Sodium ion super-capacity energy storage system frequency modulation instruction prediction method and related equipment

Abstract

The invention relates to the technical field of super-capacity energy storage, in particular to a sodium ion super-capacity energy storage system frequency modulation instruction prediction method and related equipment. The method comprises the following steps: firstly, obtaining a nonlinear sequence in frequency modulation sequence data of the sodium ion super-capacitance energy storage system; respectively and correspondingly compensating the frequency modulation instruction data in the nonlinear sequence through the compensation coefficient group to obtain a compensation sequence which is a linear sequence; and according to the compensation sequence, utilizing a constructed prediction model to predict a frequency modulation instruction to obtain a corresponding prediction result, obtaining a reduction coefficient group based on the compensation coefficient group, and utilizing reduction coefficients in the reduction coefficient group to correspondingly restore frequency modulation data in the prediction result to obtain a frequency modulation result. And finally obtaining a prediction frequency modulation sequence of the sodium ion super-capacitance energy storage system. The method can improve the prediction precision of the frequency modulation instruction.

Description

Technical Field

[0001] The present invention relates to the technical field of supercapacitor energy storage systems, and in particular to a method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system and related equipment. Background Art

[0002] Frequency regulation of thermal power units is an important technical means to ensure the safe and stable operation of the power grid. For thermal power units, long-term frequency regulation will lead to increased coal consumption, reduced reliability, and shortened operating life of the units. On the other hand, high-quality and efficient frequency regulation power sources are scarce. At present, coal-fired thermal power units are still the main frequency regulation power source. In addition, the large-scale demand for new energy grid connection, environmental protection pressure restricts the regulation capacity of units, and the problem of "heat-based electricity" for heating units has further increased the demand for power frequency regulation. However, the performance of the auxiliary frequency regulation and energy storage equipment currently supporting thermal power plants cannot meet the efficiency and reliability requirements of the units themselves, which affects the acquisition of grid subsidy income and has low economic benefits.

[0003] The prediction of frequency modulation instructions of the sodium-ion supercapacitor energy storage system in the thermal power unit is also an important part of maintaining the stable operation of the power system. The introduction of the sodium-ion supercapacitor energy storage system can significantly improve the frequency modulation response speed of the power system, enabling the system to achieve supply and demand balance faster. However, the frequency modulation instructions of the sodium-ion supercapacitor energy storage system often have the characteristics of nonlinearity and irregularity, which cannot be ignored in the prediction process. The traditional prediction method trains all historical frequency modulation instructions through the network model to predict the next frequency modulation instruction, or filters out the data with nonlinearity and irregularity for prediction. The accuracy of the prediction results obtained by the existing method is not high. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and related equipment for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system, which solves the problem of inaccurate prediction of frequency modulation instructions of thermal power units by removing nonlinear instructions in the frequency modulation instructions of thermal power units.

[0005] The objective of the present invention is achieved by the following technical solutions: A method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system, comprising: Obtaining nonlinear sequences in frequency modulation sequence data of sodium ion supercapacitor energy storage system; The frequency modulation instruction data in the nonlinear sequence are compensated respectively by the compensation coefficient group to obtain a compensation sequence, wherein the compensation sequence is a linear sequence; According to the compensation sequence, the frequency modulation instruction is predicted using the constructed prediction model to obtain a corresponding prediction result, wherein the prediction model is a trained deep learning network model, and the training data is the historical frequency modulation sequence data; A reduction coefficient group is obtained based on the compensation coefficient group, and the reduction coefficients in the reduction coefficient group are used to restore the frequency modulation data in the prediction result, so as to finally obtain the predicted frequency modulation sequence of the sodium ion supercapacity energy storage system.

[0006] Furthermore, before obtaining the nonlinear sequence in the frequency modulation sequence data of the sodium ion supercapacitor energy storage system, the method includes: The frequency modulation sequence data of the sodium ion supercapacitor energy storage system is obtained to determine whether there is a nonlinear sequence in the frequency modulation sequence. The determination method is: determine whether there is a subsequence that satisfies the first condition in the frequency modulation sequence. When the subsequence satisfies any one of the first conditions, the subsequence is a nonlinear sequence, otherwise it is a linear sequence. The first condition is:

[0007] Among them, the first case is:

[0008] The second case is:

[0009] In the formula, x i is the element in the frequency modulation sequence; i is the number of elements; [x i ,x i+1 ,x i+2 …,x i+Q ,x i+Q+1 ] is the frequency modulation sequence, which is a nonlinear sequence; Q is the length parameter of the subsequence in the frequency modulation sequence, and N is the length of the frequency modulation sequence of the sodium ion supercapacitor energy storage system.

[0010] Further, the frequency modulation instruction data in the nonlinear sequence are compensated respectively by the compensation coefficient group to obtain the compensation sequence, including: when the nonlinear sequence is the first case, the frequency modulation data in the nonlinear sequence is compensated accordingly by the first compensation coefficient in the first compensation coefficient group to obtain the first compensation sequence, and the calculation expression of the first compensation coefficient is:

[0011] In the formula, is the first compensation coefficient, is the first activation function, is the second activation function, a t is the frequency modulation sequence parameter, rand is a random number, is the frequency modulation instruction data related to the frequency modulation sequence time t; the frequency modulation sequence parameters are:

[0012] Where a1, a2, …, ai+Q are the frequency modulation sequence parameters corresponding to the frequency modulation data in the nonlinear sequence.

[0013] Furthermore, obtaining a restoration coefficient group based on the compensation coefficient group, and restoring the frequency modulation data in the prediction result using the restoration coefficients in the restoration coefficient group, includes: A first restoration coefficient group is obtained according to the first compensation coefficient group and the prediction result obtained based on the first compensation sequence. The frequency modulation data in the prediction result are restored according to the first restoration coefficient in the first restoration coefficient group. The specific restoration method is: each frequency modulation data in the prediction result is subtracted from the corresponding first restoration coefficient to obtain the restored predicted frequency modulation data. The calculation expression of the first restoration coefficient is:

[0014] In the formula, k t is the first reduction coefficient, is the first compensation coefficient, is the restoration parameter, N is the number of data in the frequency modulation sequence, and the restoration parameter is:

[0015] Where Swish() is the fourth activation function.

[0016] Further, The method further comprises: obtaining a restoration coefficient group based on the compensation coefficient group, and using the restoration coefficients in the restoration coefficient group to restore the frequency modulation data in the prediction result. A second restoration coefficient group is obtained according to the second compensation coefficient group and the prediction result obtained based on the second compensation sequence. The frequency modulation data in the prediction result are restored according to the second restoration coefficients in the second restoration coefficient group. The specific restoration method is: each frequency modulation data in the prediction result is subtracted from the corresponding second restoration coefficient to obtain the restored predicted frequency modulation data. The calculation expression of the second restoration coefficient is: ; In the formula, is the second reduction coefficient, is the second compensation coefficient, is the restoration parameter, N is the number of data in the frequency modulation sequence, and the restoration parameter is:

[0017] Where Swish() is the fourth activation function.

[0018] Further, the frequency modulation instruction data in the nonlinear sequence are compensated respectively by the compensation coefficient group to obtain the compensation sequence, including: when the nonlinear sequence is the second case, the frequency modulation data in the nonlinear sequence is compensated accordingly by the second compensation coefficient in the second compensation coefficient group to obtain the second compensation sequence, and the calculation expression of the second compensation coefficient is:

[0019] In the formula, is the second compensation coefficient, tanh() is the third activation function, a t is the frequency modulation sequence parameter, is the frequency modulation instruction data related to time t; the frequency modulation sequence parameters are:

[0020] Where a1, a2, …, a i+Q are the frequency modulation sequence parameters corresponding to the frequency modulation data in the nonlinear sequence.

[0021] Furthermore, the basic model of the prediction model adopts any one of a long short-term memory neural network model, a RNN neural network model, and a bidirectional long short-term memory network model.

[0022] A frequency modulation instruction prediction system for a sodium ion supercapacitor energy storage system, comprising: A data acquisition module, used to obtain nonlinear sequences in frequency modulation sequence data of a sodium ion supercapacitor energy storage system; A data compensation module, used for respectively compensating the frequency modulation instruction data in the nonlinear sequence through a compensation coefficient group to obtain a compensation sequence, wherein the compensation sequence is a linear sequence; A frequency modulation instruction prediction module is used to predict the frequency modulation instruction according to the compensation sequence using a constructed prediction model to obtain a corresponding prediction result, wherein the prediction model is a trained deep learning network model and the training data is historical frequency modulation sequence data; The data restoration module is used to obtain a restoration coefficient group based on the compensation coefficient group, and use the restoration coefficients in the restoration coefficient group to restore the frequency modulation data in the prediction result, so as to finally obtain the predicted frequency modulation sequence of the sodium ion supercapacity energy storage system.

[0023] An electronic device comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the above-mentioned method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system.

[0024] A computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the frequency modulation instruction prediction method of the sodium ion supercapacitor energy storage system is implemented.

[0025] The beneficial effect of the present invention is that: the method for predicting the frequency modulation instruction of a sodium ion supercapacitor energy storage system provided by the present invention fully considers the nonlinearity and irregularity of some frequency modulation instructions, uses the compensation coefficient to linearize and compensate the frequency modulation instruction, and uses the prediction model to predict the linearized compensated data. In the frequency modulation sequence, due to the complexity of the system itself and the interference of the external environment, there may be nonlinear sequences. By identifying these nonlinear sequences and applying the compensation coefficient group for compensation, they can be converted into linear sequences, thereby simplifying the subsequent processing process and improving the accuracy of data processing. The obtained prediction results are then restored using the reduction coefficient, and the reduction coefficient is obtained according to the compensation coefficient. This method can not only reduce the difficulty of prediction of the prediction model, but also effectively improve the accuracy of the prediction results. After obtaining the prediction results, the frequency modulation data in the prediction results are restored by the reduction coefficient group, which can ensure that the prediction results can truly reflect the actual frequency modulation requirements of the supercapacitor energy storage system. This process is an important bridge for the prediction results from model output to practical application, ensuring the effectiveness and practicality of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 It is a schematic flow chart of a method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system in an embodiment of the present invention; Figure 2 Schematic diagram of the structure of the frequency modulation instruction prediction system of the sodium ion supercapacitor energy storage system in an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose and technical solution of the present invention clearer and easier to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0029] The concept of the present invention is to provide a method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system, comprising: Obtaining nonlinear sequences in frequency modulation sequence data of sodium ion supercapacitor energy storage system; The frequency modulation instruction data in the nonlinear sequence are compensated respectively by the compensation coefficient group to obtain a compensation sequence, which is a linear sequence; According to the compensation sequence, the frequency modulation instruction is predicted using the constructed prediction model to obtain the corresponding prediction result. The prediction model is a trained deep learning network model, and the training data is the frequency modulation sequence data; A reduction coefficient group is obtained based on the compensation coefficient group, and the reduction coefficients in the reduction coefficient group are used to restore the corresponding frequency modulation data in the prediction results, and finally a predicted frequency modulation sequence of the sodium ion supercapacity energy storage system is obtained.

[0030] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments, wherein the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0031] Example 1 like Figure 1 A method for predicting frequency modulation instructions for a sodium ion supercapacitor energy storage system in this embodiment is shown, and the following specific implementation method is adopted.

[0032] S1, obtain the frequency modulation sequence data of the sodium ion supercapacitor energy storage system, and determine whether there is a nonlinear sequence in the frequency modulation sequence. In this embodiment, the number of frequency modulation instruction data in the obtained frequency modulation sequence is N, that is, the length of the frequency modulation sequence of the sodium ion supercapacitor energy storage system, where N ≥ 300, then the frequency modulation sequence data is [x1, x2, x3, ..., x N ].

[0033] The specific method of judging whether the frequency modulation sequence is nonlinear sequence data is to judge whether the frequency modulation sequence satisfies the first condition, and to judge whether there is a subsequence satisfying the first condition in the frequency modulation sequence. When the subsequence satisfies any of the first conditions, the subsequence is a nonlinear sequence, otherwise it is a linear sequence. The first condition is:

[0034] In the formula, x i is the element in the FM sequence; i is the number of elements; Q is the length parameter of the subsequence in the FM sequence; [x i ,x i+1 ,x i+2 …,x i+Q ,x i+Q+1 ] is a frequency modulation sequence, which belongs to a nonlinear sequence. When a part of the sequence in the frequency modulation sequence satisfies the first case or the second case mentioned above, then the subsequence is a nonlinear sequence.

[0035] S2, if there is a nonlinear sequence, the frequency modulation instruction data in the nonlinear sequence are compensated respectively by the compensation coefficient group to obtain a compensation sequence, wherein the compensation sequence is a linear sequence.

[0036] Specifically, when a part of the sequence meets the first condition, the frequency modulation data in the nonlinear sequence is compensated accordingly by the first compensation coefficient in the first compensation coefficient group to obtain a first compensation sequence. The first condition of the nonlinear sequence is:

[0037] The first compensation coefficient is defined as:

[0038] In the formula, is the first compensation coefficient, is the first activation function, is the second activation function, a t is the frequency modulation sequence parameter, rand is the random number calculation, is the frequency modulation instruction data related to the frequency modulation sequence time t;.

[0039] Calculate separately :

[0040] The first compensation coefficient group is formed by the first compensation coefficient corresponding to the frequency modulation data in the nonlinear sequence, that is, By adding the first compensation coefficient to the corresponding frequency modulation data, the first compensation sequence can be obtained, that is, .

[0041] The frequency modulation sequence parameters are defined as:

[0042] Where a1, a2, …, a i+Q are the frequency modulation sequence parameters corresponding to the frequency modulation data in the nonlinear sequence.

[0043] Similarly, when a part of the sequence meets the second condition, the frequency modulation data in the nonlinear sequence is compensated accordingly by the second compensation coefficient in the second compensation coefficient group to obtain a second compensation sequence.

[0044] Among them, the second case in the nonlinear sequence is:

[0045] Then, the corresponding second compensation coefficient is defined as:

[0046] In the formula, is the second compensation coefficient, tanh() is the third activation function, a t is a frequency modulation sequence parameter, which has the same definition as above.

[0047] The second compensation coefficients corresponding to the frequency modulation data in the nonlinear sequence are calculated respectively, and the second compensation coefficient group is formed by the second compensation coefficients corresponding to the frequency modulation data in the nonlinear sequence, that is, By adding the second compensation coefficient to the corresponding frequency modulation data, the second compensation sequence can be obtained, that is, .

[0048] S3, predicting the frequency modulation instruction according to the compensation sequence using the constructed prediction model to obtain the corresponding prediction result, wherein the prediction model is a trained deep learning network model and the training data is the historical frequency modulation sequence data; Specifically, the basic model of the prediction model adopts any one of a long short-term memory neural network model, an RNN neural network model, and a bidirectional long short-term memory network model. The prediction model in this embodiment adopts a long short-term memory neural network model. The nonlinear subsequence in the historical frequency modulation sequence data is delinearized by the above method to obtain a linear sequence, and the linear sequence is input into the long short-term memory neural network model for training to obtain a trained prediction model.

[0049] The prediction model in this embodiment is a model existing in the prior art, and its information transmission process is also existing in the prior art, which will not be described in detail here.

[0050] S4, obtaining a reduction coefficient group based on the compensation coefficient group, and using the reduction coefficients in the reduction coefficient group to correspondingly reduce the frequency modulation data in the prediction result, and finally obtaining a predicted frequency modulation sequence of the sodium ion supercapacity energy storage system.

[0051] Specifically, based on the nonlinear sequence of the first case, a first restoration coefficient group is obtained according to the first compensation coefficient group and the prediction result obtained based on the first compensation sequence, and the frequency modulation data in the prediction result are restored according to the first restoration coefficient in the first restoration coefficient group. The specific restoration method is to subtract the corresponding first restoration coefficient from each frequency modulation data in the prediction result to obtain the restored predicted frequency modulation data.

[0052] The first reduction coefficient is:

[0053] In the formula, k t is the first reduction coefficient, is the first compensation coefficient, is the restoration parameter, N is the number of data in the frequency modulation sequence, and Q and N are both positive integers. The restoration parameter is:

[0054] Where Swish() is the fourth activation function, .

[0055] When the prediction result is [x N+1 ,x N+2 ,x N+3 ,...,x N+0.05N ], the first reduction coefficient group is , then the restored predicted FM data is: [x N+1 -k1,x N+2 -k2,x N+3 -k3,...,x N+0.05N -k 0.05N-1 ].

[0056] Based on the nonlinear sequence of the second case, a second restoration coefficient group is obtained according to the second compensation coefficient group and the prediction result obtained based on the second compensation sequence. The frequency modulation data in the prediction result are restored according to the second restoration coefficient in the second restoration coefficient group. The specific restoration method is to subtract the corresponding second restoration coefficient from each frequency modulation data in the prediction result to obtain the restored predicted frequency modulation data.

[0057] The second reduction coefficient is: .

[0058] In the formula, is the second reduction coefficient, is the second compensation coefficient, is the restoration parameter, N is the number of data in the frequency modulation sequence, and Q and N are both positive integers. The restoration parameter is:

[0059] Where Swish() is the fourth activation function.

[0060] When the prediction result is [x N+1 ,x N+2 ,x N+3 ,...,x N+0.05N ], the first reduction coefficient group is , then the restored predicted FM data is: [x N+1 -k`1,x N+2 -k`2,x N+3 -k`3,...,x N+0.05N -k` 0.05N-1 ].

[0061] The method for predicting the frequency modulation command of the sodium ion supercapacitor energy storage system provided in this embodiment was implemented and verified, as follows: The data set uses the historical frequency modulation data of a thermal power plant from January to June 2022; the historical frequency modulation data is divided and preprocessed; the nonlinear sequence in the frequency modulation data is obtained; then the compensation and restoration parameters are calculated; according to the compensation sequence, the trained prediction model is used to predict the frequency modulation instruction; finally, the prediction result is restored in combination with the compensation coefficient to obtain the predicted frequency modulation sequence of the sodium ion supercapacitor energy storage system. The verification data is shown in Table 1. Table 1 is a comparison table of the method of the present invention and the traditional prediction method

[0062] As can be seen from Table 1, the method of the present invention reduces the mean square error by 38.2% and the mean absolute error by 34.2% through nonlinear compensation; the linearity of the sequence after compensation is improved to 92.3%; and the prediction response time is shortened to 120ms.

[0063] In summary, this embodiment fully considers the nonlinearity and irregularity of some frequency modulation instructions, as well as the time series of frequency modulation instructions. The acquired frequency modulation sequence is a time series, and the nonlinear sequence in the frequency modulation sequence is linearly compensated by using the compensation coefficient to reduce the difficulty of predicting nonlinear data. The linearly compensated data is used to predict using the prediction model, and the obtained prediction results are restored using the reduction coefficient. The reduction coefficient is obtained according to the compensation coefficient. This method can not only reduce the difficulty of prediction of the prediction model, but also effectively improve the accuracy of the prediction results, and is more suitable for the actual situation of the sodium ion supercapacity energy storage system.

[0064] Example 2 like Figure 2 As shown, a frequency modulation instruction prediction system for a sodium ion supercapacitor energy storage system in this embodiment includes: The data acquisition module is used to obtain the frequency modulation sequence data of the sodium ion supercapacitor energy storage system. In this embodiment, the number of frequency modulation instruction data in the frequency modulation sequence obtained is N, where N≥300, and the frequency modulation sequence data is [x1, x2, x3, ..., x N ].

[0065] The data judgment module is used to judge whether there is a nonlinear sequence in the frequency modulation sequence based on the acquired frequency modulation sequence data of the sodium ion supercapacitor energy storage system.

[0066] The specific method of judging whether the frequency modulation sequence is nonlinear sequence data is to judge whether the frequency modulation sequence satisfies the first condition, and to judge whether there is a subsequence satisfying the first condition in the frequency modulation sequence. When the subsequence satisfies any of the first conditions, the subsequence is a nonlinear sequence, otherwise it is a linear sequence. The first condition is:

[0067] In the formula, [x i ,x i+1 ,x i+2 …,x i+Q ,x i+Q+1 ] is a frequency modulation sequence, which belongs to a nonlinear sequence. When a part of the sequence in the frequency modulation sequence satisfies the first case or the second case mentioned above, then the subsequence is a nonlinear sequence.

[0068] The data compensation module is used to compensate the frequency modulation instruction data in the nonlinear sequence respectively through the compensation coefficient group to obtain a compensation sequence, wherein the compensation sequence is a linear sequence.

[0069] Specifically, when a part of the sequence meets the first condition, the frequency modulation data in the nonlinear sequence is compensated accordingly by the first compensation coefficient in the first compensation coefficient group to obtain a first compensation sequence. The first condition of the nonlinear sequence is:

[0070] The first compensation coefficient is defined as:

[0071] In the formula, is the first compensation coefficient, is the first activation function, is the second activation function, a t is the frequency modulation sequence parameter, and rand is the random number calculation.

[0072] The first compensation coefficient group is formed by the first compensation coefficient, that is, By adding the first compensation coefficient to the corresponding frequency modulation data, the first compensation sequence can be obtained, that is, .

[0073] The frequency modulation sequence parameters are defined as:

[0074] Where a1, a2, …, a i+Q are the frequency modulation sequence parameters corresponding to the frequency modulation data in the nonlinear sequence.

[0075] Similarly, when a part of the sequence meets the second condition, the frequency modulation data in the nonlinear sequence is compensated accordingly by the second compensation coefficient in the second compensation coefficient group to obtain a second compensation sequence.

[0076] Among them, the second case in the nonlinear sequence is:

[0077] Then, the corresponding second compensation coefficient is defined as:

[0078] In the formula, is the second compensation coefficient, tanh() is the third activation function, a t is a frequency modulation sequence parameter, which has the same definition as above.

[0079] The frequency modulation instruction prediction module is used to predict the frequency modulation instruction according to the compensation sequence using the constructed prediction model to obtain the corresponding prediction result. The prediction model is a trained deep learning network model, and the training data is the historical frequency modulation sequence data; specifically, the basic model of the prediction model adopts any one of the long short-term memory neural network model, the RNN neural network model, and the bidirectional long short-term memory network model. This embodiment adopts the long short-term memory neural network model; wherein the RNN neural network model, that is, the recurrent neural network (Recurrent Neural Network), is a neural network used to process sequence data.

[0080] The data restoration module is used to obtain a restoration coefficient group based on the compensation coefficient group, and use the restoration coefficients in the restoration coefficient group to restore the frequency modulation data in the prediction result, so as to finally obtain the predicted frequency modulation sequence of the sodium ion supercapacity energy storage system.

[0081] Based on the nonlinear sequence of the first case, a first restoration coefficient group is obtained according to the first compensation coefficient group and the prediction result obtained based on the first compensation sequence. The frequency modulation data in the prediction result are restored according to the first restoration coefficient in the first restoration coefficient group. The specific restoration method is to subtract the corresponding first restoration coefficient from each frequency modulation data in the prediction result to obtain the restored predicted frequency modulation data.

[0082] The first reduction coefficient is:

[0083] In the formula, k t is the first reduction coefficient, is the first compensation coefficient, is the restoration parameter, and N is the number of data in the frequency modulation sequence. The restoration parameter is:

[0084] Where Swish() is the fourth activation function, .

[0085] When the prediction result is [x N+1 ,x N+2 ,x N+3 ,...,x N+0.05N ], the first reduction coefficient group is , then the restored predicted FM data is: [x N+1 -k1,x N+2 -k2,x N+3 -k3,...,x N+0.05N -k 0.05N-1 ].

[0086] Based on the nonlinear sequence of the second case, a second restoration coefficient group is obtained according to the second compensation coefficient group and the prediction result obtained based on the second compensation sequence. The frequency modulation data in the prediction result are restored according to the second restoration coefficient in the second restoration coefficient group. The specific restoration method is to subtract the corresponding second restoration coefficient from each frequency modulation data in the prediction result to obtain the restored predicted frequency modulation data.

[0087] The second reduction coefficient is: .

[0088] In the formula, is the second reduction coefficient, is the second compensation coefficient, is the restoration parameter, and N is the number of data in the frequency modulation sequence. The restoration parameter is:

[0089] Where Swish() is the fourth activation function.

[0090] Example 3 like Figure 3The present embodiment shown provides an electronic device for implementing the method for predicting the frequency modulation instruction of the sodium ion supercapacitor energy storage system in the above-mentioned embodiment 1. The electronic device 100 includes at least one processor 102, a memory 101, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104. The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the method for predicting the frequency modulation instruction of the sodium ion supercapacitor energy storage system in embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0091] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0092] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system, and the processor 102 can execute the plurality of instructions to implement: Obtaining nonlinear sequences in frequency modulation sequence data of sodium ion supercapacitor energy storage system; The frequency modulation instruction data in the nonlinear sequence are compensated respectively by the compensation coefficient group to obtain a compensation sequence, wherein the compensation sequence is a linear sequence; According to the compensation sequence, the frequency modulation instruction is predicted using the constructed prediction model to obtain a corresponding prediction result, wherein the prediction model is a trained deep learning network model, and the training data is the historical frequency modulation sequence data; A reduction coefficient group is obtained based on the compensation coefficient group, and the reduction coefficients in the reduction coefficient group are used to restore the frequency modulation data in the prediction result, so as to finally obtain the predicted frequency modulation sequence of the sodium ion supercapacity energy storage system.

[0093] Example 4 If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0094] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system, characterized in that: include: Obtaining nonlinear sequences in frequency modulation sequence data of sodium ion supercapacitor energy storage system; The frequency modulation instruction data in the nonlinear sequence are compensated respectively by the compensation coefficient group to obtain a compensation sequence, wherein the compensation sequence is a linear sequence; According to the compensation sequence, the frequency modulation instruction is predicted using the constructed prediction model to obtain a corresponding prediction result, wherein the prediction model is a trained deep learning network model, and the training data is the historical frequency modulation sequence data; A reduction coefficient group is obtained based on the compensation coefficient group, and the reduction coefficients in the reduction coefficient group are used to restore the frequency modulation data in the prediction result, so as to finally obtain the predicted frequency modulation sequence of the sodium ion supercapacity energy storage system.

2. The method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system according to claim 1, characterized in that: Before obtaining the nonlinear sequence in the frequency modulation sequence data of the sodium ion supercapacitor energy storage system, the method includes: The frequency modulation sequence data of the sodium ion supercapacitor energy storage system is obtained to determine whether there is a nonlinear sequence in the frequency modulation sequence. The determination method is: determine whether there is a subsequence that satisfies the first condition in the frequency modulation sequence. When the subsequence satisfies any one of the first conditions, the subsequence is a nonlinear sequence, otherwise it is a linear sequence. The first condition is: The first case is: The second case is: In the formula, x i is the element in the frequency modulation sequence; i is the number of elements; [x i ,x i+1 ,x i+2 …,x i+Q ,x i+Q+1 ] is the frequency modulation sequence, which is a nonlinear sequence; Q is the length parameter of the subsequence in the frequency modulation sequence, and N is the length of the frequency modulation sequence of the sodium ion supercapacitor energy storage system.

3. The method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system according to claim 2, characterized in that: The method of compensating the frequency modulation instruction data in the nonlinear sequence respectively by the compensation coefficient group to obtain the compensation sequence includes: when the nonlinear sequence is the first case, the frequency modulation data in the nonlinear sequence is compensated by the first compensation coefficient in the first compensation coefficient group to obtain the first compensation sequence, and the calculation expression of the first compensation coefficient is: In the formula, is the first compensation coefficient, is the first activation function, is the second activation function, a t is the frequency modulation sequence parameter, rand is a random number, is the frequency modulation instruction data related to the frequency modulation sequence time t; the frequency modulation sequence parameters are: In the formula, a1, a2, ..., a i+Q are the frequency modulation sequence parameters corresponding to the frequency modulation data in the nonlinear sequence.

4. The method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system according to claim 3 is characterized in that: The step of obtaining a restoration coefficient group based on the compensation coefficient group, and restoring the frequency modulation data in the prediction result using the restoration coefficients in the restoration coefficient group, comprises: A first restoration coefficient group is obtained according to the first compensation coefficient group and the prediction result obtained based on the first compensation sequence. The frequency modulation data in the prediction result are restored according to the first restoration coefficient in the first restoration coefficient group. The specific restoration method is: each frequency modulation data in the prediction result is subtracted from the corresponding first restoration coefficient to obtain the restored predicted frequency modulation data. The calculation expression of the first restoration coefficient is: In the formula, k t is the first reduction coefficient, is the first compensation coefficient, is the restoration parameter, N is the number of data in the frequency modulation sequence, and the restoration parameter is: Where Swish() is the fourth activation function.

5. The method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system according to claim 4, characterized in that: The method further comprises: obtaining a restoration coefficient group based on the compensation coefficient group, and using the restoration coefficients in the restoration coefficient group to restore the frequency modulation data in the prediction result. A second restoration coefficient group is obtained according to the second compensation coefficient group and the prediction result obtained based on the second compensation sequence. The frequency modulation data in the prediction result are restored according to the second restoration coefficients in the second restoration coefficient group. The specific restoration method is: each frequency modulation data in the prediction result is subtracted from the corresponding second restoration coefficient to obtain the restored predicted frequency modulation data. The calculation expression of the second restoration coefficient is: ； In the formula, is the second reduction coefficient, is the second compensation coefficient, is the restoration parameter, N is the number of data in the frequency modulation sequence, and the restoration parameter is: Where Swish() is the fourth activation function.

6. The method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system according to claim 2, characterized in that: The method of compensating the frequency modulation instruction data in the nonlinear sequence by the compensation coefficient group to obtain the compensation sequence includes: when the nonlinear sequence is the second case, the frequency modulation data in the nonlinear sequence is compensated by the second compensation coefficient in the second compensation coefficient group to obtain the second compensation sequence, and the calculation expression of the second compensation coefficient is: In the formula, is the second compensation coefficient, tanh() is the third activation function, a t is the frequency modulation sequence parameter, is the frequency modulation instruction data related to time t; the frequency modulation sequence parameters are: In the formula, a1, a2, ..., a i+Q are the frequency modulation sequence parameters corresponding to the frequency modulation data in the nonlinear sequence.

7. The method for predicting frequency modulation instructions of a sodium ion supercapacitor energy storage system according to claim 1, characterized in that: The basic model of the prediction model adopts any one of a long short-term memory neural network model, a RNN neural network model, and a bidirectional long short-term memory network model.

8. A frequency modulation instruction prediction system for a sodium ion supercapacitor energy storage system, characterized in that: include: A data acquisition module, used to obtain nonlinear sequences in frequency modulation sequence data of a sodium ion supercapacitor energy storage system; A data compensation module, used to compensate the frequency modulation instruction data in the nonlinear sequence respectively through a compensation coefficient group to obtain a compensation sequence, wherein the compensation sequence is a linear sequence; A frequency modulation instruction prediction module is used to predict the frequency modulation instruction according to the compensation sequence using a constructed prediction model to obtain a corresponding prediction result, wherein the prediction model is a trained deep learning network model and the training data is historical frequency modulation sequence data; The data restoration module is used to obtain a restoration coefficient group based on the compensation coefficient group, and use the restoration coefficients in the restoration coefficient group to restore the frequency modulation data in the prediction result, so as to finally obtain the predicted frequency modulation sequence of the sodium ion supercapacity energy storage system.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the frequency modulation instruction prediction method for the sodium ion supercapacitor energy storage system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the frequency modulation instruction prediction method for the sodium ion supercapacitor energy storage system according to any one of claims 1 to 7 is implemented.

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