Multi-configuration network type converter cooperative control method and device

Abstract

The application is suitable for the field of electric power technology, and provides a multi-constructed grid type converter cooperative control method and equipment, the method comprises the following steps: taking the weighted sum of the reactive power distribution deviation index, the circulating current suppression index, the voltage stability index and the frequency fluctuation index of the power grid as a target, taking the reactive power distribution and the operation parameter of each constructed grid type converter as a decision variable, and establishing a target function; establishing a constraint condition of the target function, and solving the target function through an improved particle swarm algorithm iteration to obtain the optimal parameter of each constructed grid type converter; for each constructed grid type converter, determining a dynamic virtual impedance according to the optimal parameter, and controlling the constructed grid type converter according to the dynamic virtual impedance. The application can solve the problem that the reactive power distribution and the voltage frequency of multiple constructed grid type converters are difficult to be cooperatively controlled in the prior art.

Description

Technical Field

[0001] This invention belongs to the field of power technology, and in particular relates to a collaborative control method and device for multi-grid converters. Background Technology

[0002] Grid-type converters, by simulating the characteristics of synchronous generators, can provide voltage and frequency support to the power grid, becoming the core control unit. However, when multiple grid-type converters are operated in parallel, due to factors such as differences in line impedance and inconsistent parameter settings, uneven reactive power distribution is prone to occur, leading to overload of some equipment and decreased power supply efficiency.

[0003] Among existing solutions: the traditional virtual impedance method improves reactive power distribution characteristics by introducing a fixed virtual impedance, but the static parameters are difficult to adapt to dynamic changes in operating conditions, and cannot simultaneously meet the multiple objectives of reactive power distribution accuracy, circulating current suppression, and voltage and frequency stability; traditional particle swarm optimization algorithms, such as the standard particle swarm optimization algorithm, have the defects of slow convergence speed and easy to get trapped in local optima, resulting in poor tuning effect of core control parameters. At the same time, the power loop parameters and virtual impedance of the grid-type converter lack coordinated design, which makes voltage and frequency regulation and reactive power distribution mutually coupled and interfere, further aggravating system fluctuations and making it difficult to meet the requirements of engineering practice for control accuracy and dynamic response speed.

[0004] In the aforementioned schemes, excessive circulating current can accelerate the aging of converter components and even cause equipment failure; excessive reactive power distribution deviation can lead to bus voltage distortion, affecting the normal operation of sensitive loads; and voltage and frequency fluctuations exceeding the allowable range may cause grid disconnection. Therefore, there is an urgent need for a method that can coordinate multiple control objectives, optimize parameter configuration, and achieve collaborative control to solve the problems of reactive power distribution and voltage and frequency regulation of multiple grid-connected converters, and provide technical support for the safe and stable operation of the power grid. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method and device for coordinated control of multiple grid-type converters to solve the problem of difficulty in coordinated control of reactive power distribution and voltage frequency of multiple grid-type converters in the prior art.

[0006] A first aspect of this invention provides a cooperative control method for multi-grid converters, the method comprising: The objective function is established with the weighted sum of the reactive power distribution deviation index, circulating current suppression index, voltage stability index and frequency fluctuation index of the power grid as the objective, and the reactive power distribution and operating parameters of each grid-type converter as the decision variables. The constraints of the objective function are established, and the objective function is solved iteratively by an improved particle swarm optimization algorithm to obtain the optimal parameters of each grid-type converter. For each grid-type converter, the dynamic virtual impedance is determined based on the optimal parameters, and the grid-type converter is controlled based on the dynamic virtual impedance.

[0007] In one possible implementation, the objective function is: ; in, α 1. α 2. α 3. α 4 is the weighting coefficient, and α 1+ α 2+ α 3+ α 4 = 1; f Q The reactive power distribution deviation index. ; N This refers to the total number of grid-type converters. Q i For the first i Reactive power output of the grid-type converter; Q refi Assign reference values ​​to the target reactive power; Q basei This is the normalized reference value for rated reactive power; f I As a circulation suppression index, ; I ij circ For the first i Taiwan-type grid converter and the first j The circulating current component between the grid-type converters; I base This serves as the reference value for current normalization. f V For voltage stability indicators, ; V i For the first i Voltage amplitude of the grid-type converter; V ref This is the target value for the system voltage. V base This is the voltage normalization reference value; σ V The standard deviation of the voltage across the entire system; β V These are the weighting coefficients, and β V ≥0; f ω As a frequency fluctuation indicator, ; ω i ( t ) is the first i The instantaneous angular frequency signal of the grid-type converter varies with time; ω ref For the target frequency; T For evaluation window.

[0008] In one possible implementation, the reactive power allocation and operating parameters include: the equivalent resistance component of the virtual impedance, the equivalent inductance component of the virtual impedance, the reactive power allocation adjustment coefficient, and the frequency recovery parameter.

[0009] In one possible implementation, the constraints of the objective function include: voltage deviation constraint, peak circulating current constraint, reactive power output constraint of a single grid-connected converter, reactive power output constraint of multiple grid-connected converters, reactive power distribution ratio stability constraint, and frequency deviation constraint.

[0010] In one possible implementation, the step of iteratively solving the objective function using an improved particle swarm optimization algorithm includes: Initialize the velocity and position of the particles in the particle swarm and set the algorithm parameters; In each iteration, the dynamic inertia weight and adaptive learning factor are calculated; The particle velocity is updated based on the dynamic inertia weight and the adaptive learning factor. Based on the updated particle velocity, a Levy jump mechanism is introduced to update the particle position; Evaluate the fitness of each particle and update the optimal particle based on the fitness. The process is iterated until the convergence condition is met, and the optimal parameters of each grid converter are determined based on the optimal particle.

[0011] In one possible implementation, the calculation of the dynamic inertia weights and adaptive learning factors includes: Based on the calculation of dynamic inertia weight w k :

[0012] in, and These are the upper and lower limits of the inertia weight, respectively; k This represents the current iteration number; K This represents the maximum number of iterations. γ The shape factor of the descending curve; The adaptive learning factor is calculated according to the following formula. c 1( k )and c 2(k ):

[0013] in, and These are the initial learning factor constants; This is an adaptive factor, dimensionless, that controls the adjustment range; To avoid the denominator being zero, use extremely small positive numbers; Injecting terms for disturbances; J ( p i,k ) is the first i The particle iterates to the... k The fitness corresponding to the optimal position in time; J ( g k ) for iteration to the k The optimal position in the global time frame.

[0014] In one possible implementation, updating the particle position based on the updated particle velocity using a Levy jump mechanism includes: Update the particle's position according to the following formula. :

[0015]

[0016] in, For the first i The first particle k The velocity vector of the next iteration; For the updated number i The first particle k The position vector of the next iteration; is the perturbation amplitude; Levy(x) is the random perturbation.

[0017] In one possible implementation, determining the dynamic virtual impedance for each grid-type converter based on the optimal parameters includes: Map the optimal parameters to a dynamic virtual impedance parameter set. : ; Based on the aforementioned dynamic virtual impedance parameter set, a dynamic virtual impedance is constructed. :

[0018] in, For virtual resistance; For virtual inductance; This is a static virtual reactance; and These are the dynamic gains corresponding to frequency and voltage, respectively; and These are the time constants corresponding to frequency and voltage, respectively; s It is a Laplace complex variable.

[0019] In one possible implementation, controlling the grid-type converter based on the dynamic virtual impedance includes: Based on the dynamic virtual impedance, determine the active frequency loop control quantity and reactive voltage loop control quantity of the grid-type converter; Based on the active frequency loop control quantity, the active frequency of the grid-type converter is controlled. Based on the reactive voltage loop control quantity, reactive voltage control is performed on the grid-type converter.

[0020] A second aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in the first aspect or any possible implementation thereof.

[0021] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: This invention aims to achieve the weighted sum of the power grid's reactive power distribution deviation, circulating current suppression, voltage stability, and frequency fluctuation indices. Using the reactive power distribution and operating parameters of each grid-connected converter as decision variables, an objective function is established to achieve coordinated optimization of multiple control objectives. This overcomes the limitations of single-objective optimization and simultaneously meets the comprehensive requirements of multiple grid-connected converters for steady-state accuracy and dynamic performance, avoiding system-wide performance imbalance caused by single-objective optimization. By solving the objective function, the optimal parameters of each grid-connected converter are obtained. For each grid-connected converter, a dynamic virtual impedance is determined based on the optimal parameters, and the converter is controlled based on this dynamic virtual impedance. This deeply couples the dynamic virtual impedance with the grid-connected converter control, optimizing the system's dynamic response performance. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of the multi-grid converter collaborative control method provided in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the particle swarm optimization algorithm provided in an embodiment of the present invention; Figure 3 This is a control block diagram of a multi-grid converter provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the simulation results provided in the embodiments of the present invention. Figure 1 ; Figure 5 This is a schematic diagram of the simulation results provided in the embodiments of the present invention. Figure 2 ; Figure 6 This is a schematic diagram of the simulation results provided in the embodiments of the present invention. Figure 3 ; Figure 7 This is a schematic diagram of the simulation results provided in the embodiments of the present invention. Figure 4 ; Figure 8 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0024] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0025] To illustrate the technical solution described in this invention, specific embodiments are described below.

[0026] Figure 1 This is a schematic diagram illustrating the implementation process of the multi-grid converter cooperative control method provided in this embodiment of the invention. Figure 1 As shown, it includes: Step S101: Using the weighted sum of the power grid's reactive power distribution deviation index, circulating current suppression index, voltage stability index, and frequency fluctuation index as the objective, and using the reactive power distribution and operating parameters of each grid-type converter as decision variables, establish the objective function.

[0027] For example, the objective function can be: .

[0028] in: α 1. α 2. α 3. α 4 is the weighting coefficient, and α 1+ α 2+ α 3+α 4=1, the weight is used to make trade-offs among engineering objectives.

[0029] f Q The reactive power distribution deviation index. ; N This refers to the total number of grid-type converters. Q i For the first i Reactive power output of the grid-type converter; Q refi Assign reference values ​​to the target reactive power; Q basei It serves as a normalized reference value for rated reactive power to eliminate the influence of dimensions; f I As a circulation suppression index, ; I ij circ For the first i Taiwan-type grid converter and the first j The circulating current component between the grid-type converters; I base This serves as the reference value for current normalization. f V For voltage stability indicators, ; V i For the first i Voltage amplitude of the grid-type converter; V ref This is the target value for the system voltage. V base This is the voltage normalization reference value (usually taken as the rated voltage); σ V The standard deviation of the voltage across the entire system is used to characterize non-uniformity; β V These are the weighting coefficients, and β V ≥0 is used to suppress both mean deviation and non-uniform distribution.

[0030] f ω As a frequency fluctuation indicator, ; ω i ( t ) is the first i The instantaneous angular frequency signal of the grid-type converter varies with time; ω ref For the target frequency; TFor evaluation windows (such as the time scale of the system recovery process), the maximum deviation or settling time index can also be used instead of the integral term.

[0031] The objective function established above combines the four engineering concerns into an optimizable scalar objective. By employing normalization and additional statistics, both steady-state bias and dynamic characteristics can be considered. Weights α 1. α 2. α 3. α 4 provides degrees of freedom for trade-offs between different engineering objectives, from steady-state reactive power equilibrium to short-term frequency recovery.

[0032] In this embodiment, the decision variables are the reactive power distribution and operating parameters of each grid-type converter, such as the equivalent resistance component of the virtual impedance, the equivalent inductance component of the virtual impedance, the reactive power distribution adjustment coefficient, and the frequency recovery parameter.

[0033] Step S102: Establish the constraints of the objective function, and solve the objective function iteratively using an improved particle swarm optimization algorithm to obtain the optimal parameters of each grid-type converter.

[0034] In one possible implementation, the constraints include: (1) Voltage deviation constraint: ; In the formula, Δ V max The maximum allowable voltage deviation of the power grid is set at 5%–10%.

[0035] (2) Peak circulation constraint: ; In the formula, I base This is the rated circulating current, used to ensure the converter module operates without overload, and is set according to actual standards. If a transient current occurs during simulation or actual operation... Therefore, this result must be considered infeasible.

[0036] (3) Reactive power output constraints of a single grid-connected converter: ; In the formula, Q imin and Q imax These are the upper and lower limits for reactive power output.

[0037] (4) Reactive power output constraints of multiple grid-connected converters: ; In the formula, N This represents the total number of grid-type converter units; Q i For the first i Reactive power output of the Taiwan-type grid converterQ refi Assign a reference value to the target reactive power.

[0038] (5) Stability constraint of reactive power distribution ratio: ; In the formula, δ Q The allowable deviation is set at 2%–10%; μ i For the first i Theoretical allocation ratio of grid-type converter units.

[0039] (6) Frequency deviation constraint: ; In the formula, ω i ( t ) is the first i The instantaneous angular frequency of the converter unit as a function of time; ω 0 is the rated angular frequency; Δ ω max The maximum angular frequency deviation is set to ±5%. ω 0.

[0040] Step S103: For each grid-type converter, determine the dynamic virtual impedance based on the optimal parameters, and control the grid-type converter based on the dynamic virtual impedance.

[0041] In this embodiment, the optimal parameters are mapped to a dynamic virtual impedance parameter set. : ; Based on the dynamic virtual impedance parameter set, construct the dynamic virtual impedance in the Laplace domain. :

[0042] in, This is a virtual resistor used to actively attenuate circulating current and improve damping; As a virtual inductance, it determines the dynamic response of current to voltage changes; This is a static virtual reactance; and These are the dynamic gains corresponding to frequency and voltage, respectively; and These are the time constants corresponding to frequency and voltage, respectively, used to filter out high-frequency noise and represent mechanical / control delays; s It is a Laplace complex variable.

[0043] Then, the dynamic virtual impedance is coupled with the VSG controller, and the specific control equations are as follows: Active-frequency link:

[0044] In the formula, For the first i Phase angle of a grid-type converter; Angular frequency (rad / s or Hz); Virtual inertia, measured in kg·m²; The time derivative of the angular frequency represents the frequency acceleration; The equivalent damping coefficient is expressed in N·m·s. For the first i The actual active power output of the grid-type converter; This is the active power reference value; The additional torque / power term resulting from the virtual impedance is used for coupling frequency-voltage regulation, and can be derived from... Z v It is obtained through interaction calculation with current.

[0045] Reactive power-voltage link:

[0046] In the formula, For the first i Reference voltage setting for the Taiwan-type grid converter; This is the dynamic damping coefficient of the reactive-voltage link; This represents the actual reactive power output. This is the reactive power reference value; For the first i Dynamic virtual impedance of a grid-type converter; Dynamic damping coefficient of reactive-voltage link It can be defined as:

[0047] In the formula, As the reference damping; The damping gain coefficient is a damping term that automatically increases system damping when reactive power deviation increases, which helps to suppress oscillations more quickly and thus plays a coordinated steady-state control role on frequency dynamics and voltage dynamics.

[0048] This invention aims to achieve the weighted sum of the power grid's reactive power distribution deviation, circulating current suppression, voltage stability, and frequency fluctuation indices. Using the reactive power distribution and operating parameters of each grid-connected converter as decision variables, an objective function is established to achieve coordinated optimization of multiple control objectives. This overcomes the limitations of single-objective optimization and simultaneously meets the comprehensive requirements of multiple grid-connected converters for steady-state accuracy and dynamic performance, avoiding system-wide performance imbalance caused by single-objective optimization. By solving the objective function, the optimal parameters of each grid-connected converter are obtained. For each grid-connected converter, a dynamic virtual impedance is determined based on the optimal parameters, and the converter is controlled based on this dynamic virtual impedance. This deeply couples the dynamic virtual impedance with the grid-connected converter control, optimizing the system's dynamic response performance.

[0049] Figure 3 This is a schematic diagram illustrating the implementation process of the improved particle swarm optimization algorithm for solving the objective function provided in this embodiment of the invention. Figure 3 As shown, it includes: (1) Initialize the velocity and position of the particles in the particle swarm and set the algorithm parameters.

[0050] The particles constructed in this embodiment are:

[0051] In the formula, x i ( k () represents the core parameter vector that needs to be optimized, and also represents the position of the particle; R v,i The equivalent resistance component of the virtual impedance; L v,i This is the equivalent inductance component of the virtual impedance; G q,i This is the reactive power distribution adjustment coefficient; K ω,i For frequency recovery parameters.

[0052] (2) In each iteration, calculate the dynamic inertia weight and the adaptive learning factor.

[0053] In order to balance global search and local convergence during the design of dynamic inertia weights and adaptive learning factors, nonlinearly decreasing inertia weights are introduced. The dynamic inertia weights are calculated according to the following formula. w k :

[0054] in, and These are the upper and lower limits of the inertia weight (common value ranges). , );k This represents the current iteration number; K This represents the maximum number of iterations. γ The shape factor of the descending curve. γ >0; when γ When the value is greater than 1, the inertia weight decreases faster in the early stages, preserving the local search capability in the later stages.

[0055] To enhance the adaptability of the individual / global learning ratio, an adjustment based on fitness difference is introduced in the design of the adaptive learning factor. The adaptive learning factor is calculated according to the following formula. c 1( k )and c 2( k ):

[0056] in, and These are the initial learning factor constants, typically set to 1.5-2.0; This is an adaptive factor, dimensionless, that controls the adjustment range; To minimize positive numbers and avoid zero denominators, this design enhances local exploration (improves efficiency) when the difference between the particle's optimal state and the global optimal state is large. c 1), Conversely, it strengthens convergence (improves) c 2) Inject perturbations at each iteration or when premature convergence occurs:

[0057] In the formula, For the disturbance injection term, Let N(0,1) be the perturbation intensity, and let N(0,1) be a multidimensional normal distribution.

[0058] (3) Update the particle velocity based on the dynamic inertia weight and the adaptive learning factor.

[0059] For the i The particle in the first k In the next iteration, the speed update formula is:

[0060] In the formula, For the first i The velocity vector of each particle corresponds to the rate of change of the core parameter that needs to be optimized. x i ( k ) is the first i The position vectors of each particle; p i.k For the first i The particle iterates to the... k The optimal position of an individual in a generation;g k This represents the optimal position in the particle's history. and It is an adaptive learning factor, related to the distance from the particle to the global optimum; and The random number is in the range [0,1] and is sampled independently in each iteration; This is the gradient direction correction term; parameters The scaling factor is gradually decreased with iteration to ensure that the global search phase is not dominated by local second-order information, and is usually adjusted accordingly. k Decreasing can be defined as:

[0061] In the formula, K The maximum number of iterations, The scaling factor is the initial quasi-Newton correction factor.

[0062] If numerical gradient estimation of the target is possible, then approximate second-order information can be added to the velocity update to accelerate convergence, including a gradient direction correction term. It can be defined as:

[0063] In the formula, H k It is the Hessian approximation matrix. This is the gradient vector of the objective function with respect to the parameters.

[0064] H k The BFGS update formula is approximated:

[0065] In the formula, , Introducing BFGS into the hybrid method of particle swarm optimization can significantly accelerate convergence when gradients are computable; when the objective function is not analytical or has high noise, this approximation can be used only in the deterministic simulation evaluation or local search stage.

[0066] For the i The particle in the first k In the next iteration, the position update formula is:

[0067] in, For the first i The first particle k The velocity vector of the next iteration; For the first disturbance i The first particle k Position vector of the next iteration (4) Based on the updated particle velocity, introduce the Levy jump mechanism to update the particle position.

[0068] To prevent getting stuck in local jumps, the Levy jump mechanism is introduced:

[0069] in, For the perturbation of the first i The first particle k The position vector of the next iteration; The disturbance amplitude is 0.01–0.1; Levy(x) is the random disturbance, and n is the Levy exponent, which is usually taken as n=1.5.

[0070] (5) Evaluate the fitness of each particle and update the optimal particle based on the fitness.

[0071] Here, the fitness function is defined. f To characterize the overall performance of the system in both steady-state and transient states, considering factors such as minimizing reactive power distribution deviation, suppressing circulating current, optimizing voltage stability, and minimizing frequency fluctuation, the objective function and fitness function are as described above. f A smaller value indicates a better dynamic response and better constraint satisfaction of the system. The improved particle swarm optimization algorithm uses [a specific parameter] in each iteration. f To find the optimal search target, the velocity and position of each candidate solution in the particle swarm are updated to optimize reactive power distribution deviation, circulating current suppression, voltage stability, and minimize frequency fluctuation, thereby obtaining the optimal parameter combination.

[0072] (6) Iterate continuously until the convergence condition is met, and determine the optimal parameters of each grid converter based on the optimal particle.

[0073] This embodiment designs a dynamic nonlinear decreasing inertial weight that balances global search and local convergence, enhances the adaptability of the learning ratio by using an adaptive learning factor based on fitness difference, introduces a Levy jump mechanism to avoid local optima, and adopts the BFGS Hessian approximation to accelerate convergence. This significantly improves the optimization accuracy and speed of the algorithm, enabling rapid iteration to obtain the optimal combination of core parameters that meets the needs of multiple objectives. Compared with traditional algorithms, it effectively improves parameter tuning efficiency, and the reactive power distribution deviation and frequency fluctuation amplitude of the optimized system are reduced.

[0074] Figure 3 This is a block diagram of the control principle of a grid-type converter. The diagram illustrates how, based on a multi-objective function, the optimal parameter combination obtained through iterative optimization using an improved particle swarm optimization algorithm is introduced into a virtual impedance structure. Furthermore, the dynamic virtual impedance model is deeply coupled with the active-frequency and reactive-voltage links of the grid-type converter to collaboratively tune the control power loop parameters of the converter.

[0075] This embodiment constructs a simulation model of islanded operation of multiple grid-connected converters. Two converters are set to have the same rated capacity but different equivalent line impedances. The parallel system starts up and load 1 is connected at 0s, load 2 is connected at 1s, and load 2 is disconnected at 2s, simulating the entire islanded operation process. System stability is determined through load abrupt changes.

[0076] like Figure 4 As shown, the stability of islanded operation was verified through a load change process. Before optimization (above figure), due to interference from line impedance differences, the reactive power distribution at each load node was severely unbalanced: after starting load 1 at 0s, Q1 and Q2 already showed initial deviations; when load 2 was connected at 1s, the reactive power of Q2 surged to nearly 3000, while Q1 only reached about 2200 (a difference of over 800); when load 2 was disconnected at 2s, the fluctuation trends of the two were completely different. This is precisely the shortcoming of the traditional solution—it cannot offset the influence of line impedance differences, and is prone to circulating current due to reactive power imbalance, weakening the reactive power distribution during load changes. System stability; after optimization (see figure below), the reactive power curves of Q1 and Q2 are completely synchronized under the same load change node: the output is consistent after 0s startup, rises synchronously when load 2 is connected at 1s, and falls synchronously when load 2 is disconnected at 2s, with highly overlapping values. This directly confirms the effectiveness of this method - by improving the particle swarm algorithm to optimize parameters such as virtual impedance and adjustment coefficient, the interference of line impedance difference is offset, and the reactive power of devices with the same rated capacity is accurately distributed. At the same time, synchronous response is maintained during load change, effectively improving the system stability of islanded operation.

[0077] like Figure 5As shown in the figure, the simulation results before and after grid connection point voltage optimization are compared in the scenario of two grid-connected converters operating in parallel and in an islanded configuration. The figure clearly shows the performance difference before and after grid connection point voltage optimization: Before optimization (above), the grid connection point voltage deviates significantly from the rated voltage (310V). After starting load 1 at 0s, the voltage drops directly to about 290V, which is about 20V away from the rated value. When load 2 is connected at 1s, the voltage only fluctuates slightly but remains at a low level. After disconnecting load 2 at 2s, it does not approach the rated voltage. This is because the traditional solution cannot offset the effect of line impedance difference, and the reactive power distribution imbalance weakens the voltage support capability, making it difficult to adjust the voltage throughout the entire load change process. The voltage stability at the grid connection point is significantly improved after optimization (see figure below): after starting load 1 at 0s, the voltage stabilizes near the rated value; when load 2 is connected at 1s, there is a brief fluctuation, but the amplitude is controllable; when load 2 is disconnected at 2s, the voltage surge also quickly subsides, and finally, it consistently maintains the rated voltage of 310V. This is precisely the effect of this method—by improving the particle swarm optimization algorithm to optimize core parameters and achieve precise reactive power allocation, and by combining dynamic virtual impedance with the coordinated control of the grid-type converter, the voltage support shortcomings caused by line impedance differences are compensated for, effectively enhancing voltage stability under load change scenarios and ensuring the power supply reliability of the islanded system.

[0078] Figure 6 As shown in the figure, the simulation results of the system frequency before and after optimization are analyzed in the scenario of two grid-type converters operating in parallel and in an islanded configuration. The figure clearly shows the performance difference of the system before and after frequency optimization: Before optimization (above figure), the grid connection point frequency deviates significantly from the rated 50Hz and fluctuates out of control. After starting load 1 at 0s, the frequency is maintained at around 50.2Hz (higher than the rated value). When load 2 is connected at 1s, there is a large jump, and it is always difficult to keep up with the rated frequency thereafter. This is because the traditional solution cannot offset the interference of line impedance difference, and the reactive power distribution imbalance weakens the inertia support capability of the grid-type converter. At the same time, the lack of coordinated design of control parameters leads to a lag in frequency regulation response when the load changes suddenly, making it difficult to maintain the rated operating state; while after optimization, the frequency of the grid connection point deviates significantly from the rated 50Hz and fluctuates out of control. After optimization (see figure below), the stability of the grid connection frequency is significantly improved: after starting load 1 at 0s, the frequency closely follows the rated value of 50Hz; when load 2 is connected at 1s, although there is a brief fluctuation, the amplitude is controllable; after disconnecting load 2 at 2s, the impact is quickly smoothed out, and finally it remains stable near the rated frequency. This is the effect of this method—by improving the particle swarm algorithm to optimize core variables such as frequency recovery parameters and virtual impedance, and combining the coordinated control of dynamic virtual impedance and the active-frequency link of the grid-type converter, the adjustment shortcomings caused by line impedance differences are made up for, the virtual inertia and damping characteristics of the system are enhanced, and the precise and stable frequency adjustment is achieved throughout the entire process of load change, ensuring the frequency operation reliability of the islanded system.

[0079] like Figure 7As shown, before the introduction of this method (0.5-1.0s), the circulating current between the two grid-connected converters exhibited severe high-frequency fluctuations with an amplitude close to ±6A. This was due to the difference in equivalent line impedance between the two units, which traditional solutions could not offset, and the unbalanced reactive power distribution, leading to a large circulating current between the units. This large-amplitude circulating current significantly increases the losses of the converter switching devices, accelerates device heating and aging, and may even cause overcurrent faults, severely weakening the reliability and economy of system operation. However, after introducing this method at around 1.0s, the circulating current converged rapidly, the fluctuation amplitude decreased significantly, and subsequently stabilized within ±2A, with a noticeable reduction in fluctuation frequency. This change directly confirms the technical effectiveness of this method: by improving the particle swarm optimization algorithm to optimize core parameters such as dynamic virtual impedance components and reactive power adjustment coefficients, the interference of line impedance differences on reactive power distribution is accurately offset, achieving precise reactive power matching between the two grid-type converters; at the same time, the synergistic effect of dynamic virtual impedance and grid-type converter control effectively enhances the system damping characteristics, quickly suppresses circulating current oscillations, and ultimately significantly reduces the amplitude and fluctuation of circulating current, avoiding equipment risks caused by excessive circulating current, and improving the operating efficiency and stability of the islanded parallel system.

[0080] This invention aims to solve the technical challenges of uneven reactive power distribution, excessive circulating current, and significant voltage and frequency fluctuations when multiple grid-connected converters operate in parallel. By constructing an objective function with the core objectives of minimizing reactive power distribution deviation, suppressing circulating current, optimizing voltage stability, and minimizing frequency fluctuations, and setting constraints, an improved particle swarm optimization algorithm incorporating dynamic inertia weights, adaptive learning factors, and a Levy jump mechanism is employed to iteratively solve for the optimal combination of core parameters such as virtual impedance components and adjustment coefficients. These optimal parameters are then mapped to a dynamic virtual impedance model, coupled with the grid-connected converter control, and the power loop parameters are collaboratively tuned. The results are verified through simulation and testing. This method achieves multi-objective collaborative control, effectively improving the system's steady-state and transient performance.

[0081] The method of the present invention has the following improvements: (1) This invention achieves synergistic optimization of multiple control objectives, breaking through the limitations of single-objective optimization: Existing technologies mostly target single-objective design schemes in reactive power distribution, voltage stability, or frequency regulation. For example, the traditional virtual impedance method only focuses on improving reactive power distribution, making it difficult to simultaneously address circulating current suppression and frequency fluctuation control, which can easily lead to mutual interference between multiple objectives. This invention constructs a scalar function containing four core objectives: reactive power distribution deviation, circulating current suppression, voltage stability, and frequency fluctuation, and introduces flexibly weighted coefficients and multi-dimensional constraints to transform multi-objective optimization into a solvable scalar problem. This can simultaneously meet the comprehensive requirements of multi-grid converters for steady-state accuracy and dynamic performance, avoiding the overall system performance imbalance caused by single-objective optimization.

[0082] (2) This invention improves the particle swarm optimization algorithm to enhance the accuracy and efficiency of parameter tuning and solves the defects of traditional algorithms: conventional particle swarm optimization algorithms have the problems of slow convergence speed and easy getting trapped in local optima, resulting in poor tuning effect of core control parameters and difficulty in adapting to complex working conditions. This invention designs a dynamic nonlinear decreasing inertia weight, which takes into account both global search and local convergence, and enhances the adaptive learning ratio based on fitness difference. It also introduces the Levy jump mechanism to avoid local optima and adopts the BFGS Hessian approximation to accelerate convergence, which significantly improves the optimization accuracy and speed of the algorithm. It can quickly iterate to obtain the optimal combination of core parameters that meets the needs of multiple objectives. Compared with traditional algorithms, it effectively improves the parameter tuning efficiency, and the reactive power distribution deviation and frequency fluctuation amplitude of the optimized system are reduced.

[0083] (3) This invention optimizes the dynamic response performance of the system by deeply coupling the dynamic virtual impedance with the grid-type converter control: Existing technologies mostly use static virtual impedance, and its parameters are independently tuned with the power loop parameters of the grid-type converter. This can easily lead to voltage-frequency coupling interference and lag in dynamic response due to parameter decoupling. This invention maps the parameters obtained by algorithm optimization into a dynamic virtual impedance model containing frequency / voltage dynamic gain and time constant. It can not only adaptively adjust the impedance characteristics according to the operating conditions to actively attenuate circulating current and improve damping, but also coordinate the tuning of power loop parameters by establishing the coupling control equations of virtual impedance with the active-frequency and reactive-voltage links of the grid-type converter, so that voltage regulation and frequency recovery are linked.

[0084] It should be understood that the sequence number of each step in the above embodiments does not imply 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 embodiments of the present invention.

[0085] Figure 8 This is a schematic diagram of an electronic device 80 provided in an embodiment of the present invention. For example... Figure 8 As shown, the electronic device 80 of this embodiment includes: a processor 81, a memory 82, and a computer program 83 stored in the memory 82 and executable on the processor 81. When the processor 81 executes the computer program 83, it implements the steps in the various method embodiments described above.

[0086] For example, the computer program 83 may be divided into one or more modules / units, which are stored in the memory 82 and executed by the processor 81 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 83 in the electronic device 80.

[0087] The electronic device 80 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device 80 may include, but is not limited to, a processor 81 and a memory 82. Those skilled in the art will understand that... Figure 8 This is merely an example of electronic device 80 and does not constitute a limitation on electronic device 80. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 80 may also include input / output devices, network access devices, buses, etc.

[0088] The processor 81 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0089] The memory 82 can be an internal storage unit of the electronic device 80, such as a hard disk or RAM of the electronic device 80. The memory 82 can also be an external storage device of the electronic device 80, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 80. Furthermore, the memory 82 can include both internal and external storage units of the electronic device 80. The memory 82 is used to store the computer program and other programs and data required by the electronic device 80. The memory 82 can also be used to temporarily store data that has been output or will be output.

[0090] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0091] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0092] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0093] In the embodiments provided by this invention, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0094] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0096] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0097] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for coordinated control of multi-configuration grid type converters, characterized in that, include: The objective function is established with the weighted sum of the reactive power distribution deviation index, circulating current suppression index, voltage stability index and frequency fluctuation index of the power grid as the objective, and the reactive power distribution and operating parameters of each grid-type converter as the decision variables. The constraints of the objective function are established, and the objective function is solved iteratively by an improved particle swarm optimization algorithm to obtain the optimal parameters of each grid-type converter. For each grid-type converter, the dynamic virtual impedance is determined based on the optimal parameters, and the grid-type converter is controlled based on the dynamic virtual impedance.

2. The multi-grid converter cooperative control method as described in claim 1, characterized in that, The objective function is: ; wherein α 1, α 2, α 3, α 4 is a weight coefficient, and α 1+ α 2+ α 3+ α 4 = 1; f Q a reactive power allocation deviation index, ; N a total number of grid-forming converters; Q i a reactive power output of the i-th i grid-forming converter; Q refi a target reactive power allocation reference value; Q basei a rated reactive power normalized reference value; f I is a circulating current suppression index, ; I ij circ is a first i circulating current component between the first j grid-connected inverter and the second grid-connected inverter; I base is a current normalization reference value; f V For voltage stability indicators, ; V i For the first i Voltage amplitude of the grid-type converter; V ref This is the target value for the system voltage. V base This is the voltage normalization reference value; σ V The standard deviation of the voltage across the entire system; β V These are the weighting coefficients, and β V ≥0; f ω As a frequency fluctuation indicator, ; ω i ( t ) is the first i The instantaneous angular frequency signal of the grid-type converter varies with time; ω ref For the target frequency; T For evaluation window.

3. The multi-grid converter cooperative control method as described in claim 2, characterized in that, The reactive power distribution and operating parameters include: the equivalent resistance component of the virtual impedance, the equivalent inductance component of the virtual impedance, the reactive power distribution adjustment coefficient, and the frequency recovery parameter.

4. The multi-grid converter cooperative control method as described in claim 2, characterized in that, The constraints of the objective function include: voltage deviation constraint, peak circulating current constraint, reactive power output constraint of a single grid-type converter, reactive power output constraint of multiple grid-type converters, reactive power distribution ratio stability constraint, and frequency deviation constraint.

5. The multi-grid converter cooperative control method as described in any one of claims 1 to 4, characterized in that, The step of iteratively solving the objective function using an improved particle swarm optimization algorithm includes: Initialize the velocity and position of the particles in the particle swarm and set the algorithm parameters; In each iteration, the dynamic inertia weight and adaptive learning factor are calculated; The particle velocity is updated based on the dynamic inertia weight and the adaptive learning factor. Based on the updated particle velocity, a Levy jump mechanism is introduced to update the particle position; Evaluate the fitness of each particle and update the optimal particle based on the fitness. The process is iterated until the convergence condition is met, and the optimal parameters of each grid converter are determined based on the optimal particle.

6. The multi-grid converter cooperative control method as described in claim 5, characterized in that, The calculation of dynamic inertia weights and adaptive learning factors includes: Based on the calculation of dynamic inertia weight w k : in, and These are the upper and lower limits of the inertia weight, respectively; k This represents the current iteration number; K This represents the maximum number of iterations. γ The shape factor of the descending curve; The adaptive learning factor is calculated according to the following formula. c 1( k )and c 2( k ): in, and These are the initial learning factor constants; This is an adaptive factor, dimensionless, that controls the adjustment range; To avoid the denominator being zero, use extremely small positive numbers; Injecting terms for disturbances; J ( p i,k ) is the first i The particle iterates to the... k The fitness corresponding to the optimal position in time; J ( g k ) for iteration to the k The optimal position in the global time frame.

7. The multi-grid converter cooperative control method as described in claim 5, characterized in that, The step of updating the particle position based on the updated particle velocity, by introducing a Levy jump mechanism, includes: Update the particle's position according to the following formula. : in, For the first i The first particle k The velocity vector of the next iteration; and For the first and second moments before and after the disturbance i The first particle k The position vector of the next iteration; is the perturbation amplitude; Levy(x) is the random perturbation.

8. The multi-grid converter cooperative control method as described in any one of claims 1 to 4, characterized in that, For each grid-type converter, determining the dynamic virtual impedance based on the optimal parameters includes: Map the optimal parameters to a dynamic virtual impedance parameter set. : ； Based on the aforementioned dynamic virtual impedance parameter set, a dynamic virtual impedance is constructed. : in, For virtual resistance; For virtual inductance; This is a static virtual reactance; and These are the dynamic gains corresponding to frequency and voltage, respectively; and These are the time constants corresponding to frequency and voltage, respectively; s It is a Laplace complex variable.

9. The multi-grid converter cooperative control method as described in any one of claims 1 to 4, characterized in that, The control of the grid-type converter based on the dynamic virtual impedance includes: Based on the dynamic virtual impedance, determine the active frequency loop control quantity and reactive voltage loop control quantity of the grid-type converter; Based on the active frequency loop control quantity, the active frequency of the grid-type converter is controlled. Based on the reactive voltage loop control quantity, reactive voltage control is performed on the grid-type converter.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 9.

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