Active support regulation and control system of network construction type converter based on multi-mode cooperation

Abstract

The invention, which relates to the technical field of distributed power generation, discloses a multi-modal cooperation-based active support regulation and control system for a network-building type converter, comprising a basic configuration unit, a duty ratio adjustment unit, an optimal parameter determination unit and a control parameter optimization unit. The photovoltaic cell model is constructed through the basic configuration unit, so that the change of the output voltage of the photovoltaic cell can be visually observed in the subsequent duty ratio adjustment process, and meanwhile, the duty ratio adjustment unit monitors the output power in different sampling periods in real time in the simulation process of the photovoltaic cell panel model; the duty ratio is flexibly adjusted according to the change value of the power before and after sampling, accurate capture of the maximum power point is achieved, the increase and decrease trend of the duty ratio can be conveniently optimized according to the simulation result in the actual operation process of the photovoltaic cell panel, and therefore the operation efficiency is improved; and the optimal parameter determination unit is used for screening out the most appropriate numerical value as the initial parameter in the virtual synchronous generator, so that the time required for subsequent parameter adjustment can be shortened.

Description

Technical Field

[0001] This invention relates to the field of distributed generation technology, specifically to an active support and control system for grid-type converters based on multimodal collaboration. Background Technology

[0002] The multimodal collaborative grid-connected converter active support regulation system integrates and processes information from different modes, enabling them to cooperate and facilitating the participation of virtual generators in grid regulation and control. This provides dynamic data support to enhance grid stability and reliability. Patent application number 202211728417.0 discloses "A method, system, and terminal for optimizing the power regulation of an energy storage virtual synchronous generator, relating to the field of distributed generation technology. The key technical points are: acquiring real-time load power, real-time generation power of the distributed generation system, and the actual remaining capacity of the energy storage unit; determining the constant generation power of the traditional synchronous generator in the next regulation cycle based on the real-time load power and real-time generation power within the current regulation cycle; calculating the total regulation power of all energy storage units in the distributed generation system using the VSG control algorithm; and allocating the actual regulation power of each energy storage unit based on the distribution of all real-time generation power and all actual remaining capacity. This invention can reduce performance fluctuations of all energy storage units participating in energy storage scheduling in adjacent regulation cycles and achieves a balanced distribution of the remaining capacity of each energy storage unit, ensuring the stable operation of the entire power system even in the event of sudden failures in some energy storage units."

[0003] The aforementioned existing technologies have solved problems such as unreasonable allocation of energy storage resources. However, during system operation, the lack of a photovoltaic cell model makes it impossible to flexibly adjust the duty cycle based on the power changes before and after data collection. This makes it impossible for the system to accurately analyze the maximum power point. Furthermore, the lack of optimization of virtual inertia and damping coefficient during virtual synchronous generator control makes the system prone to frequency oscillations and voltage fluctuations during overall operation. Summary of the Invention

[0004] The purpose of this invention is to provide an active support and control system for a network-type converter based on multimodal collaboration, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-modal cooperative network-type converter active support and control system, including a control parameter optimization unit;

[0006] The basic configuration unit uses a photocurrent source, a single diode, a series resistor, and a parallel resistor to generate an equivalent circuit model of a photovoltaic cell, determines all relevant parameters in the equivalent circuit model, and constructs a corresponding photovoltaic cell model based on the relevant parameters.

[0007] The duty cycle adjustment unit sets the initial duty cycle and disturbance step size, combines multiple photovoltaic cell models to obtain a photovoltaic panel, calculates the current power and duty cycle of the panel, obtains the power value at the next measurement, determines the current power change value, obtains a new duty cycle, uses the power value at the next measurement as the current power, repeats the operation until the loop ends, and maintains the current duty cycle.

[0008] The optimal parameter determination unit builds a photovoltaic simulation model using Matla software, initializes the particle velocity and position, analyzes the fitness of each particle using a conditional function, determines the individual optimal value and the global optimal value, updates the velocity and position of each particle using the acceleration constant, and repeats the operation until the loop ends. Based on the global optimal value, it determines the virtual inertia value and damping coefficient value corresponding to the particle with the lowest fitness.

[0009] Preferably, the basic configuration unit includes an equivalent circuit generation module, a parameter setting module, and a model building module. The equivalent circuit generation module generates an equivalent circuit model of the photovoltaic cell using a photocurrent source, a single diode, a series resistor, and a parallel resistor. The parameter setting module determines all relevant parameters in the photovoltaic cell equivalent circuit model. These relevant parameters include photocurrent, forward current of the PN junction, leakage current of the PN junction, short-circuit current, open-circuit voltage, ambient temperature, reference temperature, illuminance, and diode reverse saturation current. The model building module analyzes the output current, output voltage, and output power of the photovoltaic cell based on the relevant parameters and constructs the corresponding photovoltaic cell model.

[0010] Preferably, the duty cycle adjustment unit includes a solar panel determination module, a voltage acquisition module, and a change value calculation module. The solar panel determination module sets the initial duty cycle K0, the disturbance step size ΔK0, the sampling period T0, and the power threshold ΔP. min Then, multiple photovoltaic cell models are connected in series to obtain a photovoltaic panel. The voltage acquisition module measures the current output current I0 and output voltage U0 of the photovoltaic panel, and calculates the current power P0 based on the output current I0 and output voltage U0, where P0 = U0 × I0. The change value calculation module calculates the new duty cycle K1 using the initial duty cycle K0 and the disturbance step size ΔK0, where K1 = K0 + ΔK0. The output current I1 and output voltage U1 of the photovoltaic panel at the next measurement are obtained according to the sampling period, and the corresponding power P1 is calculated through I1 and U1. Based on the current power P0 and the power P1 of the next measurement, the power change value ΔP is analyzed, where ΔP = P1 - P0.

[0011] Preferably, the duty cycle adjustment unit further includes a duty cycle update module and a power analysis module. The duty cycle update module, after obtaining the current power change value ΔP, if ΔP ≥ 0, adds the current duty cycle K1 to the disturbance step size ΔK0 to obtain a new duty cycle K2. If ΔP < 0, it subtracts the current duty cycle K1 from the disturbance step size ΔK0 to obtain a new duty cycle K2. The power analysis module uses the power P1 as the current power, determines the new duty cycle K2, obtains the output current I2 and output voltage U2 of the photovoltaic panel at the next measurement according to the sampling period, and calculates the new power P2. This operation is repeated until ΔP < ΔP. min Then, maintain the current duty cycle.

[0012] Preferably, the optimal parameter determination unit includes a model connection module, a parameter initialization module, and a fitness analysis module. The model connection module constructs a virtual synchronous generator control model using Matla simulation software, and after constructing a battery model using a bidirectional power converter, connects the virtual synchronous generator control model, the battery model, and the photovoltaic panel model to build a photovoltaic simulation model. The parameter initialization module initializes the number of particles, particle positions, particle velocities, acceleration constants, maximum number of iterations, individual optimal values, global optimal values, maximum particle velocity values, and position thresholds. The fitness analysis module analyzes the virtual inertia and damping coefficient values ​​within each particle using a conditional function to obtain the fitness of all particles. If the fitness of a particle is less than its individual optimal value, the current fitness is taken as the new individual optimal value; otherwise, no operation is performed. If the fitness of a particle is less than its global optimal value, the current fitness is taken as the new global optimal value; otherwise, no operation is performed. The specific conditional function is:

[0013]

[0014] Where G represents virtual inertia, Z represents damping coefficient, a and b represent weighting coefficients, Δf(t) represents frequency deviation, T represents simulation time, δ0 represents fundamental amplitude, and δ l Let N represent the amplitude of the l-th harmonic, N represent the upper limit of the harmonic order, l represent the parameter, t represent time, f represent the fitness value, and J(·) represent the fitness function.

[0015] Preferably, the optimal parameter determination unit further includes a position generation module and a result output module. The position generation module updates the velocity and position of each particle using the acceleration constant. If the current particle velocity value is greater than the maximum particle velocity value, the current particle velocity value is replaced with the maximum particle velocity value; otherwise, no operation is performed. If the current particle position value exceeds the position threshold, a new position value is randomly generated and used as the current particle position value. The result output module repeats the operation until the number of loops equals the maximum number of iterations. Then, based on the global optimal value, it determines the virtual inertia value and damping coefficient value corresponding to the particle with the lowest fitness and outputs them.

[0016] Preferably, the control parameter optimization unit includes a threshold setting module and a curve analysis module. The threshold setting module reads the virtual inertia value and damping coefficient value and uses them as the initial values ​​in the virtual synchronous generator control model, and sets the adjustment thresholds corresponding to the virtual inertia value and damping coefficient. During the photovoltaic simulation model simulation, the curve analysis module generates the angular velocity oscillation curve of the virtual synchronous generator rotor and the synchronous power characteristic curve. Based on the oscillation curve and characteristic curve, the angular velocity, angular acceleration and fine-tuning factor of the generator rotor at different times are analyzed.

[0017] Preferably, the control parameter optimization unit further includes an inertia calculation module and a coefficient determination module. The inertia calculation module calculates the current virtual inertia value using parameter analysis formulas based on angular velocity, angular acceleration, fine-tuning factor, and initial value when the rotor angular velocity and angular acceleration in the virtual synchronous generator control model are in the same direction and the absolute value of angular acceleration is greater than the adjustment threshold for the virtual inertia value. When the rotor angular velocity and angular acceleration in the virtual synchronous generator control model are not in the same direction, the current virtual inertia value is determined to be equal to the initial value. When the absolute value of angular acceleration is less than or equal to the adjustment threshold for the virtual inertia value, the current virtual inertia value is also set to the initial value. Similarly, the coefficient determination module calculates the current damping coefficient using parameter analysis formulas based on angular velocity, fine-tuning factor, and initial value when the absolute value of rotor angular velocity in the virtual synchronous generator control model is greater than the adjustment threshold for the damping coefficient. When the absolute value of rotor angular velocity in the virtual synchronous generator control model is less than or equal to the adjustment threshold for the damping coefficient, the current damping coefficient is determined to be equal to the initial value.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] This invention constructs a photovoltaic cell model through a basic configuration unit, facilitating intuitive observation of changes in the output voltage and current of the photovoltaic cell during subsequent duty cycle adjustments. Simultaneously, the duty cycle adjustment unit monitors the output power in real time during the photovoltaic panel model simulation, adjusting the duty cycle flexibly based on power changes before and after sampling. This achieves precise capture of the maximum power point, allowing the actual photovoltaic panel to optimize the duty cycle's increase or decrease trend based on simulation results, thereby improving operating efficiency. The optimal parameter determination unit selects the most suitable values ​​as initial parameters within the virtual synchronous generator, shortening the time required for subsequent parameter adjustments. Furthermore, the control parameter optimization unit autonomously configures the original initial parameters based on the current generator rotor's angular velocity and angular acceleration, ensuring a smoother and more efficient overall control process. Attached Figure Description

[0020] Figure 1 A schematic diagram of the overall system flow is provided for embodiments of the present invention;

[0021] Figure 2 This is an internal module block diagram of the basic configuration unit provided in an embodiment of the present invention;

[0022] Figure 3 This is an internal module block diagram of the duty cycle adjustment unit provided in an embodiment of the present invention;

[0023] Figure 4 This is an internal module block diagram of the optimal parameter determination unit provided in an embodiment of the present invention;

[0024] Figure 5 This is an internal module block diagram of the control parameter optimization unit provided in an embodiment of the present invention.

[0025] In the diagram: 1. Basic configuration unit; 101. Equivalent circuit generation module; 102. Parameter setting module; 103. Model building module; 2. Duty cycle adjustment unit; 201. Solar panel determination module; 202. Voltage acquisition module; 203. Change value calculation module; 204. Duty cycle update module; 205. Power analysis module; 3. Optimal parameter determination unit; 301. Model connection module; 302. Parameter initialization module; 303. Fitness analysis module; 304. Position generation module; 305. Result output module; 4. Control parameter optimization unit; 401. Threshold setting module; 402. Curve analysis module; 403. Inertia calculation module; 404. Coefficient determination module. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see Figures 1-5 The present invention provides a technical solution: a multimodal cooperative network converter active support control system, including a control parameter optimization unit 4;

[0028] Basic configuration unit 1 uses a photocurrent source, a single diode, a series resistor, and a parallel resistor to generate an equivalent circuit model of a photovoltaic cell, determines all relevant parameters in the equivalent circuit model, and constructs the corresponding photovoltaic cell model based on the relevant parameters.

[0029] Duty cycle adjustment unit 2 sets the initial duty cycle and disturbance step size, combines multiple photovoltaic cell models to obtain a photovoltaic panel, calculates the current power and duty cycle of the panel, obtains the power value at the next measurement, determines the current power change value, obtains the new duty cycle, uses the power value at the next measurement as the current power, repeats the operation until the loop ends, and maintains the current duty cycle.

[0030] The optimal parameter determination unit 3 uses Matla software to build a photovoltaic simulation model, initializes the particle velocity and position, analyzes the fitness of each particle using a conditional function, determines the individual optimal value and the global optimal value, updates the velocity and position of each particle by the acceleration constant, and repeats the operation until the loop ends. Based on the global optimal value, it determines the virtual inertia value and damping coefficient value corresponding to the particle with the lowest fitness.

[0031] The basic configuration unit 1 includes an equivalent circuit generation module 101, a parameter setting module 102, and a model building module 103. The equivalent circuit generation module 101 generates an equivalent circuit model of a photovoltaic cell using a photocurrent source, a single diode, a series resistor, and a parallel resistor. The parameter setting module 102 determines all relevant parameters in the equivalent circuit model of the photovoltaic cell. These relevant parameters include the photocurrent, the forward current of the PN junction, the leakage current of the PN junction, the short-circuit current, the open-circuit voltage, the ambient temperature, the reference temperature, the illuminance coefficient, and the reverse saturation current of the diode. The model building module 103 analyzes the output current, output voltage, and output power of the photovoltaic cell based on the relevant parameters and builds the corresponding photovoltaic cell model.

[0032] The duty cycle adjustment unit 2 includes a solar panel determination module 201, a voltage acquisition module 202, and a change value calculation module 203. The solar panel determination module 201 sets the initial duty cycle K0, the disturbance step size ΔK0, the sampling period T0, and the power threshold ΔP. min Then, multiple photovoltaic cell models are connected in series to obtain a photovoltaic panel. The voltage acquisition module 202 measures the current output current I0 and output voltage U0 of the photovoltaic panel and calculates the current power P0 based on the output current I0 and output voltage U0, where P0 = U0 × I0. The change value calculation module 203 calculates the new duty cycle K1 using the initial duty cycle K0 and the disturbance step size ΔK0, where K1 = K0 + ΔK0. The output current I1 and output voltage U1 of the photovoltaic panel at the next measurement are obtained according to the sampling period. The corresponding power P1 is calculated through I1 and U1. The power change value ΔP is analyzed based on the current power P0 and the power P1 of the next measurement, where ΔP = P1 - P0.

[0033] The duty cycle adjustment unit 2 also includes a duty cycle update module 204 and a power analysis module 205. After the duty cycle update module 204 obtains the current power change value ΔP, if ΔP ≥ 0, it adds the current duty cycle K1 to the disturbance step size ΔK0 to obtain a new duty cycle K2. If ΔP < 0, it subtracts the current duty cycle K1 from the disturbance step size ΔK0 to obtain a new duty cycle K2. The power analysis module 205 takes the power P1 as the current power, determines the new duty cycle K2, obtains the output current I2 and output voltage U2 of the photovoltaic panel at the next measurement according to the sampling period, and calculates the new power P2. The operation is repeated until ΔP < ΔP min Then, maintain the current duty cycle;

[0034] The optimal parameter determination unit 3 includes a model connection module 301, a parameter initialization module 302, and a fitness analysis module 303. The model connection module 301 constructs a virtual synchronous generator control model using Matla simulation software, and after constructing a battery model using a bidirectional power converter, connects the virtual synchronous generator control model, the battery model, and the photovoltaic panel model to build a photovoltaic simulation model. The parameter initialization module 302 initializes the number of particles, particle positions, particle velocities, acceleration constants, maximum number of iterations, individual optimal values, global optimal values, maximum particle velocity values, and position thresholds. The fitness analysis module 303 analyzes the virtual inertia and damping coefficient values ​​within each particle using conditional functions to obtain the fitness of all particles. If the fitness of a particle is less than its individual optimal value, the current fitness is taken as the new individual optimal value; otherwise, no operation is performed. Similarly, if the fitness of a particle is less than its global optimal value, the current fitness is taken as the new global optimal value; otherwise, no operation is performed. The specific conditional functions are as follows:

[0035]

[0036] Where G represents virtual inertia, Z represents damping coefficient, a and b represent weighting coefficients, Δf(t) represents frequency deviation, T represents simulation time, δ0 represents fundamental amplitude, and δ l Let N represent the amplitude of the l-th harmonic, N represent the upper limit of the harmonic order, l represent the parameter, t represent time, f represent the fitness value, and J(·) represent the fitness function.

[0037] The optimal parameter determination unit 3 also includes a position generation module 304 and a result output module 305. After updating the velocity and position of each particle using the acceleration constant, the position generation module 304 replaces the current particle velocity value with the maximum particle velocity value if the current particle velocity value is greater than the maximum particle velocity value, otherwise no operation is performed. If the current particle position value exceeds the position threshold, a new position value is randomly generated and used as the current particle position value. The result output module 305 repeats the operation until the number of loops equals the maximum number of iterations. Then, it determines the virtual inertia value and damping coefficient value corresponding to the particle with the lowest fitness based on the global optimal value and outputs them.

[0038] The control parameter optimization unit 4 includes a threshold setting module 401 and a curve analysis module 402. The threshold setting module 401 reads the virtual inertia value and damping coefficient value and uses them as the initial values ​​in the virtual synchronous generator control model, and sets the adjustment thresholds corresponding to the virtual inertia value and damping coefficient. During the photovoltaic simulation model simulation, the curve analysis module 402 generates the angular velocity oscillation curve of the virtual synchronous generator rotor and the synchronous power characteristic curve. Based on the oscillation curve and characteristic curve, the angular velocity, angular acceleration and fine-tuning factor of the generator rotor at different times are analyzed.

[0039] The control parameter optimization unit 4 also includes an inertia calculation module 403 and a coefficient determination module 404. The inertia calculation module 403 calculates the current virtual inertia value using parameter analysis formulas based on angular velocity, angular acceleration, fine-tuning factor, and initial value when the rotor angular velocity and angular acceleration in the virtual synchronous generator control model are in the same direction and the absolute value of angular acceleration is greater than the adjustment threshold of the virtual inertia value. When the rotor angular velocity and angular acceleration in the virtual synchronous generator control model are not in the same direction, the current virtual inertia value is determined to be equal to the initial value. When the absolute value of angular acceleration is less than or equal to the adjustment threshold of the virtual inertia value, the current virtual inertia value is also set to the initial value. The coefficient determination module 404 calculates the current damping coefficient using parameter analysis formulas based on angular velocity, fine-tuning factor, and initial value when the absolute value of rotor angular velocity in the virtual synchronous generator control model is greater than the adjustment threshold of the damping coefficient. When the absolute value of rotor angular velocity in the virtual synchronous generator control model is less than or equal to the adjustment threshold of the damping coefficient, the current damping coefficient is determined to be equal to the initial value. The parameter analysis formulas are as follows:

[0040]

[0041] Where G represents the virtual inertia and Z represents the damping coefficient. This represents the initial value of the virtual inertia. H represents the initial value of the damping coefficient. G H represents the fine-tuning factor of the virtual inertia. Z The damping coefficient is a fine-tuning factor, γ represents the rotor angular velocity, γ′ represents the rotor angular acceleration, and R... G R represents the adjustment threshold for the virtual inertia value. Z This represents the adjustment threshold for the damping coefficient value.

[0042] Working Principle: This invention constructs an equivalent circuit model of a photovoltaic cell using the equivalent circuit generation module 101 in the basic configuration unit 1, analyzes relevant parameters using the parameter setting module 102, and generates a photovoltaic cell model using the model construction module 103. Multiple photovoltaic cell models are connected in series using the panel determination module 201 in the duty cycle adjustment unit 2 to obtain a photovoltaic panel. The current power is calculated using the voltage acquisition module 202, and the power change value is analyzed by the change value calculation module 203 based on the current power and the power to be measured next. The new duty cycle is analyzed by the duty cycle update module 204 based on the power change value, and the current duty cycle is maintained using the power analysis module 205 when the power change value is less than the power threshold. The model is then processed using the optimal parameter determination unit 3. The photovoltaic simulation model is built by the connection module 301, the parameter initialization module 302 sets the particle parameters, the fitness analysis module 303 uses the condition function to analyze the fitness of each particle to obtain the current individual optimal value and the global optimal value, the position generation module 304 updates the velocity and position of each particle, the result output module 305 determines the virtual inertia value and damping coefficient value corresponding to the particle with the lowest fitness based on the global optimal value, the threshold setting module 401 in the control parameter optimization unit 4 determines the adjustment threshold, the curve analysis module 402 obtains the angular velocity, angular acceleration and fine adjustment factor of the generator rotor at different times, the inertia calculation module 403 determines the virtual inertia value at different times, and the damping coefficient at different times is analyzed by the coefficient determination module 404.

[0043] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-modal cooperative network-type converter active support control system, comprising a control parameter optimization unit (4), characterized in that: The basic configuration unit (1) uses a photocurrent source, a single diode, a series resistor and a parallel resistor to generate an equivalent circuit model of a photovoltaic cell, determines all relevant parameters in the equivalent circuit model, and constructs a corresponding photovoltaic cell model based on the relevant parameters. Duty cycle adjustment unit (2): After setting the initial duty cycle and disturbance step size, the duty cycle adjustment unit (2) combines multiple photovoltaic cell models to obtain a photovoltaic cell panel, calculates the current power and duty cycle of the cell panel, obtains the power value at the next measurement, determines the current power change value, obtains the new duty cycle, takes the power value at the next measurement as the current power, repeats the operation until the loop ends, and maintains the current duty cycle. The optimal parameter determination unit (3) builds a photovoltaic simulation model using Matla software, initializes the particle velocity and position, analyzes the fitness of each particle using a conditional function, determines the individual optimal value and the global optimal value, updates the velocity and position of each particle using the acceleration constant, repeats the operation until the loop ends, and determines the virtual inertia value and damping coefficient value corresponding to the particle with the lowest fitness based on the global optimal value.

2. The active support and control system for a network-type converter based on multimodal collaboration according to claim 1, characterized in that: The basic configuration unit (1) includes an equivalent circuit generation module (101), a parameter setting module (102), and a model building module (103). The equivalent circuit generation module (101) generates an equivalent circuit model of a photovoltaic cell using a photocurrent source, a single diode, a series resistor, and a parallel resistor. The parameter setting module (102) determines all relevant parameters in the equivalent circuit model of the photovoltaic cell. The relevant parameters include photocurrent, forward current of the PN junction, leakage current of the PN junction, short-circuit current, open-circuit voltage, ambient temperature, reference temperature, illuminance coefficient, and diode reverse saturation current. The model building module (103) analyzes the output current, output voltage, and output power of the photovoltaic cell based on the relevant parameters and builds the corresponding photovoltaic cell model.

3. The active support and control system for a network-type converter based on multimodal collaboration according to claim 1, characterized in that: The duty cycle adjustment unit (2) includes a solar panel determination module (201), a voltage acquisition module (202), and a change value calculation module (203). The solar panel determination module (201) sets the initial duty cycle K0, the disturbance step size ΔK0, the sampling period T0, and the power threshold ΔP. min Then, multiple photovoltaic cell models are connected in series to obtain a photovoltaic panel. The voltage acquisition module (202) measures the current output current I0 and output voltage U0 of the photovoltaic panel and calculates the current power P0 based on the output current I0 and output voltage U0, where P0 = U0 × I0. The change value calculation module (203) calculates the new duty cycle K1 using the initial duty cycle K0 and the disturbance step size ΔK0, where K1 = K0 + ΔK0. The output current I1 and output voltage U1 of the photovoltaic panel at the next measurement are obtained according to the sampling period. The corresponding power P1 is calculated through I1 and U1. The power change value ΔP is analyzed based on the current power P0 and the power P1 of the next measurement, where ΔP = P1 - P0.

4. The active support and control system for a network-type converter based on multimodal cooperation according to claim 3, characterized in that: The duty cycle adjustment unit (2) further includes a duty cycle update module (204) and a power analysis module (205). After the duty cycle update module (204) obtains the current power change value ΔP, if ΔP≥0, it adds the current duty cycle K1 to the disturbance step size ΔK0 to obtain a new duty cycle K2. If ΔP<0, it subtracts the current duty cycle K1 from the disturbance step size ΔK0 to obtain a new duty cycle K2. The power analysis module (205) takes the power P1 as the current power, determines the new duty cycle K2, obtains the output current I2 and output voltage U2 of the photovoltaic panel at the next measurement according to the sampling period, and calculates the new power P2. The operation is repeated until ΔP<ΔP min Then, maintain the current duty cycle.

5. The active support and control system for a network-type converter based on multimodal collaboration according to claim 1, characterized in that: The optimal parameter determination unit (3) includes a model connection module (301), a parameter initialization module (302), and a fitness analysis module (303). The model connection module (301) constructs a virtual synchronous generator control model using Matla simulation software, constructs a battery model using a bidirectional power converter, and then connects the virtual synchronous generator control model, the battery model, and the photovoltaic panel model to build a photovoltaic simulation model. The parameter initialization module (302) initializes the number of particles, particle positions, particle velocities, acceleration constants, maximum number of iterations, individual optimal values, global optimal values, maximum particle velocity values, and position thresholds. The fitness analysis module (303) analyzes the virtual inertia value and damping coefficient value of each particle using conditional functions to obtain the fitness of all particles. If the fitness of a particle is less than the individual optimal value, the current fitness is taken as the new individual optimal value; otherwise, no operation is performed. If the fitness of a particle is less than the global optimal value, the current fitness is taken as the new global optimal value; otherwise, no operation is performed.

6. The active support and control system for a network-type converter based on multimodal cooperation according to claim 5, characterized in that: The optimal parameter determination unit (3) further includes a position generation module (304) and a result output module (305). The position generation module (304) updates the velocity and position of each particle using the acceleration constant. If the current particle velocity value is greater than the maximum particle velocity value, the current particle velocity value is replaced with the maximum particle velocity value. Otherwise, no operation is performed. If the current particle position value exceeds the position threshold, a new position value is randomly generated and used as the current particle position value. The result output module (305) repeats the operation until the number of loops equals the maximum number of iterations. Then, it determines the virtual inertia value and damping coefficient value corresponding to the particle with the lowest fitness based on the global optimal value and outputs them.

7. The active support and control system for a network-type converter based on multimodal cooperation according to claim 1, characterized in that: The control parameter optimization unit (4) includes a threshold setting module (401) and a curve analysis module (402). The threshold setting module (401) reads the virtual inertia value and damping coefficient value and uses them as the initial values ​​in the virtual synchronous generator control model, and sets the adjustment thresholds corresponding to the virtual inertia value and damping coefficient. During the photovoltaic simulation model simulation process, the curve analysis module (402) generates the angular velocity oscillation curve of the virtual synchronous generator rotor and the synchronous power characteristic curve. Based on the oscillation curve and characteristic curve, it analyzes the angular velocity, angular acceleration and fine-tuning factor of the generator rotor at different times.

8. The active support and control system for a network-type converter based on multimodal cooperation according to claim 7, characterized in that: The control parameter optimization unit (4) further includes an inertia calculation module (403) and a coefficient determination module (404). The inertia calculation module (403) calculates the virtual inertia value at the current moment using parameter analysis formulas based on angular velocity, angular acceleration, fine-tuning factor, and initial value when the directions of rotor angular velocity and angular acceleration in the virtual synchronous generator control model are the same and the absolute value of angular acceleration is greater than the adjustment threshold of virtual inertia value. When the directions of rotor angular velocity and angular acceleration in the virtual synchronous generator control model are not the same, it determines that the virtual inertia value at the current moment is equal to the initial value. When the absolute value of angular acceleration is less than or equal to the adjustment threshold of virtual inertia value, it also sets the virtual inertia value at the current moment to the initial value. The coefficient determination module (404) calculates the damping coefficient at the current moment using parameter analysis formulas based on angular velocity, fine-tuning factor, and initial value when the absolute value of rotor angular velocity in the virtual synchronous generator control model is greater than the adjustment threshold of damping coefficient. When the absolute value of rotor angular velocity in the virtual synchronous generator control model is less than or equal to the adjustment threshold of damping coefficient, it determines that the damping coefficient at the current moment is equal to the initial value.

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