Virtual synchronous generator adaptive control method based on line impedance identification

Abstract

The invention provides a virtual synchronous generator adaptive control method based on line impedance identification, and relates to the technical field of power system control. The line impedance identification-based adaptive control method for the virtual synchronous generator comprises the steps of dynamically adjusting the virtual inertia J, the damping coefficient D and the droop coefficient K of the virtual synchronous generator through a multivariable constraint optimization algorithm according to the impedance-frequency coupling model, and realizing real-time matching between the output power of the virtual synchronous generator and the impedance of a power grid. By collecting power grid signals in real time and extracting impedance characteristics, the dynamic change of power grid impedance can be timely and accurately mastered, and a reliable basis is provided for subsequent control strategy adjustment, so that real-time matching of the output power of the virtual synchronous generator and the power grid impedance is realized, power fluctuation is effectively reduced, and the stability of a power grid is improved; according to the invention, a self-adaptive modulation strategy and a multi-resonant controller are adopted, harmonic oscillation can be effectively suppressed, and the quality of output electric energy of the inverter is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system control, and particularly to an adaptive control method for a virtual synchronous generator based on line impedance identification. Background Art

[0002] With the large-scale grid connection of new energy sources (such as photovoltaic and wind power), the inertia and damping characteristics of the power system have been significantly reduced, leading to increasingly prominent problems in grid frequency and voltage stability. The virtual synchronous generator (VSG) technology can provide inertia and damping support for the grid by simulating the operating characteristics of synchronous generators, but its control performance is greatly affected by the grid line impedance. Traditional VSG control methods usually adopt fixed parameter design and are difficult to adapt to the dynamic changes of grid impedance. Especially in weak grids or high-impedance scenarios, it is easy to cause subsynchronous oscillation, harmonic resonance and other problems, seriously threatening the safe and stable operation of the grid.

[0003] In modern power systems, with the wide access of distributed energy sources, the structure and operating characteristics of the grid have become increasingly complex. The virtual synchronous generator (VSG) technology, as a control strategy that can effectively improve the grid connection performance of distributed energy sources, has received extensive attention. However, in practical applications, the uncertainty of grid line impedance will have a significant impact on the control performance of virtual synchronous generators. For example, changes in line impedance may cause fluctuations in the output power of virtual synchronous generators, thereby affecting the stability of the grid; in weak grids and high-impedance scenarios, traditional virtual synchronous generator control strategies are difficult to ensure the transient stability of the system and are prone to problems such as harmonic oscillations. In addition, when intermittent new energy power sources are connected, due to the volatility of their power, it will further exacerbate the difficulty of virtual synchronous generator control.

[0004] Therefore, how to achieve real-time matching between virtual synchronous generators and grid impedance and improve their control performance and stability in complex grid environments has become an urgent problem to be solved in the current power system control field. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an adaptive control method for a virtual synchronous generator based on line impedance identification, aiming to solve problems such as output power fluctuations of virtual synchronous generators when grid impedance changes, poor transient stability, and control difficulties brought about by the access of new energy.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An adaptive control method for a virtual synchronous generator based on line impedance identification, comprising the following steps:

[0007] S1: Real-time collect grid node voltage and current signals through distributed sensors, and combine online harmonic detection technology to extract the amplitude-frequency characteristics and phase-frequency characteristics of the grid line impedance;

[0008] S2: Based on the improved recursive least squares algorithm and impedance phase compensation strategy, online identify the dynamic changes of the grid equivalent impedance, and construct an impedance-frequency coupling model;

[0009] S3: According to the impedance-frequency coupling model, dynamically adjust the virtual inertia J, damping coefficient D, and droop coefficient K of the virtual synchronous generator through a multivariable constraint optimization algorithm to achieve real-time matching of the output power of the virtual synchronous generator and the grid impedance;

[0010] S4: Generate a power command signal for the virtual synchronous generator based on the dynamic parameters, drive the full-bridge inverter to output the target power through an adaptive modulation strategy, and use a multi-resonant controller to suppress the harmonic oscillation caused by impedance mismatch;

[0011] S5: Construct a composite feedback index including grid frequency deviation, voltage deviation, and impedance change rate, and real-time correct the control parameters through a fuzzy sliding mode control algorithm to ensure the transient stability of the virtual synchronous generator in weak grid and high impedance scenarios.

[0012] Preferably, in the step S1, the online harmonic detection technology specifically includes:

[0013] S1.1: Inject a small wide-band signal perturbation with a frequency of 0.1f n -2f n into the grid, where f n is the fundamental frequency;

[0014] S1.2: Through the complex admittance analysis method in the synchronous rotating coordinate system, separate the resistive component R grid and inductive component L grid of the grid impedance;

[0015] S1.3: Use the Prony algorithm to fit the impedance frequency characteristic curve and extract the key resonance frequency points and impedance phase mutation thresholds.

[0016] Preferably, the impedance phase compensation strategy in the step S2 is specifically:

[0017] S2.1: Introduce a lead-lag compensation network into the control loop of the virtual synchronous generator to compensate for the phase lag caused by the line impedance;

[0018] S2.2: Based on the slope change of the impedance amplitude-frequency characteristic curve, dynamically adjust the zero-pole positions of the compensation network to suppress the impedance resonance in the medium and high frequency bands.

[0019] Preferably, the multivariable constraint optimization algorithm in the step S3 specifically includes:

[0020] S3.1: Establish a cost function with the goal of the shortest power grid frequency stabilization time and the minimum power fluctuation of the virtual synchronous generator:

[0021]

[0022] Among them, α and β are weight coefficients, which are dynamically adjusted according to the power grid operation state;

[0023] S3.2: Combine the impedance identification results with the dynamic equation of the virtual synchronous generator to generate a collaborative optimization interval for the virtual inertia J and the damping coefficient D, and avoid subsynchronous oscillation caused by parameter overshoot.

[0024] Preferably, the adaptive modulation strategy in step S4 is specifically as follows:

[0025] S4.1: Dynamically adjust the inverter PWM carrier frequency according to the resistance-to-inductance ratio R grid / L grid of the power grid impedance to suppress switching subharmonics;

[0026] S4.2: Adopt current feedforward control based on impedance matching to cancel the influence of line impedance on power transmission.

[0027] Preferably, the fuzzy sliding mode control algorithm in step S5 specifically includes:

[0028] S5.1: Define the sliding mode surface s = e + λ∫edt, where e is the frequency deviation;

[0029] S5.2: Dynamically adjust the switching gain of the sliding mode reaching law through fuzzy rules to eliminate the chattering phenomenon of traditional sliding mode control.

[0030] Preferably, the virtual synchronous generator adaptive control method further includes the following fault-tolerant control steps:

[0031] When it is detected that the power grid impedance mutation exceeds the set threshold, start the virtual impedance reshaping mode of the virtual synchronous generator, and cancel the negative impact of the actual impedance by adding a virtual impedance loop;

[0032] In the island operation mode, switch to the self-synchronization control strategy based on impedance identification to achieve parallel current sharing of multiple virtual synchronous generators without communication.

[0033] Preferably, the virtual impedance reshaping mode is specifically as follows:

[0034] Connect a virtual impedance Z virtual = R v + jωL v in series at the output end of the VSG, where R v = k 1 ΔZ grid and L v= k 2 ΔZ grid ,K 1 , K 2 is the proportionality coefficient, and ΔZ grid is the impedance mutation variable;

[0035] Optimize K 1 and K 2 in real time through the particle swarm optimization algorithm to ensure that the system damping ratio is always greater than the critical value.

[0036] Preferably, the virtual synchronous generator adaptive control method further includes an extended application in the new energy access scenario:

[0037] When photovoltaic or wind power intermittent power sources are connected, predict the power fluctuation trend through the impedance identification result, and adjust the virtual inertia of the VSG in advance to smooth the power fluctuation;

[0038] For the harmonic problems caused by the grid connection of new energy, embed an adaptive harmonic impedance controller in the VSG control loop to suppress specific sub-harmonic components.

[0039] Preferably, the method is verified by the HIL experimental platform, specifically including:

[0040] Build an RT-LAB real-time simulation system to simulate the high-impedance grid scenario;

[0041] Use FPGA to achieve nanosecond-level impedance identification and parameter adjustment to ensure that the control delay is less than 1 ms;

[0042] Compared with the traditional virtual synchronous generator control strategy, this method shortens the frequency recovery time by 40% and reduces the total harmonic distortion rate to less than 3%.

[0043] The present invention provides a virtual synchronous generator adaptive control method based on line impedance identification.

[0044] It has the following beneficial effects:

[0045] 1. By collecting grid signals in real time and extracting impedance characteristics, the present invention can timely and accurately grasp the dynamic changes of the grid impedance, provide a reliable basis for subsequent control strategy adjustment, thereby realizing the real-time matching of the output power of the virtual synchronous generator and the grid impedance, effectively reducing power fluctuations, and improving grid stability.

[0046] 2. The present invention uses an improved algorithm and compensation strategy for impedance identification and phase compensation, constructs an impedance-frequency coupling model, and adjusts control parameters through a multivariable constraint optimization algorithm, which not only improves the stability of the system, but also avoids problems such as subsynchronous oscillation caused by parameter overshoot.

[0047] 3. The present invention adopts an adaptive modulation strategy and a multi-resonant controller, which can effectively suppress harmonic oscillations, improve the power quality of the inverter output, and meet the strict requirements of the power system for power quality.

[0048] 4. The present invention constructs a composite feedback index and uses a fuzzy sliding mode control algorithm to correct the control parameters in real time, ensuring the transient stability of the virtual synchronous generator in complex scenarios such as weak power grids and high impedance, and enhancing the adaptability and reliability of the system.

[0049] 5. The fault-tolerant control strategy designed for special situations such as sudden changes in grid impedance and island operation, as well as its extended application in the scenario of new energy access, further improves the practicability and universality of the control method of the present invention, providing more comprehensive guarantee for the stable operation of the power system. Verified by the HIL experimental platform, the control method of the present invention has significantly improved key indicators such as frequency recovery time and harmonic distortion rate compared with traditional strategies, proving its effectiveness and superiority. Specific embodiments

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0051] Embodiment 1:

[0052] The embodiment of the present invention provides a virtual synchronous generator adaptive control method based on line impedance identification, including:

[0053] Collecting signals and extracting characteristics: In a certain actual power grid area, distributed sensors are set to collect grid node voltage and current signals. The fundamental frequency f of the power grid in this area n = 50Hz. According to the method of "injecting a broadband small-signal disturbance with a frequency of 0.1f n -2f n into the power grid, where f n is the fundamental frequency", a broadband small-signal disturbance with a frequency range of 5Hz - 100Hz is injected into the power grid. Using the complex admittance analysis method in the synchronous rotating coordinate system to "separate the resistive component R grid and the inductive component L grid of the grid impedance", after calculation, R grid = 0.3Ω, L grid= 0.0015H. The Prony algorithm is used to fit the impedance-frequency characteristic curve, "extract the key resonance frequency points and the impedance phase mutation threshold", and the key resonance frequency points are determined to be 50 Hz and 70 Hz, and the impedance phase mutation threshold is ±10°.

[0054] Impedance identification and model construction: Based on the improved recursive least squares algorithm (RLS), the equivalent impedance of the power grid is identified online. According to the impedance phase compensation strategy of "introducing a lead-lag compensation network into the virtual synchronous generator control loop to compensate for the phase lag caused by the line impedance", a lead-lag compensation network is introduced into the control loop. At the same time, according to the slope change of the impedance amplitude-frequency characteristic curve, "dynamically adjust the zero-pole positions of the compensation network to suppress the impedance resonance in the medium and high frequency bands". After multiple adjustments, the impedance resonance in the medium and high frequency bands is effectively suppressed, and an impedance-frequency coupling model is successfully constructed.

[0055] Parameter adjustment for matching: According to the constructed impedance-frequency coupling model, the multi-variable constraint optimization algorithm is used to adjust the parameters. First, a cost function is established with the goal of the shortest power grid frequency stabilization time and the smallest power fluctuation of the virtual synchronous generator where α and β are weight coefficients, which are dynamically adjusted according to the operating state of the power grid. Considering the current light load and frequency stable operating state of the power grid, after analysis and calculation, α = 0.4 and β = 0.6 are determined. Then, "combining the impedance identification results with the dynamic equation of the virtual synchronous generator, generate the collaborative optimization interval of the virtual inertia J and the damping coefficient D to avoid the subsynchronous oscillation caused by over-adjustment of the parameters", and the optimization interval of the virtual inertia J is [0.1, 0.3] kg·m 2 , and the optimization interval of the damping coefficient D is [10, 30] N·m·s / rad. Finally, J = 0.2 kg·m 2 , D = 20 N·m·s / rad, and the droop coefficient K is adjusted according to the actual situation to achieve the real-time matching of the output power of the virtual synchronous generator and the power grid impedance.

[0056] Generate commands and suppress oscillations: Generate a power command signal based on the adjusted dynamic parameters. According to the resistance-inductance ratio R grid / L grid = 200 of the power grid impedance, according to the "adaptive modulation strategy of dynamically adjusting the inverter PWM carrier frequency according to the resistance-inductance ratio R grid / L grid of the power grid impedance to suppress the switching sub-harmonics", the inverter PWM carrier frequency is adjusted from 8 kHz to 10 kHz, effectively suppressing the switching sub-harmonics. At the same time, "adopt current feed-forward control based on impedance matching to cancel the influence of line impedance on power transmission", and drive the full-bridge inverter to accurately output the target power. Use a multi-resonant controller to suppress the harmonic oscillation caused by impedance mismatch. After detection, the total harmonic distortion rate is significantly reduced.

[0057] Constructing indicators and correction parameters: Construct a composite feedback indicator that includes grid frequency deviation, voltage deviation, and impedance change rate. Define the sliding mode surface according to "Define the sliding mode surface s = e + λ∫edt, where e is the frequency deviation". Through the fuzzy rule "Dynamically adjust the switching gain of the sliding mode reaching law to eliminate the chattering phenomenon of traditional sliding mode control", the chattering phenomenon is effectively eliminated in weak grid and high impedance scenarios, ensuring the transient stability of the virtual synchronous generator, and both the grid frequency deviation and voltage deviation are within the allowable range.

[0058] Fault tolerance control: During the operation of the system, if it is detected that the grid impedance mutation exceeds the set threshold (the set threshold is ±15%), start the virtual impedance reshaping mode of the virtual synchronous generator. According to "Connect a virtual impedance Z in series at the output of the VSG" virtual = R v + jwL v where R v = k 1 ΔZ grid , L v = k 2 ΔZ grid , K 1 , K 2 are proportionality coefficients, and ΔZ grid is the impedance mutation amount; optimize K 1 , K 2 in real time through the particle swarm optimization algorithm. According to the virtual impedance reshaping mode of "Ensure that the system damping ratio is always greater than the critical value", connect the virtual impedance in series and optimize K 1 , K 2 using the particle swarm optimization algorithm, effectively offsetting the negative impact of the actual impedance mutation and maintaining the stable operation of the system. When the system enters the island operation mode, switch to the self-synchronization control strategy based on impedance identification, realizing parallel current sharing of multiple virtual synchronous generators without communication and ensuring stable power supply for the island system.

[0059] Extended application in the scenario of new energy access: When the photovoltaic intermittent power source accesses this grid area, predict the power fluctuation trend through the impedance identification result. For example, if it is predicted that the photovoltaic power will drop significantly within the next 15 minutes, according to the method of "Predict the power fluctuation trend through the impedance identification result and adjust the virtual inertia of the VSG in advance to smooth the power fluctuation", adjust the virtual inertia of the VSG from 0.2 kg·m 2 to 0.25 kg·m 2 in advance, effectively smoothing the power fluctuation. For the harmonic problems caused by the grid connection of new energy, embed an adaptive harmonic impedance controller in the VSG control loop to suppress specific harmonic components and improve the power quality of the grid.

[0060] Embodiment 2:

[0061] An embodiment of the present invention provides an adaptive control method for a virtual synchronous generator based on line impedance identification, including:

[0062] Collecting signals and extracting characteristics: In another different power grid area, its fundamental frequency f n = 60Hz. Distributed sensors collect grid node voltage and current signals in real time, and inject broadband small-signal disturbances with a frequency range of 6Hz - 120Hz into the power grid. The complex admittance analysis method in the synchronous rotating coordinate system is used to separate the resistive component R grid = 0.4Ω and the inductive component L grid = 0.0018H of the grid impedance. The Prony algorithm is used to fit the impedance frequency characteristic curve, and the key resonance frequency points are extracted as 60Hz and 85Hz, and the impedance phase mutation threshold is ±12°.

[0063] Impedance identification and model construction: Online identification of the grid equivalent impedance is carried out based on an improved recursive least squares algorithm (RLS). A lead-lag compensation network is introduced in the virtual synchronous generator control loop to compensate for phase lag, and the zero and pole positions of the compensation network are dynamically adjusted according to the slope change of the impedance amplitude-frequency characteristic curve to suppress impedance resonance in the middle and high frequency bands, and an impedance-frequency coupling model is constructed.

[0064] Parameter adjustment for matching: According to the impedance-frequency coupling model, a multivariable constraint optimization algorithm is adopted. A cost function is established, and combined with the current operating state of the power grid with heavy load and large frequency fluctuations, α = 0.6 and β = 0.4 are determined. Combining the impedance identification results with the dynamic equation of the virtual synchronous generator, the optimization interval of the virtual inertia J is [0.15, 0.35] kg·m 2 , and the optimization interval of the damping coefficient D is [15, 35] N·m·s / rad. Finally, J = 0.25 kg·m 2 , D = 25 N·m·s / rad, and the droop coefficient K is adjusted to achieve the matching of the output power and the grid impedance.

[0065] Generating commands and suppressing oscillations: According to the resistance-inductance ratio R grid / L grid = 222.22 of the grid impedance, the PWM carrier frequency of the inverter is dynamically adjusted, such as from 9kHz to 11kHz. Current feedforward control based on impedance matching is adopted to drive the full-bridge inverter to output the target power, and a multi-resonant controller is used to suppress harmonic oscillations and reduce the harmonic distortion rate.

[0066] Construction of indicators and correction parameters: Construct a composite feedback indicator that includes grid frequency deviation, voltage deviation, and impedance change rate. Define the sliding mode surface \(s = e+\lambda\int edt\), and dynamically adjust the switching gain of the sliding mode reaching law through fuzzy rules to ensure the transient stability of the virtual synchronous generator in weak grid and high impedance scenarios.

[0067] Fault tolerance control: When it is detected that the grid impedance mutation exceeds the set threshold (set as ±20%), start the virtual impedance reshaping mode, connect a virtual impedance in series at the VSG output end, and use the particle swarm optimization algorithm to optimize the relevant parameters to maintain system stability. When operating in island mode, switch to the self-synchronization control strategy based on impedance identification to achieve parallel current sharing of multiple virtual synchronous generators.

[0068] Application scenarios of new energy access: When wind power is connected to this grid, predict the power fluctuation trend through impedance identification, and adjust the virtual inertia of the VSG in advance to smooth the power fluctuation. Embed an adaptive harmonic impedance controller in the VSG control loop to suppress specific harmonic components and improve the power quality of the grid.

[0069] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A virtual synchronous generator adaptive control method based on line impedance identification, characterized in that: The following steps are involved: S1: Use distributed sensors to collect voltage and current signals of power grid nodes in real time, and combine online harmonic detection technology to extract the impedance amplitude-frequency characteristics and phase-frequency characteristics of power grid lines; S2: Based on the improved recursive least squares algorithm and impedance phase compensation strategy, the dynamic changes of the equivalent impedance of the power grid are identified online and the impedance-frequency coupling model is constructed; S3: According to the impedance-frequency coupling model, the virtual inertia J, damping coefficient D and droop coefficient K of the virtual synchronous generator are dynamically adjusted through a multivariable constrained optimization algorithm to achieve real-time matching of the output power of the virtual synchronous generator with the grid impedance; S4: Generate a power command signal for the virtual synchronous generator based on dynamic parameters, drive the full-bridge inverter to output the target power through an adaptive modulation strategy, and use a multi-resonance controller to suppress harmonic oscillation caused by impedance mismatch; S5: Construct a composite feedback index including grid frequency deviation, voltage deviation and impedance change rate, and correct the control parameters in real time through the fuzzy sliding mode control algorithm to ensure the transient stability of the virtual synchronous generator in weak grid and high impedance scenarios.

2. The method for adaptive control of a virtual synchronous generator based on line impedance identification according to claim 1 is characterized in that: In the step S1, the online harmonic detection technology specifically includes: S1.1: Inject frequency into the grid at 0.1f n -2f n The broadband small signal disturbance of f n is the fundamental frequency; S1.2: Separate the resistive component R of the grid impedance by complex admittance analysis in a synchronous rotating coordinate system grid and the inductive component L grid ; S1.3: Use the Prony algorithm to fit the impedance frequency characteristic curve and extract the key resonant frequency points and impedance phase mutation threshold.

3. The method for adaptive control of a virtual synchronous generator based on line impedance identification according to claim 1, characterized in that: The impedance phase compensation strategy in step S2 is specifically as follows: S2.1: Introduce a lead-lag compensation network in the virtual synchronous generator control loop to compensate for the phase lag caused by line impedance; S2.2: Based on the slope change of the impedance amplitude-frequency characteristic curve, the zero-pole position of the compensation network is dynamically adjusted to suppress the impedance resonance in the mid- and high-frequency bands.

4. The method for adaptive control of a virtual synchronous generator based on line impedance identification according to claim 1, characterized in that: The multivariable constrained optimization algorithm in step S3 specifically includes: S3.1: Establish a cost function with the goal of minimizing the grid frequency stabilization time and minimizing the power fluctuation of the virtual synchronous generator: Among them, α and β are weight coefficients, which are dynamically adjusted by the grid operation status; S3.2: Combining the impedance identification results with the dynamic equation of the virtual synchronous generator, the coordinated optimization interval of the virtual inertia J and the damping coefficient D is generated to avoid subsynchronous oscillation caused by parameter over-adjustment.

5. The method for adaptive control of a virtual synchronous generator based on line impedance identification according to claim 1, characterized in that: The adaptive modulation strategy in step S4 is specifically: S4.1: According to the resistance-inductance ratio R of the grid impedance grid / L grid , dynamically adjust the inverter PWM carrier frequency and suppress switching subharmonics; S4.2: Use current feedforward control based on impedance matching to offset the influence of line impedance on power transmission.

6. The method for adaptive control of a virtual synchronous generator based on line impedance identification according to claim 1, characterized in that: The fuzzy sliding mode control algorithm in step S5 specifically includes: S5.1: Define the sliding surface , where e is the frequency deviation; S5.2: Dynamically adjust the switching gain of the sliding mode reaching law through fuzzy rules to eliminate the chattering phenomenon of traditional sliding mode control.

7. The method for adaptive control of a virtual synchronous generator based on line impedance identification according to claim 1, characterized in that: The following fault-tolerant control steps are also included: When it is detected that the grid impedance mutation exceeds the set threshold, the virtual impedance reshaping mode of the virtual synchronous generator is started to offset the negative impact of the actual impedance by adding a virtual impedance loop; In the island operation mode, the system switches to the self-synchronization control strategy based on impedance identification to achieve current sharing in parallel of multiple virtual synchronous generators without communication.

8. The method for adaptive control of a virtual synchronous generator based on line impedance identification according to claim 7, characterized in that: The virtual impedance reshaping mode is specifically: Add virtual impedance in series at the output of VSG ,in , K1, K2 are proportional coefficients, is the impedance mutation amount; The particle swarm optimization algorithm is used to optimize K1 and K2 in real time to ensure that the system damping ratio is always greater than the critical value.

9. The method for adaptive control of a virtual synchronous generator based on line impedance identification according to claim 1, characterized in that: It also includes extended applications in new energy access scenarios: When intermittent photovoltaic or wind power sources are connected, the power fluctuation trend is predicted through the impedance identification results, and the virtual inertia of the VSG is adjusted in advance to smooth the power fluctuation; To address the harmonic problems caused by the grid connection of renewable energy, an adaptive harmonic impedance controller is embedded in the VSG control loop to suppress specific subharmonic components.

10. The method for adaptive control of a virtual synchronous generator based on line impedance identification according to claim 1, characterized in that: The method is verified by the HIL experimental platform, specifically including: Build RT-LAB real-time simulation system to simulate high-impedance power grid scenarios; Adopt FPGA to realize nanosecond impedance identification and parameter adjustment, ensuring the control delay is less than 1ms; Compared with the traditional virtual synchronous generator control strategy, this method shortens the frequency recovery time by 40% and reduces the harmonic distortion rate to below 3%.

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