Short-time high-frequency super capacitor energy storage charging and discharging stability control method and system

Abstract

The invention discloses a short-time high-frequency super capacitor energy storage charging and discharging stability control method and system, and relates to the technical field of electric energy storage, and the method comprises the steps: monitoring the data information of a super capacitor, and carrying out the preprocessing; constructing a mathematical model of the supercapacitor, and dynamically adjusting charging and discharging current and voltage; constructing a first prediction model to output predicted values of the inverter side output current, the grid-connected current and the power grid voltage; and constructing a second value function constraint condition and a second prediction model, generating a charging and discharging optimization plan, and optimizing an energy distribution strategy. Through real-time monitoring and accurate control of the control algorithm module, the stability of the supercapacitor in the short-time high-frequency charging and discharging process can be remarkably improved. The super capacitor is ensured to always run in an optimal state in the charging and discharging process through an optimization algorithm, so that the energy loss is reduced. By accurately controlling the charging and discharging process, the stress level of the supercapacitor can be remarkably reduced, and the service life of the supercapacitor is further prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage, and in particular to a short-time high-frequency supercapacitor energy storage charging and discharging stable control method and system. Background Art

[0002] In modern society, the rapid development of power electronics technology has led to the rise of supercapacitors, a new type of energy storage component. Supercapacitors have attracted widespread attention due to their significant advantages such as high power density, fast charging and discharging capabilities, and long cycle life. However, during short-term and high-frequency charging and discharging operations, the voltage and current of supercapacitors fluctuate greatly, which may cause system instability and even damage the supercapacitor. Therefore, it is particularly important to develop an effective control strategy to stabilize the charging and discharging process of supercapacitors. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is: how to improve the stability of the supercapacitor during high-frequency charging and discharging, reduce energy loss, and thus extend the service life of the supercapacitor.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a short-time high-frequency supercapacitor energy storage charging and discharging stable control method, which comprises the following steps: The data information of the supercapacitor is monitored by sensors for preprocessing; Based on the preprocessed data, a mathematical model of the supercapacitor is constructed to dynamically adjust the charging and discharging current and voltage of the supercapacitor; Constructing a first prediction model, a first value function constraint condition and a switching number constraint condition, and outputting predicted values ​​of inverter-side output current, grid-connected current and grid voltage; Constructing a second value function constraint condition based on the switching number constraint condition; A second prediction model is constructed based on the constraints of the second value function to generate a charging and discharging optimization plan and optimize the energy allocation strategy.

[0006] As a preferred solution of the short-time high-frequency supercapacitor energy storage charging and discharging stable control method described in the present invention, the data information of the monitoring supercapacitor includes: Use sensors to monitor the voltage, current, resistance, capacitance, and inductance data of supercapacitors in real time; The preprocessing is to filter the monitoring data, remove high-frequency noise, use a correction algorithm to correct the data deviation caused by sensor error, and discretize the processed data.

[0007] As a preferred solution of the short-time high-frequency supercapacitor energy storage charging and discharging stable control method described in the present invention, wherein: the mathematical model of the supercapacitor is constructed based on the preprocessed data, and the charging and discharging current and voltage of the supercapacitor are dynamically adjusted, including: The supercapacitor energy storage grid-connected inverter adopts a midpoint clamped inverter. In the coordinate system, the output voltage of the supercapacitor energy storage grid-connected inverter is expressed as: , The grid side voltage expression is: , Supercapacitor energy storage grid-connected inverter side inductor current , grid-side inductor current With filter capacitor The relationship expression is: , in, , is the three-phase voltage on the grid side; , Output three-phase voltage for supercapacitor energy storage grid-connected inverter; , is the three-phase voltage of the filter capacitor; , is the three-phase current on the inverter side; , is the three-phase current on the grid side; is the DC side voltage; They are the parasitic resistance and inductance of the AC side of the supercapacitor energy storage grid-connected inverter; are the parasitic resistance and inductance of the grid side respectively; is the LCL filter capacitor.

[0008] As a preferred solution of a short-time high-frequency supercapacitor energy storage charging and discharging stable control method described in the present invention, wherein: the first prediction model, the first value function constraint condition and the switching number constraint condition are constructed, and the predicted values ​​of the output current, grid-connected current and grid voltage on the output inverter side include: In the sampling period Under this condition, the load current is discretized, and the derivative expression of the three-phase current on the grid side is: , in, for The three-phase current on the grid side at the moment, for The three-phase current on the grid side at the moment, is the sampling period; Substituting the derivative of the three-phase current on the grid side into the mathematical model of the supercapacitor, the predicted values ​​of the output current, grid-connected current and grid voltage on the output inverter side are expressed as follows: , in, , , , They are The values ​​of the inverter side output current, grid side current, inverter output voltage and filter capacitor voltage after Clark transformation at the moment; , , yes The predicted values ​​of the inverter side output current, grid-connected current and grid voltage at the moment.

[0009] As a preferred solution of the short-time high-frequency supercapacitor energy storage charging and discharging stable control method described in the present invention, wherein: the first prediction model, the first value function constraint condition and the switching number constraint condition are constructed, and the predicted values ​​of the output current, grid-connected current and grid voltage on the output inverter side also include: The square error is used to construct the value function as the constraint condition of the prediction model, and the expression is: , in, , for The current reference value component of a sampling period, for Output current of inverter side under the shaft, They are Output current of inverter side under the shaft; The constraint expression for the number of on and off switching of the switching device of the supercapacitor energy storage grid-connected inverter is: , in, , , The three-phase switching states of a, b, and c of the supercapacitor energy storage grid-connected inverter at time k; , , It is the three-phase switching state of a, b, and c of the supercapacitor energy storage grid-connected inverter at time k+1.

[0010] As a preferred solution of the short-time high-frequency supercapacitor energy storage charging and discharging stable control method described in the present invention, wherein: the second value function constraint condition constructed based on the switching number constraint condition includes: The second value function constraint is constructed based on the switching number constraint to suppress the voltage deviation between the upper and lower bridge arms on the DC side. The expression is: , in, , is the weight coefficient, , They are the filter capacitor voltages in the α and β coordinate systems at time k, , They are the filter capacitor voltages in the α and β coordinate systems at time k+1 respectively.

[0011] As a preferred solution of the short-time high-frequency supercapacitor energy storage charging and discharging stable control method described in the present invention, wherein: the second prediction model is constructed based on the second value function constraint condition, the charging and discharging optimization plan is generated, and the energy allocation strategy is optimized, including: The second prediction model is constructed based on the constraint condition of the second value function. The second step of prediction is performed after the current prediction value at the moment. and Complete voltage vector control and generate a charge and discharge optimization plan, the expression is: , in, , for The voltage vector control output voltage of the supercapacitor energy storage grid-connected inverter in the coordinate system; according to and Perform rolling optimization, output the switch sequence that makes the second value function result optimal, and place the switch sequence in The data is output to the switch tube at all times, reducing the influence of sampling frequency and transmission rate on model predictive control.

[0012] Another object of the present invention is to provide a short-time high-frequency supercapacitor energy storage charging and discharging stable control system, which can realize precise control of the supercapacitor energy storage process by real-time monitoring of the operating status of the supercapacitor, optimizing the charging and discharging strategy, limiting the number of switches, and generating an optimized charging and discharging plan, thereby effectively solving the problems of inaccurate monitoring, low energy distribution efficiency, high hardware loss, and insufficient system stability in the prior art.

[0013] In order to solve the above technical problems, the present invention provides the following technical solutions: a short-time high-frequency supercapacitor energy storage charging and discharging stable control system, comprising: a data acquisition and preprocessing module, a mathematical model construction and dynamic adjustment module, a first prediction model and value function construction module, and a second prediction model and optimization strategy module; The data acquisition and preprocessing module uses sensors to monitor the voltage, current, resistance, capacitance and inductance data of the supercapacitor in real time, and performs filtering, correction and discretization processing on the data; The mathematical model construction and dynamic adjustment module constructs a mathematical model of the supercapacitor according to the preprocessed data, and dynamically adjusts the charging and discharging current and voltage of the supercapacitor through the model; The first prediction model and value function construction module predicts the inverter side output current, grid-connected current and grid voltage by constructing the first prediction model, the first value function constraint condition and the switching number constraint condition; The second prediction model and optimization strategy module constructs a second prediction model based on the switching number constraint and the second value function constraint, generates a charging and discharging optimization plan, optimizes the energy allocation strategy, and outputs an optimized switching sequence to control the operation of the inverter.

[0014] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned short-time high-frequency supercapacitor energy storage charging and discharging stable control method are implemented.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned short-time high-frequency supercapacitor energy storage charging and discharging stability control method.

[0016] Beneficial effects of the present invention: The present invention can significantly improve the stability of supercapacitors during short-term high-frequency charging and discharging through real-time monitoring and precise control of the control algorithm module. The optimization algorithm ensures that the supercapacitor always operates in the optimal state during the charging and discharging process, thereby reducing energy loss. By precisely controlling the charging and discharging process, the present invention can significantly reduce the stress level of the supercapacitor, thereby extending its service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 An overall flow chart of a short-time high-frequency supercapacitor energy storage charging and discharging stability control method provided for the first embodiment of the present invention.

[0019] Figure 2A topological structure diagram of a supercapacitor energy storage grid-connected inverter in a short-time high-frequency supercapacitor energy storage charging and discharging stability control method provided in the first embodiment of the present invention.

[0020] Figure 3 A single-phase voltage drop supercapacitor energy storage control voltage and current waveform diagram of a short-time high-frequency supercapacitor energy storage charge and discharge stability control method provided in the third embodiment of the present invention.

[0021] Figure 4 A short-time high-frequency supercapacitor energy storage charging and discharging stability control method provided in the third embodiment of the present invention is a single-phase voltage drop supercapacitor energy storage control voltage content waveform diagram under different task numbers. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0023] Example 1, reference Figure 1~Figure 2 As an embodiment of the present invention, a short-time high-frequency supercapacitor energy storage charging and discharging stable control method is provided, comprising: The data information of the supercapacitor is monitored by sensors for preprocessing; Based on the preprocessed data, a mathematical model of the supercapacitor is constructed to dynamically adjust the charging and discharging current and voltage of the supercapacitor; Constructing a first prediction model, a first value function constraint condition and a switching number constraint condition, and outputting predicted values ​​of inverter-side output current, grid-connected current and grid voltage; Constructing a second value function constraint condition based on the switching number constraint condition; A second prediction model is constructed based on the constraints of the second value function to generate a charging and discharging optimization plan and optimize the energy allocation strategy.

[0024] Sensors are used to monitor the voltage, current, resistance, capacitance and inductance data of supercapacitors in real time. The monitoring data is filtered to remove high-frequency noise, and a correction algorithm is used to correct the data deviation caused by sensor errors, and the processed data is discretized.

[0025] like Figure 2 As shown in the figure, the supercapacitor energy storage grid-connected inverter adopts a midpoint clamped inverter, and its topology is as follows Figure 2 shown. , is the three-phase voltage on the grid side; , Output three-phase voltage for supercapacitor energy storage grid-connected inverter; , is the three-phase voltage of the filter capacitor; , is the three-phase current on the inverter side; , is the three-phase current on the grid side; is the DC side voltage; They are the parasitic resistance and inductance of the AC side of the supercapacitor energy storage grid-connected inverter; are the parasitic resistance and inductance of the grid side respectively; is the LCL filter capacitor.

[0026] In the coordinate system, the output voltage of the supercapacitor energy storage grid-connected inverter can be expressed as: (1) In the coordinate system, the grid side voltage can be expressed as: (2) Supercapacitor energy storage grid-connected inverter side inductor current , grid-side inductor current With filter capacitor There exists between: (3) It can be seen from formula (1-3): Axis and The two sets of state variables of the axis are two-phase orthogonal independent variables, and the control algorithm can be designed in this coordinate system.

[0027] During the sampling week Under this condition, the load current is discretized and the derivative It can be expressed as: (4) According to the derivative of the three-phase current on the grid side, the predicted values ​​of the output current, grid-connected current and grid voltage on the output inverter side are substituted into the mathematical model of the supercapacitor. Formula (4) is substituted into formula (1-3) and the following is obtained: (5) in: , , , They are The values ​​of the inverter side output current, grid side current, inverter output voltage and filter capacitor voltage after Clark transformation at the moment; , , yes The predicted values ​​of the inverter side output current, grid-connected current and grid voltage at the moment.

[0028] The present invention uses square error to construct the first value function constraint, which can achieve accurate control of voltage and current and improve the stability of the supercapacitor energy storage control system. The value function can be expressed as: (6) in: , for The current reference value component of a sampling period.

[0029] The constraint expression for the number of on and off switching of the switching device of the supercapacitor energy storage grid-connected inverter is: (7) in: , , The three-phase switching states of a, b, and c of the supercapacitor energy storage grid-connected inverter at time k; , , It is the three-phase switching state of a, b, and c of the supercapacitor energy storage grid-connected inverter at time k+1.

[0030] In order to reduce the energy loss of the supercapacitor energy storage grid-connected inverter at high switching frequency, ensure the stable balance of the midpoint potential, and effectively suppress the voltage deviation phenomenon of the upper and lower bridge arms on the DC side, an improvement measure can be taken. Specifically, according to formula (7), a second value function constraint condition can be reconstructed, which can be expressed as: (8) in: , is the weight coefficient.

[0031] In this way, not only can the energy loss of the inverter in high-frequency switching operation be reduced, but also the stability of the midpoint potential can be ensured, avoiding the degradation of system performance due to potential imbalance. In addition, through the reconstructed value function, the voltage deviation between the upper and lower bridge arms on the DC side can be effectively suppressed, thereby improving the stability and reliability of the entire system. This improvement method not only optimizes the performance of the system, but also improves the overall efficiency of the system, making it more competitive in practical applications.

[0032] In the actual supercapacitor energy storage grid-connected inverter control system, the prediction model control will be affected by the sampling frequency and transmission rate, and there is a delay problem. In order to solve the delay problem, the present invention adopts a two-step prediction method to compensate for the delay. The second step of prediction is performed after the current prediction value at the moment. and To realize voltage vector control, it can be expressed as: (9) in: , for The voltage vector control output voltage of the supercapacitor energy storage grid-connected inverter in the coordinate system. and Perform rolling optimization, output the switch sequence that makes the value function optimal, and place the switch sequence in The output is sent to the switch tube at all times, thereby reducing the influence of sampling frequency and transmission rate on model predictive control.

[0033] In a specific coordinate system, the supercapacitor energy storage grid-connected inverter can accurately control the output voltage through voltage vector control technology. To achieve this goal, the system will perform a detailed rolling optimization operation based on the two key variables in voltage vector control, namely the amplitude and phase of the voltage vector. Through this optimization process, the system can find a set of optimal switching sequences so that the value function achieves the best result. Subsequently, this set of optimal switching sequences will be output to the switch tube at a specific time, thereby effectively reducing the adverse effects of sampling frequency and transmission rate on model predictive control.

[0034] Embodiment 2 is an embodiment of the present invention, which provides a short-time high-frequency supercapacitor energy storage charging and discharging stable control system, including: a data acquisition and preprocessing module, a mathematical model construction and dynamic adjustment module, a first prediction model and value function construction module, and a second prediction model and optimization strategy module.

[0035] The data acquisition and preprocessing module uses sensors to monitor the voltage, current, resistance, capacitance and inductance data of the supercapacitor in real time, and performs filtering, correction and discretization processing on the data.

[0036] The mathematical model building and dynamic adjustment module builds a mathematical model of the supercapacitor according to the preprocessed data, and dynamically adjusts the charging and discharging current and voltage of the supercapacitor through the model.

[0037] The first prediction model and value function construction module predicts the inverter side output current, grid-connected current and grid voltage by constructing a first prediction model, a first value function constraint condition and a switching number constraint condition.

[0038] The second prediction model and optimization strategy module constructs a second prediction model based on the switching number constraint and the second value function constraint, generates a charging and discharging optimization plan, optimizes the energy allocation strategy, and outputs an optimized switching sequence to control the operation of the inverter.

[0039] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0040] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0041] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0042] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0043] Example 3, reference Figure 3~Figure 4 This is another embodiment of the present invention. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. This embodiment experiments the existing traditional method and the method of this embodiment respectively.

[0044] In order to further verify the stability of the supercapacitor energy storage control system and improve its dynamic response performance, a switchable power grid module was built in the simulation environment. By simulating the single-phase voltage drop, the performance of the supercapacitor energy storage control system in actual application can be observed. In this way, the stability and response speed of the system in the face of power grid fluctuations can be verified. The specific simulation parameters are shown in Table 1 below. These parameters provide a detailed setting basis for the simulation and ensure the accuracy and reliability of the simulation.

[0045] Table 1 Simulation parameters of three-level model predictive control system , Figure 3 The voltage and current waveforms of the supercapacitor energy storage control under single-phase voltage drop are shown. In the figure, it can be observed that the supercapacitor energy storage control system can maintain a stable output voltage and current when the red voltage drops. In the case of voltage drop, the voltage and current can be stabilized at 121V and 13A. The system can still keep the output voltage close to the set value when the red voltage fluctuates, and the blue current waveform also remains stable, proving the stability and response speed of the system in dealing with power grid fluctuations.

[0046] Figure 4 The waveform of the voltage content of the supercapacitor energy storage control under single-phase voltage drop is shown. As can be seen from the figure, after the red voltage drops, the voltage distortion content increases slightly, but remains at a low level overall, indicating that the system can maintain good voltage quality under different working conditions and the harmonic distortion is effectively controlled. Although the voltage content has increased, it is still far below the critical value that may cause system instability. During the entire sampling period, the harmonic distortion rate remained below 3.29%.

[0047] By applying the predictive model control technology, the switching frequency of the supercapacitor energy storage control system has been effectively reduced. Specifically, the switching frequency has been reduced by 10.98% under the condition that the event-triggered control mechanism ensures the output stability. This means that under the same operating time and sampling frequency conditions, the switching frequency of the system has been significantly reduced, thereby improving the overall performance and efficiency of the system.

[0048] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A short-time high-frequency supercapacitor energy storage charging and discharging stable control method, characterized in that: include: The data information of the supercapacitor is monitored by sensors for preprocessing; Based on the preprocessed data, a mathematical model of the supercapacitor is constructed to dynamically adjust the charging and discharging current and voltage of the supercapacitor; Constructing a first prediction model, setting a first value function constraint and a switching number constraint, and outputting predicted values ​​of inverter-side output current, grid-connected current, and grid voltage; Constructing a second value function constraint condition based on the switching number constraint condition; A second prediction model is constructed based on the constraints of the second value function to generate a charging and discharging optimization plan and optimize the energy allocation strategy.

2. A short-time high-frequency supercapacitor energy storage charging and discharging stable control method as claimed in claim 1, characterized in that: The data information of monitoring the supercapacitor includes: Use sensors to monitor the voltage, current, resistance, capacitance, and inductance data of supercapacitors in real time; The preprocessing is to filter the monitoring data, remove high-frequency noise, use a correction algorithm to correct the data deviation caused by sensor error, and discretize the processed data.

3. A short-time high-frequency supercapacitor energy storage charging and discharging stable control method as claimed in claim 2, characterized in that: The method of constructing a mathematical model of the supercapacitor based on the preprocessed data and dynamically adjusting the charging and discharging current and voltage of the supercapacitor includes: The supercapacitor energy storage grid-connected inverter adopts a midpoint clamped inverter. In the coordinate system, the output voltage of the supercapacitor energy storage grid-connected inverter is expressed as: ， The grid side voltage expression is: ， Supercapacitor energy storage grid-connected inverter side inductor current , grid-side inductor current With filter capacitor The relationship expression is: ， in, , is the three-phase voltage on the grid side; , Output three-phase voltage for supercapacitor energy storage grid-connected inverter; , is the three-phase voltage of the filter capacitor; , is the three-phase current on the inverter side; , is the three-phase current on the grid side; is the DC side voltage; They are the parasitic resistance and inductance of the AC side of the supercapacitor energy storage grid-connected inverter; are the parasitic resistance and inductance of the grid side respectively; is the LCL filter capacitor.

4. A short-time high-frequency supercapacitor energy storage charging and discharging stable control method as claimed in claim 3, characterized in that: Construct a first prediction model, set the first value function constraint and the switching number constraint, and output the predicted values ​​of the inverter side output current, grid-connected current and grid voltage, including: In the sampling period Under this condition, the load current is discretized, where the derivative of the three-phase current i on the grid side is The expression is: ， in, for The three-phase current on the grid side at the moment, for The three-phase current on the grid side at the moment, is the sampling period; Substitute the derivative of the three-phase current on the grid side into the mathematical model of the supercapacitor to output the predicted values ​​of the output current, grid-connected current and grid voltage on the inverter side. The expression is: ， in, , , , They are The values ​​of the inverter side output current, grid side current, inverter output voltage and filter capacitor voltage after Clark transformation at the moment; , , yes The predicted values ​​of the inverter side output current, grid-connected current and grid voltage at the moment.

5. A short-time high-frequency supercapacitor energy storage charging and discharging stable control method as claimed in claim 4, characterized in that: The first prediction model, the first value function constraint condition and the switching number constraint condition are constructed, and the predicted values ​​of the output current, the grid-connected current and the grid voltage on the output inverter side are output, and further include: The square error is used to construct the first value function as the constraint condition of the prediction model, and the expression is: ， in, , for The current reference value component of a sampling period, for Output current of inverter side under the shaft, They are Output current of inverter side under the shaft; The constraint expression for the number of on and off switching of the switching device of the supercapacitor energy storage grid-connected inverter is: ， in, , , The three-phase switching states of a, b, and c of the supercapacitor energy storage grid-connected inverter at time k; , , It is the three-phase switching state of a, b, and c of the supercapacitor energy storage grid-connected inverter at time k+1.

6. A short-time high-frequency supercapacitor energy storage charging and discharging stable control method as claimed in claim 5, characterized in that: The second value function constraint condition constructed based on the switching number constraint condition includes: The second value function constraint is constructed based on the switching number constraint to suppress the voltage deviation between the upper and lower bridge arms on the DC side. The expression is: ， in, , is the weight coefficient, , They are the filter capacitor voltages in the α and β coordinate systems at time k, , They are the filter capacitor voltages in the α and β coordinate systems at time k+1 respectively.

7. A short-time high-frequency supercapacitor energy storage charging and discharging stable control method as claimed in claim 6, characterized in that: The second prediction model is constructed based on the second value function constraint condition, the charge and discharge optimization plan is generated, and the energy allocation strategy is optimized, including: The second prediction model is constructed based on the constraint condition of the second value function. The second step of prediction is performed after the current prediction value at the moment. and Complete voltage vector control and generate a charge and discharge optimization plan, the expression is: ， in, , for The voltage vector control output voltage of the supercapacitor energy storage grid-connected inverter in the coordinate system; according to and Perform rolling optimization, output the switch sequence that makes the second value function result optimal, and place the switch sequence in The data is output to the switch tube at all times, reducing the influence of sampling frequency and transmission rate on model predictive control.

8. A system using a short-time high-frequency supercapacitor energy storage charging and discharging stable control method as claimed in any one of claims 1 to 7, characterized in that: It includes a data acquisition and preprocessing module, a mathematical model building and dynamic adjustment module, a first prediction model and value function building module, and a second prediction model and optimization strategy module; The data acquisition and preprocessing module uses sensors to monitor the voltage, current, resistance, capacitance and inductance data of the supercapacitor in real time, and performs filtering, correction and discretization processing on the data; The mathematical model construction and dynamic adjustment module constructs a mathematical model of the supercapacitor according to the preprocessed data, and dynamically adjusts the charging and discharging current and voltage of the supercapacitor through the model; The first prediction model and value function construction module predicts the inverter side output current, grid-connected current and grid voltage by constructing the first prediction model, the first value function constraint condition and the switching number constraint condition; The second prediction model and optimization strategy module constructs a second prediction model based on the switching number constraint and the second value function constraint, generates a charging and discharging optimization plan, optimizes the energy allocation strategy, and outputs an optimized switching sequence to control the operation of the inverter.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a short-time high-frequency supercapacitor energy storage charging and discharging stability control method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a short-time high-frequency supercapacitor energy storage charging and discharging stability control method described in any one of claims 1 to 7 are implemented.

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