequency modulation method based on ultracapacitor energy storage provided by the embodiments of the present disclosure uses a new type of supercapacitor and lithium battery as energy storage devices and performs frequency modulation when the grid frequency in the power plant fluctuates. The state of the power plant grid is monitored in real time. When the power supply frequency in the power plant grid fluctuates, a corresponding frequency modulation command signal is generated. After obtaining this signal, the frequency modulation command signal is cyclically inhibited by adding an inhibition function, calculating the average aliasing number, and other constraint conditions, and the power plant grid is frequency modulated using the command allocation corresponding to the optimal inhibition function.

[0079] In another embodiment provided by the present disclosure, in the above step “S102. Decompose the frequency modulation command signal into several modal subsequences based on a preset algorithm, and calculate the original average aliasing number using the average aliasing number algorithm. In the average aliasing number algorithm, first calculate the aliasing characteristic sequence of each subsequence, then calculate the shared aliasing characteristic sequence of adjacent subsequences, and obtain the aliasing number of adjacent subsequences. Sum and average the aliasing numbers of all pairwise adjacent subsequences to obtain an average aliasing number”, it includes:

[0080] Step 1. Receive a frequency modulation command; assume the frequency modulation command is Pt; Pt is a function of the time parameter t.

[0081] Step 2: Decompose the frequency modulation command signal based on a preset algorithm; determine the decomposition level, and decompose the frequency modulation command using VMD decomposition; assume the decomposition level of VMD is K, and decompose the frequency modulation command into K subsequences IMF1, IMF2, IMF3, ..., IMF K .

[0082] Step 3: Calculate the aliasing feature sequences of each subsequence, and successively obtain the aliasing feature sequences of the IMF1 - IMF K sequences.

[0083] Assume a certain subsequence IMF i = f(x), find the points where the derivative of this function is 0, and the points where the corresponding derivative does not exist. Assume that the total number of points where the derivative is 0 and the points where the derivative does not exist found is m.

[0084] The above m points are arranged in the order from front to back along the coordinate axis as (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 aliasing feature sequence of the subsequence IMF i can be expressed as

[0085] [(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 function to calculate the mean value.

[0086] Regarding the subsequence IMF i in Step 3, its adjacent subsequence IMF i-1 ,

[0087] Assume the adjacent subsequence IMF i-1 = f(x), find the points where the derivative of this function is 0, and the points where the corresponding derivative does not exist. Assume that the total number of points where the derivative is 0 and the points where the derivative does not exist found is n.

[0088] Similarly, the above n points are arranged in the order from front to back along the coordinate axis as (t 12 , f(t 12 )), (t 22 , f(t 22 ))), (t 32 , f(t32 )) , … , (t n2 , f(t n2 )) , the aliasing feature sequence of the subsequence IMF i-1 is expressed as,

[0089] [(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 )))]。

[0090] Step 4: Calculate the shared aliasing feature sequence of adjacent subsequences to obtain the aliasing number of adjacent subsequences.

[0091] As the shared aliasing feature sequence of IMF i and IMF i-1 , when comparing and operating between each pair of aliasing feature quantities in adjacent subsequences, the following conditions need to be met:

[0092] Condition 1: The ratio of the maximum abscissa to the minimum abscissa is not greater than 1.1;

[0093] Condition 2: The ratio of the maximum ordinate to the minimum ordinate is not greater than 1.05.

[0094] For example,

[0095] The aliasing feature sequence of the IMF i subsequence includes 3 aliasing feature quantities [1, 1], [2, 3], [1.8, 7.6];

[0096] The aliasing feature sequence of the IMF i-1 subsequence includes 3 aliasing feature quantities [1.09, 1.02], [2.01, 3.11], [1.8, 12];

[0097] Among them, after comparing and operating between each pair of aliasing feature quantities,

[0098] 【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, satisfying the above two ratio conditions;

[0099] Then, the shared aliasing feature sequences of two adjacent subsequences include the following two pairs of aliasing feature quantities, 【1,1】 and 【1.09,1.02】, 【2,3】 and 【2.01,3.11】.

[0100] It can be known that the aliasing number of its adjacent subsequences is 2.

[0101] Step Five: After calculating the aliasing numbers of all pairwise adjacent subsequences, sum and average them to obtain an average aliasing number Q. The average aliasing number Q is used to represent the aliasing degree of the K sequences obtained by decomposing the current frequency modulation command.

[0102] In another embodiment provided by the present disclosure, in the above step "S103. Add an inhibition function to the frequency modulation command signal according to the average aliasing number; and calculate the average aliasing number after inhibition", it includes:

[0103] Step One: Add an inhibition function r for inhibiting the aliasing degree to the frequency modulation command Pt according to the average aliasing number to obtain a corrected frequency modulation command Pt + r,

[0104] ,

[0105] where gelu() is an activation function, sin() is a trigonometric function, Q is the average aliasing number, and t is a time parameter.

[0106] Step Two: Use the average aliasing number algorithm in step S102 to decompose the corrected frequency modulation command Pt + r and obtain the average aliasing number Q1 after inhibition.

[0107] In another embodiment provided by the present disclosure, in the above step "S104. Compare the average aliasing number after inhibition with a predetermined threshold condition; when it does not meet the predetermined threshold condition, further generate an inhibition function based on the average aliasing number after inhibition; repeat steps S103 - S104", it includes:

[0108] Step One: Determine whether the average aliasing number Q1 after inhibition is less than half of the original average aliasing number Q;

[0109] If it is not less than half of Q, generate a new inhibition function r1;

[0110] ,

[0111] Among them, gelu() is the activation function, sin() is the trigonometric function, Q and Q1 are the average aliasing numbers, and t is the time parameter.

[0112] Step 2: Decompose and correct the frequency modulation command Pt+r1 using the average aliasing number algorithm in Step S102, and obtain the suppressed average aliasing number Q2.

[0113] Determine whether the suppressed average aliasing number Q2 is less than half of the original average aliasing number Q;

[0114] If it is not less than half of Q, generate a new suppression function r2;

[0115] ,

[0116] Among them, gelu() is the activation function, sin() is the trigonometric function, Q, Q1, and Q2 are the average aliasing numbers, and t is the time parameter.

[0117] Continuously generate a new average aliasing number Q j , and compare the average aliasing number Q j with half of the original average aliasing number Q.

[0118] Step 3: Until the predetermined threshold condition is met, that is, the average aliasing number Q j is less than half of the original average aliasing number Q.

[0119] In another embodiment provided by the present disclosure, in the above step "S105, after the suppressed average aliasing number reaches the predetermined threshold condition, restore several modal subsequences; apply the restored modal subsequences to the power distribution in the hybrid energy storage", it includes:

[0120] When the predetermined threshold condition is met and the average aliasing number Q j is less than half of the original average aliasing number Q, the calculated Q j is based on the corrected frequency modulation command Pt+r j At this time, it is necessary to perform a restoration operation on the subsequences IMF1-IMF j generated based on the corrected frequency modulation command Pt+r K . The calculation formula for the restoration operation is

[0121] ,

[0122] Among them, sigmoid() is the activation function and rand() is the random function.

[0123] Through the restoration operation, regenerate the subsequences IMF1-IMF for the frequency modulation command Pt K .

[0124] Apply each of the restored modal components to the power distribution in the hybrid energy storage.

[0125] Corresponding to the method described above, Figure 1 Embodiments of the present disclosure further provide a frequency modulation system based on supercapacitor energy storage, including:

[0126] An instruction acquisition module, which acquires a frequency modulation instruction signal;

[0127] An initial decomposition module, which decomposes the frequency modulation instruction signal into a number of modal subsequences based on a preset algorithm, calculates the original average aliasing number using the average aliasing number algorithm. In the average aliasing number algorithm, first calculate the aliasing characteristic sequence of each subsequence, then calculate the shared aliasing characteristic sequence of adjacent subsequences, and obtain the aliasing number of adjacent subsequences. Sum and average the aliasing numbers of all pairwise adjacent subsequences to obtain an average aliasing number;

[0128] An inhibition correction module, which adds an inhibition function to the frequency modulation instruction signal according to the average aliasing number; and calculates the average aliasing number after inhibition using the average aliasing number algorithm;

[0129] A judgment and comparison module, which compares the average aliasing number after inhibition with a predetermined threshold condition;

[0130] When the predetermined threshold condition is not met, further generate an inhibition function based on the average aliasing number after inhibition; perform modal decomposition and calculate the average aliasing number in a loop;

[0131] When the predetermined threshold condition is met, exit the loop;

[0132] A restoration and distribution module, after the average aliasing number after inhibition reaches the predetermined threshold condition, restore a number of modal subsequences; apply the restored modal subsequences to the power distribution in the hybrid energy storage.

[0133] To further verify the advantages of the present invention, the optimized VMD decomposition method and the prediction method of direct VMD decomposition in the present invention are respectively used to perform prediction analysis on the sample frequency modulation sequence. The performance evaluation results are as follows, and the algorithm of the present invention is better.

[0134]

[0135] The four evaluation indexes in the table are as follows,

[0136]

[0137] N represents the sample size, and respectively represent the actual value and the predicted value at time n.

[0138] An embodiment of the present disclosure provides a computer device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps provided in any embodiment of the present disclosure are executed.

[0139] The computer device provided in an embodiment of the present application includes a processor, a memory, and a bus. Among them, the memory is used to store execution instructions, including internal memory and external memory; here, the internal memory is also called main memory, which is used to temporarily store operation data in the processor and data exchanged with external memories such as hard disks. The processor exchanges data with the external memory through the internal memory. When the electronic device runs, the processor communicates with the memory through the bus, so that the processor executes the following instructions:

[0140] Obtain a frequency modulation instruction signal;

[0141] Decompose the frequency modulation instruction signal into several modal subsequences based on a preset algorithm, and calculate the original average aliasing number using the average aliasing number algorithm. In the average aliasing number algorithm, first calculate the aliasing feature sequence of each subsequence, then calculate the shared aliasing feature sequence of adjacent subsequences, and obtain the aliasing number of adjacent subsequences. Sum and average the aliasing numbers of all pairwise adjacent subsequences to obtain an average aliasing number;

[0142] Add an inhibition function to the frequency modulation instruction signal according to the average aliasing number; and calculate the average aliasing number after inhibition using the average aliasing number algorithm;

[0143] Compare the average aliasing number after inhibition with a predetermined threshold condition;

[0144] When the predetermined threshold condition is not met, further generate an inhibition function based on the average aliasing number after inhibition; loop through modal decomposition and calculation of the average aliasing number;

[0145] When the predetermined threshold condition is met, exit the loop;

[0146] After the average aliasing number after inhibition reaches the predetermined threshold condition, restore several modal subsequences; apply the restored modal subsequences to power distribution in a hybrid energy storage.

[0147] An embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the steps provided in any embodiment of the present disclosure are executed. Among them, the storage medium can be a volatile or non-volatile computer-readable storage medium.

[0148] Through the description of the above embodiments, 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 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 (which can be a read-only optical disc, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present disclosure.

[0149] Those skilled in the art can understand that the drawings are only schematic diagrams of a preferred embodiment, and the modules or processes in the drawings are not necessarily essential for implementing the present disclosure.

[0150] Those skilled in the art can understand that the modules in the device in the embodiments can be distributed in the device in the embodiments according to the description of the embodiments, or can be correspondingly changed and located in one or more devices different from the present embodiments. The modules of the above embodiments can be combined into one module, or further split into multiple sub-modules.

[0151] The serial numbers of the above embodiments of the present disclosure are only for description and do not represent the advantages and disadvantages of the embodiments.

[0152] 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 equivalent technologies, the present disclosure also intends to include these changes and modifications.

[0153] Finally, it should be noted that the above is only an explanation of the present invention and is not used to limit the present invention. Although the present invention has been described in detail, for those skilled in the art, they can still modify the above-described technical solutions, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, 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 the mode aliasing amount of VMD; characterized in that, S101. Obtain a frequency modulation command signal; S102. Decompose the frequency modulation command signal into several modal subsequences based on a preset algorithm, and calculate the original average aliasing number using the average aliasing number algorithm. In the average aliasing number algorithm, first calculate the aliasing characteristic sequence of each subsequence, then calculate the shared aliasing characteristic sequence of adjacent subsequences, and obtain the aliasing number of adjacent subsequences. Sum and average the aliasing numbers of all pairwise adjacent subsequences to obtain an average aliasing number; S103. Add an inhibition function to the frequency modulation command signal according to the average aliasing number; and calculate the average aliasing number after inhibition using the average aliasing number algorithm as in step S102; S104. Compare the average aliasing number after inhibition with a predetermined threshold condition; When it does not meet the predetermined threshold condition, generate an inhibition function again based on the average aliasing number after inhibition; repeat steps S103 - S104; When it meets the predetermined threshold condition, enter step S105; S105. After the average aliasing number after inhibition reaches the predetermined threshold condition, restore several modal subsequences; Apply the restored modal subsequences to the power distribution in the hybrid energy storage; In step S104, it further includes: Judge whether the average aliasing number Q1 after inhibition is less than half of the original average aliasing number; If it is not less than half of the original average aliasing number, generate a new inhibition function r1; , where Q1 is the average aliasing number, Decompose the corrected frequency modulation command Pt + r1 using the average aliasing number algorithm in step S102 and obtain the average aliasing number Q2 after inhibition; Judge whether the average aliasing number Q2 after inhibition is less than half of the original average aliasing number; If it is not less than half of the original average aliasing number, generate a new inhibition function r2; , where Q2 is the average aliasing number, The loop continuously generates a new average aliasing number Q j , and compares the average aliasing number Q j with half of the original average aliasing number; until a predetermined threshold condition is met, i.e., the average aliasing number Q j is less than half of the original average aliasing number; In step S105, it further includes: When the average aliasing number Q j is less than half of the original average aliasing number, the calculated Q j is based on the corrected frequency modulation command Pt+r j for the subsequence IMF1-IMF j generated based on the corrected frequency modulation command Pt+r K to 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 reduction operation, the subsequence IMFs for the frequency modulation command Pt are regenerated K ; Apply each restored modal component to the power distribution in the hybrid energy storage.

2. The hybrid energy storage frequency modulation method for calculating the mode aliasing amount of VMD according to claim 1, characterized in that, In step S102, decompose the frequency modulation command signal based on a preset algorithm; determine the decomposition level, and decompose the frequency modulation command by using VMD decomposition; the decomposition level of VMD is K, and decompose the frequency modulation command into K sub-sequences IMF1, IMF2, IMF3, ..., IMF K .

3. The hybrid energy storage frequency modulation method for calculating the mode aliasing amount of VMD according to claim 2, characterized in that, In step S102, calculate the aliasing characteristic sequences of each subsequence IMF1 - IMF K ; perform differentiation on the subsequence IMF i to form an aliasing characteristic sequence from the points where the derivative is 0 and the points where the derivative does not exist among them.

4. The hybrid energy storage frequency modulation method for calculating the mode aliasing amount of VMD according to claim 3, characterized in that, Subsequence IMF i = f(x), and there are m points where the derivative is 0 and points where the derivative does not exist; m points are arranged in the order from front to back along the coordinate axis as (t1, f(t1)), (t2, f(t2)), (t3, f(t3)), …, (t m , f(t m ))), then the aliasing characteristic sequence of the subsequence IMF i is represented 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 function for calculating the mean value.

5. The hybrid energy storage frequency modulation method for calculating the mode aliasing amount of VMD according to claim 4, characterized in that, Calculate the shared aliasing characteristic sequence of adjacent subsequences, and the number of shared aliasing characteristic sequences of two adjacent subsequences is the aliasing number of adjacent subsequences; The following conditions need to be satisfied between the aliasing characteristic quantities in the shared aliasing characteristic sequence: The ratio of the maximum abscissa to the minimum abscissa 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 the mode aliasing amount of VMD according to claim 5, characterized in that, After calculating the aliasing numbers of all pairwise adjacent subsequences, sum and average them to obtain an average aliasing number; the average aliasing number is used to represent the aliasing degree of the K sequences decomposed from the current frequency modulation command.

7. The hybrid energy storage frequency modulation method for calculating the mode aliasing amount of VMD according to claim 6, characterized in that, In step S103, it includes: Add an aliasing suppression function r to the frequency modulation command Pt according to the average aliasing number to obtain the corrected frequency modulation command 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; Decompose the corrected frequency modulation command Pt + r using the average aliasing number algorithm in step S102, and obtain the average aliasing number Q1 after suppression.

8. A system for a hybrid energy storage frequency modulation method for calculating the VMD modal aliasing amount according to claim 1; characterized in that An instruction acquisition module for acquiring a frequency modulation instruction signal; An initial decomposition module that decomposes the frequency modulation instruction signal into several modal subsequences based on a preset algorithm, calculates the original average aliasing number using the average aliasing number algorithm. In the average aliasing number algorithm, first calculate the aliasing characteristic sequence of each subsequence, then calculate the shared aliasing characteristic sequence of adjacent subsequences, and obtain the aliasing number of adjacent subsequences. Sum and average the aliasing numbers of all pairwise adjacent subsequences to obtain an average aliasing number. A suppression correction module that adds a suppression function to the frequency modulation instruction signal according to the average aliasing number; and calculates the average aliasing number after suppression using the average aliasing number algorithm. A judgment comparison module that compares the average aliasing number after suppression with a predetermined threshold condition; When the predetermined threshold condition is not met, generate a suppression function again based on the average aliasing number after suppression; perform modal decomposition and calculation of the average aliasing number in a loop; When the predetermined threshold condition is met, exit the loop; A restoration allocation module that restores several modal subsequences after the average aliasing number after suppression reaches the predetermined threshold condition; and applies the restored modal subsequences to the power allocation of the hybrid energy storage.

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