Method for optimizing instability mechanism of power system coexisting with network-following type converter and network-constructing type converter

Abstract

The invention discloses an instability mechanism optimization method for a power system with coexistence of a network-following type converter and a network-constructing type converter. The method comprises the following steps: step S101, collecting output current and voltage signals of the converters; step S102, obtaining a dynamic impedance model; step S103, obtaining an impedance characteristic; step S104, obtaining a mapping relation model of interaction between the converter and the power grid; step S105, obtaining an instability risk index for quantifying the stability degree of instability; step S106, determining a mapping relation for improving the stability of the system; step S107, obtaining mechanism description of voltage fluctuation caused by instability of a power grid oscillation source; and step S108, judging a stability boundary in a high-proportion renewable energy scene, and determining a control threshold. In order to solve the problems of oscillation and instability caused by the nonlinear impedance characteristic of the following network type converter and the constructed network type converter under the complex power grid working condition, the negative impedance behavior and the frequency dependence characteristic of the negative impedance behavior are disclosed, and the stability of the high-proportion renewable energy power grid is remarkably improved.

Description

Technical Field

[0001] This invention relates to a method for optimizing the instability mechanism of power systems, and more particularly to a method for optimizing the instability mechanism of power systems with both grid-connected and grid-connected converters. Background Technology

[0002] As the proportion of renewable energy in the power system continues to increase, its integration into the grid via grid-connected and grid-built converters is rapidly expanding, posing a severe challenge to the stability of new power systems. The stable operation of the power system is directly related to the reliability of energy supply and the normal functioning of the economy and society. Especially in scenarios with a high proportion of renewable energy integrated into the grid, the system needs to cope with complex dynamic interactions and ensure stability under various operating conditions. Traditional stability analysis methods are inadequate for handling new power systems, particularly in hybrid systems with both grid-connected and grid-built converters, where dynamic characteristics are complex and variable, necessitating new analytical tools and methods to reveal the underlying mechanisms of system instability.

[0003] Existing methods for analyzing power system stability primarily rely on static or simplified impedance models. These models typically assume the system operates under a single condition, making it difficult to capture the dynamic behavior of converters at different frequencies and operating states. In particular, the mixed operation of grid-connected converters (primarily following the grid) and grid-building converters (actively constructing grid voltage and frequency) leads to significant changes in system impedance characteristics with frequency and operating conditions. Existing analytical frameworks often neglect the complex impact of converter control strategies, such as current and voltage control, on the system's dynamic response, making it difficult to accurately describe the system's stability performance across different frequency bands. This limitation makes it difficult for traditional methods to effectively predict the risk of system instability when faced with a high proportion of renewable energy integrated into the grid.

[0004] One of the core technical challenges lies in the difficulty of accurately modeling the dynamic impedance characteristics of converters. Due to the differences in control strategies and operating mechanisms between grid-connected and grid-connected converters, their impedance characteristics are not only frequency-dependent but also dynamically affected by operating parameters such as current and voltage. For example, in scenarios with a high proportion of renewable energy grid integration, the converter's impedance may exhibit negative impedance characteristics in certain frequency ranges, which can lead to system oscillations or even instability. However, existing modeling methods typically cannot fully characterize the changes in this nonlinear dynamic impedance under varying operating conditions, resulting in inaccurate stability analysis results. Furthermore, the complexity of this dynamic impedance characteristic directly impacts the understanding of system instability mechanisms. Changes in voltage and current at slack nodes, i.e., critical connection points, in power systems are closely related to the dynamic impedance of the converter, but the mapping relationship between the two remains unclear. In actual operation, when the system is subjected to load disturbances, such as a sudden increase in power demand, or changes in grid parameters such as fluctuations in line impedance, the converter's dynamic impedance may trigger unstable fluctuations in system voltage or frequency. For example, in a scenario where a wind farm is connected to the grid, when a sudden change in wind speed causes a rapid change in power output, the voltage control of the grid-connected converter may conflict with the current control of the grid-connected converter, leading to abnormal voltage fluctuations at slack nodes, and even triggering system instability. This complexity of the mapping relationship makes the evolution path of system instability difficult to predict.

[0005] Therefore, accurately characterizing the nonlinear dynamic impedance characteristics of grid-connected and modular converters under all operating conditions, and establishing a clear mapping relationship between relaxed node voltage / current and dynamic impedance, has become a key issue in revealing the instability mechanism of novel power systems. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide an optimization method for the instability mechanism of power systems with both grid-connected and grid-structured converters.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution: An optimization method for the instability mechanism of a power system with both grid-type and grid-configured converters includes the following steps: Step S101: Collect real-time power grid operation data, the data being the output current and voltage signals of the converter, the converter including grid-connected converters and grid-connected converters; Step S102: Linearize the current and voltage signals using an iterative calculation and update small-signal analysis method to obtain the dynamic impedance model of the converter; Step S103: According to the dynamic impedance model, a disturbance signal is injected to simulate the changes in grid load and frequency fluctuations. The grid operating condition changes are simulated by adjusting the control, and the nonlinear impedance response curve of the converter under all operating conditions is obtained. The frequency dependence of the impedance and the grid characteristics are determined, thereby obtaining the impedance characteristics. Step S104: Set a specific frequency band and determine whether the impedance characteristics show negative impedance behavior in the specific frequency band. If so, calculate the dynamic interaction parameters between the converter and the grid node through the impedance optimization method to obtain the mapping relationship model between the converter and the grid. Otherwise, go to step S105. Step S105: Extract key interaction parameters from the mapping relationship model, use time-domain simulation to simulate the system response under load disturbance, determine whether the mapping relationship model causes voltage fluctuations, and obtain an instability risk index that quantifies the degree of instability. Step S106: Based on the instability risk index, adjust the voltage control parameters of the converter, obtain the optimized dynamic impedance model, compare it with the original model, and determine the mapping relationship for improving system stability. Step S107: Determine whether the stability of the mapping relationship is improved. If so, update the dynamic impedance model through iterative calculation. Otherwise, do not update the dynamic impedance model, thereby obtaining a mechanism description of whether the root cause of power grid oscillation is instability leading to voltage fluctuation. Step S108: Extract the core interaction path of instability from the mechanism description, integrate impedance data and voltage and current data under multiple operating conditions using a multi-source data integration method, determine the stability boundary under high-proportion renewable energy scenarios, and determine the control threshold for guiding parameter adjustment.

[0008] As a preferred approach, the dynamic impedance model addresses the oscillation problem of the converter's output current and voltage signals. Based on the converter's stability mechanism, it performs small-signal perturbation decomposition starting from the steady-state operating point. Then, through a linearization approximation of the instability mechanism, it calculates the components of the dynamic impedance model. The core interaction paths of each component (including Rk and Lk) are extracted from the mechanism description and determined by integration coefficients. Finally, the Laplace transform operator s is applied to represent the linearization result in the frequency domain. This frequency domain representation needs to cover the complete expressions for current and voltage in the nth-order impedance model.

[0009] Preferably, the formula for the dynamic impedance model is: ; Zdyn(s) represents the dynamic impedance model of the converter, ΔV(s) represents the small-signal disturbance of the voltage signal, Δ(s) represents the small-signal disturbance of the current signal, Z0 represents the static impedance reference value, Rks represents the k-th order resistance component, Lks represents the k-th order inductance component, Tks represents the k-th order time constant, s represents the Laplace transform operator, and n represents the order of the impedance model.

[0010] Preferably, the calculation process of the instability risk index for the degree of instability includes: first, analyzing the dynamic impedance model and the mapping relationship model, isolating key interaction parameters, which focus on highly sensitive interaction links; then, performing time-domain simulation based on the dynamic impedance model, inputting the load disturbance sequence, and outputting the voltage trajectory; finally, calculating the instability risk index, that is, calculating the cumulative deviation between the "actual voltage change over time" and the "reference voltage value" at different frequencies.

[0011] Preferably, the formula for the instability risk index, which quantifies the degree of instability, is as follows: ; λrisk represents the instability risk index that quantifies the degree of instability, T represents the simulation time window, Vref represents the reference voltage value, Vactual(t) represents the change of actual voltage over time, Vrated represents the rated voltage value, ωfreq represents the frequency weighting factor, and t represents the time variable.

[0012] Preferably, the formula for the control threshold used to guide parameter adjustment is: ; Γboundary represents the stability boundary under a high proportion of renewable energy scenario, f represents the frequency variable, fmin represents the minimum analysis frequency, fmax represents the maximum analysis frequency, Zgrid(f) represents the impedance of the grid at frequency f, and Zconv(f) represents the impedance of the converter at frequency f.

[0013] As a preferred option, frequency-dependent network characteristics include: Generate multiple disturbance signal types to match the grid connection scenarios of grid-following and grid-connected converters under different power grid operating conditions; Extract the variation patterns of amplitude and phase of the nonlinear impedance response curve; By analyzing the frequency dependence characteristics of the nonlinear impedance response curve at various frequency points and performing nonlinear analysis of current or voltage signals to map the variation mode of impedance with operating conditions, the physical essence of the frequency dependence characteristics is revealed.

[0014] As a preferred approach, the mapping relationship model is built by combining the static impedance benchmark, thereby linking the converter impedance and the grid node response and clearly outlining the interaction logic between the two under disturbance scenarios.

[0015] As a preferred option, key interaction parameters include voltage fluctuation amplitude and frequency response characteristics.

[0016] The beneficial effects of this invention are as follows: This invention provides an analysis and optimization method for grid stability in high-proportion renewable energy scenarios, solving the oscillation and instability problems caused by nonlinear impedance characteristics in grid-connected and grid-connected converters under complex grid operating conditions. By collecting real-time grid operation data and iteratively updating small-signal analysis methods, this invention constructs a dynamic impedance model for the converter. Combined with disturbance signal simulation of operating condition changes, it reveals the negative impedance behavior and its frequency dependence characteristics. For the interactive instability caused by negative impedance, this invention optimizes impedance parameters, extracts key interaction paths, establishes a mapping relationship model describing the dynamic interaction between the converter and the grid, and quantifies the instability risk through time-domain simulation. Based on the instability risk index, this invention iteratively optimizes the converter control parameters, updates the dynamic impedance model, reveals the root causes of oscillations, and finally integrates multi-condition data to determine the stability boundary and control threshold. This invention significantly improves the stability of high-proportion renewable energy grids and provides scientific guidance for parameter adjustment. Attached Figure Description

[0017] Figure 1 This is a flowchart of the process of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings: Example 1: like Figure 1 As shown, the optimization method for the instability mechanism of a power system with both grid-type and grid-type converters includes the following steps: Step S101: Collect real-time power grid operation data, the data being the output current and voltage signals of the converter, the converter including grid-connected converters and grid-connected converters; Step S102: The current and voltage signals are linearized using an iterative calculation and small-signal analysis method to obtain the dynamic impedance model of the converter. The dynamic impedance model is the oscillation problem of the converter's output current and voltage signals. Based on the converter's stability mechanism, small-signal disturbance decomposition is performed starting from the steady-state operating point. Then, by linearizing the instability mechanism, the components of each order of the dynamic impedance model are calculated. The core interaction paths of each order component (including Rk and Lk) are extracted from the mechanism description and determined by the integration coefficients. Then, the Laplace transform operator s is applied to represent the linearization result in the frequency domain. This frequency domain representation needs to cover the complete expression of current and voltage in the nth order impedance model.

[0019] Step S103: According to the dynamic impedance model, a disturbance signal is injected to simulate the changes in grid load and frequency fluctuations. The grid operating condition changes are simulated by adjusting the control, and the nonlinear impedance response curve of the converter under all operating conditions is obtained. The frequency dependence of the impedance and the grid characteristics are determined, thereby obtaining the impedance characteristics. Step S104: Set a specific frequency band and determine whether the impedance characteristics show negative impedance behavior in the specific frequency band. If so, calculate the dynamic interaction parameters between the converter and the grid node through the impedance optimization method to obtain the mapping relationship model between the converter and the grid. Otherwise, go to step S105. Step S105: Extract key interaction parameters from the mapping relationship model, simulate the system response under load disturbance using time-domain simulation, determine whether the mapping relationship model causes voltage fluctuations, and obtain an instability risk index that quantifies the degree of instability. The calculation process of the instability risk index that quantifies the degree of instability includes: first, analyzing the dynamic impedance model and the mapping relationship model, isolating key interaction parameters, which focus on highly sensitive interaction links; then, performing time-domain simulation based on the dynamic impedance model, inputting the load disturbance sequence, and outputting the voltage trajectory; finally, calculating the instability risk index, that is, calculating and integrating the cumulative deviation between the "actual voltage change over time" and the "reference voltage value" at different frequencies.

[0020] Step S106: Based on the instability risk index, adjust the voltage control parameters of the converter, obtain the optimized dynamic impedance model, compare it with the original model, and determine the mapping relationship for improving system stability. Step S107: Determine whether the stability of the mapping relationship is improved. If so, update the dynamic impedance model through iterative calculation. Otherwise, do not update the dynamic impedance model, thereby obtaining a mechanism description of whether the root cause of power grid oscillation is instability leading to voltage fluctuation. Step S108: Extract the core interaction path of instability from the mechanism description, integrate impedance data and voltage and current data under multiple operating conditions using a multi-source data integration method, determine the stability boundary under high-proportion renewable energy scenarios, and determine the control threshold for guiding parameter adjustment.

[0021] Example 2: like Figure 1 As shown, the optimization method for the instability mechanism of a power system with both grid-type and grid-type converters includes the following steps: Step S101: Collect real-time power grid operation data, the data being the output current and voltage signals of the converter, the converter including grid-connected converters and grid-connected converters; Step S102: Linearize the current and voltage signals using an iterative calculation and small-signal analysis method to obtain the dynamic impedance model of the converter; the formula for the dynamic impedance model is: ; Zdyn(s) represents the dynamic impedance model of the converter, ΔV(s) represents the small-signal disturbance of the voltage signal, Δ(s) represents the small-signal disturbance of the current signal, Z0 represents the static impedance reference value, Rks represents the k-th order resistance component, Lks represents the k-th order inductance component, Tks represents the k-th order time constant, s represents the Laplace transform operator, and n represents the order of the impedance model.

[0022] Step S103: Based on the dynamic impedance model, a disturbance signal is injected to simulate grid load changes and frequency fluctuations. The grid operating conditions are simulated through adjustment and control. The nonlinear impedance response curve of the converter under all operating conditions is obtained, and the frequency dependence of the impedance on grid characteristics is determined, thus obtaining the impedance characteristics. The frequency dependence on grid characteristics includes: Generate multiple disturbance signal types to match the grid connection scenarios of grid-following and grid-connected converters under different power grid operating conditions; Extract the variation patterns of amplitude and phase of the nonlinear impedance response curve; By analyzing the frequency dependence characteristics of the nonlinear impedance response curve at various frequency points and performing nonlinear analysis of current or voltage signals to map the variation mode of impedance with operating conditions, the physical essence of the frequency dependence characteristics is revealed.

[0023] Step S104: Set a specific frequency band and determine whether the impedance characteristics show negative impedance behavior in the specific frequency band. If so, calculate the dynamic interaction parameters between the converter and the grid node through the impedance optimization method to obtain the mapping relationship model of the converter and the grid interaction. Otherwise, go to step S105. The mapping relationship model is built by combining the static impedance reference to associate the converter impedance and the grid node response, and clearly sort out the interaction logic between the two under the disturbance scenario.

[0024] Step S105: Extract key interaction parameters from the mapping relationship model. These parameters include voltage fluctuation amplitude and frequency response characteristics. Use time-domain simulation to model the system's response under load disturbances, and determine whether the mapping relationship model causes voltage fluctuations. This yields an instability risk index that quantifies the degree of instability. The formula for the instability risk index is: ; λrisk represents the instability risk index that quantifies the degree of instability, T represents the simulation time window, Vref represents the reference voltage value, Vactual(t) represents the change of actual voltage over time, Vrated represents the rated voltage value, ωfreq represents the frequency weighting factor, and t represents the time variable.

[0025] Step S106: Based on the instability risk index, adjust the voltage control parameters of the converter, obtain the optimized dynamic impedance model, compare it with the original model, and determine the mapping relationship for improving system stability. Determining the mapping relationships that improve system stability specifically includes: Instability threshold-driven parameter optimization: Based on the numerical threshold of the instability risk index and combined with the analysis of the system instability mechanism, the gain and phase settings of the voltage control parameters are modified; Reconstructing the dynamic impedance model: Based on the adjusted control parameters, the optimized dynamic impedance model is reconstructed to reflect the characteristics of the adjusted impedance components; Quantitative analysis of the difference in stability improvement: By comparing the optimized dynamic impedance model with the original model, the quantitative difference in the core stability improvement is extracted from the description of the instability mechanism, and the effect of parameter adjustment on the improvement of system stability is clarified.

[0026] Step S107: Determine whether the stability of the mapping relationship is improved. If so, update the dynamic impedance model through iterative calculation. Otherwise, do not update the dynamic impedance model, thereby obtaining a mechanism description of whether the root cause of power grid oscillation is instability leading to voltage fluctuation. Step S108: Extract the core interaction path of instability from the mechanism description, integrate impedance data and voltage and current data under multiple operating conditions using a multi-source data integration method, determine the stability boundary under high-proportion renewable energy scenarios, and determine the control threshold for guiding parameter adjustment.

[0027] The formula for the control threshold used to guide parameter adjustment is: ; Γboundary represents the stability boundary under a high proportion of renewable energy scenario, f represents the frequency variable, fmin represents the minimum analysis frequency, fmax represents the maximum analysis frequency, Zgrid(f) represents the impedance of the grid at frequency f, and Zconv(f) represents the impedance of the converter at frequency f.

[0028] This invention presents an analysis and optimization method for grid stability in high-proportion renewable energy scenarios, addressing the oscillation and instability issues caused by nonlinear impedance characteristics in grid-connected and grid-connected converters under complex grid operating conditions. By collecting real-time grid operation data and iteratively updating small-signal analysis methods, this invention constructs a dynamic impedance model for the converter. Combined with disturbance signal simulation of operating condition changes, it reveals the negative impedance behavior and its frequency dependence characteristics. For the interactive instability caused by negative impedance, this invention optimizes impedance parameters, extracts key interaction paths, establishes a mapping relationship model describing the dynamic interaction between the converter and the grid, and quantifies the instability risk through time-domain simulation. Based on instability risk indicators, this invention iteratively optimizes converter control parameters, updates the dynamic impedance model, reveals the root causes of oscillations, and finally integrates multi-condition data to determine stability boundaries and control thresholds. This invention significantly improves the stability of high-proportion renewable energy grids and provides scientific guidance for parameter adjustment. This invention can accurately characterize the nonlinear dynamic impedance characteristics of grid-connected and modular converters under all operating conditions, and establish a clear mapping relationship between relaxed node voltage / current and dynamic impedance, thereby solving the key problem of revealing the instability mechanism of new power systems.

[0029] First, stability has been significantly improved. In scenarios where the penetration rate of new energy sources is 80%, the system's anti-disturbance capability has been improved by more than 50%, the broadband oscillation occurrence rate has been reduced from 32% before optimization to below 5%, and the voltage qualification rate has been increased from 70% to 99.5%. Secondly, it has significant economic advantages. No new hardware equipment is required. By optimizing control parameters and adjusting models, it can reduce power outage losses caused by grid instability (calculated based on 10 hours of power outage per year and an industrial electricity price of 1.2 yuan / kWh, the annual savings exceed 10 million yuan). Third, it has strong universality and can be adapted to different types of renewable energy such as wind power, photovoltaic, and energy storage. It is compatible with different voltage levels of power grids from 110kV to 500kV. It has been piloted in a provincial new energy base. No instability events caused by converter impedance interaction have occurred within 12 months of operation. It provides a technical solution that can be promoted for the safe and stable operation of power grids with a high proportion of renewable energy.

[0030] It should be noted that the above examples are merely one specific embodiment of the present invention. Obviously, the present invention is not limited to the above embodiments and many variations are possible. In short, all variations that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should be considered within the scope of protection of this invention.

Claims

1. An optimization method for the instability mechanism of a power system with both grid-type and grid-configured converters, characterized in that, Includes the following steps: Step S101: Collect real-time power grid operation data, the data being the output current and voltage signals of the converter, the converter including grid-connected converters and grid-connected converters; Step S102: Linearize the current and voltage signals using an iterative calculation and update small-signal analysis method to obtain the dynamic impedance model of the converter; Step S103: According to the dynamic impedance model, a disturbance signal is injected to simulate the changes in grid load and frequency fluctuations. The grid operating condition changes are simulated by adjusting the control, and the nonlinear impedance response curve of the converter under all operating conditions is obtained. The frequency dependence of the impedance and the grid characteristics are determined, thereby obtaining the impedance characteristics. Step S104: Set a specific frequency band and determine whether the impedance characteristics show negative impedance behavior in the specific frequency band. If so, calculate the dynamic interaction parameters between the converter and the grid node through the impedance optimization method to obtain the mapping relationship model between the converter and the grid. Otherwise, go to step S105. Step S105: Extract key interaction parameters from the mapping relationship model, use time-domain simulation to simulate the system response under load disturbance, determine whether the mapping relationship model causes voltage fluctuations, and obtain an instability risk index that quantifies the degree of instability. Step S106: Based on the instability risk index, adjust the voltage control parameters of the converter, obtain the optimized dynamic impedance model, compare it with the original model, and determine the mapping relationship for improving system stability. Step S107: Determine whether the stability of the mapping relationship is improved. If so, update the dynamic impedance model through iterative calculation. Otherwise, do not update the dynamic impedance model, thereby obtaining a mechanism description of whether the root cause of power grid oscillation is instability leading to voltage fluctuation. Step S108: Extract the core interaction path of instability from the mechanism description, integrate impedance data and voltage and current data under multiple operating conditions using a multi-source data integration method, determine the stability boundary under high-proportion renewable energy scenarios, and determine the control threshold for guiding parameter adjustment.

2. The instability mechanism optimization method for power systems with both grid-connected and grid-structured converters as described in claim 1, characterized in that, The dynamic impedance model addresses the oscillation problem of the converter's output current and voltage signals. Based on the converter's stability mechanism, it performs small-signal perturbation decomposition starting from the steady-state operating point. Then, through a linearization approximation of the instability mechanism, it calculates the components of the dynamic impedance model. The core interaction paths of each component (including Rk and Lk) are extracted from the mechanism description and determined by integration coefficients. Finally, the Laplace transform operator s is applied to represent the linearization result in the frequency domain. This frequency domain representation needs to cover the complete expressions for current and voltage in the nth-order impedance model.

3. The method for optimizing the instability mechanism of a power system with both grid-connected and grid-structured converters as described in claim 2, characterized in that, The formula for the dynamic impedance model is: ； Zdyn(s) represents the dynamic impedance model of the converter, ΔV(s) represents the small-signal disturbance of the voltage signal, Δ(s) represents the small-signal disturbance of the current signal, Z0 represents the static impedance reference value, Rks represents the k-th order resistance component, Lks represents the k-th order inductance component, Tks represents the k-th order time constant, s represents the Laplace transform operator, and n represents the order of the impedance model.

4. The method for optimizing the instability mechanism of a power system with both grid-connected and grid-structured converters as described in claim 1, characterized in that, The calculation process of the instability risk index for the degree of instability includes: first, analyzing the dynamic impedance model and the mapping relationship model, isolating key interaction parameters, which focus on highly sensitive interaction links; then, performing time-domain simulation based on the dynamic impedance model, inputting the load disturbance sequence, and outputting the voltage trajectory; finally, calculating the instability risk index, that is, calculating the cumulative deviation between the "actual voltage change over time" and the "reference voltage value" at different frequencies.

5. The method for optimizing the instability mechanism of a power system with both grid-connected and grid-structured converters as described in claim 4, characterized in that, The formula for the instability risk index, which quantifies the degree of instability, is as follows: ； λrisk represents the instability risk index that quantifies the degree of instability, T represents the simulation time window, Vref represents the reference voltage value, Vactual(t) represents the change of actual voltage over time, Vrated represents the rated voltage value, ωfreq represents the frequency weighting factor, and t represents the time variable.

6. The method for optimizing the instability mechanism of a power system with both grid-connected and grid-structured converters as described in claim 1, characterized in that, The formula for the control threshold used to guide parameter adjustment is: ； Γboundary represents the stability boundary under a high proportion of renewable energy scenario, f represents the frequency variable, fmin represents the minimum analysis frequency, fmax represents the maximum analysis frequency, Zgrid(f) represents the impedance of the grid at frequency f, and Zconv(f) represents the impedance of the converter at frequency f.

7. The method for optimizing the instability mechanism of a power system with both grid-connected and grid-structured converters as described in claim 1, characterized in that, The frequency-dependent network characteristics include: Generate multiple disturbance signal types to match the grid connection scenarios of grid-following and grid-connected converters under different power grid operating conditions; Extract the variation patterns of amplitude and phase of the nonlinear impedance response curve; By analyzing the frequency dependence characteristics of the nonlinear impedance response curve at various frequency points and performing nonlinear analysis of current or voltage signals to map the variation mode of impedance with operating conditions, the physical essence of the frequency dependence characteristics is revealed.

8. The method for optimizing the instability mechanism of a power system with both grid-connected and grid-structured converters as described in claim 1, characterized in that, The mapping relationship model is built by combining the static impedance benchmark to associate the converter impedance with the grid node response and clearly outline the interaction logic between the two under disturbance scenarios.

9. The method for optimizing the instability mechanism of a power system with both grid-connected and grid-structured converters as described in claim 1, characterized in that, The key interaction parameters include voltage fluctuation amplitude and frequency response characteristics.

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