A method and system for evaluating the broadband oscillation risk of an AC / DC system of a multi-constituent network device

Abstract

The present application relates to a kind of multi-structure network equipment AC-DC system broadband oscillation risk assessment method and system, belong to power system stability analysis and new energy grid-connected control technical field, solve after the large disturbance stability margin of multiple equipment access system is difficult to quantify, stable boundary is difficult to construct and the unclear law of influence of key parameters etc.Problem.Establish the dynamic mathematical model of each subsystem;Using first integral method constructs energy function to obtain system energy, and according to interactive energy obtains interactive energy change rate;Based on interactive energy change rate determines interactive stability factor, and quantitatively analyzes the influence of key control parameters on system stability to construct stability discrimination function;Large disturbance energy margin is introduced into power injection space, constructs stability domain boundary function to obtain stability domain boundary;Search stability domain boundary in power injection space, obtain the disturbance stability domain analysis result of AC-DC sending-out system.The convergence operating area and instability risk area under large disturbance are directly distinguished by stability domain.

Description

Technical Field

[0001] This invention relates to the fields of power system stability analysis and new energy grid connection control technology, and in particular to a method and system for assessing broadband oscillation risk in AC / DC systems of multi-grid equipment. Background Technology

[0002] With the continuous increase in the proportion of new energy power generation, wind power, photovoltaics, electric vehicles, and energy storage power electronic devices are being connected to AC / DC hybrid systems in large numbers. Compared with traditional synchronous generator-dominated systems, grid-type converters in new energy AC / DC transmission systems have the characteristics of fast response and high control freedom. However, their inertia support, damping configuration, power outer loop, inner loop current, and DC-side energy exchange are all closely coupled with the dynamics of the AC network. When the system experiences large disturbances such as short circuits on the receiving-end AC bus, DC power steps, line disconnection, or converter station control switching, multi-timescale energy exchange will occur between grid-driven direct-drive wind turbines, electric vehicle aggregates, and AC / DC channels, leading to voltage drops, frequency shifts, power oscillations, and even transient instability.

[0003] Existing analytical methods mainly include time-domain simulation, small-signal eigenvalue analysis, and direct methods based on Lyapunov functions. Time-domain simulation can reflect the nonlinear dynamic process after a fault, but it requires repeated calculations for a large number of operating points and control parameters, making it difficult to directly provide stability boundaries. Small-signal analysis is suitable for identifying damped and oscillating modes near the equilibrium point, but it is difficult to characterize the stability domain of operating points that exceed the boundary under large disturbances. Although traditional energy function methods have clear physical interpretations, they often suffer from problems such as inconsistent models, difficulty in analytically decomposing interactive energy terms, and lack of quantitative mapping between key control parameters and stability boundaries when dealing with systems with grid-connected direct-drive wind turbines, grid-connected electric vehicles, AC networks, and DC transmission links. Summary of the Invention

[0004] Based on the above analysis, the embodiments of the present invention aim to provide a method and system for assessing the broadband oscillation risk of AC / DC systems with multi-network equipment, in order to solve the technical problems in the prior art where it is difficult to quantify the stability margin of large disturbances, difficult to construct the stability boundary, and unclear influence law of key parameters after multi-network equipment is connected to AC / DC transmission systems.

[0005] On one hand, embodiments of the present invention provide a method for assessing the broadband oscillation risk of a multi-grid AC / DC system, comprising: establishing dynamic mathematical models of each subsystem of the grid-connected direct-drive wind turbine and grid-connected electric vehicle in the AC / DC transmission system; based on the dynamic mathematical models of the subsystems, constructing an energy function of the AC / DC transmission system using the first integral method to obtain the system energy, decomposing the system energy into stored energy, dissipated energy, and interactive energy, and obtaining the rate of change of interactive energy based on the interactive energy; determining an interactive stability factor based on the rate of change of interactive energy, and using the interactive stability factor to quantitatively analyze the impact of key control parameters on system stability to construct a stability discrimination function; based on the stability discrimination function, introducing a large disturbance energy margin into the power injection space, constructing a stability domain boundary function with the active power injection of the grid-connected direct-drive wind turbine and the power of the grid-connected electric vehicle as the dominant coordinates to obtain the stability domain boundary; searching for the stability domain boundary in the power injection space, and obtaining the disturbance stability domain analysis result of the AC / DC transmission system based on the relative position of the operating point and the stability domain boundary.

[0006] The beneficial effects of the above technical solution are as follows: by constructing an energy function using a unified dynamic model and the first integral method, the energy interaction process between multi-network devices can be analytically decomposed; the impact of key control parameters on system stability can be quantified through the interaction stability factor; by constructing a stability domain in the power injection space, the convergence operation region and the instability risk region under large disturbances can be directly distinguished, providing a theoretical basis for parameter tuning, operation mode arrangement, and stability margin assessment of AC / DC transmission systems containing multi-network devices.

[0007] Further improvements to the above method introduce a large disturbance energy margin into the power injection space. The construction of a stability domain boundary function with the active power injection from grid-connected direct-drive wind turbines and the power of grid-connected electric vehicles as the dominant coordinates further includes: selecting the combined active power injection from grid-connected direct-drive wind turbines and the power of grid-connected electric vehicles as the dominant coordinates of the power injection space; calculating the first large disturbance energy margin; and establishing a stability domain boundary function based on the first large disturbance energy margin. Specifically, when the first large disturbance energy margin is greater than zero, the operating point is located inside the stability domain; when the first large disturbance energy margin is equal to zero, the operating point is located at the stability domain boundary; and when the operating point crosses the stability boundary, it is determined that the system has an instability risk region.

[0008] Further improvements to the above method include searching for the stable domain boundary in the power injection space, which further comprises: generating candidate power injection points by gradually changing the power injection amount along multiple power injection search directions, with the stable operating point before the fault as the center; performing power flow calculation and transient calculation on each candidate power injection point to obtain the second largest disturbance energy margin; when the sign of the second largest disturbance energy margin of adjacent candidate points changes, using the bisection method to correct the critical step size to obtain the stable boundary point in the corresponding search direction; forming a set of stable boundary points after traversing multiple search directions, and determining the stable domain boundary based on the set of stable boundary points.

[0009] Further improvements to the above method include establishing dynamic mathematical models for each subsystem of the grid-connected direct-drive wind turbine, which further comprises: dividing the grid-connected direct-drive wind turbine into an inner current loop subsystem, an outer voltage loop subsystem, an AC line subsystem, an active frequency subsystem, and a reactive voltage subsystem; and establishing dynamic mathematical models of the inner current loop subsystem, the outer voltage loop subsystem, the AC line subsystem, the active frequency subsystem, and the reactive voltage subsystem in the d-axis and q-axis coordinates, respectively.

[0010] Further improvements to the above method include establishing dynamic mathematical models for each subsystem of the grid-connected electric vehicle, which further includes: dividing the grid-connected electric vehicle into a vehicle transmission coupling subsystem, a motor current subsystem, and a battery SOC subsystem; and establishing dynamic mathematical models for the vehicle transmission coupling subsystem, the motor current subsystem, and the battery SOC subsystem, respectively.

[0011] Further improvements to the above method, based on the dynamic mathematical models of each subsystem, and using the first integration method to construct the energy function of the AC / DC transmission system, further include: organizing the dynamic mathematical models of each subsystem into unified dynamic equations for each subsystem, including generalized capacitance, generalized inductance, generalized resistance, generalized voltage, generalized current, and interactive elements; performing the first integration on the unified dynamic equations of each subsystem to obtain the energy function of the AC / DC transmission system, wherein the energy function includes the energy stored in the electric field, the energy stored in the electromagnetic field, the energy dissipated by the generalized resistance, and the energy of the interaction between the subsystems.

[0012] Further improvements to the above method involve decomposing the system energy into stored energy, dissipated energy, and interactive energy, and obtaining the rate of change of interactive energy. This further includes: selecting the interactive energy component of the energy function of the AC / DC transmission system as the interactive energy function between each subsystem, wherein the sum of the interactive energy functions between each subsystem is taken as the interactive energy function of the AC / DC transmission system; calculating the time derivative of the interactive energy function of the AC / DC transmission system to obtain the rate of change of interactive energy of the AC / DC transmission system; and analyzing the rate of change of interactive energy of the AC / DC transmission system. Specifically, when the interactive energy is greater than zero and the rate of change of interactive energy is increasing, the interactive energy of the AC / DC transmission system increases in the divergent direction, which is detrimental to system stability; when the interactive energy is greater than zero and the rate of change of interactive energy is decreasing, the system interactive energy increases in the convergent direction and gradually decreases, which is beneficial to system stability; when the interactive energy is less than zero and the rate of change of interactive energy is increasing, the system interactive energy decreases in the convergent direction but gradually increases, which is detrimental to system stability; and when the interactive energy is less than zero and the rate of change of interactive energy is decreasing, the system interactive energy decreases in the convergent direction, which is beneficial to system stability.

[0013] Further improvements to the above method, determining the interaction stability factor based on the interaction energy change rate further includes: taking the partial derivative of the interaction energy change rate with respect to the key control parameter to obtain the interaction stability factor; and using the interaction stability factor to quantitatively analyze the impact of the key control parameter on system stability further includes: when the interaction stability factor decreases as the corresponding key control parameter increases, it is determined that the change in the key control parameter is beneficial to system stability; when the interaction stability factor increases as the corresponding key control parameter increases, it is determined that the change in the key control parameter is detrimental to system stability.

[0014] Based on further improvements to the above method, the key control parameters include a first set of key control parameters for grid-connected direct-drive wind turbines and a second set of key control parameters for grid-connected electric vehicles. The first set of key control parameters includes the current loop proportional coefficient, active frequency control parameters, active damping parameters, and virtual inertia parameters. The second set of key control parameters includes the drive shaft torsional stiffness, rotor excitation flux linkage, voltage margin, DC-side power parameters, and energy dissipation parameters.

[0015] On the other hand, embodiments of the present invention provide a broadband oscillation risk assessment system for AC / DC systems with multi-grid equipment, comprising: a model building module for establishing dynamic mathematical models of subsystems of the AC / DC transmission system, including direct-drive wind turbines and electric vehicles in the grid; an energy modeling and analysis module, including an energy modeling module and an interactive energy analysis module, wherein the energy modeling module is used to construct the energy function of the AC / DC transmission system based on the subsystem dynamic mathematical models using the first integration method to obtain the system energy, and decompose the system energy into stored energy, dissipated energy, and interactive energy; the interactive energy analysis module is used to obtain the rate of change of interactive energy based on the interactive energy; and the interactive energy analysis module is used to obtain the rate of change of interactive energy based on the interactive energy. The stability factor calculation module is used to determine the interaction stability factor based on the interaction energy change rate, and to use the interaction stability factor to quantitatively analyze the impact of key control parameters on system stability in order to construct a stability discrimination function; the stability domain construction module is used to introduce the large disturbance energy margin into the power injection space based on the stability discrimination function, and to construct a stability domain boundary function with the active power injection of grid-connected direct-drive wind turbines and the aggregated power of grid-connected electric vehicles as the dominant coordinates to obtain the stability domain boundary; the stability boundary search module is used to search for the stability domain boundary in the power injection space, and to obtain the stability domain analysis results of the AC / DC transmission system based on the relative position of the operating point and the stability domain boundary.

[0016] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. By constructing an energy function using a unified dynamic model and the first integration method, the energy interaction process between multi-network devices can be analytically decomposed; the interaction stability factor can quantify the impact of key control parameters on system stability; by constructing a stability domain in the power injection space, the convergence operation region and the instability risk region under large disturbances can be directly distinguished, providing a theoretical basis for parameter tuning, operation mode arrangement, and stability margin assessment of AC / DC transmission systems containing multi-network devices.

[0017] 2. When a stable-instability state change occurs between adjacent search points, a bisection method is used to perform a locally refined search on that interval until the preset error accuracy requirement is met, thereby obtaining the critical stable point in that direction. Subsequently, the accuracy of the stable boundary representation is improved by combining the boundary correction parameter identification method. The above process is repeated in multiple search directions to obtain a set of stable domain boundary points, and these boundary points are used for surface fitting to obtain the system's stable domain.

[0018] 3. This method can effectively distinguish between the convergence operation region and the instability risk region under large disturbances, providing a basis for parameter tuning, operation mode arrangement and stability margin assessment of AC / DC transmission systems with multi-network equipment.

[0019] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0020] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0021] Figure 1 This invention provides a topology model for a new energy AC / DC transmission system containing multi-network equipment, as provided in an embodiment of the invention. Figure 2 This is a topology model of a direct-drive wind turbine system provided in an embodiment of the present invention; Figure 3 A flowchart for constructing a stable region based on power injection space provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a hardware-in-the-loop test platform provided in an embodiment of the present invention; Figure 5A and Figure 5B Key parameters K of the grid-connected direct-drive wind turbine system provided in the embodiments of the present invention are as follows: p1 and J S Verification diagram; Figure 6A and Figure 6B The key parameters λ of the grid-connected electric vehicle system provided in the embodiments of the present invention are as follows: f Verification diagram of D0; Figure 7 This is a system overall stability margin surface diagram provided in an embodiment of the present invention; Figure 8A , Figure 8B , Figure 8C and Figure 8D These are comparison charts showing the rapid decay of deep points in the time domain response of the overall stable domain of the system provided in the embodiments of the present invention, the longer duration of oscillations at points near the boundary, the slow instability at points near the boundary, and the faster divergence trend at points far from the boundary. Figure 9A and Figure 9B Key parameters K of the grid-connected direct-drive wind turbine provided in the embodiments of the present invention are as follows: p1 and D p Schematic diagram of the impact on the stability region; Figure 10A and Figure 10B The key parameters λ of the receiving-end electric vehicle provided in the embodiments of the present invention are as follows: fA schematic diagram illustrating the impact of D0 on the stability region; Figure 11 This is a flowchart of a method for assessing the broadband oscillation risk of AC / DC systems in multi-network equipment according to an embodiment of the present invention; Figure 12 This is a block diagram of a broadband oscillation risk assessment system for AC / DC systems of multi-network equipment according to an embodiment of the present invention; Figure 13 This is a block diagram of a broadband oscillation risk assessment system for AC / DC systems of multi-network equipment according to another embodiment of the present invention. Detailed Implementation

[0022] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0023] The stability domain construction method and system for a new energy AC / DC transmission system with multiple grid-connected devices according to embodiments of the present invention are applicable to large disturbance stability margin assessment, stability boundary search, and operation control tuning of new energy bases including grid-connected direct-drive wind turbines and grid-connected electric vehicles.

[0024] refer to Figure 11 A specific embodiment of the present invention discloses a method for assessing the broadband oscillation risk of AC / DC systems in multi-network equipment, comprising: In step S111, dynamic mathematical models of each subsystem of the AC / DC power transmission system, including the grid-connected direct-drive wind turbine and the grid-connected electric vehicle, are established.

[0025] Specifically, establishing dynamic mathematical models for each subsystem of the grid-connected direct-drive wind turbine further includes: dividing the grid-connected direct-drive wind turbine into an inner current loop subsystem, an outer voltage loop subsystem, an AC line subsystem, an active frequency subsystem, and a reactive voltage subsystem; and establishing dynamic mathematical models for the inner current loop subsystem, the outer voltage loop subsystem, the AC line subsystem, the active frequency subsystem, and the reactive voltage subsystem in the d-axis and q-axis coordinates, respectively.

[0026] Specifically, establishing dynamic mathematical models for each subsystem of a grid-connected electric vehicle further includes: dividing the grid-connected electric vehicle into a vehicle transmission coupling subsystem, a motor current subsystem, and a battery SOC subsystem; and establishing dynamic mathematical models for each of the vehicle transmission coupling subsystem, motor current subsystem, and battery SOC subsystem.

[0027] In step S112, based on the subsystem dynamic mathematical model, the energy function of the AC / DC transmission system is constructed using the first integration method to obtain the system energy. The system energy is decomposed into stored energy, dissipated energy, and interactive energy, and the rate of change of interactive energy is obtained based on the interactive energy.

[0028] Specifically, based on the dynamic mathematical models of each subsystem, the energy function of the AC / DC transmission system constructed using the first integration method further includes: organizing the dynamic mathematical models of each subsystem into unified dynamic equations for each subsystem, including generalized capacitance, generalized inductance, generalized resistance, generalized voltage, generalized current, and interactive elements; performing the first integration on the unified dynamic equations of each subsystem to obtain the energy function of the AC / DC transmission system, which includes the energy functions of electric field stored energy, electromagnetic stored energy, generalized resistance dissipated energy, and energy of interaction between subsystems.

[0029] Specifically, decomposing the system energy into stored energy, dissipated energy, and interactive energy, and obtaining the rate of change of interactive energy, further includes: selecting the interactive energy component in the energy function of the AC / DC transmission system as the interactive energy function between each subsystem, wherein the sum of the interactive energy functions between each subsystem is taken as the interactive energy function of the AC / DC transmission system; calculating the time derivative of the interactive energy function of the AC / DC transmission system to obtain the rate of change of interactive energy of the AC / DC transmission system; analyzing the rate of change of interactive energy of the AC / DC transmission system, wherein when the interactive energy is greater than zero and the rate of change of interactive energy is increasing, the interactive energy of the AC / DC transmission system increases in the divergent direction, which is not conducive to system stability; when the interactive energy is greater than zero and the rate of change of interactive energy is decreasing, the system interactive energy increases in the convergent direction and gradually decreases, which is conducive to system stability; when the interactive energy is less than zero and the rate of change of interactive energy is increasing, the system interactive energy decreases in the convergent direction but gradually increases, which is not conducive to system stability; when the interactive energy is less than zero and the rate of change of interactive energy is decreasing, the system interactive energy decreases in the convergent direction, which is conducive to system stability.

[0030] In step S113, the interaction stability factor is determined based on the interaction energy change rate, and the impact of key control parameters on system stability is quantitatively analyzed using the interaction stability factor to construct a stability discrimination function. This stability domain boundary function is essentially the zero-value contour line of the stability discrimination function in the power injection space: when the stability discrimination function is equal to zero, the corresponding power injection point constitutes the stability domain boundary; when the stability discrimination function is greater than zero, the operating point is located inside the stability domain; when the stability discrimination function is less than zero, the operating point is located in the instability risk region.

[0031] Specifically, determining the interaction stability factor based on the interaction energy change rate further includes: taking the partial derivative of the interaction energy change rate with respect to the key control parameter to obtain the interaction stability factor; and using the interaction stability factor to quantitatively analyze the impact of the key control parameter on system stability further includes: when the interaction stability factor decreases as the corresponding key control parameter increases, it is determined that the change in the key control parameter is beneficial to system stability; when the interaction stability factor increases as the corresponding key control parameter increases, it is determined that the change in the key control parameter is detrimental to system stability.

[0032] Specifically, the key control parameters include the first set of key control parameters for grid-connected direct-drive wind turbines and the second set of key control parameters for grid-connected electric vehicles. The first set of key control parameters includes the current loop proportional coefficient, active frequency control parameters, active damping parameters, and virtual inertia parameters. The second set of key control parameters includes the drive shaft torsional stiffness, rotor excitation flux linkage, voltage margin, DC-side power parameters, and energy dissipation parameters.

[0033] Key control parameters affect the system's dynamic response to disturbances by altering the power injection distribution, energy exchange characteristics, and interactive stability factor. When key control parameters change, the system's disturbance energy margin changes accordingly, leading to changes in the location, size, and shape of the stability domain boundary. Proper parameter configuration can improve system stability and expand the stability domain, while parameter mismatch or excessive fluctuations can reduce the system's disturbance immunity, causing stability domain contraction or even system instability.

[0034] In step S114, based on the stability discriminant function, the large disturbance energy margin is introduced into the power injection space to construct a stability domain boundary function with the active power injection of grid-connected direct-drive wind turbines and the power of grid-connected electric vehicles as the dominant coordinates, thereby obtaining the stability domain boundary. There is a correspondence between the stability discriminant function and the stability domain boundary function, representing the energy criterion and the spatial boundary. The stability discriminant function provides a criterion for system stability from the perspective of the large disturbance energy margin. By introducing this discriminant function into the power injection space, and using the active power injection of grid-connected direct-drive wind turbines and the power of grid-connected electric vehicles as the dominant coordinates, the stability domain boundary function can be obtained. Therefore, the stability discriminant function provides the energy criterion basis for constructing the stability domain boundary function, while the stability domain boundary function maps the stability discriminant result to the stability boundary in the power injection space.

[0035] Specifically, introducing a large disturbance energy margin into the power injection space and constructing a stability domain boundary function with the active power injection of grid-connected direct-drive wind turbines and the power of grid-connected electric vehicles as the dominant coordinates further includes: selecting the active power injection of grid-connected direct-drive wind turbines and the aggregated power of grid-connected electric vehicles as the dominant coordinates of the power injection space, calculating the first large disturbance energy margin, and establishing a stability domain boundary function based on the first large disturbance energy margin; wherein, when the first large disturbance energy margin is greater than zero, the operating point is located inside the stability domain; when the first large disturbance energy margin is equal to zero, the operating point is located at the stability domain boundary; when the operating point crosses the stability boundary, it is determined that the system has an unstable risk region.

[0036] The first major disturbance energy margin is a specific stability index obtained through power flow and transient calculations for each candidate power injection point, used to characterize the system's ability to resist large disturbances under a single operating condition.

[0037] In step S115, the stability domain boundary is searched in the power injection space, and the disturbance stability domain analysis results of the AC / DC transmission system are obtained based on the relative position of the operating point and the stability domain boundary.

[0038] Specifically, searching for the stable domain boundary in the power injection space further includes: taking the stable operating point before the fault as the center, gradually changing the power injection amount along multiple power injection search directions to generate candidate power injection points; performing power flow calculation and transient calculation on each candidate power injection point to obtain the second largest disturbance energy margin; when the sign of the second largest disturbance energy margin of adjacent candidate points changes, the critical step size is corrected using the bisection method to obtain the stable boundary point in the corresponding search direction; after traversing multiple search directions, a set of stable boundary points is formed, and the stable domain boundary is determined based on the set of stable boundary points.

[0039] The second largest disturbance energy margin is determined by spatial mapping and correlation analysis of the margin results at different operating points after selecting the active power injection of grid-connected direct-drive wind turbines and the aggregated power of grid-connected electric vehicles as the dominant coordinates. This is used to describe the stability distribution law in the power injection space and further construct the system stability domain boundary.

[0040] Compared with existing technologies, the broadband oscillation risk assessment method for AC / DC systems with multi-network equipment provided in this embodiment can analytically decompose the energy interaction process between multi-network equipment by constructing an energy function through a unified dynamic model and the first integration method; the impact of key control parameters on system stability can be quantified through the interaction stability factor; and by constructing a stability domain in the power injection space, the convergence operation region and the instability risk region under large disturbances can be directly distinguished, providing a theoretical basis for parameter tuning, operation mode arrangement, and stability margin assessment of AC / DC transmission systems containing multi-network equipment.

[0041] Example 1 This embodiment discloses a method for assessing broadband oscillation risk in AC / DC systems with multi-network equipment based on energy modeling. The topology model of a renewable energy AC / DC transmission system containing multi-network equipment is as follows: Figure 1 As shown, the receiving-end slave station has grid-connecting equipment such as direct-drive wind turbines and electric vehicles. During normal operation, both the sending-end converter stations MFVSC1 and MFVSC2 adopt a constant AC voltage and constant AC frequency control strategy. GSVSC1 is the receiving-end master station and adopts a constant DC voltage and constant AC voltage control strategy, while GSVSC2 is the receiving-end slave station and adopts a constant active power and constant AC voltage control strategy.

[0042] First, the network-connected equipment is modularized according to the system control components. For example, the network-connected direct-drive wind turbine system... Figure 2 As shown, it includes an inner current loop subsystem, an outer voltage loop subsystem, an AC line subsystem, an active frequency subsystem, and a reactive voltage subsystem, each of which is as follows: (1) Current inner loop dq shaft subsystem ; ; In the formula, I d , I q These are the d-axis and q-axis components of the converter output; I d_ref , I q_ref This is the reference value for the current loop (given by the outer loop / power loop). U kd The d / q axis components of the control voltage output by the current PI regulator; k i1 For the coefficients / time constant parameters of the current loop integral element; k p1 The corresponding coefficient / time constant parameter of the current loop proportional element; L For filter inductance / equivalent inductance; R The equivalent resistance of the filter inductor; ω c Angular velocity in the coordinate system; U pccd , U pccq This represents the d / q-axis components of the voltage at point PCC.

[0043] (2) Voltage outer loop dq axis subsystem ; ; In the formula, u od , u oq The d / q axis component of the filter capacitor voltage; u odref , u oqref This is the voltage loop reference value; U ud , U uq For voltage loop regulator output; k iu The voltage loop integral state coefficient; k pu This is the voltage loop proportional state coefficient; C f (This is a filter capacitor).

[0044] (3) AC line dq shaft subsystem ; ; In the formula, C t For line capacitance; U ctd , U ctq This represents the d / q axis components of the line capacitor voltage; L 1 represents the line inductance; R 1 represents the line resistance; I d1 , I q1 The d / q axis components of the line inductance current; I d2 , I q2 Inject the d / q axis component of the current into the other end of the line; ω 0 represents the fundamental frequency angular velocity.

[0045] (4) Active frequency subsystem ; In the formula, ω c To control the internal frequency; ω n This is the rated angular frequency; ω ref For frequency reference; ω 0 represents the measurement frequency or reference frequency; δ Phase angle state; P ref For reference only; P e For electrical output active power; P m Equivalent mechanical input power / primary frequency modulation input power; J s Equivalent moment of inertia / virtual moment of inertia; D p Active frequency damping; K p This is the droop coefficient of the power loop; K f For frequency feedback gain; U pccd , U pccq The dq component of the PCC voltage; I pccd , I pccq This represents the dq component of the PCC current.

[0046] (5) Reactive voltage subsystem ; In the formula, E This represents the equivalent internal potential / voltage amplitude state. E ref For voltage reference; Q To measure reactive power; Q ref For reactive power reference; J q The reactive-voltage loop inertia time constant parameter; K q This is the reactive voltage droop factor; X E This refers to the internal state of the voltage loop; K 1, K 2, K 3 represents the voltage reference channel gain, reactive current channel gain, and reactive power channel gain.

[0047] A grid-connected electric vehicle comprises a vehicle drivetrain coupling subsystem, a motor current subsystem, and a battery SOC subsystem. The systems are as follows: (1) The transmission vehicle coupling subsystem is as follows: ; In the formula, ω m This refers to the mechanical angular velocity of the motor. v The longitudinal speed of the vehicle; T e This refers to the electromagnetic torque of the motor. θ The road slope angle; J m This is the equivalent rotational inertia on the motor side; m eq The longitudinal equivalent mass of the vehicle; η For the efficiency of the transmission system; G This is the total transmission ratio; R ω This is the equivalent tire radius; f v f is the slope of the resistance with respect to the velocity. θ The slope of the resistance with respect to the slope angle; k s It is the equivalent coupling viscosity coefficient.

[0048] (2) The motor current subsystem is as follows: ; In the formula, i d This refers to the d-axis current. i q for q shaft current; for q Combined variables of shaft current;v d for d Shaft voltage; v q for q Shaft voltage; L d It is the d-axis inductance; L q for q Shaft inductance; R s λ is the stator phase resistance; f The equivalent magnetic flux generated by the permanent magnet; ω e ω is the electric angular velocity.

[0049] (3) The battery SOC subsystem is as follows: ; In the formula, SOC represents the battery's state of charge. v 1 represents the polarization voltage of the first-order RC branch of the battery; P dc This refers to the DC-side power of the battery. Q n This refers to the battery's rated capacity. R 1 represents the polarization resistance of the dynamic branch; C 1 is the polarization capacitor of the dynamic branch; I 0 represents the operating point value of the battery current; This is the derivative of the open-circuit voltage at the operating point; D 0 represents the first-order sensitivity of the power equation to current.

[0050] Secondly, the standard forms of each subsystem are uniformly written as dynamic equations including generalized voltage, generalized current, generalized capacitance, generalized inductance, generalized resistance, and interactive elements, and an energy model is constructed using the first integration method. The constructed system energy function consists of the electric and electromagnetic energy stored in the generalized capacitors and inductors, the dissipated energy on the generalized resistor, and the interaction energy between different subsystems.

[0051] All of the above standard types can be written in the following general form: ; In this context, generalized capacitance, inductance, and resistance are uniformly represented as: , and , and For constant terms, generalized voltage and current are uniformly expressed as: and Affecting voltage Interactive segment , and All are uniformly represented as Affecting the current Interactive segment , and All are uniformly represented as .

[0052] Based on the above equation, the energy models of each subsystem in the interconnected system are constructed, and expressed using the first integration method as follows: ; in, The first and second terms in the table represent the generalized capacitance, respectively. and inductor The electric field energy and electromagnetic energy stored in it. Representing generalized resistance Dissipated energy. This represents the interaction energy between different subsystems. The first item involves an interactive process. Reflecting the interaction energy, the second term is through the interaction link. Reflects interaction energy. From It can be seen that each interaction corresponds to an interaction energy term.

[0053] According to Lyapunov's second method, the rate of change of stored energy... At that time, the stored energy gradually decays over time, and the DC transmission system is gradually stable.

[0054] Therefore, the rate of change of interactive energy in the new energy AC / DC transmission system can be obtained as follows: ; ; Furthermore, the interaction energy change rate of the new energy AC / DC transmission system is analyzed: when the interaction energy is greater than zero and the interaction energy change rate is increasing, the system interaction energy increases in the divergent direction, which is not conducive to system stability; when the interaction energy is greater than zero and the interaction energy change rate is decreasing, the system interaction energy increases in the convergent direction and gradually decreases, which is conducive to system stability; when the interaction energy is less than zero and the interaction energy change rate is increasing, the system interaction energy decreases in the convergent direction but gradually increases, which is not conducive to system stability; when the interaction energy is less than zero and the interaction energy change rate is decreasing, the system interaction energy decreases in the convergent direction, which is conducive to system stability.

[0055] Based on the above analysis, this embodiment defines an interaction stability factor to characterize the influence of key control parameters on the rate of change of interaction energy.

[0056] Define the interaction stability factor : ; when It decreases as the control parameter increases, which is beneficial to system stability; It increases with the increase of control parameters, which is detrimental to system stability.

[0057] Analysis of key parameters of the current loop in grid-connected direct-drive wind turbines includes: ; Analyzing the above equation, after a large disturbance fault occurs, it is clear that IdrefId>0 and IqrefIq>0, indicating that... Increase This increase is detrimental to system stability.

[0058] The key parameters of active frequency in a grid-connected direct-drive wind turbine system include: ; ; Analyzing the above equation, after a large disturbance fault occurs, Then there is ,illustrate Increase This increase is detrimental to system stability; due to Then there is ,illustrate Increase This reduction is beneficial to system stability.

[0059] Analysis of key parameters of the current loop in the receiving end of the electric vehicle reveals the following: ; ; Analyzing the above equation, after a large disturbance fault occurs, >0, >0, then we have ,illustrate Increase This increase is detrimental to system stability; Then there is ,illustrate Increase This increase is detrimental to system stability.

[0060] Analysis of key parameters for receiving-end electric vehicle energy storage includes: ; ; Analyzing the above equation, after a large disturbance fault occurs, , Then there is ,illustrate Increase This decrease is detrimental to system stability; therefore, we have ,illustrate Increase This reduction is beneficial to system stability.

[0061] Therefore, taking the key parameters of a grid-connected direct-drive wind turbine as an example, an increase in the proportional coefficient of the current loop leads to an increase in the rate of change of interactive energy, which is detrimental to system stability. In the active power frequency control loop, when the active power reference and the electrical output active power satisfy a preset relationship, an increase in the damping parameter can reduce the rate of change of interactive energy, which is beneficial to system stability. Taking the key parameters of a receiving-end electric vehicle as an example, an increase in the torsional stiffness of the drive shaft and the rotor excitation flux linkage will enhance the coupling of disturbance energy, which is detrimental to system stability. However, an improvement in the energy dissipation capability corresponding to changes in DC power is beneficial to system stability.

[0062] Considering that the operation mode of the AC / DC transmission system is mainly determined by the wind power injection, the aggregated power of electric vehicles, and the power exchange status of the receiving-end network, this embodiment uses the power injection space to describe the system stability domain and uses the large disturbance energy margin as the stability criterion to construct the system stability domain and its stability boundary.

[0063] The stability region method can directly provide the range of power injections within which the system can remain stable under a given disturbance, and characterizes the system's stability margin by the relative position between the operating point and the stability boundary. Let the selected key power injection quantities in the system constitute the power injection vector. P Based on the research object of this embodiment, the active power injection of the grid-connected direct-drive wind turbine, the active power injection of the electric vehicle aggregate, and the DC power output can be used as the main coordinate variables.

[0064] The process of constructing the stability region based on the power injection space is as follows: Figure 3 As shown, the process includes: determining the power injection space; determining the reference operating point; establishing stability criteria; setting typical large disturbance scenarios; searching the boundary along the power injection direction; determining the critical stability point and boundary correction parameters; drawing the stability region and calculating the stability margin.

[0065] When determining the power injection space, based on the topology, power transmission relationship and energy interaction path of the new energy AC / DC transmission system, variables that have a significant impact on system stability, such as active power injection from grid-connected wind turbines, active power injection from electric vehicles, and DC transmission power, are selected to form the power injection vector.

[0066] When determining the baseline operating point, the normal operating condition of the system is used as the baseline operating point. P At point 0, power flow calculations and stability checks are performed to ensure the system is in a stable operating state. The baseline operating point should be located within the stability domain and serve as the starting point for different search directions.

[0067] When establishing stability discrimination conditions, the influence of key parameters on system stability is analyzed based on the rate of change of interactive energy. A system stability discrimination function is constructed, and the zero equivalent condition of the discrimination function is used as the mathematical expression of the boundary of the stability domain to distinguish between the stable operating region and the unstable region.

[0068] When searching for the boundary along the power injection direction, starting from the reference operating point P0, the power injection vector P is gradually changed along the given power injection direction, and power flow calculation and large disturbance stability determination are performed for each candidate operating point. By comparing the stability states of adjacent search points, it is determined whether the system has changed from stable to unstable; if no change has occurred, the search continues along the current direction.

[0069] When a stable-instability state change occurs between adjacent search points, a bisection method is used to perform a locally refined search on that interval until the preset error accuracy requirement is met, thereby obtaining the critical stable point in that direction. Subsequently, a boundary correction parameter identification method is combined to improve the accuracy of the stable boundary representation. The above process is repeated in multiple search directions to obtain a set of stable domain boundary points, and these boundary points are used for surface fitting to obtain the system's stable domain.

[0070] Example 2 To verify the effectiveness of the method described in this invention, this embodiment constructs a simulation verification system based on the RT-LAB hardware-in-the-loop platform, such as... Figure 4 As shown, the proposed method is validated through key parameter scanning, stability domain boundary comparison, and time-domain response at typical operating points.

[0071] The key system parameters in this embodiment are shown in Table 1.

[0072] Table 1 Key System Parameters

[0073] Simulation Scenario: The total simulation duration is 10.0 seconds. A large disturbance fault in a system containing multi-network equipment is simulated by disconnecting the electrical connection between the AC power grid and the converter station at 7.0 seconds on the AC side. Furthermore, the stability analysis of key control components for direct-drive wind turbines and electric vehicles connected to the receiving-end power grid under this fault scenario is verified through simulation.

[0074] The impact of key parameters of grid-connected direct-drive wind turbines on system stability, such as Figure 5A and Figure 5BAs shown; the impact of key parameters of grid-connected electric vehicles on system stability is as follows. Figure 6A and Figure 6B As shown; the overall stability region of the system is as follows Figure 7 As shown; the time-domain response results of the equivalent power angle deviation δ at the receiving end of the system are as follows. Figure 8A , Figure 8B , Figure 8C and Figure 8D As shown; the impact of key parameters of the grid-connected direct-drive wind turbine on the stability region of the system is as follows. Figure 9A and Figure 9B As shown: The impact of key parameters of a grid-connected electric vehicle on the stability region of the system. Figure 10A and Figure 10B As shown.

[0075] Depend on Figure 5A It can be observed that after applying a large AC-side disturbance at t=7.0s, the rate of change of the interactive energy corresponding to the current loop of the grid-connected direct-drive wind turbine system rapidly deviates from the steady-state operating point, and exhibits significant positive fluctuations in the post-fault stage. With the current loop proportionality coefficient... K p1 As the value increases, the overall level of the rate of change of interaction energy gradually rises, with both the peak amplitude and oscillation intensity increasing, indicating that a larger value... K p1 This will exacerbate the energy coupling between the current loop and other dynamic components of the system, making it easier for disturbance energy to accumulate and amplify within the system. Therefore, an excessively large current loop proportionality coefficient is detrimental to the transient energy convergence of the system after a large disturbance, weakening the transient stability of the grid-connected direct-drive wind turbine system. Figure 5B It can be seen that the coefficient of inertia J s After the change, the rate of change of system interaction energy exhibited a relatively obvious hierarchical variation characteristic after the fault. Under the influence of a large disturbance, the interaction energy change initially fluctuated drastically, and then gradually evolved towards a new dynamic level over time. J s The changes in the energy change rate alter both the extreme range and the oscillation region, indicating that the inertial element affects the transmission speed and release process of disturbance energy in the network control system. J s When the value is not chosen appropriately, the system may experience a large negative or positive rate of energy change after a fault, making it difficult to dissipate the interactive energy in a timely manner, thereby reducing the system's transient stability margin. Therefore, J s The selection of the control network needs to take into account both the inertial support capability of the network control and the energy convergence capability after disturbance.

[0076] Depend on Figure 6AIt can be seen that after a large disturbance fault is applied, the rate of change of interactive energy in the receiving-end grid-connected electric vehicle system increases rapidly and maintains a high positive fluctuation after the fault. With the flux linkage parameter... As the value increases, the peak value of the rate of change of interaction energy rises significantly, and the amplitude of local oscillations also increases accordingly, indicating that... Changes in this will directly affect the dynamic response characteristics of the current loop in the grid-connected converter of an electric vehicle. Larger changes... This will enhance the energy exchange intensity during electromagnetic coupling, making the energy shift caused by fault disturbances more significant, thus hindering the system's rapid recovery after the disturbance. Figure 6B It can be seen that the damping parameters D The value 0 has a significant impact on the rate of change of interactive energy after a system fault. After a disturbance occurs, the rate of change of interactive energy shows a large negative shift, accompanied by obvious oscillating changes, indicating that energy is rapidly exchanged between the current loop and the receiving-end network within the system. With... D The change from 0 significantly alters the amplitude and duration of the negative energy change rate, indicating that the damping parameter can affect the dissipation capacity of disturbance energy.

[0077] Depend on Figure 7 It can be seen that, under the condition that the power of the wind turbine and the power of the electric vehicle change together, the overall stability domain of the system exhibits a distinct three-dimensional hill-like distribution. The horizontal axis represents the wind turbine power, the vertical axis represents the electric vehicle power, and the center axis represents the system stability margin index. The stability domain gradually rises from the bottom constraint boundary towards the central region, forming a relatively high stability peak in the middle. This indicates that when the wind turbine output and the charging / discharging power of the electric vehicle are within a reasonable matching range, the system has a large stability margin and strong disturbance tolerance.

[0078] Depend on Figure 8A and Figure 8B It can be seen that when the operating point is located within the overall stability region of the system, although the system will experience short-term oscillations of varying degrees after a fault, the power angle deviation can gradually decay and converge back to the stable equilibrium point. The decay rate at the deep point A is faster and the damping characteristics are more pronounced, while the oscillation duration at the boundary-adjacent point B is longer, indicating that its stability margin is relatively smaller. Figure 8C and Figure 8D It can be seen that when the operating point crosses the boundary of the stability region and enters the outside of the stability region, the amplitude of the power angle deviation oscillation will continue to increase and gradually diverge under the same disturbance. Among them, point C near the boundary shows slow instability, while point D far from the boundary shows a faster divergence trend. This indicates that the overall stability region constructed in this paper can not only effectively distinguish between the convergent operating region and the instability risk region of the system under large disturbances, but also reflect the correspondence between the operating point's relative boundary position and the system's stability margin from a time domain perspective, thus further verifying the effectiveness of the overall stability region criterion.

[0079] Depend on Figure 9A It can be seen that when K p1 When the value is 0.7 pu, the system stability range is the largest, and as... K p1 As the voltage increases from 0.7 pu to 1.3 pu, the system's stable region gradually shrinks, indicating that an excessively large voltage range is detrimental. K p1 This will reduce the system's stability margin under large disturbances. Figure 9B It can be seen that when D p When the voltage is 0.6 pu, the system's stability domain is relatively small, indicating that the system's damping support capability is relatively insufficient at this point. D p As the voltage increases from 0.6 pu to 1.4 pu, the system's stable domain shows a continuous expanding trend, indicating that increasing the voltage... D p This helps to enhance the system's damping capacity and stability under large disturbances, consistent with the aforementioned theoretical analysis.

[0080] Depend on Figure 10A It can be seen that when λ f When the value is 0.7 pu, the system stability range is relatively large. λ f As the voltage increases from 0.7 pu to 1.3 pu, the overall system stability domain shows a shrinking trend, indicating that the excessively large voltage range is not conducive to stability. λ f This will reduce the system's recovery capability and stability margin after disturbances. Figure 10B It can be seen that when D When 0 = 0.6 PU, the system stability range is relatively small. D As the voltage increases from 0.6 pu to 1.4 pu, the system's stable region gradually expands, indicating that improving the voltage... D 0 is beneficial for enhancing the system's energy dissipation capacity and stability under large disturbances, which is consistent with the aforementioned theoretical analysis.

[0081] In summary, the energy model of electric vehicles and pumped storage connected to the receiving-end power grid established in this invention can reflect the characteristics of the energy interaction of each subsystem, and the proposed interaction stability factor can clarify the impact of key parameters on system stability. The proposed method can effectively distinguish between the stable operating area and the instability risk area of ​​the system, and can be used to guide the operation mode arrangement and control parameter optimization of AC / DC transmission systems with multi-network equipment.

[0082] refer to Figure 12The following is another specific embodiment of the present invention, disclosing a broadband oscillation risk assessment system for AC / DC systems of multi-network equipment. The system includes: a data acquisition module M1 that acquires operating data through a simulation interface, measurement and control device, PMU, or converter station control system, and uploads the acquired voltage, current, power, frequency, SOC, and fault clearing status to a model and energy construction module M2; the model and energy construction module M2 converts each subsystem into a unified dynamic model and constructs stored energy, dissipated energy, and interactive energy to form an energy model that can be used for stability analysis; an interactive stability factor calculation module M3 calculates the rate of change of interactive energy and calculates the interactive stability factor of key control parameters to obtain the direction of parameter influence on stability; a stability domain construction module M4 establishes the power injection space, physical feasible domain, large disturbance energy margin, and stability boundary expressions; a boundary search and parameter identification module M5 completes multi-directional stability boundary search, critical point correction, and boundary parameter identification; and an evaluation and early warning output module M6 outputs the current operating point's stability state, stability margin, stability domain graph, instability risk level, and parameter tuning suggestions. The tuning suggestions include reducing the factors that cause instability. Furthermore, control parameters with larger amplitudes, when increased, cause Furthermore, it can improve the damping parameters that enhance energy dissipation capacity, and adjust the power distribution of grid-connected wind turbines and electric vehicles to keep the operating point away from the stability boundary.

[0083] refer to Figure 13 Another specific embodiment of the present invention discloses a broadband oscillation risk assessment system for AC / DC systems with multi-grid equipment, comprising: a model building module 131, used to establish dynamic mathematical models of subsystems of the AC / DC transmission system including direct-drive wind turbines and electric vehicles in the AC / DC transmission system; an energy modeling and analysis module 132, including an energy building module and an interactive energy analysis module, wherein the energy modeling module is used to construct the energy function of the AC / DC transmission system based on the subsystem dynamic mathematical model using the first integration method to obtain the system energy, and decompose the system energy into stored energy, dissipated energy, and interactive energy; the interactive energy analysis module is used to obtain the rate of change of interactive energy based on the interactive energy; AC / DC transmission system... The mutual stability factor calculation module 133 is used to determine the mutual stability factor based on the mutual energy change rate, and to use the mutual stability factor to quantitatively analyze the impact of key control parameters on system stability in order to construct a stability discrimination function; the stability domain construction module 134 is used to introduce the large disturbance energy margin into the power injection space based on the stability discrimination function, and to construct a stability domain boundary function with the active power injection of grid-connected direct-drive wind turbines and the aggregated power of grid-connected electric vehicles as the dominant coordinates to obtain the stability domain boundary; the stability boundary search module 135 is used to search for the stability domain boundary in the power injection space, and to obtain the stability domain analysis results of the AC / DC transmission system based on the relative position of the operating point and the stability domain boundary.

[0084] Compared with existing technologies, the stability domain construction method for new energy AC / DC transmission systems with multi-network equipment provided in this embodiment can organically combine unified modeling of multi-network equipment, energy function construction, interactive stability factor calculation, power injection spatial stability domain boundary search, and system operation risk assessment, so as to improve the accuracy and engineering applicability of large disturbance stability assessment of new energy AC / DC transmission systems. Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0085] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing the broadband oscillation risk of AC / DC systems with multi-network equipment, characterized in that, include: Establish dynamic mathematical models for each subsystem of the AC / DC power transmission system, including the grid-connected direct-drive wind turbine and the grid-connected electric vehicle. Based on the dynamic mathematical model of the subsystem, the energy function of the AC / DC transmission system is constructed using the first integration method to obtain the system energy. The system energy is decomposed into stored energy, dissipated energy, and interactive energy, and the rate of change of interactive energy is obtained based on the interactive energy. The interaction stability factor is determined based on the interaction energy change rate, and the interaction stability factor is used to quantitatively analyze the impact of key control parameters on system stability in order to construct a stability discrimination function. Based on the stability discrimination function, the large disturbance energy margin is introduced into the power injection space, and a stability domain boundary function is constructed with the active power injection of grid-connected direct-drive wind turbines and the power of grid-connected electric vehicles as the dominant coordinates to obtain the stability domain boundary. The stability domain boundary is searched in the power injection space, and the disturbance stability domain analysis results of the AC / DC transmission system are obtained based on the relative position of the operating point and the stability domain boundary.

2. The method for assessing the broadband oscillation risk of AC / DC systems with multi-network equipment according to claim 1, characterized in that, Introducing a large disturbance energy margin into the power injection space, the stability domain boundary function, with the active power injection from grid-connected direct-drive wind turbines and the power of grid-connected electric vehicles as the dominant coordinates, further includes: The active power injection from grid-connected direct-drive wind turbines and the aggregated power from grid-connected electric vehicles are selected as the dominant coordinates in the power injection space. The first maximum disturbance energy margin is calculated, and the boundary function of the stability domain is established based on the first maximum disturbance energy margin. When the energy margin of the first major disturbance is greater than zero, the operating point is located inside the stability region; When the first maximum disturbance energy margin is zero, the operating point is located at the boundary of the stability region. When the operating point crosses the stability boundary, the system is determined to be in an unstable risk region.

3. The method for assessing the broadband oscillation risk of AC / DC systems with multi-network equipment according to claim 2, characterized in that, Searching for the stability region boundary in the power injection space further includes: Using the stable operating point before the fault as the center, candidate power injection points are generated by gradually changing the power injection amount along multiple power injection search directions. Power flow and transient calculations are performed for each candidate power injection point to obtain the second largest disturbance energy margin; When the sign of the second largest perturbation energy margin of adjacent candidate points changes, the critical step size is corrected by the bisection method to obtain the stable boundary point in the corresponding search direction. After traversing multiple search directions, a set of stable boundary points is formed, and the boundary of the stable domain is determined based on the set of stable boundary points.

4. The method for assessing the broadband oscillation risk of AC / DC systems with multi-network equipment according to claim 1, characterized in that, The establishment of dynamic mathematical models for each subsystem of the grid-connected direct-drive wind turbine further includes: The grid-connected direct-drive wind turbine is divided into an inner current loop subsystem, an outer voltage loop subsystem, an AC line subsystem, an active frequency subsystem, and a reactive voltage subsystem. Dynamic mathematical models of the inner current loop subsystem, the outer voltage loop subsystem, the AC line subsystem, the active frequency subsystem, and the reactive voltage subsystem are established in d-axis and q-axis coordinates, respectively.

5. The method for assessing the broadband oscillation risk of AC / DC systems for multi-network equipment according to claim 4, characterized in that, The establishment of dynamic mathematical models for each subsystem of a grid-connected electric vehicle further includes: The electric vehicle with grid structure is divided into a vehicle transmission coupling subsystem, a motor current subsystem, and a battery SOC subsystem. Dynamic mathematical models of the vehicle transmission coupling subsystem, the motor current subsystem, and the battery SOC subsystem are established respectively.

6. The method for assessing the broadband oscillation risk of AC / DC systems with multi-network equipment according to claim 1, characterized in that, Based on the dynamic mathematical models of each subsystem, constructing the energy function of the AC / DC transmission system using the first integration method further includes: The dynamic mathematical models of each subsystem are organized into a unified form of dynamic equations for each subsystem, which includes generalized capacitance, generalized inductance, generalized resistance, generalized voltage, generalized current, and interactive elements. The unified dynamic equations of each subsystem are integrated for the first time to obtain the energy function of the AC / DC transmission system. The energy function includes the energy stored in the electric field, the energy stored in the electromagnetic field, the energy dissipated by the generalized resistance, and the energy of the interaction between the subsystems.

7. The method for assessing the broadband oscillation risk of AC / DC systems in multi-network equipment according to claim 6, characterized in that, The system energy is decomposed into stored energy, dissipated energy, and interactive energy, and the rate of change of interactive energy is further obtained by: The interactive energy component in the energy function of the AC / DC transmission system is selected as the interactive energy function between each subsystem, wherein the sum of the interactive energy functions between each subsystem is taken as the interactive energy function of the AC / DC transmission system. The time derivative of the interactive energy function of the AC / DC transmission system is obtained to obtain the rate of change of the interactive energy of the AC / DC transmission system. The rate of change of interactive energy of the AC / DC transmission system is analyzed, wherein, When the interaction energy is greater than zero and the rate of change of interaction energy is increasing, the interaction energy of the AC / DC transmission system increases in the divergent direction, which is not conducive to system stability. When the interaction energy is greater than zero and the rate of change of the interaction energy is decreasing, the system interaction energy increases towards the convergence direction and gradually decreases, which is beneficial to system stability. When the interaction energy is less than zero and the rate of change of the interaction energy is increasing, the system interaction energy decreases towards the convergence direction but gradually increases, which is not conducive to system stability. When the interaction energy is less than zero and the rate of change of the interaction energy is decreasing, the system interaction energy decreases in the direction of convergence, which is beneficial to system stability.

8. The method for assessing the broadband oscillation risk of AC / DC systems in multi-network equipment according to claim 6, characterized in that, Determining the interaction stability factor based on the interaction energy change rate further includes: taking the partial derivative of the interaction energy change rate with respect to the key control parameters to obtain the interaction stability factor; The quantitative analysis of the impact of key control parameters on system stability using the interactive stability factor further includes: when the interactive stability factor decreases as the corresponding key control parameter increases, it is determined that the change in the key control parameter is beneficial to system stability; when the interactive stability factor increases as the corresponding key control parameter increases, it is determined that the change in the key control parameter is detrimental to system stability.

9. The method for assessing the broadband oscillation risk of AC / DC systems of multi-network equipment according to any one of claims 1 to 8, characterized in that, The key control parameters include the first set of key control parameters for grid-connected direct-drive wind turbines and the second set of key control parameters for grid-connected electric vehicles, wherein... The first set of key control parameters includes the current loop proportional coefficient, active frequency control parameters, active damping parameters, and virtual inertia parameters; and The second set of key control parameters includes drive shaft torsional stiffness, rotor excitation flux linkage, voltage margin, DC side power parameters, and energy dissipation parameters.

10. A broadband oscillation risk assessment system for AC / DC systems of multi-network equipment, characterized in that, include: The model building module is used to establish dynamic mathematical models of the subsystems of the AC / DC transmission system, namely the grid-connected direct-drive wind turbine and the grid-connected electric vehicle. The energy modeling and analysis module includes an energy modeling module and an interactive energy analysis module. The energy modeling module is used to construct the energy function of the AC / DC transmission system based on the dynamic mathematical model of the subsystem using the first integration method to obtain the system energy, and decompose the system energy into stored energy, dissipated energy, and interactive energy. The interactive energy analysis module is used to obtain the rate of change of interactive energy based on the interactive energy. The interaction stability factor calculation module is used to determine the interaction stability factor based on the interaction energy change rate, and to use the interaction stability factor to quantitatively analyze the impact of key control parameters on system stability in order to construct a stability discrimination function. The stability domain construction module is used to introduce the large disturbance energy margin into the power injection space based on the stability discrimination function, and construct a stability domain boundary function with the active power injection of grid-connected direct-drive wind turbines and the aggregated power of grid-connected electric vehicles as the dominant coordinates to obtain the stability domain boundary. The stability boundary search module is used to search for the stability domain boundary in the power injection space and obtain the stability domain analysis results of the AC / DC transmission system based on the relative position of the operating point and the stability domain boundary.

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