Self-adaptive control method and device for network construction type converter

Abstract

The invention provides a self-adaptive control method and device for a network-forming converter, and relates to the technical field of electric power grids. According to the method, disturbance analysis is carried out on the power grid operation data, the disturbance type is determined, the initial control target of the network construction type converter is determined according to the disturbance type, then multi-target optimization is carried out on the control parameters of the network construction type converter, and the final control target is determined, so that a self-adaptive control scheme is generated. According to the method, different conditions of power grid fluctuation are considered, different control targets are set for different fluctuation conditions, multi-target optimization is carried out, and adjustment conflicts among all control parameters of the network construction type converter are coordinated, so that the adjustment process of the network construction type converter adapts to the actual fluctuation condition of a power grid and also adapts to the actual fluctuation condition of the power grid. And various conflicts in the adjustment process are fully reconciled, so that the control effect of the network construction type converter is improved on the basis of realizing adaptive control of the network construction type converter, and the stability and the reliability of a power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power grid, and in particular to an adaptive control method and device for a grid-connected converter. Background Art

[0002] With the large-scale access of renewable energy and the widespread application of power electronic equipment, the power grid is gradually shifting towards power electronics. This shift has promoted the optimization of energy structure and the improvement of energy utilization efficiency, but it has also brought many challenges. Due to the intermittent and uncertain nature of renewable energy and the characteristics of power electronic equipment themselves, the system inertia of the power grid has decreased, the strength has weakened, and the stability has faced severe tests. Especially in the complex and changeable power grid environment, traditional control methods are often difficult to ensure the stable operation of the power grid.

[0003] For example, when the grid voltage fluctuates, the reactive power injected by the grid-type converter needs to be adjusted, but the injected reactive power may affect the active power balance of the grid, resulting in grid power imbalance, and there is a conflict between voltage stability and power balance. For another example, when the grid frequency fluctuates, the grid-type converter needs to quickly adjust the active power output, which may cause unstable voltage of the load, and there is a conflict between frequency stability and load response. For another example, the fast response speed of the grid-type converter may cause system oscillation, and the slow response speed may not be able to cope with emergencies. The response speed of the grid-type converter is difficult to set.

[0004] Therefore, there are many conflicts, unstable control and poor effects in the application process of grid-connected converters, which affect the stability and reliability of the power grid. Summary of the invention

[0005] The present invention provides an adaptive control method and device for a grid-type converter, which can realize adaptive control of the grid-type converter, improve the control effect of the grid-type converter, and improve the stability and reliability of the power grid.

[0006] In a first aspect, the present invention provides an adaptive control method for a grid-type converter, the method comprising: obtaining current control parameters of the grid-type converter and grid operation data, the grid operation data including voltage, current, frequency and power factor; performing disturbance analysis based on the grid operation data to determine the disturbance type of the grid; the disturbance types include transient disturbances, static disturbances and dynamic disturbances; determining an initial control target of the grid-type converter based on the disturbance type of the grid, and the inertial characteristics and damping characteristics of the grid-type converter; performing multi-objective optimization based on the current control parameters of the grid-type converter and the initial control target to determine the final control target of the grid-type converter; and determining an adaptive control scheme of the grid-type converter based on the final control target.

[0007] In a possible implementation, a disturbance analysis is performed based on the grid operation data to determine the disturbance type of the grid, including: calculating the difference between each grid operation data and the standard data; the standard data includes standard voltage, standard current, standard frequency and standard power factor; generating a difference time series sequence of each type of grid operation data based on the difference of each type of grid operation data at each moment; the difference time series sequence is used to characterize the change trend of the grid operation data; calculating the statistical data of each grid operation data and the difference of each grid operation data, the statistical data includes the mean, standard deviation, mode and median; generating grid parameter characteristics based on each grid operation data, as well as the difference time series sequence and statistical data of each grid operation data; determining the disturbance type of the grid based on the grid parameter characteristics and the disturbance analysis model.

[0008] In a possible implementation, before performing disturbance analysis based on grid operation data and determining the disturbance type of the grid, it also includes: obtaining grid operation data and disturbance types when the grid is disturbed multiple times at a grid-type converter; calculating the difference time series and statistical data at each disturbance based on the grid operation data at each disturbance; generating grid parameter characteristics at each disturbance based on the grid operation data, the difference time series and statistical data at each disturbance; determining multiple training samples using the grid parameter characteristics at each disturbance as input and the disturbance type of each disturbance as output; and performing neural network training based on the multiple training samples to obtain a disturbance analysis model.

[0009] In a possible implementation, the initial control target includes an initial target voltage, an initial target current, an initial target active power and an initial target reactive power; based on the disturbance type of the power grid, and the inertia characteristics and damping characteristics of the grid-type converter, the initial control target of the grid-type converter is determined, including: determining the initial target voltage based on the disturbance type and the difference time series sequence of the voltage in the power grid operation data; determining the initial target active power based on the disturbance type and the difference time series sequence of the frequency in the power grid operation data; determining the initial target reactive power based on the disturbance type, the difference time series sequence of the voltage in the power grid operation data, and the difference time series sequence of the frequency; determining the current limit of the grid-type converter based on the disturbance type and the device properties of the grid-type converter; adjusting the initial target active power and the initial target reactive power based on the current limit to obtain updated initial target active power and initial target reactive power.

[0010] In one possible implementation, multi-objective optimization is performed based on current control parameters of the grid-type converter and initial control targets to determine the final control target of the grid-type converter, including: determining constraints of each control parameter based on the current control parameters of the grid-type converter and device properties of the grid-type converter; determining multiple combinations based on the initial control targets and constraints, each combination including a set of control parameters; and determining the final control target of the grid-type converter based on multiple combinations and a particle swarm algorithm.

[0011] In a possible implementation, based on multiple combinations and a particle swarm algorithm, the final control target of the grid-type converter is determined, including: step one, initializing the particle swarm; setting the initial speed and initial position of each particle, and the maximum number of iterations; wherein each particle in the particle swarm is a set of control parameters; step two, calculating the comprehensive fitness of each particle in the current iteration process; step three, based on the comprehensive fitness, using the Pareto sorting algorithm to sort each particle in the current iteration process, and determining the non-dominated solution and the Pareto sequence; step four, calculating the comprehensive crowding distance of each non-dominated solution; step five, calculating the comprehensive crowding distance of each non-dominated solution; and step six, calculating the comprehensive crowding distance of each non-dominated solution. Step 5: Based on the speed and position update formula of the particle swarm algorithm, update the speed and position of each particle; Step 6: Based on the Pareto sequence and the comprehensive crowding distance of each non-dominated solution, screen the particles in the current iteration process and determine the particles in the next iteration process; Step 7: If the current number of iterations is greater than or equal to the maximum number of iterations, exit the iteration and execute step 8; If the current number of iterations is less than the maximum number of iterations, repeat steps 2 to 7 until the current number of iterations is greater than or equal to the maximum number of iterations; Step 8: Based on the Pareto sequence, determine the final control target of the grid-connected converter.

[0012] In one possible implementation, the final control target includes the output voltage, output active power and output reactive power of the grid-type converter; based on the final control target, an adaptive control scheme of the grid-type converter is determined, including: based on the final control target and the fluctuation type, determining the adjustment duration of each final control target; based on each final control target and the adjustment duration of each final control target, performing adaptive adjustment to determine an adaptive control scheme; the adaptive control scheme includes the adjustment values ​​of each control parameter of the grid-type converter at each moment within the adjustment period.

[0013] In one possible implementation, after determining the adaptive control scheme of the grid-type converter based on the final control target, it also includes: obtaining the grid operation data of the area where the grid-type converter is located after the disturbance occurs; performing stability analysis based on the real-time grid data after the disturbance occurs, and determining the grid stability parameters; the multiple grid stability parameters include voltage stability, frequency stability and power factor stability; based on the grid stability parameters, presetting standard stability parameters, verifying the grid stability, and determining the adjustment result of the grid-type converter; based on the adjustment result, fine-tuning the adaptive control scheme of the grid-type converter and determining the fine-tuning scheme.

[0014] In one possible implementation, after determining the adaptive control scheme of the grid-type converter based on the final control target, it also includes: obtaining control parameter data of the grid-type converter after a disturbance occurs; performing parameter comparison based on the control parameter data of the grid-type converter after the disturbance occurs and the adaptive control scheme to determine the adjustment deviation of the grid-type converter; updating the adaptive control scheme based on the adjustment deviation of the grid-type converter to determine the updated adaptive control scheme.

[0015] In the second aspect, an embodiment of the present invention provides an adaptive control device for a grid-type converter, the device comprising: a communication module, for acquiring current control parameters of the grid-type converter and grid operation data, the grid operation data including voltage, current, frequency and power factor; a processing module, for performing disturbance analysis based on the grid operation data to determine the disturbance type of the grid; the disturbance types include transient disturbances, static disturbances and dynamic disturbances; based on the disturbance type of the grid, and the inertial characteristics and damping characteristics of the grid-type converter, determining an initial control target of the grid-type converter; based on the current control parameters of the grid-type converter and the initial control target, performing multi-objective optimization to determine the final control target of the grid-type converter; based on the final control target, determining an adaptive control scheme of the grid-type converter.

[0016] In one possible implementation, the processing module is specifically used to calculate the difference between each power grid operation data and the standard data; the standard data includes standard voltage, standard current, standard frequency and standard power factor; based on the difference of each type of power grid operation data at each moment, a difference time series sequence of each type of power grid operation data is generated; the difference time series sequence is used to characterize the changing trend of the power grid operation data; the statistical data of each power grid operation data and the difference of each power grid operation data are calculated, and the statistical data include the mean, standard deviation, mode and median; based on each power grid operation data, and the difference time series sequence and statistical data of each power grid operation data, a power grid parameter feature is generated; based on the power grid parameter feature and the disturbance analysis model, the disturbance type of the power grid is determined.

[0017] In one possible implementation, the processing module is specifically used to determine the initial target voltage based on the disturbance type and the difference time series sequence of the voltage in the power grid operation data; determine the initial target active power based on the disturbance type and the difference time series sequence of the frequency in the power grid operation data; determine the initial target reactive power based on the disturbance type, the difference time series sequence of the voltage in the power grid operation data, and the difference time series sequence of the frequency; determine the current limit of the grid-type converter based on the disturbance type and the equipment properties of the grid-type converter; and adjust the initial target active power and the initial target reactive power based on the current limit to obtain updated initial target active power and initial target reactive power.

[0018] In one possible implementation, the processing module is specifically used to determine the constraints of each control parameter based on the current control parameters of the grid-type converter and the device properties of the grid-type converter; determine multiple combinations based on the initial control objectives and the constraints, each combination including a set of control parameters; and determine the final control objective of the grid-type converter based on the multiple combinations and a particle swarm algorithm.

[0019] In one possible implementation, the processing module is specifically used to determine the adjustment duration of each final control target based on the final control target and the fluctuation type; based on each final control target and the adjustment duration of each final control target, perform adaptive adjustment to determine an adaptive control scheme; the adaptive control scheme includes the adjustment values ​​of each control parameter of the grid-connected converter at each moment within the adjustment period.

[0020] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, the memory storing a computer program, the processor being used to call and run the computer program stored in the memory to perform the steps of the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein when the computer program is executed by a processor, the steps of the method described in the first aspect and any possible implementation method of the first aspect are implemented.

[0022] The present invention provides an adaptive control method and device for a grid-type converter. The present invention performs disturbance analysis on the operation data of the power grid to determine the disturbance type, and determines the initial control target of the grid-type converter according to the disturbance type, and then performs multi-objective optimization on the control parameters of the grid-type converter to determine the final control target, thereby generating an adaptive control scheme. The present invention not only takes into account different situations of power grid fluctuations, sets different control targets for different fluctuation situations, but also performs multi-objective optimization and coordinates the adjustment conflicts between the various control parameters of the grid-type converter, so that the adjustment process of the grid-type converter not only adapts to the actual fluctuation situation of the power grid, but also fully reconciles various conflicts in the adjustment process. On the basis of realizing the adaptive control of the grid-type converter, the control effect of the grid-type converter is improved, and the stability and reliability of the power grid are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, 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 only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0024] Figure 1 It is a flow chart of an adaptive control method of a grid-connected converter provided by an embodiment of the present invention;

[0025] Figure 2 It is a structural schematic diagram of an adaptive control device for a grid-type converter provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0027] In the description of the present invention, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" and "plurality" refer to two or more. The words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not limit them to be different.

[0028] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0029] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include other steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0030] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be described through specific embodiments in conjunction with the accompanying drawings of the present invention.

[0031] As described in the background art, there are many conflicts, unstable control, and poor effects in the regulation process of the grid-connected converter, which affect the stability and reliability of the power grid.

[0032] To solve this technical problem, Figure 1 As shown, an embodiment of the present invention provides an adaptive control method for a grid-connected converter. The method includes steps S101-S106.

[0033] S101. Obtain current control parameters of a grid-connected converter and grid operation data.

[0034] In the embodiment of the present application, the power grid operation data includes voltage, current, frequency and power factor.

[0035] In some embodiments, the control parameters of the grid-connected converter include output voltage, output active power, and output reactive power.

[0036] S102: Perform disturbance analysis based on power grid operation data to determine the disturbance type of the power grid.

[0037] In the embodiment of the present application, the disturbance types include transient disturbance, static disturbance and dynamic disturbance.

[0038] For example, a transient disturbance refers to a sudden and short-duration change in voltage, current or power in a power grid. Such disturbances are usually caused by events such as short-circuit faults, lightning strikes, and switching operations (such as circuit breaker tripping or reclosing). Transient disturbances can cause distortion of the voltage and current waveforms in the power grid, which may cause problems such as overcurrent and overvoltage, posing a threat to power equipment and system stability. For grid-type converters, transient disturbances require them to have fast response and strong control capabilities so that they can quickly adjust the output voltage and current when disturbances occur to maintain the stable operation of the power grid.

[0039] For example, a static disturbance refers to a deviation in voltage, current or power that persists and changes slowly in the power grid. Such disturbances may be caused by factors such as load changes, generator output adjustments, and line impedance changes. Although static disturbances are not as severe as transient disturbances, their long-term existence will affect the voltage quality and frequency stability of the power grid. When dealing with static disturbances, the grid-type converter needs to be able to accurately sense the operating status of the power grid and compensate for these deviations by adjusting its control parameters to maintain the voltage and frequency of the power grid within the allowable range. This requires the converter to have precise control capabilities and good steady-state performance.

[0040] For example, dynamic disturbances refer to periodic or non-periodic changes in voltage, current and power in the power grid due to rapid changes in load, fluctuations in generator output or changes in system structure. Such disturbances are usually closely related to the dynamic characteristics of the power grid, such as the inertia, damping and oscillation mode of the system. When dealing with dynamic disturbances, the grid-connected converter needs to have strong dynamic response capabilities and stability control capabilities. It needs to adjust its control strategy according to the dynamic characteristics of the power grid to suppress system oscillations, improve system damping and enhance system stability.

[0041] As a possible implementation manner, step S102 may be specifically implemented as steps S1021 - S1025 .

[0042] S1021. Calculate the difference between the operating data of each power grid and the standard data.

[0043] In some embodiments, the standard data includes a standard voltage, a standard current, a standard frequency, and a standard power factor.

[0044] In some embodiments, the difference is used to reflect the degree of deviation between the grid operation data and the standard data.

[0045] Exemplarily, the difference includes a voltage difference, a current difference, a frequency difference and a power factor difference.

[0046] S1022. Generate a time series sequence of the difference of each type of power grid operation data based on the difference of each type of power grid operation data at each time.

[0047] In some embodiments, the difference time series sequence is used to characterize the change trend of the power grid operation data.

[0048] S1023. Calculate statistical data of each power grid operation data and the difference between each power grid operation data.

[0049] In some embodiments, the statistics include mean, standard deviation, mode, and median.

[0050] S1024. Generate grid parameter characteristics based on the operation data of each grid, and the difference time series and statistical data of each grid operation data.

[0051] S1025. Determine the disturbance type of the power grid based on the power grid parameter characteristics and the disturbance analysis model.

[0052] In some embodiments, the disturbance analysis model is obtained by training a neural network based on grid operation data and disturbance types when the grid at the grid-connected converter is disturbed multiple times.

[0053] S103: Determine an initial control target of the grid-forming converter based on the disturbance type of the power grid and the inertia characteristics and damping characteristics of the grid-forming converter.

[0054] In some embodiments, the initial control targets include an initial target voltage, an initial target current, an initial target active power, and an initial target reactive power.

[0055] In some embodiments, the inertial characteristics and damping characteristics of the grid-connected converter are key indicators that describe the response characteristics and stability of the converter when facing grid disturbances. The inertial characteristics are the characteristics of the converter's response speed and recovery ability when subjected to external disturbances. Traditional synchronous generators have mechanical rotational inertia, which can provide a certain buffering effect, allowing the power system to transition smoothly when subjected to disturbances. However, modern power electronic converters themselves do not have mechanical rotating parts, so this inertial behavior needs to be simulated through software algorithms.

[0056] Converters with good inertia characteristics can respond faster when disturbances occur, slowing down sharp changes in frequency or voltage. Providing inertial support can help the grid return to a stable state more quickly after a disturbance. When the grid frequency fluctuates, the speed of frequency change can be slowed down by providing inertial support. When the voltage drops or rises, the rapid response of the energy storage element can provide temporary voltage support.

[0057] In some embodiments, the damping characteristic refers to the ability of the converter to suppress system oscillation after a grid disturbance. Good damping characteristics can help the system recover to a stable state faster and prevent oscillation from occurring.

[0058] Converters with good damping characteristics can better suppress oscillations and prevent the system from entering an unstable state. After a disturbance occurs, the system can smoothly transition to a new stable state instead of fluctuating violently. When frequency oscillations occur in the power grid, good damping characteristics can quickly calm the oscillations. When voltage oscillates, damping control can quickly restore voltage stability.

[0059] In practical applications, the design of grid-type converters usually needs to consider both inertia characteristics and damping characteristics to ensure that the system can respond quickly and remain stable under various disturbance conditions. For example: transient disturbance: when the power grid experiences a voltage drop or a frequency mutation, the converter needs to have good inertia characteristics to provide immediate energy support, and to suppress possible oscillations through good damping characteristics. Static disturbance: in the case of slow changes in voltage or frequency caused by load changes, the converter needs to use appropriate damping characteristics to smooth the transition while maintaining a certain inertia to cope with potential rapid changes. Dynamic disturbance: for continuous frequency fluctuations or voltage fluctuations, the converter needs to use a comprehensive control strategy to use inertia characteristics to provide energy buffering and damping characteristics to suppress oscillations.

[0060] As a possible implementation manner, step S103 may be specifically implemented as steps S1031 - S1035 .

[0061] S1031. Determine an initial target voltage based on the disturbance type, the inertia characteristics of the grid-connected converter, and the voltage difference time series in the grid operation data.

[0062] In some embodiments, if the converter has a large inertia, it means that its response speed is slow, so it is necessary to set a milder target voltage to avoid frequent adjustments. Adjustment strategy: For transient disturbances, a target voltage recovery value within a short time can be set; for static and dynamic disturbances, a target voltage recovery value within a longer time can be set.

[0063] Exemplarily, if it is a transient disturbance: if a voltage drop is detected, a target voltage can be set according to the voltage difference sequence, for example, to recover to 98% of the rated voltage in a short time (such as a few milliseconds to a few seconds).

[0064] For example, if it is a static disturbance: if a slow voltage drop is detected, a target voltage within a longer time can be set according to the voltage difference sequence, for example, the target voltage can be restored to 99% of the rated voltage within a few seconds to tens of seconds.

[0065] For example, if it is a dynamic disturbance: if voltage fluctuation is detected, a target voltage can be set according to the voltage difference sequence, for example, to stabilize within a certain range within a few seconds.

[0066] S1032. Determine the initial target active power based on the disturbance type, the damping characteristics of the grid-connected converter, and the frequency difference time series in the grid operation data.

[0067] In some embodiments, if the converter has good damping characteristics, which means that it can better suppress frequency fluctuations, a more aggressive active power adjustment strategy can be set. Adjustment strategy: For frequency fluctuations, a faster response speed can be set to restore frequency stability.

[0068] Exemplarily, if it is a transient disturbance: if a sudden frequency change is detected, a target active power can be set according to the frequency difference sequence, for example, to restore the active power to near the reference frequency in a short time (such as a few seconds).

[0069] Exemplarily, if it is a static disturbance: if a slow frequency change is detected, a target active power within a longer period of time can be set according to the frequency difference sequence, for example, the active power is restored to a level near the reference frequency within tens of seconds.

[0070] For example, if it is a dynamic disturbance: if frequency fluctuation is detected, a target active power can be set according to the frequency difference sequence, for example, to stabilize within a certain range within a few seconds.

[0071] S1033. Determine the initial target reactive power based on the disturbance type, the inertia characteristics and damping characteristics of the grid-forming converter, the voltage difference time series sequence in the grid operation data, and the frequency difference time series sequence.

[0072] In some embodiments, if the converter has a large inertia, a milder target reactive power needs to be set to avoid frequent adjustments. If the converter has good damping characteristics, a more aggressive reactive power adjustment strategy can be set.

[0073] Exemplarily, if it is a transient disturbance: if voltage drop and frequency mutation are detected, a target reactive power can be set according to the voltage and frequency difference sequence, for example, the reactive power required to restore to stable voltage and frequency in a short time (such as a few milliseconds to a few seconds).

[0074] Exemplarily, if it is a static disturbance: if a slow drop in voltage and a slow change in frequency are detected, a target reactive power for a longer period of time can be set based on the voltage and frequency difference sequence, for example, the reactive power required to restore to a stable voltage and frequency within a few seconds to tens of seconds.

[0075] Exemplarily, in case of a dynamic disturbance: if voltage fluctuation and frequency fluctuation are detected, a target reactive power may be set according to a voltage and frequency difference sequence, for example, to stabilize within a certain range within a few seconds.

[0076] S1034. Determine a current limit value of the grid-type converter based on the disturbance type and device properties of the grid-type converter.

[0077] Exemplary, device attributes: set the current limit value according to the maximum output current, maximum voltage and other parameters of the converter. Adjustment strategy: set a reasonable current limit value according to the device attributes of the converter to ensure that it does not exceed the safety range. According to the device attributes: set a reasonable current limit value, such as 80% or 90% of the maximum output current.

[0078] S1035. Based on the current limit value, adjust the initial target active power and the initial target reactive power to obtain updated initial target active power and initial target reactive power.

[0079] Exemplarily, if the combination of the initial target active power and the initial target reactive power may cause the current to exceed the limit value, the target value needs to be adjusted to ensure that the current limit value is not exceeded. If the calculated combination of the target active power and the target reactive power may cause the current to exceed the limit value, one or both of the target values ​​need to be adjusted to ensure that the current limit value is not exceeded. For example, if the combination of the initial target active power and the reactive power causes the current to exceed the limit value, the target reactive power may be appropriately reduced, or the target active power may be reduced if necessary, to ensure that the current does not exceed the limit value.

[0080] S104: Based on the current control parameters of the grid-type converter and the initial control target, multi-objective optimization is performed to determine the final control target of the grid-type converter.

[0081] In some embodiments, the final control target includes the output voltage, output active power and output reactive power of the grid-connected converter.

[0082] As a possible implementation manner, step S104 may be specifically implemented as steps S1041 - S1043 .

[0083] S1041. Based on current control parameters of the grid-type converter and device properties of the grid-type converter, determine constraint conditions of each control parameter.

[0084] In some embodiments, the constraints of the output voltage include an upper voltage limit and a lower voltage limit, for example, not exceeding ±5% of the rated voltage.

[0085] Exemplarily, the constraint condition of the output current is a current upper limit, for example, not exceeding a maximum allowable current of the converter.

[0086] Exemplarily, the constraints of the output active power are an upper limit of active power and a lower limit of active power, for example, not exceeding the maximum output power of the converter.

[0087] Exemplarily, the constraints of output reactive power are a reactive power upper limit and a reactive power lower limit.

[0088] Exemplarily, the frequency constraints are an upper frequency limit and a lower frequency limit.

[0089] S1042. Determine multiple combinations based on the initial control objectives and constraints.

[0090] In some embodiments, each combination includes a set of control parameters.

[0091] S1043. Based on multiple combinations and a particle swarm algorithm, determine a final control target of the grid-connected converter.

[0092] Exemplarily, step S1043 may be specifically implemented as steps one to eight.

[0093] Step 1: Initialize the particle swarm; set the initial velocity and initial position of each particle, as well as the maximum number of iterations.

[0094] Among them, each particle in the particle swarm is a set of control parameters.

[0095] Step 2: Calculate the comprehensive fitness of each particle in the current iteration.

[0096] Exemplarily, the fitness function is the key to evaluating the fitness of particles in the solution space. Since this is a multi-objective optimization problem, the fitness function needs to comprehensively consider multiple objectives. The objectives of multi-objective optimization in this application include voltage stability, frequency stability, power factor, etc. The fitness function can use a weighted summation method or other multi-objective fitness calculation methods.

[0097] Voltage stability is a measure of the deviation of the output voltage from a standard voltage. Ideally, the output voltage should be as close to the standard voltage as possible. Frequency stability is a measure of the deviation of the output frequency from a standard frequency. Ideally, the output frequency should be as close to the standard frequency as possible. Power factor is a measure of the ratio of the actual active power consumed to the apparent power. Ideally, the power factor should be as close to 1 as possible.

[0098] Exemplarily, the embodiment of the present invention can directly determine the multi-objective fitness vector as the comprehensive fitness, or perform weighted summation on the multi-objective fitness to determine the comprehensive fitness.

[0099] Step 3: Based on the comprehensive fitness, the Pareto sorting algorithm is used to sort each particle in the current iteration process to determine the non-dominated solution and the Pareto sequence.

[0100] In multi-objective optimization, the Pareto ranking algorithm is a very effective means to identify those solutions that cannot be simultaneously outperformed by other solutions in all objectives, that is, non-dominated solutions. These non-dominated solutions constitute part of the Pareto optimal solution set.

[0101] It should be noted that the definition of a non-dominated solution is: if there is no other solution B such that B is not inferior to A in all objectives and is superior to A in at least one objective, then solution A is defined as a non-dominated solution.

[0102] Pareto sorting: Traverse all particles and determine which particles are non-dominated solutions. The specific steps are as follows: For each particle i, check whether other particles j dominate i. If there is a domination relationship, then i is not a non-dominated solution. If no particle dominates i, then i is a non-dominated solution. Pareto hierarchy: All non-dominated solutions are divided into different levels, and the non-dominated solutions of each level constitute a Pareto front. Hierarchical sorting: Starting from the first level, non-dominated solutions are screened layer by layer until all particles are assigned to a certain level.

[0103] For example, assume that we have two objective functions f1 (voltage stability) and f2 (frequency stability), and five particles P1, P2, P3, P4, P5.

[0104] initialization:

[0105] P=[P1,P2,P3,P4,P5]

[0106] Fronts = []

[0107] Q=[]

[0108] First level:

[0109] For each particle i in P:

[0110] Initialize n(i)=0 and S(i)=[].

[0111] For each particle j in P (j≠i):

[0112] If i dominates j, then j is added to S(i).

[0113] If j dominates i, then n(i)++.

[0114] If n(i)=0, then i is a non-dominated solution and i is added to Q.

[0115] Add Q to Fronts.

[0116] Remove all particles in Q from P.

[0117] Subsequent levels:

[0118] For each particle i in Q:

[0119] For each particle j in S(i):

[0120] Decrease the value of n(j).

[0121] If n(j)=0, j is a non-dominated solution and j is added to Q.

[0122] If Q is not empty, repeat "subsequent levels" in step 2 until P is empty.

[0123] Fronts contains non-dominated solutions of all levels.

[0124] Step 4: Calculate the comprehensive crowding distance of each non-dominated solution.

[0125] Exemplarily, initialize the distance: for each objective function, set the crowding distance of the minimum and maximum solutions to infinity. Calculate the distance: for each objective function, sort the non-dominated solutions by the objective function value. Then, calculate the sum of the objective function value differences between adjacent solutions, i.e., the crowding distance. Comprehensive crowding distance: for each solution, combine its crowding distances on all objectives to obtain the final crowding distance of the solution.

[0126] Step 5: Update the speed and position of each particle based on the speed and position update formula of the particle swarm algorithm.

[0127] Exemplarily, the speed update formula is as follows:

[0128]

[0129] Where w is the inertia weight, is the velocity of the ith particle in the dth dimension during the t+1th iteration, is the velocity of the ith particle in the dth dimension during the tth iteration, c1 is the first acceleration constant, c2 is the second acceleration constant, r1 and r2 are random numbers between 0 and 1, and pbest i,d is the personal optimal position of the ith particle in the dth dimension, gbest d is the global optimal position of the ith particle in the dth dimension, is the position of the i-th particle in the d-th dimension during the t-th iteration.

[0130] Exemplarily, the speed update formula is as follows:

[0131]

[0132] in, is the position of the i-th particle in the d-th dimension during the t-th iteration, is the position of the i-th particle in the d-th dimension during the t+1-th iteration, is the velocity of the i-th particle in the d-th dimension during the t+1-th iteration.

[0133] Step 6: Based on the Pareto sequence and the comprehensive crowding distance of each non-dominated solution, the particles in the current iteration process are screened to determine the particles in the next iteration process.

[0134] Exemplarily, the present invention selects solutions from non-dominated solutions to enter the next generation. If the number of non-dominated solutions exceeds a predetermined number, the crowding distance is used to select solutions. The specific steps are as follows: Sort the solutions in the non-dominated hierarchy by crowding distance. Select the solution with the largest crowding distance to enter the next generation population until the population size reaches a predetermined number.

[0135] Step 7: If the current number of iterations is greater than or equal to the maximum number of iterations, exit the iteration and execute step 8; if the current number of iterations is less than the maximum number of iterations, repeat steps 2 to 7 until the current number of iterations is greater than or equal to the maximum number of iterations.

[0136] Step eight, based on the Pareto sequence, determine the final control target of the grid-connected converter.

[0137] Exemplarily, the present invention selects the final solution: selects one or more solutions from the Pareto front as the final control target. This is usually based on engineering practice or decision maker's preference. Determine the control target: select one or more non-dominated solutions as the final control target, which should have good performance on all targets and be evenly distributed in the target space.

[0138] S105. Determine an adaptive control scheme for the grid-connected converter based on the final control target.

[0139] As a possible implementation manner, step S105 can be specifically implemented as steps S1051 - S1052 .

[0140] S1051. Based on the final control target and the fluctuation type, determine the adjustment time of each final control target.

[0141] S1052: Based on each final control target and the adjustment duration of each final control target, perform adaptive adjustment to determine an adaptive control scheme.

[0142] In some embodiments, the adaptive control scheme includes adjustment values ​​of various control parameters of the grid converter at various times within an adjustment period.

[0143] The present invention provides an adaptive control method for a grid-type converter, which performs disturbance analysis on grid operation data to determine the type of disturbance, determines the initial control target of the grid-type converter according to the type of disturbance, and then performs multi-objective optimization on the control parameters of the grid-type converter to determine the final control target, thereby generating an adaptive control scheme. The present invention not only takes into account different situations of grid fluctuations, sets different control targets for different fluctuation situations, but also performs multi-objective optimization and coordinates the adjustment conflicts between various control parameters of the grid-type converter, so that the adjustment process of the grid-type converter not only adapts to the actual fluctuation situation of the grid, but also fully reconciles various conflicts in the adjustment process, and on the basis of realizing the adaptive control of the grid-type converter, improves the control effect of the grid-type converter, and improves the stability and reliability of the grid.

[0144] Optionally, the adaptive control method for a grid-connected converter provided in an embodiment of the present invention further includes steps S201 to S205 before step S102.

[0145] S201. Obtaining grid operation data and disturbance types when the grid at the grid-forming converter is disturbed multiple times.

[0146] S202. Based on the power grid operation data at each disturbance, calculate the difference time series and statistical data at each disturbance.

[0147] S203. Generate grid parameter characteristics at each disturbance based on the grid operation data, difference time series and statistical data at each disturbance.

[0148] S204: Taking the power grid parameter characteristics at each disturbance as input and the disturbance type of each disturbance as output, a plurality of training samples are determined.

[0149] S205: Based on multiple training samples, neural network training is performed to obtain a disturbance analysis model.

[0150] Exemplarily, the embodiments of the present invention can select a suitable neural network model, such as a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory network (LSTM), according to the complexity of the problem and the characteristics of the data. Standardize or normalize the input features to ensure data consistency. Using the prepared training samples, adjust the network weights through the back propagation algorithm to minimize the difference between the predicted disturbance type and the actual disturbance type. Use the validation set to adjust the hyperparameters during the training process, and use an independent test set to evaluate the model performance after the training is completed.

[0151] In this way, the embodiment of the present invention can obtain a disturbance analysis model through training with historical disturbance data, so as to facilitate real-time data analysis in the power grid, obtain the disturbance type, and improve the control effect of the grid-connected converter.

[0152] Optionally, the adaptive control method for a grid-connected converter provided in an embodiment of the present invention further includes steps S301 - S304 after step S105 .

[0153] S301. Obtaining power grid operation data of the area where the grid-forming converter is located after a disturbance occurs.

[0154] S302: Perform stability analysis based on real-time grid data after the disturbance occurs to determine grid stability parameters.

[0155] In some embodiments, the grid stability multi-parameters include voltage stability, frequency stability, and power factor stability.

[0156] For example, voltage stability: evaluates whether the grid voltage remains within the allowable range to avoid voltage collapse. Frequency stability: checks whether the grid frequency deviates from the standard value (such as 50Hz or 60Hz) to ensure that the frequency deviation is within the acceptable range. Power factor stability: analyzes the power factor of the grid to ensure that it is close to the ideal 1 to improve the efficiency of power transmission.

[0157] S303: Based on the grid stability parameters and preset standard stability parameters, the grid stability is verified to determine the adjustment result of the grid-connecting converter.

[0158] Exemplarily, the embodiments of the present invention may preset a set of standard stability parameters as reference benchmarks, which reflect the expected state of the power grid during normal operation. Comparative analysis: Compare the currently measured power grid stability parameters with the preset standards to determine whether the power grid is in a stable state. Result determination: Based on the comparison results, determine whether it is necessary to adjust the working mode or parameter setting of the grid-connected converter.

[0159] S304: Based on the adjustment result, fine-tune the adaptive control scheme of the grid-connected converter and determine the fine-tuning scheme.

[0160] Exemplary, adjustment decision: based on the verification results, decide whether the control strategy of the converter needs to be adjusted and how to adjust it. Fine-tuning plan: design a specific fine-tuning plan, which may include changing the output power of the converter, adjusting its reactive power compensation capability, adjusting the frequency response characteristics, etc. Implementation adjustment: adjust the converter according to the fine-tuning plan in order to restore or enhance the stability of the power grid.

[0161] In this way, the embodiment of the present invention can continuously monitor the operating data of the power grid after a disturbance occurs, monitor the stability of the power grid during the adjustment process of the grid-connected converter, and adjust the adaptive control scheme in real time to determine the fine-tuning scheme to ensure stable and reliable operation of the power grid.

[0162] Optionally, the adaptive control method for a grid-connected converter provided in an embodiment of the present invention further includes steps S401 to S403 after step S105.

[0163] S401. Obtain control parameter data of a grid-connected converter after a disturbance occurs.

[0164] S402: Based on the control parameter data of the grid-type converter after the disturbance occurs and the adaptive control scheme, parameter comparison is performed to determine the adjustment deviation of the grid-type converter.

[0165] Exemplarily, embodiments of the present invention may be based on ideal control parameter settings in an adaptive control scheme, which are the expected optimal operating states in the absence of disturbances. Deviation calculation: Compare the actual control parameter data with the ideal parameters in the adaptive control scheme to calculate the deviation between the two. For example, if the converter should ideally output a certain amount of reactive power to maintain voltage stability, and the actual output reactive power is lower than this value, there is a deviation. Deviation evaluation: Analyze the degree of deviation and its impact on grid stability to determine whether the control strategy needs to be adjusted.

[0166] S403: Based on the adjustment deviation of the grid-connected converter, the adaptive control scheme is updated to determine an updated adaptive control scheme.

[0167] Exemplarily, the embodiments of the present invention can determine the specific parameters that need to be adjusted and their adjustment directions based on the results of the above-mentioned deviation analysis. Scheme update: Modify the control parameters in the adaptive control scheme to reduce or eliminate the deviation. For example, if it is found that the reactive power output of the converter is insufficient, the reactive power output can be increased by adjusting the gain of the controller or changing the reactive power reference value. Scheme determination: Reapply the updated control parameters to the converter and observe their effects.

[0168] In this way, the embodiment of the present invention can adjust and update the adaptive control scheme during the adjustment process of the grid-type converter, thereby ensuring the real-time adjustment of the grid-type converter and improving the adjustment effect of the grid-type converter.

[0169] Optionally, an embodiment of the present invention further provides an adaptive control method for a grid-connected converter, the method comprising steps S501 - S505 .

[0170] S501. Obtain current control parameters of the grid-connected converter and grid operation data.

[0171] In an embodiment of the present application, the power grid operation data includes voltage, current, frequency and power factor.

[0172] S502: Perform disturbance analysis based on the power grid operation data to determine whether the disturbance type of the power grid is a transient disturbance.

[0173] S503: If the disturbance type of the power grid is a transient disturbance, an initial control target of the grid-connected converter is determined based on the transient disturbance characteristics and the inertia characteristics and damping characteristics of the grid-connected converter.

[0174] S504: Based on the current control parameters of the grid-type converter and the initial control target, multi-objective optimization is performed to determine the final control target of the grid-type converter.

[0175] S505: Determine an adaptive control scheme for the grid-connected converter based on the final control target.

[0176] After determining that the disturbance type is a transient disturbance, the present invention determines the initial control target of the grid-type converter according to the transient disturbance characteristics, and then performs multi-objective optimization on the control parameters of the grid-type converter to determine the final control target, thereby generating an adaptive control scheme. The present invention not only takes into account the actual fluctuation of transient disturbances, but also fully reconciles various conflicts in the adjustment process. On the basis of realizing the adaptive control of the grid-type converter, the control effect of the grid-type converter is improved, and the stability and reliability of the power grid are improved.

[0177] Optionally, an embodiment of the present invention further provides an adaptive control method for a grid-connected converter, the method comprising steps S601-S605.

[0178] S601. Obtain current control parameters of the grid-connected converter and grid operation data.

[0179] In an embodiment of the present application, the power grid operation data includes voltage, current, frequency and power factor.

[0180] S602: Perform disturbance analysis based on the power grid operation data to determine whether the disturbance type of the power grid is a static disturbance.

[0181] S603: If the disturbance type of the power grid is a static disturbance, determine an initial control target of the grid-connected converter based on the static disturbance characteristics and the inertia characteristics and damping characteristics of the grid-connected converter.

[0182] S604: Based on the current control parameters of the grid-type converter and the initial control target, multi-objective optimization is performed to determine the final control target of the grid-type converter.

[0183] S605: Determine an adaptive control scheme for the grid-connected converter based on the final control target.

[0184] After determining that the disturbance type is a static disturbance, the present invention determines the initial control target of the grid-type converter according to the static disturbance characteristics, and then performs multi-objective optimization on the control parameters of the grid-type converter to determine the final control target, thereby generating an adaptive control scheme. The present invention not only takes into account the actual fluctuation of the static disturbance, but also fully reconciles various conflicts in the adjustment process. On the basis of realizing the adaptive control of the grid-type converter, the control effect of the grid-type converter is improved, and the stability and reliability of the power grid are improved.

[0185] Optionally, an embodiment of the present invention further provides an adaptive control method for a grid-connected converter, the method comprising steps S701-S705.

[0186] S701. Obtain current control parameters of the grid-connected converter and grid operation data.

[0187] In an embodiment of the present application, the power grid operation data includes voltage, current, frequency and power factor.

[0188] S702: Perform disturbance analysis based on the power grid operation data to determine whether the disturbance type of the power grid is a dynamic disturbance.

[0189] S703: If the disturbance type of the power grid is a dynamic disturbance, determine an initial control target of the grid-connected converter based on the dynamic disturbance characteristics and the inertia characteristics and damping characteristics of the grid-connected converter.

[0190] S704: Based on the current control parameters of the grid-type converter and the initial control target, multi-objective optimization is performed to determine the final control target of the grid-type converter.

[0191] S705: Determine an adaptive control scheme for the grid-connected converter based on the final control target.

[0192] After determining that the disturbance type is a dynamic disturbance, the present invention determines the initial control target of the grid-type converter according to the dynamic disturbance characteristics, and then performs multi-objective optimization on the control parameters of the grid-type converter to determine the final control target, thereby generating an adaptive control scheme. The present invention not only takes into account the actual fluctuation of the dynamic disturbance, but also fully reconciles various conflicts in the adjustment process. On the basis of realizing the adaptive control of the grid-type converter, the control effect of the grid-type converter is improved, and the stability and reliability of the power grid are improved.

[0193] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0194] The following is an embodiment of the device of the present invention. For details not described in detail, reference may be made to the corresponding method embodiments described above.

[0195] Figure 2 The schematic diagram of the structure of an adaptive control device for a grid-connected converter provided by an embodiment of the present invention is shown. The adaptive control device 800 includes a communication module 801 and a processing module 802 .

[0196] The communication module 801 is used to obtain the current control parameters of the grid-connected converter and the grid operation data, where the grid operation data includes voltage, current, frequency and power factor.

[0197] Processing module 802 is used to perform disturbance analysis based on power grid operation data to determine the disturbance type of the power grid; the disturbance types include transient disturbances, static disturbances and dynamic disturbances; based on the disturbance type of the power grid, as well as the inertia characteristics and damping characteristics of the grid-type converter, determine the initial control target of the grid-type converter; based on the current control parameters of the grid-type converter and the initial control target, perform multi-objective optimization to determine the final control target of the grid-type converter; based on the final control target, determine the adaptive control scheme of the grid-type converter.

[0198] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An adaptive control method for a grid-connected converter, characterized in that: include: Acquire current control parameters of the grid-forming converter and grid operation data, wherein the grid operation data includes voltage, current, frequency and power factor; Based on the power grid operation data, disturbance analysis is performed to determine the disturbance type of the power grid; the disturbance type includes transient disturbance, static disturbance and dynamic disturbance; Determining an initial control target of the grid-forming converter based on the disturbance type of the grid and the inertia characteristics and damping characteristics of the grid-forming converter; Based on the current control parameters of the grid-type converter and the initial control target, multi-objective optimization is performed to determine the final control target of the grid-type converter; Based on the final control target, an adaptive control scheme for the grid-connected converter is determined.

2. The adaptive control method of a grid-connected converter according to claim 1, characterized in that: The performing disturbance analysis based on the power grid operation data to determine the disturbance type of the power grid includes: Calculating the difference between the operation data of each power grid and the standard data; the standard data includes standard voltage, standard current, standard frequency and standard power factor; Based on the difference of each type of power grid operation data at each time, a difference time series sequence of each type of power grid operation data is generated; the difference time series sequence is used to characterize the change trend of the power grid operation data; Calculate the statistical data of each power grid operation data and the difference between each power grid operation data, wherein the statistical data includes the mean value, standard deviation, mode and median; Generate grid parameter characteristics based on the operation data of each grid, as well as the difference time series and statistical data of the operation data of each grid; Based on the grid parameter characteristics and the disturbance analysis model, the disturbance type of the grid is determined.

3. The adaptive control method of a grid-connected converter according to claim 2, characterized in that: Before performing disturbance analysis based on the power grid operation data to determine the disturbance type of the power grid, the method further includes: Obtaining grid operation data and disturbance types when the grid at the grid-forming converter is disturbed multiple times; Based on the power grid operation data at each disturbance, the difference time series and statistical data at each disturbance are calculated; Based on the grid operation data, difference time series and statistical data at each disturbance, the grid parameter characteristics at each disturbance are generated; Taking the power grid parameter characteristics at each disturbance as input and the disturbance type of each disturbance as output, a plurality of training samples are determined; Based on the multiple training samples, neural network training is performed to obtain the disturbance analysis model.

4. The adaptive control method of a grid-connected converter according to claim 1, characterized in that: The initial control targets include initial target voltage, initial target current, initial target active power and initial target reactive power; The determining of the initial control target of the grid-forming converter based on the disturbance type of the grid and the inertia characteristics and damping characteristics of the grid-forming converter comprises: Determining an initial target voltage based on the disturbance type, the inertia characteristics of the grid-forming converter, and the voltage difference time series in the grid operation data; Determining the initial target active power based on the disturbance type, the damping characteristics of the grid-forming converter, and the difference time series of the frequency in the grid operation data; Determine the initial target reactive power based on the disturbance type, the inertia characteristics and damping characteristics of the grid-forming converter, the voltage difference time series sequence, and the frequency difference time series sequence in the grid operation data; Determine a current limit of the grid-type converter based on the disturbance type and device properties of the grid-type converter; Based on the current limit value, the initial target active power and the initial target reactive power are adjusted to obtain updated initial target active power and initial target reactive power.

5. The adaptive control method of a grid-connected converter according to claim 1, characterized in that: The multi-objective optimization is performed based on the current control parameters of the grid-type converter and the initial control target to determine the final control target of the grid-type converter, including: Determining constraints of each control parameter based on current control parameters of the grid-type converter and device properties of the grid-type converter; Based on the initial control target and the constraint condition, determining a plurality of combinations, each combination including a set of control parameters; Based on the multiple combinations and the particle swarm algorithm, a final control target of the grid-connected converter is determined.

6. The adaptive control method of a grid-connected converter according to claim 1, characterized in that: The determining of the final control target of the grid-connected converter based on the multiple combinations and the particle swarm algorithm comprises: Step 1: Initialize the particle swarm; set the initial velocity and initial position of each particle, as well as the maximum number of iterations; wherein each particle in the particle swarm is a set of control parameters, Step 2: Calculate the comprehensive fitness of each particle in the current iteration process; Step 3: Based on the comprehensive fitness, the Pareto sorting algorithm is used to sort each particle in the current iteration process to determine the non-dominated solution and the Pareto sequence; Step 4, calculate the comprehensive crowding distance of each non-dominated solution; Step 5: Based on the speed and position update formula of the particle swarm algorithm, update the speed and position of each particle; Step 6: Based on the Pareto sequence and the comprehensive crowding distance of each non-dominated solution, the particles in the current iteration process are screened to determine the particles in the next iteration process; Step 7: If the current number of iterations is greater than or equal to the maximum number of iterations, then exit the iteration and execute step 8; if the current number of iterations is less than the maximum number of iterations, then repeat steps 2 to 7 until the current number of iterations is greater than or equal to the maximum number of iterations; Step eight: determining a final control target of the grid-connected converter based on the Pareto sequence.

7. The adaptive control method of a grid-connected converter according to claim 1, characterized in that: The final control target includes the output voltage, output active power and output reactive power of the grid-connected converter; The step of determining the adaptive control scheme of the grid-connected converter based on the final control target includes: Based on the final control target and the fluctuation type, determining the adjustment duration of each final control target; Based on each final control target and the adjustment duration of each final control target, adaptive adjustment is performed to determine the adaptive control scheme; the adaptive control scheme includes the adjustment values ​​of each control parameter of the grid-connected converter at each moment within the adjustment period.

8. The adaptive control method of a grid-connected converter according to claim 1, characterized in that: After determining the adaptive control scheme of the grid-connected converter based on the final control target, the method further includes: Obtaining the grid operation data of the area where the grid-forming converter is located after the disturbance occurs; Based on the real-time grid data after the disturbance occurs, stability analysis is performed to determine grid stability parameters; the grid stability parameters include voltage stability, frequency stability and power factor stability; Based on the grid stability parameters, standard stability parameters are preset to verify grid stability and determine the adjustment result of the grid-forming converter; Based on the adjustment result, the adaptive control scheme of the grid-connected converter is fine-tuned to determine the fine-tuning scheme.

9. The adaptive control method of a grid-connected converter according to claim 1, characterized in that: After determining the adaptive control scheme of the grid-connected converter based on the final control target, the method further includes: Obtaining control parameter data of the grid-type converter after a disturbance occurs; Based on the control parameter data of the grid-type converter after the disturbance occurs and the adaptive control scheme, a parameter comparison is performed to determine the adjustment deviation of the grid-type converter; Based on the adjustment deviation of the grid-connected converter, the adaptive control scheme is updated to determine an updated adaptive control scheme.

10. An adaptive control method for a grid-connected converter, characterized in that: include: Acquire current control parameters of the grid-forming converter and grid operation data, wherein the grid operation data includes voltage, current, frequency and power factor; Based on the power grid operation data, a disturbance analysis is performed to determine whether the disturbance type of the power grid is a transient disturbance; If the disturbance type of the power grid is a transient disturbance, then based on the transient disturbance characteristics, and the inertia characteristics and damping characteristics of the grid-forming converter, an initial control target of the grid-forming converter is determined; Based on the current control parameters of the grid-type converter and the initial control target, multi-objective optimization is performed to determine the final control target of the grid-type converter; Based on the final control target, an adaptive control scheme for the grid-connected converter is determined.

11. An adaptive control method for a grid-connected converter, characterized in that: include: Acquire current control parameters of the grid-forming converter and grid operation data, wherein the grid operation data includes voltage, current, frequency and power factor; Based on the power grid operation data, a disturbance analysis is performed to determine whether the disturbance type of the power grid is a static disturbance; If the disturbance type of the power grid is a static disturbance, then based on the static disturbance characteristics, and the inertia characteristics and damping characteristics of the grid-forming converter, an initial control target of the grid-forming converter is determined; Based on the current control parameters of the grid-type converter and the initial control target, multi-objective optimization is performed to determine the final control target of the grid-type converter; Based on the final control target, an adaptive control scheme for the grid-connected converter is determined.

12. An adaptive control method for a grid-connected converter, characterized in that: include: Acquire current control parameters of the grid-forming converter and grid operation data, wherein the grid operation data includes voltage, current, frequency and power factor; Based on the power grid operation data, a disturbance analysis is performed to determine whether the disturbance type of the power grid is a dynamic disturbance; If the disturbance type of the power grid is a dynamic disturbance, then based on the dynamic disturbance characteristics, and the inertia characteristics and damping characteristics of the grid-forming converter, an initial control target of the grid-forming converter is determined; Based on the current control parameters of the grid-type converter and the initial control target, multi-objective optimization is performed to determine the final control target of the grid-type converter; Based on the final control target, an adaptive control scheme for the grid-connected converter is determined.

13. An adaptive control device for a grid-connected converter, characterized in that: include: A communication module, used to obtain current control parameters of the grid-forming converter and grid operation data, wherein the grid operation data includes voltage, current, frequency and power factor; A processing module is used to perform disturbance analysis based on the grid operation data to determine the disturbance type of the grid; the disturbance type includes transient disturbance, static disturbance and dynamic disturbance; based on the disturbance type of the grid, and the inertia characteristics and damping characteristics of the grid-type converter, determine the initial control target of the grid-type converter; based on the current control parameters of the grid-type converter and the initial control target, perform multi-objective optimization to determine the final control target of the grid-type converter; based on the final control target, determine the adaptive control scheme of the grid-type converter.

14. The adaptive control device for a grid-connected converter according to claim 10, characterized in that: The processing module is specifically used to calculate the difference between each type of power grid operation data and the standard data; the standard data includes standard voltage, standard current, standard frequency and standard power factor; based on the difference of each type of power grid operation data at each time, a difference time series sequence of each type of power grid operation data is generated; the difference time series sequence is used to characterize the change trend of the power grid operation data; Calculate the statistical data of each power grid operation data and the difference of each power grid operation data, wherein the statistical data includes the mean value, standard deviation, mode and median; generate power grid parameter characteristics based on each power grid operation data, and the difference time series and statistical data of each power grid operation data; Based on the grid parameter characteristics and the disturbance analysis model, the disturbance type of the grid is determined.

15. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the steps of the method according to any one of claims 1 to 9.

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