A PfU inertia adaptive control method and system for converter

Abstract

The present application relates to the field of converter control technology, and specifically to a method and system for adaptive control of the P-f-U inertia of a converter, the method comprising: obtaining an operating condition parameter matrix corresponding to each acquisition moment; mapping the characteristic roots of the operating condition parameter matrix to a two-dimensional coordinate system to obtain a characteristic root trajectory diagram; confirming the characteristic root distance of each acquisition moment based on the distance distribution and quantity distribution characteristics of the characteristic roots in the characteristic root trajectory diagram; constructing a fitness function of a particle swarm algorithm; comparing the difference characteristics of the fitness function at each acquisition moment in each iteration with the overall distribution of the fitness function of all acquisition moments to obtain the particle swarm inertia weight at each acquisition moment in each iteration, and obtaining the optimal inertia weight after optimization; confirming the inertia coefficient at each acquisition moment and controlling the P-f-U inertia of the converter. The goal of the present application is to enable the converter to adaptively adjust the inertia value according to the operating conditions.

Description

Technical Field

[0001] The present application relates to the technical field of converter control, and in particular to a method and system for adaptively controlling the PfU inertia of a converter. Background Art

[0002] With the increasing penetration of renewable energy sources like wind and photovoltaic power, the scale of distributed generators (DG) connected to the grid and the increasing feeder load rates have significantly impacted the existing energy structure and system. Renewable energy generation is characterized by flexibility, randomness, and instability. The increasing number of new energy distributed electronic power devices connected to the grid, coupled with the inertia and lack of damping of traditional power systems, has negatively impacted the security and stability of the power grid.

[0003] The topology of a virtual synchronous engine 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, it can effectively change the way distributed renewable energy power generation is connected to the grid, thereby effectively improving the stability of the power grid system operation.

[0004] Existing control methods for virtual synchronous motors mostly focus on AC characteristics or DC characteristics, ignoring the consideration of both DC voltage and AC frequency control. In addition, the virtual synchronous motor parameters determined based on human experience are overly dependent on the operator's professional and technical level, and cannot effectively reduce the impact volatility of the power grid when load fluctuates. Summary of the Invention

[0005] In view of the above, it is necessary to provide a method and system for adaptively controlling the PfU inertia of a converter to solve the above problems.

[0006] A first aspect of the present application provides a PfU inertia adaptive control method for a converter, the method comprising:

[0007] Recording a matrix consisting of operating condition electrical parameters at all acquisition moments within a preset time range at each acquisition moment as an operating condition parameter matrix; mapping the characteristic roots of the operating condition parameter matrix to a two-dimensional coordinate system to obtain a characteristic root locus diagram; and determining the characteristic root distance at each acquisition moment based on the distance distribution and quantity distribution characteristics of the characteristic roots in the characteristic root locus diagram;

[0008] Based on the error function and power quality function of the virtual synchronous motor, the fitness function of the particle swarm algorithm is determined. The difference characteristics of the fitness function at each acquisition moment in each iteration are compared with the overall distribution of the fitness function of all acquisition moments. Combined with the characteristic root distance at each acquisition moment, the inertia weight of the particle swarm at each acquisition moment in each iteration is obtained. After a preset number of iterations, the optimal inertia weight is obtained.

[0009] Based on the obtained optimal inertia weight and the difference between the angular frequency of the virtual synchronous motor and the rated angular frequency, the inertia coefficient at each acquisition moment is determined, and the PfU inertia of the converter is controlled.

[0010] Wherein, each row of the operating condition parameter matrix represents an operating condition parameter of the virtual synchronous motor converter at a collection moment, and each column represents an operating condition parameter.

[0011] The process of obtaining the characteristic root locus diagram is specifically as follows:

[0012] The real part of the characteristic root corresponding to each acquisition moment in the operating condition parameter matrix is ​​used as the abscissa, and the imaginary part of the characteristic root is used as the ordinate.

[0013] The determination of the characteristic root distance at each acquisition moment is specifically as follows:

[0014] Get the distances and values ​​between all pairwise combinations of points in the characteristic root locus diagram corresponding to each acquisition moment;

[0015] The minimum value of the horizontal coordinate of all points in the characteristic root trajectory diagram is obtained, and the mean value of the difference between the horizontal coordinate of each point and the minimum value of the horizontal coordinate is forward fused with the distance sum value to obtain the characteristic root distance at each acquisition moment.

[0016] The characteristic root distance at each acquisition moment is specifically the product of the difference mean and the distance sum value.

[0017] The fitness function of the particle swarm algorithm is specifically a weighted sum of the error function of the virtual synchronous motor and the power quality function.

[0018] The step of obtaining the particle swarm inertia weight at each iteration at each acquisition moment includes:

[0019] Calculate the product of the normalized value of the characteristic root distance at each acquisition moment and the preset reduction factor;

[0020] 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 function obtained at all moments, the sum of the preset particle swarm inertia weight initial value and the multiplication result is used as the particle swarm inertia weight at each acquisition moment of each iteration;

[0021] Otherwise, the difference between the preset initial value of the particle swarm inertia weight and the multiplication result is used as the particle swarm inertia weight at each acquisition moment in each iteration.

[0022] Among them, the condition for the particle swarm algorithm to converge is that the number of iterations is greater than the preset maximum number of iterations.

[0023] The formula for determining the inertia coefficient at each acquisition moment is: Where, 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 offset is infinite.

[0024] In a second aspect, an embodiment of the present application further provides a PfU 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, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0025] This application has at least the following beneficial effects:

[0026] Traditional virtual synchronous motor control methods use a fixed inertia coefficient and cannot be adjusted according to actual operating conditions, thus affecting the control effect under different operating conditions. However, the adaptive inertia coefficient method in this application combines the operating condition influencing factors 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 traditional methods, enabling the converter to adaptively adjust the inertia value according to the operating conditions, thereby improving the control effect.

[0027] The inertia coefficient used in traditional virtual synchronous motor control methods cannot simultaneously meet the dynamic response requirements of both the AC and DC sides. However, the adaptive inertia coefficient method proposed in this application can take into account the dynamic response of both the AC and DC sides. When the DC voltage and power reference values ​​fluctuate significantly, the system prioritizes a smaller inertia coefficient. When the AC frequency fluctuates significantly, the system adopts a larger inertia coefficient, and when the fluctuation is smaller, the inertia coefficient is reduced. This method can adaptively adjust the inertia coefficient based on actual operating conditions, effectively reducing control overshoot and improving dynamic response speed.

[0028] Furthermore, the virtual synchronous motor control method incorporates a particle swarm algorithm to adjust the converter inertia coefficient. By tracking the operating conditions of the virtual synchronous motor in real time, the particle swarm algorithm's inertia weight is dynamically adjusted, thereby improving the algorithm's optimization efficiency. This adjustment method effectively optimizes the dynamic performance and stability of the virtual synchronous motor during operation, ensuring efficient and reliable system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flowchart of a method for adaptively controlling the PfU inertia of a converter provided in one embodiment of the present application;

[0030] Figure 2 A topological diagram of a virtual synchronous motor converter provided in one embodiment of the present application. DETAILED DESCRIPTION

[0031] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the art of this application. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0033] It should also be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the methods. Without departing from the scope of protection of this application, the order of executing multiple steps can be interchanged with each other, and some steps can also be deleted.

[0034] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0035] The following describes in detail a method and system for adaptively controlling the PfU inertia of a converter provided by the present application with reference to the accompanying drawings.

[0036] See also Figure 1 , which shows a flowchart of a method for adaptively controlling the PfU inertia of a converter provided by one embodiment of the present application, the method comprising the following steps:

[0037] The first step is to obtain all operating electrical parameters of the virtual synchronous motor at each acquisition moment.

[0038] In this application, the PfU of the converter specifically refers to the power, frequency and voltage of the converter.

[0039] A topology model was established in PSCAD / EMTDC and simulation was performed with a sampling frequency of 10 kHz and a simulation step size of 0.01 ms. The simulation parameters are shown in Table 1:

[0040] Table 1: Simulation parameters

[0041] System parameters Numerical unit AC rated voltage 0.38 kV DC rated voltage 1.8 kV filter inductors 0.001 H AC side equivalent resistance 0.01 Ω filter capacitors 0.0025 uF

[0042] Among them, the topological structure of the virtual synchronous motor converter is shown in the figure below: Figure 2 As shown in the figure, the DC grid is connected to the AC grid via a converter module. The system uses pulse width modulation (PWM) technology to precisely regulate power. During this process, the adaptive inertia control method adjusts the inertia coefficient in real time, ensuring the system's rapid response to load fluctuations and operating condition changes, thus ensuring stable operation of the power system. In this way, an effective power transmission and control system can achieve efficient conversion and reliable dispatch of power, optimize the grid's operational performance, and enhance its adaptability to dynamic changes.

[0043] Because renewable energy power generation systems operate differently from traditional power generation systems, interactions between distributed power sources and the grid often lead to insufficient damping, resulting in low-frequency oscillations and minor instability disturbances. The operating electrical parameters of the virtual synchronous motor are obtained, including its DC voltage, frequency, input power, and output power.

[0044] Since the operating parameters of the virtual synchronous motor are very sensitive to time changes, the stable changes in the operating conditions of the virtual synchronous motor at different times are also different.

[0045] The second step is to record the matrix composed of the operating condition electrical parameters of all acquisition moments within the preset time range of each acquisition moment as the operating condition parameter matrix; map the characteristic roots of the operating condition parameter matrix to a two-dimensional coordinate system to obtain a characteristic root trajectory diagram; and confirm the characteristic root distance of each acquisition moment based on the distance distribution and quantity distribution characteristics of the characteristic roots in the characteristic root trajectory diagram.

[0046] This application sets the time interval for collecting the operating condition electrical parameters to 1s, and analyzes the changes in the operating condition parameters of the virtual synchronous motor converter at each collection moment.

[0047] In the power system, if the system is subjected to a small disturbance and becomes unstable, the oscillation amplitude of the generator rotor will continue to increase, causing the system to lose stability.

[0048] Randomly select the operating condition parameters of the virtual synchronous motor converter at each collection moment, take the collection moment as the starting point, extend N collection moments backward as the time range of each collection moment, and form the operating condition parameters of all collection moments within the time range into an operating condition parameter matrix of the virtual synchronous motor converter; wherein N is a preset value, which is 8; each row of the matrix represents an operating condition parameter of the virtual synchronous motor converter at a collection moment, and each column represents an operating condition parameter.

[0049] The operating condition parameter matrix of the virtual synchronous motor converter is used as the input of the eigenvalue solution algorithm to obtain the eigenvalues ​​and eigenvectors at any time interval. In this embodiment, the eigenvalue solution algorithm uses the singular value decomposition (SVD) algorithm, which is a well-known technology and will not be described in detail in this application.

[0050] Generally, the characteristic roots in the virtual synchronous motor control system are mostly complex numbers. Therefore, a corresponding characteristic root trajectory diagram is established for the characteristic roots of the operating condition parameter matrix corresponding to each acquisition moment. The real part of the characteristic root corresponding to each acquisition moment in the operating condition parameter matrix is ​​used as the horizontal coordinate, and the imaginary part of the characteristic root is used as the vertical coordinate to indicate the position of the complex characteristic root at each acquisition moment on the complex plane.

[0051] Based on the distance distribution and number of characteristic roots in the characteristic root locus diagram obtained at each acquisition moment, the characteristic root distance at each acquisition moment is determined. Specifically, the sum of the distances between all pairs of points in the characteristic root locus diagram corresponding to each acquisition moment is obtained; the minimum value of the horizontal coordinates of all points in the characteristic root locus diagram is obtained, and the mean difference between the horizontal coordinate of each point and the minimum value of the horizontal coordinate is forward fused with the sum of the distances to obtain the characteristic root distance at each acquisition moment. In this embodiment, the distance between points is calculated using Euclidean distance; the difference between variables is calculated using the absolute value of the difference, and the mean difference is specifically the average of the absolute value of the difference between the horizontal coordinate of each point and the minimum value of the horizontal coordinate.

[0052] It should be understood that when a virtual synchronous motor system is subjected to a small perturbation, the rotor will oscillate. If the oscillation amplitude continues to increase, the system will lose stability. For a virtual synchronous motor system that maintains stable operation, the real part of its characteristic roots gradually decreases, and different characteristic roots tend to be clustered. Therefore, the characteristic root distance in a stable virtual synchronous motor system is relatively small.

[0053] The third step is to determine the fitness function of the particle swarm algorithm based on the error function and power quality function of the virtual synchronous motor. The fitness function at each acquisition moment of each iteration is compared with the overall distribution of the fitness function at all acquisition moments. The characteristic root distance of each acquisition moment is combined to obtain the inertia weight of the particle swarm at each acquisition moment in each iteration. After a preset number of iterations, the optimal inertia weight is obtained.

[0054] Traditional virtual synchronous motor control suffers from long adjustment times and poor stability. Existing technologies use particle swarm optimization (PSO) algorithms to adjust key control parameters of virtual synchronous motors. However, improper inertia weight settings in the PSO control process can lead to poor global search capabilities during the PSO optimization process, making it impossible to accurately and adaptively adjust the inertia weight coefficient of the virtual synchronous motor. Therefore, this paper optimizes the inertia weight of the PSO algorithm based on the changing parameters of the virtual synchronous motor's operating conditions.

[0055] Typically, a fitness function is used to evaluate the performance of each individual particle in a swarm. It scores the performance of a particle at its current position, helping the algorithm determine how well it performs in the search space. A higher fitness value indicates a better solution for that particle; a lower fitness value indicates a poorer solution. The particle swarm algorithm continuously optimizes the fitness function to ultimately find the optimal solution.

[0056] In this embodiment, the weighted result of the error function and the power quality function is used as the fitness function of the converter particle swarm. In this embodiment, the weighting coefficient is 0.5. 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 functions are existing well-known technologies and are not described in detail in this application.

[0057] Generally, the particle swarm inertia weight represents the search capability of the parameter optimization process. In the early stages of the virtual synchronous motor system, a larger particle swarm inertia weight should be set to ensure a reasonable range of parameter acquisition. In the later stages of the virtual synchronous motor system, a smaller particle swarm inertia weight should be set to ensure local accuracy in parameter acquisition.

[0058] The characteristic root distance reflects the operating state of the virtual synchronous motor system. Therefore, the characteristic root distance is used to calculate the inertia weight of the virtual synchronous motor particle swarm. Specifically, the normalized value of the characteristic root distance at each acquisition moment is multiplied by a 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 function obtained at all times, the sum of the preset particle swarm inertia weight initial value and the multiplication result is used as the particle swarm inertia weight at each acquisition moment in each iteration. Otherwise, the difference between the preset particle swarm inertia weight initial value and the multiplication result is used as the particle swarm inertia weight at each acquisition moment in each iteration. In this embodiment, the preset reduction factor is 0.1. In this embodiment, the normalization function is a hyperbolic tangent function, and the preset particle swarm inertia weight initial value is 0.5.

[0059] It should be understood that when the fitness function value of the converter particle swarm after each iteration is greater than the average fitness function value of all converter particle swarms, the virtual synchronous motor system is in the early stage of operation and the particle swarm algorithm is still in an unconverged state. At this time, the particle swarm inertia weight of each iteration should be appropriately increased; conversely, when the virtual synchronous motor system is in a stable state in the late stage 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 particle swarm inertia weight should be relatively small.

[0060] By optimizing the inertia weight of the particle swarm algorithm, we ensure that the particle swarm inertia weight follows the adaptive dynamic adjustment of the virtual synchronous motor system during operation. 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, the particle swarm algorithm is considered to have converged and the optimal inertia weight has been obtained.

[0061] The fourth step is to determine the inertia coefficient at each acquisition moment based on the obtained optimal inertia weight and the difference between the angular frequency of the virtual synchronous motor and the rated angular frequency, and to control the PfU inertia of the converter.

[0062] The inertia coefficient plays a key role in control. In different scenarios, the size of the inertia coefficient has different effects on converter control. On the one hand, when the converter is subjected to a sudden change in AC frequency, an increase in the inertia coefficient can reduce the frequency offset and make the frequency fluctuation smoother. On the other hand, when the converter's own power and DC voltage change, reducing the virtual inertia coefficient can reduce the converter's active power and DC voltage offset. Therefore, an adaptive inertia coefficient is proposed to adapt to different working conditions. The specific formula is: Where, 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, which is 100 in this embodiment; H 0 is the virtual inertia coefficient at power frequency, which is 0.5 in this embodiment; H h is the virtual inertia coefficient when the frequency offset is infinite, and in this embodiment, the value is 1.5.

[0063] After obtaining the inertia coefficient at each acquisition moment, the virtual inertia coefficient of the converter is synchronously adjusted in combination with the operating conditions of the virtual synchronous grid. Through multiple iterations, the real-time optimal inertia coefficient of the virtual synchronous grid converter is obtained.

[0064] When the power flowing from the converter output to the AC grid is considered positive, the converter output power is positive, indicating that the converter is operating in inverter mode, delivering active power to the AC grid while also providing power to the DC grid. The converter can dynamically adjust to changes on the AC side. When the AC side load fluctuates, the converter effectively mitigates frequency deviation by adjusting its inertia coefficient in real time.

[0065] Based on the same inventive concept as the above method, an embodiment of the present application also provides a PfU 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-mentioned PfU inertia adaptive control methods for a converter.

[0066] 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 operating conditions. The adaptive inertia coefficient method in this application, combined with the operating condition influencing factors in different scenarios, 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.

[0067] The inertia coefficient used in traditional virtual synchronous motor control methods cannot simultaneously meet the dynamic response requirements of both the AC and DC sides. However, the adaptive inertia coefficient method proposed in this application can take into account the dynamic response of both the AC and DC sides. When the DC voltage and power reference values ​​fluctuate significantly, the system prioritizes a smaller inertia coefficient. When the AC frequency fluctuates significantly, the system adopts a larger inertia coefficient, and when the fluctuation is smaller, the inertia coefficient is reduced. This method can adaptively adjust the inertia coefficient based on actual operating conditions, effectively reducing control overshoot and improving dynamic response speed.

[0068] Furthermore, the virtual synchronous motor control method incorporates a particle swarm algorithm to adjust the converter inertia coefficient. By tracking the operating conditions of the virtual synchronous motor in real time, the particle swarm algorithm's inertia weight is dynamically adjusted, thereby improving the algorithm's optimization efficiency. This adjustment method effectively optimizes the dynamic performance and stability of the virtual synchronous motor during operation, ensuring efficient and reliable system operation.

[0069] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0070] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, from any point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive; modifications to the technical solutions described in the above embodiments, or equivalent replacement of some of the technical features therein, do not deviate from the essence of the corresponding technical solutions within the scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application.

Claims

1. A method for adaptively controlling the PfU inertia of a converter, characterized in that: The method comprises the following steps: Recording a matrix consisting of operating condition electrical parameters at all acquisition moments within a preset time range at each acquisition moment as an operating condition parameter matrix; mapping the characteristic roots of the operating condition parameter matrix to a two-dimensional coordinate system to obtain a characteristic root locus diagram; and determining the characteristic root distance at each acquisition moment based on 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, the fitness function of the particle swarm algorithm is determined. The difference characteristics of the fitness function at each acquisition moment in each iteration are compared with the overall distribution of the fitness function of all acquisition moments. Combined with the characteristic root distance at each acquisition moment, the inertia weight of the particle swarm at each acquisition moment in each iteration is obtained. After a preset number of iterations, the optimal inertia weight is obtained. Based on the obtained optimal inertia weight and the difference between the angular frequency of the virtual synchronous motor and the rated angular frequency, the inertia coefficient at each acquisition moment is determined, and the PfU inertia of the converter is controlled.

2. The PfU inertia adaptive control method for a converter according to claim 1, wherein: Each row of the operating condition parameter matrix represents an operating condition parameter of the virtual synchronous motor converter at a collection moment, and each column represents an operating condition parameter.

3. The PfU inertia adaptive control method for a converter according to claim 1, wherein: The process of obtaining the characteristic root locus diagram is specifically as follows: The real part of the characteristic root corresponding to each acquisition moment in the operating condition parameter matrix is ​​used as the abscissa, and the imaginary part of the characteristic root is used as the ordinate.

4. The PfU inertia adaptive control method for a converter according to claim 1, wherein: The determination of the characteristic root distance at each acquisition moment is specifically as follows: Get the distances and values ​​between all pairs of points in the characteristic root locus diagram corresponding to each acquisition moment; The minimum value of the horizontal coordinate of all points in the characteristic root trajectory diagram is obtained, and the mean value of the difference between the horizontal coordinate of each point and the minimum value of the horizontal coordinate is forward fused with the distance sum value to obtain the characteristic root distance at each acquisition moment.

5. The PfU inertia adaptive control method for a converter according to claim 4, characterized in that: The characteristic root distance at each acquisition moment is specifically the product of the difference mean and the distance sum value.

6. The PfU inertia adaptive control method for a converter according to claim 1, characterized in that: The fitness function of the particle swarm algorithm is specifically a weighted result of the error function of the virtual synchronous motor and the power quality function.

7. The PfU inertia adaptive control method for a converter according to claim 1, wherein: The method of obtaining the particle swarm inertia weight at each iteration at each acquisition moment includes: Calculate the product of the normalized value of the characteristic root distance 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 function obtained at all moments, the sum of the preset particle swarm inertia weight initial value and the multiplication result is used as the particle swarm inertia weight at each acquisition moment of each iteration; Otherwise, the difference between the preset initial value of the particle swarm inertia weight and the multiplication result is used as the particle swarm inertia weight at each acquisition moment in each iteration.

8. The PfU inertia adaptive control method for a converter according to claim 1, wherein: The condition for the particle swarm algorithm to converge is that the number of iterations is greater than the preset maximum number of iterations.

9. The PfU inertia adaptive control method for a converter according to claim 1, wherein: The formula for determining the inertia coefficient at each acquisition moment is: Where, 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 offset is infinite.

10. A PfU 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, the steps of the method according to any one of claims 1 to 9 are implemented.

Images