Converter P-f-U inertia adaptive control method and system

Abstract

The invention relates to the technical field of converter control, in particular to a converter P-f-U inertia self-adaptive control method and system, and the method comprises the steps: obtaining an operation condition parameter matrix corresponding to each collection moment; mapping the characteristic root of the operation condition parameter matrix into a two-dimensional coordinate system to obtain a characteristic root locus diagram; according to distance distribution and number distribution features of feature roots in the feature root trajectory diagram, determining a feature root distance at each acquisition moment; constructing a fitness function of a particle swarm algorithm; comparing the overall distribution difference characteristics of the fitness function of each collection moment in each iteration and the fitness functions of all collection moments, obtaining the particle swarm inertia weight of each collection moment in each iteration, and obtaining the optimal inertia weight after optimization; and determining an inertia coefficient at each acquisition moment, and controlling the P-f-U inertia of the converter. The objective of the invention is to enable the converter to adjust the inertia value in a self-adaptive manner according to the operation condition.

Description

Technical Field

[0001] This application relates to the technical field of converter control, and specifically relates to a P-f-U inertia adaptive control method and system for a converter. Background Art

[0002] With the continuous increase in the penetration rate of renewable new energy power generation such as wind power and photovoltaic power, the grid connection scale of distributed power generation (DG) electronic power equipment and the feeder load rate are continuously increasing, which has brought a greater impact on the existing energy structure and energy system. The new energy power generation form has the characteristics of flexibility, randomness, and instability. With the increase in the number of new energy distributed electronic power equipment connected to the grid, the inertia and undamped characteristics of the traditional power system have had a negative impact on the safety and stability of the power grid system.

[0003] The virtual synchronous generator topology generally includes a DC power supply, a power electronic converter, and an output LC filter. By simulating the operating principle of a synchronous generator in the control system, the grid connection method of distributed new energy power generation can be effectively changed, thereby effectively improving the operating stability of the power grid system.

[0004] Most of the existing control methods for virtual synchronous generators focus on AC characteristics or DC characteristics, ignoring the consideration of both DC voltage and AC side frequency control. Moreover, the virtual synchronous motor parameters determined based on human experience are too dependent on the professional technical level of operators and cannot effectively reduce the grid impact volatility when the load fluctuates. Summary of the Invention

[0005] In view of the above, it is necessary to provide a P-f-U inertia adaptive control method and system for a converter to solve the above problems.

[0006] The first aspect of this application provides a P-f-U inertia adaptive control method for a converter, and the method includes: Denote the matrix composed of the operating condition electrical parameters at all acquisition times within the preset time range at each acquisition time as the operating condition parameter matrix; map the characteristic roots of the operating condition parameter matrix into a two-dimensional coordinate system to obtain a characteristic root locus diagram; confirm the characteristic root distance at each acquisition time according to the distance distribution and quantity distribution characteristics of the characteristic roots in the characteristic root locus diagram; Based on the error function and power quality function of the virtual synchronous motor, determine the fitness function of the particle swarm optimization algorithm; compare the difference characteristics between the fitness function at each acquisition time in each iteration and the overall distribution of the fitness functions at all acquisition times, and combine the characteristic root distance at each acquisition time to obtain the particle swarm inertia weight at each acquisition time in each iteration. After a preset number of iterations, obtain the optimal inertia weight; According to the obtained optimal inertia weight, combined with the difference between the angular frequency of the virtual synchronous generator and the rated angular frequency, confirm the inertia coefficient at each acquisition moment, and control the P-f-U inertia of the converter.

[0007] Among them, each row of the operating condition parameter matrix represents the operating condition parameters of the virtual synchronous generator converter at an acquisition moment, and each column represents an operating condition parameter.

[0008] Among them, the process of obtaining the characteristic root locus diagram is specifically as follows: Take the real part of the characteristic root corresponding to each acquisition moment in the operating condition parameter matrix as the abscissa, and the imaginary part of the characteristic root as the ordinate.

[0009] Among them, the confirmation of the distance between the characteristic roots at each acquisition moment is specifically as follows: Obtain the sum of the distances between all pairwise combinations of points in the characteristic root locus diagram corresponding to each acquisition moment; Obtain the minimum value of the abscissas of all points in the characteristic root locus diagram, and perform positive fusion on the mean difference between the abscissa of each point and the minimum value of the abscissa and the sum of the distances to obtain the distance between the characteristic roots at each acquisition moment.

[0010] Among them, the distance between the characteristic roots at each acquisition moment is specifically the product of the mean difference and the sum of the distances.

[0011] Among them, the fitness function of the particle swarm optimization algorithm is specifically the result of weighting the error function of the virtual synchronous generator and the power quality function.

[0012] Among them, obtaining the particle swarm inertia weight at each acquisition moment in each iteration includes: Calculate the product of the normalized value of the distance between the characteristic roots at each acquisition moment and the preset reduction factor; When the value of the converter particle swarm fitness function obtained at each acquisition moment after each iteration is greater than the average value of the converter particle swarm fitness functions obtained at all moments, use the sum of the preset initial value of the particle swarm inertia weight and the product result as the particle swarm inertia weight at each acquisition moment in each iteration; Otherwise, use the difference between the preset initial value of the particle swarm inertia weight and the product result as the particle swarm inertia weight at each acquisition moment in each iteration.

[0013] Among them, the condition for the convergence of the particle swarm optimization algorithm is that the number of iterations is greater than the preset maximum number of iterations.

[0014] Among them, the formula for confirming the inertia coefficient at each acquisition moment is: ; In the formula, is the inertia coefficient at the i-th acquisition moment;k a is the optimal inertia weight; is the angular frequency of the virtual synchronous generator at the i-th acquisition moment; is the reference rated angular frequency; H 0 is the virtual inertia coefficient at power frequency; H h is the virtual inertia coefficient when the frequency deviation is infinite.

[0015] In a second aspect, an embodiment of the present application further provides a P-f-U inertia adaptive control system for a converter, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0016] The present application has at least the following beneficial effects: Traditional virtual synchronous generator control methods use fixed inertia coefficients and cannot be adjusted according to actual operating conditions, thus affecting the control effect under different conditions. However, the adaptive inertia coefficient method in the present application combines the influencing factors of operating conditions in different scenarios and can automatically optimize the value of the inertia coefficient according to real-time conditions. This method effectively overcomes the limitation of fixed inertia coefficients in traditional methods, enabling the converter to adaptively adjust the inertia value according to operating conditions, thereby improving the control effect.

[0017] The inertia coefficients in traditional virtual synchronous generator control methods cannot simultaneously meet the dynamic response requirements of both the AC and DC sides, while the proposed adaptive inertia coefficient method in the present application can take into account the dynamic responses of both the AC and DC sides. When the DC voltage and power reference value fluctuate greatly, the system preferentially uses a smaller inertia coefficient; when the AC frequency fluctuates greatly, the system uses a larger inertia coefficient, and reduces the inertia coefficient when the fluctuation is small. This method can adaptively adjust the inertia coefficient according to actual operating conditions, thereby effectively reducing control overshoot and improving the dynamic response speed.

[0018] In addition, in the virtual synchronous generator control method, the inertia coefficient of the converter is adjusted by combining the particle swarm optimization algorithm. By real-time tracking the changes in the operating conditions of the virtual synchronous generator, the inertia weight of the particle swarm optimization algorithm is dynamically adjusted, thereby improving the optimization efficiency of the particle swarm optimization algorithm. This adjustment method can effectively optimize the dynamic performance and stability of the virtual synchronous generator during operation, ensuring the efficient and reliable operation of the system. Description of the Drawings

[0019] Figure 1 is a flowchart of the steps of a P-f-U inertia adaptive control method for a converter provided by an embodiment of the present application; Figure 2Topological structure diagram of a virtual synchronous motor converter provided by an embodiment of the present application. Detailed implementation manners

[0020] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example", etc. are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "exemplary", "or", "for example", etc. aims to present relevant concepts in a specific manner.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used in the description of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0022] In addition, it should be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects and are not used to describe a specific order or sequence. For the method disclosed in the embodiments of this application or the method shown in the flowchart, including one or more steps for implementing the method, without departing from the protection scope of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of this application.

[0024] The following specifically describes the specific solutions of a P-f-U inertia adaptive control method and system for a converter provided by this application with reference to the accompanying drawings.

[0025] Please refer to Figure 1 , which shows a step flowchart of a P-f-U inertia adaptive control method for a converter provided by an embodiment of the present application. The method includes the following steps: The first step: Obtain all operating condition electrical parameters of the virtual synchronous motor at each acquisition moment.

[0026] In this application, P-f-U of the converter specifically refers to the power, frequency and voltage of the converter.

[0027] Establish a topological model in PSCAD / EMTDC and perform simulation. The sampling frequency is 10KHz, the simulation step size is taken as 0.01ms, and the simulation parameters are shown in Table 1: Table 1: Simulation parameter table System parameters Value Unit AC rated voltage 0.38 kV DC rated voltage 1.8 kV Filter inductor 0.001 H AC side equivalent resistance 0.01 Ω Filter capacitor 0.0025 uF Among them, the topological structure diagram of the virtual synchronous motor converter is as shown in Figure 2 . The DC power grid is connected to the AC power grid through the conversion module, and the system realizes precise regulation of electric energy through Pulse Width Modulation (PWM) technology. In this process, the adaptive inertia control method ensures that the system can respond quickly according to load fluctuations and operating conditions changes by adjusting the inertia coefficient in real time, ensuring the stable operation of the power system. In this way, an effective power transmission and control system can achieve efficient conversion and reliable scheduling of electric energy, optimize the operating performance of the power grid, and enhance the adaptability of the power grid to dynamic changes.

[0028] Since the operation mode of the new energy power generation system is different from that of the traditional power generation system, interactions between distributed power sources and the power grid often occur, resulting in insufficient damping and small stability interference phenomena such as low-frequency oscillations. Obtain the operating condition electrical parameters of the virtual synchronous motor, specifically including the DC voltage, frequency, input power, and output power of the virtual synchronous motor.

[0029] Since the operating condition parameters of the virtual synchronous motor are very sensitive to time changes, the stable change conditions of the operating conditions of the virtual synchronous motor are also different at different times.

[0030] The second step: Denote the matrix composed of the operating condition electrical parameters at all acquisition times within the preset time range at each acquisition time as the operating condition parameter matrix; map the characteristic roots of the operating condition parameter matrix into a two-dimensional coordinate system to obtain the characteristic root locus diagram; confirm the distance between the characteristic roots at each acquisition time according to the distance distribution and quantity distribution characteristics of the characteristic roots in the characteristic root locus diagram.

[0031] In this application, the time interval for collecting the operating condition electrical parameters is set to 1 s, and the change situation of the operating condition parameters of the virtual synchronous motor converter at each acquisition time is analyzed.

[0032] In the power system, if the system is subjected to small disturbances and becomes unstable, the oscillation amplitude of the generator rotor will increase continuously at this time, resulting in the loss of system stability.

[0033] Arbitrarily take the operating condition parameters of the virtual synchronous motor converter at each acquisition time, starting from this acquisition time, extend backward by N acquisition times as the time range at each acquisition time, and form the operating condition parameter matrix of the virtual synchronous motor converter with the operating condition parameters at all acquisition times within the time range; where N is a preset value, with a value of 8; each row of the matrix represents the operating condition parameters of the virtual synchronous motor converter at one acquisition time, and each column represents one kind of operating condition parameter.

[0034] Taking the operating condition parameter matrix of the virtual synchronous machine converter as the input of the eigenvalue solving algorithm, eigenvalues and eigenvectors at any time interval are obtained. In this embodiment, the eigenvalue solving algorithm uses the Singular Value Decomposition (SVD) algorithm, which is a well-known existing technology and will not be elaborated in this application.

[0035] Under normal circumstances, the characteristic roots in the virtual synchronous machine control system are mostly complex numbers. Therefore, a corresponding characteristic root locus diagram is established for the characteristic roots of the operating condition parameter matrix corresponding to each acquisition moment. The real part of the characteristic roots corresponding to each acquisition moment in the operating condition parameter matrix is used as the abscissa, and the imaginary part of the characteristic roots is used as the ordinate to represent the position of the complex characteristic roots at each acquisition moment on the complex plane.

[0036] According to the distance distribution and quantity of the characteristic roots in the characteristic root locus diagram obtained at each acquisition moment, the distance between the characteristic roots at each acquisition moment is confirmed. Specifically: obtain the sum of the distances between all pairwise combination points in the characteristic root locus diagram corresponding to each acquisition moment; obtain the minimum value of the abscissas of all points in the characteristic root locus diagram, and fuse the mean difference between the abscissa of each point and the minimum abscissa value in a positive direction with the sum of the distances to obtain the distance between the characteristic roots at each acquisition moment. In this embodiment, the distance between points is calculated using the Euclidean distance; the difference between variables is calculated using the absolute value of the difference, and the mean difference is specifically the average value of the absolute value of the difference between the abscissa of each point and the minimum abscissa value.

[0037] It should be understood that when the virtual synchronous machine system is subjected to small disturbances, the rotor will oscillate. If the oscillation amplitude continues to increase, the system will lose stability. For a virtual synchronous machine system operating stably, the real part of its characteristic roots will gradually decrease, and different characteristic roots tend to be distributed in a clustered manner. Therefore, the distance between the characteristic roots in a stably operating virtual synchronous machine system is relatively small.

[0038] The third step: Based on the error function and power quality function of the virtual synchronous machine, determine the fitness function of the particle swarm algorithm; compare the difference characteristics between the fitness function at each acquisition moment in each iteration and the overall distribution of the fitness functions at all acquisition moments, and combine the distance between the characteristic roots at each acquisition moment to obtain the particle swarm inertia weight at each acquisition moment in each iteration. After a preset number of iterations, the optimal inertia weight is obtained.

[0039] In the control process of traditional virtual synchronous motors, there are problems of long adjustment time and poor stability. The existing technology adjusts the key control parameters of virtual synchronous motors through the particle swarm optimization algorithm. However, in the control process of virtual synchronous motors, there is still a problem that the global search ability is poor during the optimization process of the particle swarm algorithm due to unreasonable setting of the inertia weight, and it is unable to adaptively and accurately adjust the inertia weight coefficient of the virtual synchronous motor. Therefore, the inertia weight of the particle algorithm is optimized in combination with the change of the operating condition parameters of the virtual synchronous motor.

[0040] Generally, the fitness function is used to evaluate the quality of each individual in the particle swarm. It scores the performance of the particle at the current position, helping the algorithm judge the performance of the particle in the search space. The higher the fitness value, the better the solution corresponding to the particle; the lower the fitness value, the worse the solution of the particle. The particle swarm algorithm finally finds the optimal solution to the problem by continuously optimizing the value of the fitness function.

[0041] In this embodiment, the result of weighting the error function and the power quality function is used as the fitness function of the converter particle swarm. The weighting coefficient in this embodiment takes a value of 0.5; among them, the error function is specifically the integral of the product of the absolute value of the error and time, which is usually used to measure the error of the system; the power quality function is used to describe power quality problems such as harmonic distortion and frequency fluctuation of voltage and current in the system. Both of these functions are well-known existing technologies, and this application will not elaborate on them.

[0042] Generally, the particle swarm inertia weight represents the size of the search ability in the parameter optimization process. In the initial stage of the operation of the virtual synchronous motor system, a larger particle swarm inertia weight should be set to ensure the rationality of the parameter acquisition range; in the later stage of the operation of the virtual synchronous motor system, a smaller particle swarm inertia weight should be set to ensure the local accuracy of the parameter acquisition.

[0043] The characteristic root distance reflects the operating condition state of the virtual synchronous motor system. Therefore, the inertia weight of the virtual synchronous motor particle swarm is calculated using the characteristic root distance. Specifically: calculate the multiplication result of the normalized value of the characteristic root distance at each acquisition moment and the preset reduction factor; when the value of the fitness function of the converter particle swarm obtained at each acquisition moment after each iteration is greater than the average value of the fitness function of the converter particle swarm obtained at all moments, use the sum of the preset initial value of the particle swarm inertia weight and the multiplication result as the particle swarm inertia weight at each acquisition moment for each iteration; otherwise, use the difference between the preset initial value of the particle swarm inertia weight and the multiplication result as the particle swarm inertia weight at each acquisition moment for each iteration. Among them, the preset reduction factor takes a value of 0.1 in this embodiment; the hyperbolic tangent function is selected as the normalization function in this embodiment, and the preset initial value of the particle swarm inertia weight is 0.5.

[0044] It should be understood that when the fitness function value of the converter particle swarm after each iteration is greater than the average value of the fitness function values of all converter particle swarms, at this time, the virtual synchronous motor system is in the early stage of operation, and the particle swarm algorithm is still in the non-convergent state. At this time, the inertia weight of the particle swarm in each iteration should be appropriately increased; on the contrary, when the virtual synchronous motor system is in the stable state in the middle and late stages of operation, the particle swarm algorithm should also tend to converge synchronously, and attention should be paid to the accuracy of local parameters. At this time, the calculated inertia weight of the particle swarm should be relatively small.

[0045] By optimizing the inertia weight of the particle swarm algorithm, it is ensured that during the operation of the virtual synchronous motor, the inertia weight of the particle swarm adaptively adjusts dynamically following the virtual synchronous motor system. In this embodiment, the maximum number of iterations of the particle swarm is set to 100. When the number of iterations exceeds the maximum value, it is considered that the particle swarm algorithm converges at this time, and the optimal inertia weight is obtained.

[0046] The fourth step: According to the obtained optimal inertia weight, combined with the difference between the angular frequency of the virtual synchronous motor and the rated angular frequency, confirm the inertia coefficient at each acquisition moment, and control the P-f-U inertia of the converter.

[0047] The inertia coefficient plays a key role in control. In different scenarios, the magnitude of the inertia coefficient has different effects on the control effect of the converter. On the one hand, when the converter is subjected to a sudden change in the AC frequency, the increase in the inertia coefficient can reduce the frequency deviation and make the frequency fluctuation smoother. On the other hand, when the self-power and DC voltage of the converter change, reducing the virtual inertia coefficient can reduce the active power and DC voltage deviation of the converter. Therefore, an adaptive inertia coefficient is proposed to adapt to different working conditions. The specific formula is: ; In the formula, is the inertia coefficient at the i-th acquisition moment; k a is the optimal inertia weight; is the angular frequency of the virtual synchronous motor at the i-th acquisition moment; is the reference rated angular frequency, and the value in this embodiment is 100; H 0 is the virtual inertia coefficient at power frequency, and the value in this embodiment is 0.5; H h is the virtual inertia coefficient when the frequency deviation is infinite, and the value in this embodiment is 1.5.

[0048] After obtaining the inertia coefficient at each acquisition moment, synchronously adjust the virtual inertia coefficient of the converter in combination with the operating conditions of the virtual synchronous power grid. Through multiple iterations, the real-time optimal inertia coefficient of the converter of the virtual synchronous power grid is obtained.

[0049] When the power flowing from the converter outlet to the AC grid is taken as the positive direction, a positive converter output power indicates that the converter is operating in the inversion mode, delivering active power to the AC grid and providing power to the DC grid simultaneously. The converter can make dynamic adjustments according to the changes on the AC side. When the AC side load fluctuates, the converter effectively reduces the frequency deviation by adjusting its inertia coefficient in real time.

[0050] Based on the same inventive concept as the above method, the embodiment of the present application also provides a P-f-U inertia adaptive control system for a converter, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods of the P-f-U inertia adaptive control method for a converter.

[0051] In summary, the traditional virtual synchronous motor control method uses a fixed inertia coefficient and cannot be adjusted according to the actual operating conditions, thus affecting the control effect under different conditions. The adaptive inertia coefficient method in the present application combines the influencing factors of the operating conditions in different scenarios and can automatically optimize the value of the inertia coefficient according to the real-time operating conditions. This method effectively overcomes the limitation of the fixed inertia coefficient in the traditional method, enabling the converter to adaptively adjust the inertia value according to the operating conditions, thereby improving the control effect.

[0052] The inertia coefficient in the traditional virtual synchronous motor control method cannot simultaneously meet the dynamic response requirements of both the AC and DC sides, while the proposed adaptive inertia coefficient method in the present application can take into account the dynamic responses of both the AC and DC sides. When the DC voltage and power reference value fluctuate greatly, the system preferentially uses a smaller inertia coefficient; when the AC frequency fluctuates greatly, the system uses a larger inertia coefficient, and reduces the inertia coefficient when the fluctuation is small. This method can adaptively adjust the inertia coefficient according to the actual operating conditions, thereby effectively reducing the control overshoot and improving the dynamic response speed.

[0053] In addition, in the virtual synchronous motor control method, the particle swarm algorithm is combined to adjust the inertia coefficient of the converter. By real-time tracking the changes in the operating conditions of the virtual synchronous motor, the inertia weight of the particle swarm algorithm is dynamically adjusted, thereby improving the optimization efficiency of the particle swarm algorithm. This adjustment method can effectively optimize the dynamic performance and stability of the virtual synchronous motor during operation, ensuring the efficient and reliable operation of the system.

[0054] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0055] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and without departing from the basic features of the present application, the present application can be implemented in other specific forms. Therefore, from any point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive; modifying the technical solutions recorded in the foregoing embodiments, or equivalently replacing some of the technical features, does not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A P-f-U inertia adaptive control method for a converter, characterized in that The method includes the following steps: Denote the matrix composed of the operating condition electrical parameters at all acquisition times within the preset time range at each acquisition time as the operating condition parameter matrix; map the eigenvalues of the operating condition parameter matrix into a two-dimensional coordinate system to obtain an eigenvalue locus diagram; confirm the eigenvalue distance at each acquisition time according to the distance distribution and quantity distribution characteristics of the eigenvalues in the eigenvalue locus diagram; Based on the error function of the virtual synchronous generator and the power quality function, determine the fitness function of the particle swarm optimization algorithm; compare the difference characteristics between the fitness function at each acquisition time in each iteration and the overall distribution of the fitness functions at all acquisition times, and combine the eigenvalue distance at each acquisition time to obtain the particle swarm inertia weight at each acquisition time in each iteration. After a preset number of iterations, obtain the optimal inertia weight; According to the obtained optimal inertia weight, combine the difference between the angular frequency of the virtual synchronous generator and the rated angular frequency to confirm the inertia coefficient at each acquisition time, and control the P-f-U inertia of the converter.

2. The P-f-U inertia self-adaptive control method of a converter according to claim 1, characterized in that, Each row of the operating condition parameter matrix represents the operating condition parameters of the virtual synchronous generator converter at one acquisition time, and each column represents one type of operating condition parameter.

3. The P-f-U inertia self-adaptive control method of a converter according to claim 1, characterized in that The process of obtaining the eigenvalue locus diagram is specifically as follows: Take the real part of the eigenvalue corresponding to each acquisition time in the operating condition parameter matrix as the abscissa, and the imaginary part of the eigenvalue as the ordinate.

4. The P-f-U inertia self-adaptive control method of a converter according to claim 1, characterized in that, The confirmation of the eigenvalue distance at each acquisition time is specifically as follows: Obtain the sum of the distances between all pairwise combinations of points in the eigenvalue locus diagram corresponding to each acquisition time; Obtain the minimum value of the abscissas of all points in the eigenvalue locus diagram, and positively fuse the mean difference between the abscissa of each point and the minimum value of the abscissas with the sum of the distances to obtain the eigenvalue distance at each acquisition time.

5. The P-f-U inertia self-adaptive control method of a converter according to claim 4, characterized in that, The eigenvalue distance at each acquisition time is specifically the product of the mean difference and the sum of the distances.

6. The P-f-U inertia self-adaptive control method for a converter according to claim 1, characterized in that, The fitness function of the particle swarm optimization algorithm is specifically the weighted result of the error function of the virtual synchronous generator and the power quality function.

7. The P-f-U inertia self-adaptive control method for a converter according to claim 1, characterized in that The obtaining of the particle swarm inertia weight at each acquisition time in each iteration includes: Calculate the product result of the normalized value of the eigenvalue distance at each acquisition time and a preset reduction factor; When the value of the converter particle swarm fitness function obtained at each acquisition time after each iteration is greater than the average value of the converter particle swarm fitness functions obtained at all times, take the sum of the preset initial value of the particle swarm inertia weight and the product result as the particle swarm inertia weight at each acquisition time in each iteration; Otherwise, take the difference between the preset initial value of the particle swarm inertia weight and the product result as the particle swarm inertia weight at each acquisition time in each iteration.

8. The P-f-U inertia self-adaptive control method for a converter according to claim 1, characterized in that The convergence condition of the particle swarm optimization algorithm is that the number of iterations is greater than the preset maximum number of iterations.

9. The P-f-U inertia self-adaptive control method for a converter according to claim 1, characterized in that The formula for confirming the inertia coefficient at each acquisition moment is as follows: ; In the formula, is the inertia coefficient at the i-th acquisition moment; k a is the optimal inertia weight; is the angular frequency of the virtual synchronous motor at the i-th acquisition moment; is the reference rated angular frequency; H 0 is the virtual inertia coefficient at power frequency; H h is the virtual inertia coefficient when the frequency deviation is infinite.

10. A P-f-U inertia adaptive control system for a converter, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-9.

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