Method and device for evaluating local inertia supporting capability of new energy delivery type AC / DC power system

Abstract

The invention provides a method and device for evaluating the local inertia supporting capacity of a new energy delivery type AC / DC power system, and relates to the technical field of power system operation and control, and the method comprises the steps: building a parameterized differential-algebraic equation model; deriving an inertia distribution matrix of the power system by using a generalized energy framework, and calculating a local inertia supporting capability index of each network node based on the inertia distribution matrix; determining the network region of which the index value is lower than a preset index threshold value as an inertia weak region, and predicting the dynamic evolution trend of the inertia weak region based on a hybrid automaton model to obtain a prediction result; and establishing a mapping relation between the inertia supporting capability of the inertia weak area and the commutation failure risk of the high-voltage direct-current converter station, and evaluating the stability margin of the system. According to the method provided by the invention, the problem that the inertia space-time heterogeneity cannot be identified and evaluated by a traditional method can be solved, and accurate identification and risk evaluation of a region with weak local inertia supporting capability can be realized.

Description

Technical Field

[0001] This application relates to the field of power system operation and control technology, and in particular to a method and apparatus for evaluating the local inertia support capability of AC / DC power systems for new energy transmission. Background Technology

[0002] The penetration rate of inverter-based resources (IBRs), represented by wind and solar power, in power systems is rising rapidly, and the physical foundation of the system is undergoing a fundamental paradigm shift from synchronous generator dominance to power electronic equipment dominance. In traditional power system analysis, system inertia is regarded as a slowly changing global scalar parameter based on the sum of the rotational inertia of synchronous units, namely the Center of Inertia (COI) model.

[0003] However, this model can no longer meet the usage requirements in scenarios with a high proportion of IBR access, especially in "new energy base" scenarios where data is collected and transmitted via high-voltage direct current (HVDC). Summary of the Invention

[0004] The purpose of this application is to provide a method and apparatus for evaluating the local inertia support capability of AC / DC power systems for new energy transmission, so as to solve the problem that traditional methods cannot identify and evaluate the spatiotemporal heterogeneity of inertia, and to achieve accurate identification and risk assessment of areas with weak local inertia support capability.

[0005] This application provides a method for evaluating the local inertia support capability of AC / DC power systems that transmit new energy sources to other regions, including: Based on the acquired real-time operation data and network topology data of the power system, a parameterized differential-algebraic equation model including synchronous generators and converter interface new energy power generation equipment is established. Based on the differential-algebraic equation model, the inertia distribution matrix of the power system is derived using a generalized energy framework, and the local inertia support capability index of each network node is calculated based on the inertia distribution matrix. Based on the local inertia support capability index of each network node, network areas with index values ​​lower than a preset index threshold are identified as inertia-weak areas, and the dynamic evolution trend of the inertia-weak areas is predicted based on a hybrid automata model to obtain prediction results. Based on the prediction results, a first mapping relationship is established between the inertia support capability of the inertia-weak areas and the commutation failure risk of the high-voltage DC converter station, and the stability margin of the system is evaluated based on the first mapping relationship.

[0006] Optionally, the step of establishing a parameterized differential-algebraic equation model for new energy power generation equipment including synchronous generators and converter interfaces based on the acquired real-time operation data and network topology data of the power system includes: constructing a set of differential equations and a set of algebraic equations based on the acquired real-time operation data and network topology data of the power system; the set of differential equations is used to describe the internal physical dynamics or control dynamics of the synchronous generator and converter interface equipment; the differential state variables of the synchronous generator include at least one of the following: rotor angle, rotor angular velocity, transient potential; the differential state variables of the converter interface resources include at least one of the following: phase-locked loop output phase, integral term of active / reactive power control loop, DC side capacitor voltage; the set of algebraic equations is used to describe the network topology constraints and the power balance relationship of the system; the key parameters of the system are introduced into the set of differential equations and the set of algebraic equations in vector form to obtain the parameterized differential-algebraic equation model.

[0007] Optionally, the step of deriving the inertia distribution matrix of the power system using the generalized energy framework based on the differential-algebraic equation model, and calculating the local inertia support capability index of each network node based on the inertia distribution matrix, includes: providing a unified mathematical description of the physical rotational kinetic energy of the synchronous generator and the electromagnetic energy provided by the converter interface resources through virtual inertia control based on the differential-algebraic equation model, and constructing the system dynamic equation using the linearized system power flow equation combined with the generator sway equation; obtaining a second mapping relationship between the angular acceleration vector of all network nodes and the power disturbance vector of the nodes based on the system dynamic equation, and defining the inertia distribution matrix as the inverse matrix of the mapping matrix between the node angular acceleration vector and the power disturbance vector based on the second mapping relationship; determining the diagonal elements of the inertia distribution matrix as the local inertia support capability index of the corresponding network node, and interpreting the off-diagonal elements of the inertia distribution matrix as coefficients characterizing the inertia support coupling relationship between different nodes.

[0008] Optionally, the prediction of the dynamic evolution trend of the inertia-weak region based on the hybrid automaton model to obtain the prediction result includes: defining a discrete state set and a continuous state variable space of the hybrid automaton; the discrete state set includes at least one of the following: normal operation state, current limiting protection action state, and energy reserve depletion state; the variable continuous state variable space includes at least one of the following: converter DC capacitor voltage, wind turbine rotor speed, energy storage system state of charge, and converter output current; based on the discrete state set, establishing a continuous dynamic equation set corresponding to each discrete state, and setting guard conditions to trigger discrete state transitions; different discrete states correspond to different virtual inertia parameters; the guard conditions include current over-limit conditions and energy reserve lower limit conditions; based on the hybrid automaton model, simulating and predicting the jump time points of the virtual inertia parameters and the position migration trend of the inertia-weak region in the power grid to obtain the prediction result.

[0009] Optionally, the rules for the virtual inertia parameter to take values ​​under different discrete states include: under normal operation, the virtual inertia parameter is the design value; under current limiting mode, the virtual inertia parameter is zero; under energy depletion state, the virtual inertia parameter is zero and does not respond to frequency support requests.

[0010] Optionally, establishing a first mapping relationship between the inertia support capability of the weak inertia region and the commutation failure risk of the HVDC converter station based on the prediction results includes: analyzing the impact of power disturbances in the weak inertia region on the AC voltage phase angle based on the prediction results, and establishing a correlation model between the local frequency change rate and power angle fluctuation; calculating the impact of AC voltage phase angle fluctuation on the inverter turn-off angle based on the correlation model, and establishing a first mapping relationship between the turn-off angle margin and the local inertia support capability index; wherein, when the turn-off angle margin is lower than a preset safety threshold, the commutation failure risk level is determined to be high risk.

[0011] Optionally, the step of evaluating the stability margin of the system based on the first mapping relationship includes: calculating the upper limit of the local frequency change rate of the weak inertia region based on the first mapping relationship, and comparing the upper limit of the local frequency change rate with the system protection setting to obtain a comparison result; if the comparison result indicates that the upper limit of the local frequency change rate exceeds the system protection setting, then outputting a warning signal of insufficient frequency stability margin.

[0012] Optionally, the method further includes: calculating the local inertia support capability index of each node within a preset time interval, and generating a spatiotemporal distribution visualization map of inertia based on the calculation results; and identifying and providing early warnings for weak areas where the local inertia support capability index is lower than a preset index threshold in the spatiotemporal distribution visualization map.

[0013] This application also provides a device for evaluating the local inertia support capability of AC / DC power systems for new energy transmission, comprising: The system comprises the following modules: a data acquisition module, which establishes a parameterized differential-algebraic equation model for new energy power generation equipment, including synchronous generators and converter interfaces, based on the acquired real-time operating data and network topology data of the power system; an inertia distribution calculation module, which derives the inertia distribution matrix of the power system using a generalized energy framework based on the differential-algebraic equation model, and calculates the local inertia support capability index of each network node based on the inertia distribution matrix; an inertia-weak region identification module, which identifies network regions with index values ​​lower than a preset index threshold as inertia-weak regions based on the local inertia support capability index of each network node, and predicts the dynamic evolution trend of the inertia-weak regions based on a hybrid automata model to obtain prediction results; and a risk assessment module, which establishes a first mapping relationship between the inertia support capability of the inertia-weak regions and the commutation failure risk of the high-voltage DC converter station based on the prediction results, and assesses the stability margin of the system based on the first mapping relationship.

[0014] Optionally, the data acquisition module is specifically used to construct a system of differential equations and a system of algebraic equations based on the acquired real-time operating data and network topology data of the power system. The system of differential equations is used to describe the internal physical dynamics or control dynamics of the synchronous generator and converter interface equipment. The differential state variables of the synchronous generator include at least one of the following: rotor angle, rotor angular velocity, and transient electromotive force. The differential state variables of the converter interface resources include at least one of the following: phase-locked loop output phase, integral term of active / reactive power control loop, and DC side capacitor voltage. The system of algebraic equations is used to describe the network topology constraints and the power balance relationship of the system. The data acquisition module is also specifically used to introduce the key parameters of the system into the system of differential equations and the system of algebraic equations in vector form to obtain a parameterized differential-algebraic equation model.

[0015] Optionally, the inertia distribution calculation module is specifically used to provide a unified mathematical description of the physical rotational kinetic energy of the synchronous generator and the electromagnetic energy provided by the converter interface resources through virtual inertia control based on the differential-algebraic equation model, and to construct the system dynamic equation using the linearized system power flow equation combined with the generator sway equation; the inertia distribution calculation module is also specifically used to obtain a second mapping relationship between the angular acceleration vector of all network nodes and the power disturbance vector of the nodes based on the system dynamic equation, and to define the inertia distribution matrix as the inverse matrix of the mapping matrix between the node angular acceleration vector and the power disturbance vector based on the second mapping relationship; the inertia distribution calculation module is also specifically used to determine the diagonal elements of the inertia distribution matrix as the local inertia support capability index of the corresponding network node, and to interpret the off-diagonal elements of the inertia distribution matrix as coefficients characterizing the inertia support coupling relationship between different nodes.

[0016] Optionally, the inertia-weak region identification module is specifically used to define the discrete state set and continuous state variable space of the hybrid automaton; the discrete state set includes at least one of the following: normal operation state, current limiting protection action state, and energy reserve depletion state; the variable continuous state variable space includes at least one of the following: converter DC capacitor voltage, wind turbine rotor speed, energy storage system state of charge, and converter output current; the inertia-weak region identification module is further used to establish a set of continuous dynamic equations corresponding to each discrete state based on the discrete state set, and set guard conditions to trigger discrete state transitions; different discrete states correspond to different virtual inertia parameters; the guard conditions include current over-limit conditions and energy reserve lower limit conditions; the inertia-weak region identification module is further used to simulate and predict the jump time points of virtual inertia parameters and the location migration trend of inertia-weak regions in the power grid based on the hybrid automaton model, and obtain the prediction results.

[0017] Optionally, the risk assessment module is specifically used to analyze the impact of power disturbances in the weak inertia region on the AC voltage phase angle based on the prediction results, and to establish a correlation model between the local frequency change rate and the power angle fluctuation; the risk assessment module is also specifically used to calculate the impact of AC voltage phase angle fluctuations on the inverter turn-off angle based on the correlation model, and to establish a first mapping relationship between the turn-off angle margin and the local inertia support capability index; wherein, when the turn-off angle margin is lower than a preset safety threshold, the commutation failure risk level is determined to be high risk.

[0018] Optionally, the risk assessment module is specifically used to calculate the upper limit of the local frequency change rate of the weak inertia region based on the first mapping relationship, and compare the upper limit of the local frequency change rate with the system protection setting to obtain a comparison result; the risk assessment module is also specifically used to output a warning signal of insufficient frequency stability margin if the comparison result indicates that the upper limit of the local frequency change rate exceeds the system protection setting.

[0019] Optionally, the device further includes: a visualization module and an early warning module; the visualization module is used to calculate the local inertia support capability index of each node within a preset time interval, and generate a spatiotemporal distribution visualization map of inertia based on the calculation results; the early warning module is used to identify and provide early warning prompts for weak areas in the spatiotemporal distribution visualization map where the local inertia support capability index is lower than a preset index threshold.

[0020] This application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method for evaluating the local inertia support capability of a renewable energy-exporting AC / DC power system as described above.

[0021] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above for evaluating the local inertia support capability of a new energy transmission AC / DC power system.

[0022] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for evaluating the local inertia support capability of a renewable energy-exporting AC / DC power system as described above.

[0023] The method and apparatus provided in this application for evaluating the local inertia support capability of AC / DC power systems for new energy transmission, firstly, establishes a parameterized differential-algebraic equation model including synchronous generators and converter interface new energy power generation equipment based on the acquired real-time operating data and network topology data of the power system; then, based on the differential-algebraic equation model, derives the inertia distribution matrix of the power system using a generalized energy framework, and calculates the local inertia support capability index of each network node based on the inertia distribution matrix; based on the local inertia support capability index of each network node, network areas with index values ​​lower than a preset index threshold are identified as inertia-weak areas, and the dynamic evolution trend of the inertia-weak areas is predicted based on a hybrid automata model to obtain prediction results; finally, based on the prediction results, establishes a first mapping relationship between the inertia support capability of the inertia-weak areas and the commutation failure risk of the high-voltage DC converter station, and evaluates the stability margin of the system based on the first mapping relationship. This solves the problem that traditional methods cannot identify and evaluate the spatiotemporal heterogeneity of inertia, and achieves accurate identification and risk assessment of areas with weak local inertia support capability. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating the method provided in this application for evaluating the local inertia support capability of AC / DC power systems for transmitting new energy sources. Figure 2 This is a schematic diagram of the state transitions of the hybrid automaton model provided in this application; Figure 3 This is a schematic diagram of the topology and inertia distribution of the AC / DC hybrid system provided in this application; Figure 4 This is a schematic diagram of the device provided in this application for evaluating the local inertia support capability of AC / DC power systems for new energy transmission. Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

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

[0027] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship. All actions involving the acquisition of signal information or data in this application are performed in accordance with the relevant data protection laws and policies of the country where the application is located and with authorization from the owner of the relevant device.

[0028] The technical solutions in related technologies mainly have the following shortcomings: 1. It cannot identify areas in the network with insufficient local inertia support. When disturbances occur in these areas, they can cause a sharp increase in the local rate of change of frequency (RoCoF), which can lead to HVDC commutation failure or protection malfunction. Traditional evaluation methods based on the total inertia of the entire network are ineffective in addressing this issue.

[0029] 2. The virtual inertia of an IBR is not derived from physical mass, but is a product of the control algorithm. Its response speed is limited by the delay of frequency measurement, its response amplitude is limited by the instantaneous available energy of DC-side energy storage or the prime mover, and its response mode can even be switched by software under different operating conditions. Related technologies treat virtual inertia as a constant parameter, failing to capture its nonlinear, time-varying, and state-dependent characteristics.

[0030] 3. The construction of new energy power plants is geographically concentrated, while traditional synchronous generator units are distributed in load centers, resulting in an extremely non-uniform distribution of system inertia resources in geographical space. Related technical solutions are based on the assumption of a uniform frequency across the entire network, which fails to reflect the risk of local instability caused by uneven inertia distribution.

[0031] 4. The system's equivalent inertia varies drastically across different time scales: at the day-ahead scheduling scale, it is affected by unit combination; at the real-time operation scale, it is affected by fluctuations in wind and solar power output; and during millisecond-level fault transients, it may undergo instantaneous changes due to IBR control mode switching. Related technical solutions cannot uniformly describe this multi-scale time-varying characteristic.

[0032] To address the aforementioned technical problems in related technologies, this application provides a method for evaluating the local inertia support capability of AC / DC power systems for new energy transmission. This method can solve the problem that traditional methods cannot identify and evaluate the spatiotemporal heterogeneity of inertia, and achieve accurate identification and risk assessment of areas with weak local inertia support capability.

[0033] The method for evaluating the local inertia support capability of AC / DC power systems for new energy transmission, provided in this application, will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0034] like Figure 1 As shown in the embodiment of this application, a method for evaluating the local inertia support capability of a renewable energy-exporting AC / DC power system is provided. This method may include the following steps 101 to 104: Step 101: Based on the real-time operation data and network topology data of the power system obtained, establish a parameterized differential-algebraic equation model for new energy power generation equipment including synchronous generators and converter interfaces.

[0035] For example, the aforementioned network topology data is directly used to construct the algebraic equations describing the network topology constraints in subsequent steps, and is the fundamental basis for forming the entire network model and calculating the electrical distance between nodes. The aforementioned system operation data is used to initialize the state variables (such as generator power angle, speed, converter control variables, etc.) of the aforementioned differential-algebraic equation model, and to provide real-time values ​​for the system parameter vector in the model, enabling the model to reflect the current actual operating point of the system, thereby calculating the local inertia support capability index.

[0036] Specifically, step 101 above, the step of constructing the differential-algebraic equation model, may further include the following steps 101a1 and 101a2: Step 101a1: Based on the obtained real-time operation data and network topology data of the power system, construct a system of differential equations and a system of algebraic equations.

[0037] The differential equations are used to describe the internal physical dynamics or control dynamics of the interface equipment between the synchronous generator and the converter; the differential state variables of the synchronous generator include at least one of the following: rotor angle, rotor angular velocity, and transient electromotive force; the differential state variables of the converter interface resources include at least one of the following: phase-locked loop output phase, integral term of active / reactive power control loop, and DC side capacitor voltage; the algebraic equations are used to describe the network topology constraints and the power balance relationship of the system.

[0038] Step 101a2: Introduce the key parameters of the system into the differential equation system and the algebraic equation system in vector form to obtain a parameterized differential-algebraic equation model.

[0039] For example, the dynamic behavior of a power system including synchronous generator and converter interface resources is described by a parameterized differential-algebraic equation model, which can be represented by the following set of differential-algebraic equations: in, This is a vector of differential state variables. For a synchronous generator, this includes the rotor angle. Rotor angular velocity Transient potential etc.; for converter interface resources, including phase-locked loop phases Active / reactive control integral term, DC capacitor voltage wait. This is an algebraic variable vector, primarily referring to the voltage amplitudes at each node of the network. V and phase angle It obeys the power balance equation. This is a system parameter vector, including unit combination status, renewable energy penetration rate, DC transmission power, etc. (The above...) It is a system of differential equations; It is a system of algebraic equations.

[0040] It is understood that the aforementioned converter interface resources are an abstraction of the capabilities or services provided by the aforementioned converter interface device hardware.

[0041] Step 102: Based on the differential-algebraic equation model, derive the inertia distribution matrix of the power system using the generalized energy framework, and calculate the local inertia support capability index of each network node based on the inertia distribution matrix.

[0042] For example, in order to extract a quantitative analysis tool for the spatial distribution of inertia from a complex differential-algebraic equation model, this application also proposes the concept of an inertia distribution matrix, and then calculates the local inertia support capability index of each network node based on the inertia distribution matrix.

[0043] Specifically, step 102 above may also include steps 102a1 to 102a3: Step 102a1: Based on the differential-algebraic equation model, the physical rotational kinetic energy of the synchronous generator and the electromagnetic energy provided by the converter interface resources through virtual inertia control are given a unified mathematical description. The system dynamic equation is constructed by using the linearized system power flow equation and combining it with the generator sway equation.

[0044] Step 102a2: Based on the system dynamic equations, obtain the second mapping relationship between the angular acceleration vector of all network nodes and the power disturbance vector of nodes, and based on the second mapping relationship, define the inertia distribution matrix as the inverse matrix of the mapping matrix between the node angular acceleration vector and the power disturbance vector.

[0045] Step 102a3: Determine the diagonal elements of the inertia distribution matrix as the local inertia support capability index of the corresponding network node, and interpret the off-diagonal elements of the inertia distribution matrix as coefficients characterizing the inertia support coupling relationship between different nodes.

[0046] For example, firstly, the physical rotational kinetic energy of the synchronous generator and the electromagnetic energy provided by the converter interface resources through virtual inertia control are mathematically described in a unified manner; then, the system power flow equation is linearized, and the system dynamic equation is established by combining the generator sway equation; finally, the inertia distribution matrix is ​​defined as the inverse matrix of the mapping matrix between the node angular acceleration vector and the power disturbance vector. The diagonal elements of the above inertia distribution matrix are the local inertia support capacity index of the corresponding node, and the off-diagonal elements are the inertia coupling coefficients between nodes.

[0047] Specifically, in order to extract a quantitative analysis tool for the spatial distribution of inertia from complex differential-algebraic equation models, this invention proposes the concept of an inertia distribution matrix. For the linearized system power-power angle relationship: Where K is a coefficient. Combining the generator pendulum equation, the system dynamic equation can be obtained: in, Let this be the inertia matrix. Define the inertia distribution matrix. for: For example, the diagonal elements of the aforementioned inertia distribution matrix The local inertia support capability index of node k The local inertial support capacity index can be intuitively understood as... in, For nodes k Local inertia, For the inertia of other synchronous generator nodes, IBR For converter interface resources, The virtual inertia of the converter interface resources. and Here, is the inertia penetration coefficient, which is inversely proportional to the electrical distance between nodes. For example, inertia penetration coefficient Quantitatively describes the nodes m Inertia can "penetrate" the network and reach the nodes. k The degree of this coefficient is related to the node. k , m The electrical distance between them is inversely proportional.

[0048] Step 103: Based on the local inertia support capability index of each network node, network areas with index values ​​lower than the preset index threshold are identified as inertia-weak areas, and the dynamic evolution trend of the inertia-weak areas is predicted based on the hybrid automata model to obtain the prediction results.

[0049] For example, the inertia-weak region is defined as: the local inertia support capability index of a node. The network area is much smaller than the level expected based on the total system inertia and local installed capacity.

[0050] Specifically, step 103 above may also include steps 103a1 to 103a3: Step 103a1: Define the discrete state set and continuous state variable space of the hybrid automaton.

[0051] The discrete state set includes at least one of the following: normal operation state, current limiting protection operation state, and energy reserve depletion state; the variable space of the variable continuous state includes at least one of the following: converter DC capacitor voltage, wind turbine rotor speed, energy storage system state of charge, and converter output current.

[0052] Step 103a2: Based on the set of discrete states, establish the corresponding set of continuous dynamic equations for each discrete state, and set the guard conditions that trigger the transition of discrete states.

[0053] Different discrete states correspond to different virtual inertia parameters; the guarding conditions include current over-limit conditions and energy reserve lower limit conditions.

[0054] It is understandable that if the node k If it is electrically close to a cluster of synchronous machines with huge inertia, then Approaching 1, It will be very large. If the node k It is a converter interface resource aggregation station located at the end of the power grid, connected to the main grid's synchronous machine via a long-distance, high-impedance line. Therefore, even with a high total system inertia, the corresponding... The factor will also be very small, resulting in a low local inertia support capacity index. This inertia may be far below the system average, forming a weak inertia region. Equipment located in a weak inertia region will experience extremely severe frequency fluctuations and rate of change of frequency (RoCoF) when faced with local power disturbances, which is the root cause of local instability.

[0055] Step 103a3: Based on the hybrid automata model, simulate and predict the jump time point of the virtual inertia parameter and the location migration trend of the weak inertia region in the power grid to obtain the prediction results.

[0056] For example, to uniformly describe the multi-scale time-varying characteristics of inertia, especially to capture inertia jumps caused by IBR control mode switching, a hybrid automaton model is used in this embodiment. The hybrid automaton model HA can be formally defined as: in, The set of discrete states includes: (Normal network configuration operation, providing virtual inertia) (Current limiting mode, virtual inertia is zero) (State of charge too low, frequency response off). The state variable space is continuous, including the command capacitor voltage. Fan speed Energy storage state of charge (SOC), current wait. Let be a vector field function, describing the evolution of the continuous state under each discrete state. The core idea is that different discrete states correspond to different sets of differential equations. In the current state, the model contains a complete virtual inertia response term; while in the previous state... In this state, the virtual inertia coefficient becomes zero. The guard condition function defines the conditions that trigger discrete state transitions, for example: Specifically, the rules for the virtual inertia parameter to take values ​​under different discrete states include: under normal operating conditions, the virtual inertia parameter is the design value; under current limiting mode, the virtual inertia parameter is zero; under energy depletion state, the virtual inertia parameter is zero and does not respond to frequency support requests.

[0057] For example, such as Figure 2 The diagram shown is a schematic diagram of the state transition of the hybrid automaton model provided in the embodiment of this application. Ellipses represent discrete states, arrows represent state transitions, and guard conditions are marked.

[0058] Step 104: Based on the prediction results, establish a first mapping relationship between the inertia support capacity of the weak inertia region and the commutation failure risk of the high-voltage DC converter station, and evaluate the stability margin of the system based on the first mapping relationship.

[0059] For example, the commutation process at the LCC-HVDC inverter terminal depends on the voltage amplitude and phase of the AC system. When a power disturbance occurs in the AC system, the frequency change of the node directly corresponds to the rate of change of the node's power angle.

[0060] Specifically, step 104 above may also include the following steps 104a1 and 104a2: Step 104a1: Based on the prediction results, analyze the impact of power disturbance in the weak inertia region on the AC voltage phase angle, and establish a correlation model between the local frequency change rate and the power angle fluctuation.

[0061] Step 104a2: Based on the aforementioned correlation model, calculate the impact of AC voltage phase angle fluctuations on the inverter turn-off angle, and establish the first mapping relationship between the turn-off angle margin and the local inertia support capability index.

[0062] Among them, when the turn-off angle margin is lower than the preset safety threshold, the commutation failure risk level is determined to be high risk.

[0063] For example, in regions with weak inertia, due to If the frequency change rate in this region is extremely small, it will be amplified dramatically: For example, rapid changes in frequency inevitably lead to changes in the power angle. Severe and rapid fluctuations occur. For LCC-HVDC inverters, if the AC voltage phase angle changes drastically within the commutation interval, it may lead to a significant change in the actual turn-off angle. Reduce to below the minimum shut-off angle This leads to commutation failure.

[0064] For example, embodiments of this application establish a mapping relationship between the turn-off angle margin and the characteristics of the inertia-weak region: Where, when Δγ < At that time, the risk of commutation failure was determined to be high.

[0065] For example, such as Figure 3 The diagram shown is a schematic representation of the topology and inertia distribution of an AC / DC hybrid system provided in an embodiment of this application. Figure 3 As shown, SG is a synchronous generator, AC is an AC tie line, and HVDC is a high-voltage direct current transmission line. The red area represents the region with weak inertia. (The value is far below the system average, indicating a high risk of commutation failure).

[0066] In one possible implementation, this application embodiment also provides a visualization method.

[0067] For example, after step 104 above, the method for evaluating the local inertia support capability of a new energy transmission AC / DC power system provided in this application embodiment may further include steps 105 and 106: Step 105: Within a preset time interval, calculate the local inertia support capacity index of each node, and generate a visualization map of the spatiotemporal distribution of inertia based on the calculation results.

[0068] Step 106: In the spatiotemporal distribution visualization map, weak areas where the local inertia support capacity index is lower than the preset index threshold are identified and given early warning prompts.

[0069] For example, spatiotemporal distribution visualization maps transform abstract matrix data into intuitive, geographically correlated inertia heatmaps or contour maps. Operators can immediately grasp the spatial distribution and temporal trends of inertia across the entire network, a crucial situational awareness capability that traditional digital reports cannot provide.

[0070] The method for evaluating the local inertia support capability of AC / DC power systems for transmitting new energy sources, provided in this application embodiment, has the following technical effects: 1. Theoretical Breakthrough: The concept of "local inertia support capability index" and "inertia distribution matrix" is proposed for the first time. The network topology and electrical distance are intrinsically integrated into the inertia model, breaking through the limitations of the traditional "total system inertia" and enabling precise location of the power grid's weak points in inertia.

[0071] 2. Mechanism Revealed: The mechanism by which insufficient local inertia support leads to drastic fluctuations in voltage phase angle, thereby inducing HVDC commutation failure, was revealed, and a direct link between inertia distribution and DC stability was established.

[0072] 3. Dynamic adaptability: By introducing a hybrid system model, it can handle the time-varying characteristics of inertia caused by fluctuations in new energy output (continuous dynamic) and switching of control modes (discrete events). It can not only identify the current weak areas of inertia, but also predict the future risk evolution trend.

[0073] 4. Evaluation accuracy: Compared with the traditional method based on the uniform inertia of the entire network, the evaluation method based on the inertia distribution matrix can more accurately identify the weak links of the system, providing a reliable basis for the design of subsequent control strategies.

[0074] The method for evaluating the local inertia support capability of a renewable energy-exporting AC / DC power system provided in this application embodiment first establishes a parameterized differential-algebraic equation model including renewable energy power generation equipment with synchronous generators and converter interfaces based on the acquired real-time operating data and network topology data of the power system. Then, based on the differential-algebraic equation model, the inertia distribution matrix of the power system is derived using a generalized energy framework, and the local inertia support capability index of each network node is calculated based on the inertia distribution matrix. Based on the local inertia support capability index of each network node, network areas with index values ​​lower than a preset index threshold are identified as inertia-weak areas, and the dynamic evolution trend of the inertia-weak areas is predicted based on a hybrid automata model to obtain prediction results. Finally, based on the prediction results, a first mapping relationship is established between the inertia support capability of the inertia-weak areas and the commutation failure risk of the high-voltage DC converter station, and the stability margin of the system is evaluated based on the first mapping relationship. This solves the problem that traditional methods cannot identify and evaluate the spatiotemporal heterogeneity of inertia, achieving accurate identification and risk assessment of areas with weak local inertia support capability.

[0075] It should be noted that the method for evaluating the local inertia support capability of a renewable energy-exporting AC / DC power system provided in this application embodiment can be executed by an apparatus for evaluating the local inertia support capability of a renewable energy-exporting AC / DC power system, or by a control module within that apparatus for performing the method. This application embodiment uses an apparatus for evaluating the local inertia support capability of a renewable energy-exporting AC / DC power system executing the method as an example to illustrate the apparatus for evaluating the local inertia support capability of a renewable energy-exporting AC / DC power system provided in this application embodiment.

[0076] It should be noted that, in the embodiments of this application, the methods shown in the accompanying drawings for evaluating the local inertia support capability of AC / DC power systems for new energy transmission are all illustrated by way of example with reference to one accompanying drawing in the embodiments of this application. In specific implementation, the methods shown in the accompanying drawings for evaluating the local inertia support capability of AC / DC power systems for new energy transmission can also be implemented in conjunction with any other accompanying drawings shown in the above embodiments, which will not be elaborated here.

[0077] The apparatus for evaluating the local inertia support capability of AC / DC power systems for new energy transmission provided in this application is described below. The method for evaluating the local inertia support capability of AC / DC power systems for new energy transmission described above can be referred to in correspondence with the method described below.

[0078] Figure 4 This is a schematic diagram of the structure of the device for evaluating the local inertia support capability of a new energy transmission AC / DC power system provided in the embodiments of this application, as shown below. Figure 4 As shown, it specifically includes: The data acquisition module 401 is used to establish a parameterized differential-algebraic equation model for new energy power generation equipment, including synchronous generators and converter interfaces, based on the acquired real-time operation data and network topology data of the power system. The inertia distribution calculation module 402 is used to derive the inertia distribution matrix of the power system using a generalized energy framework based on the differential-algebraic equation model, and calculate the local inertia support capability index of each network node based on the inertia distribution matrix. The inertia weak area identification module 403 is used to identify network areas with index values ​​lower than a preset index threshold as inertia weak areas based on the local inertia support capability index of each network node, and predict the dynamic evolution trend of the inertia weak areas based on a hybrid automata model to obtain the prediction result. The risk assessment module 404 is used to establish a first mapping relationship between the inertia support capability of the inertia weak areas and the commutation failure risk of the high-voltage DC converter station based on the prediction result, and evaluate the stability margin of the system based on the first mapping relationship.

[0079] Optionally, the data acquisition module 401 is specifically used to construct a system of differential equations and a system of algebraic equations based on the acquired real-time operating data and network topology data of the power system. The system of differential equations is used to describe the internal physical dynamics or control dynamics of the synchronous generator and converter interface equipment. The differential state variables of the synchronous generator include at least one of the following: rotor angle, rotor angular velocity, and transient electromotive force. The differential state variables of the converter interface resources include at least one of the following: phase-locked loop output phase, integral term of active / reactive power control loop, and DC side capacitor voltage. The system of algebraic equations is used to describe the network topology constraints and the power balance relationship of the system. The data acquisition module 401 is also specifically used to introduce the key parameters of the system into the system of differential equations and the system of algebraic equations in vector form to obtain a parameterized differential-algebraic equation model.

[0080] Optionally, the inertia distribution calculation module 402 is specifically used to perform a unified mathematical description of the physical rotational kinetic energy of the synchronous generator and the electromagnetic energy provided by the converter interface resources through virtual inertia control based on the differential-algebraic equation model, and to construct the system dynamic equation using the linearized system power flow equation combined with the generator sway equation; the inertia distribution calculation module 402 is also specifically used to obtain a second mapping relationship between the angular acceleration vector of all network nodes and the power disturbance vector of the nodes based on the system dynamic equation, and to define the inertia distribution matrix as the inverse matrix of the mapping matrix between the node angular acceleration vector and the power disturbance vector based on the second mapping relationship; the inertia distribution calculation module 402 is also specifically used to determine the diagonal elements of the inertia distribution matrix as the local inertia support capability index of the corresponding network node, and to interpret the off-diagonal elements of the inertia distribution matrix as coefficients characterizing the inertia support coupling relationship between different nodes.

[0081] Optionally, the inertia-weak region identification module 403 is specifically used to define the discrete state set and continuous state variable space of the hybrid automaton; the discrete state set includes at least one of the following: normal operation state, current limiting protection action state, and energy reserve depletion state; the variable continuous state variable space includes at least one of the following: converter DC capacitor voltage, wind turbine rotor speed, energy storage system state of charge, and converter output current; the inertia-weak region identification module 403 is further used to establish a continuous dynamic equation set corresponding to each discrete state based on the discrete state set, and set guard conditions to trigger discrete state migration; different discrete states correspond to different virtual inertia parameters; the guard conditions include current over-limit conditions and energy reserve lower limit conditions; the inertia-weak region identification module 403 is further used to simulate and predict the jump time point of the virtual inertia parameter and the location migration trend of the inertia-weak region in the power grid based on the hybrid automaton model, and obtain the prediction result.

[0082] Optionally, the risk assessment module 404 is specifically used to analyze the impact of power disturbances in the weak inertia region on the AC voltage phase angle based on the prediction results, and to establish a correlation model between the local frequency change rate and the power angle fluctuation; the risk assessment module 404 is also specifically used to calculate the impact of AC voltage phase angle fluctuations on the inverter turn-off angle based on the correlation model, and to establish a first mapping relationship between the turn-off angle margin and the local inertia support capability index; wherein, when the turn-off angle margin is lower than a preset safety threshold, the commutation failure risk level is determined to be high risk.

[0083] Optionally, the risk assessment module 404 is specifically used to calculate the upper limit of the local frequency change rate of the weak inertia region based on the first mapping relationship, and compare the upper limit of the local frequency change rate with the system protection setting to obtain a comparison result; the risk assessment module 404 is also specifically used to output a warning signal of insufficient frequency stability margin if the comparison result indicates that the upper limit of the local frequency change rate exceeds the system protection setting.

[0084] Optionally, the device further includes: a visualization module and an early warning module; the visualization module is used to calculate the local inertia support capability index of each node within a preset time interval, and generate a spatiotemporal distribution visualization map of inertia based on the calculation results; the early warning module is used to identify and provide early warning prompts for weak areas in the spatiotemporal distribution visualization map where the local inertia support capability index is lower than a preset index threshold.

[0085] The device provided in this application for evaluating the local inertia support capability of AC / DC power systems for new energy transmission firstly establishes a parameterized differential-algebraic equation model including synchronous generators and converter interface new energy power generation equipment based on the acquired real-time operating data and network topology data of the power system. Then, based on the differential-algebraic equation model, the inertia distribution matrix of the power system is derived using a generalized energy framework, and the local inertia support capability index of each network node is calculated based on the inertia distribution matrix. Based on the local inertia support capability index of each network node, network areas with index values ​​lower than a preset index threshold are identified as inertia-weak areas, and the dynamic evolution trend of the inertia-weak areas is predicted based on a hybrid automata model to obtain prediction results. Finally, based on the prediction results, a first mapping relationship is established between the inertia support capability of the inertia-weak areas and the commutation failure risk of the high-voltage DC converter station, and the stability margin of the system is evaluated based on the first mapping relationship. This solves the problem that traditional methods cannot identify and evaluate the spatiotemporal heterogeneity of inertia, achieving accurate identification and risk assessment of areas with weak local inertia support capability.

[0086] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communications bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other through the communications bus 540. The processor 510 can call logic instructions in the memory 530 to execute a method for evaluating the local inertia support capability of a new energy-exporting AC / DC power system. This method includes: first, establishing a parameterized differential-algebraic equation model containing new energy power generation equipment including synchronous generators and converter interfaces, based on the acquired real-time operating data and network topology data of the power system; then, deriving the inertia distribution matrix of the power system using a generalized energy framework based on the differential-algebraic equation model, and calculating the local inertia support capability index of each network node based on the inertia distribution matrix; based on the local inertia support capability index of each network node, identifying network regions with index values ​​below a preset index threshold as inertia-weak regions, and predicting the dynamic evolution trend of the inertia-weak regions based on a hybrid automata model to obtain prediction results; finally, based on the prediction results, establishing a first mapping relationship between the inertia support capability of the inertia-weak regions and the commutation failure risk of the high-voltage DC converter station, and evaluating the stability margin of the system based on the first mapping relationship. In this way, the problem that traditional methods cannot identify and assess the spatiotemporal heterogeneity of inertia can be solved, and the accurate identification and risk assessment of areas with weak local inertia support capacity can be achieved.

[0087] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0088] On the other hand, this application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the method provided by the above methods for evaluating the local inertia support capability of a new energy-exporting AC / DC power system. The method includes: first, establishing a parameterized differential-algebraic equation model including synchronous generators and converter interface new energy power generation equipment based on the acquired real-time operating data and network topology data of the power system; then, deriving the inertia distribution matrix of the power system using a generalized energy framework based on the differential-algebraic equation model, and calculating the local inertia support capability index of each network node based on the inertia distribution matrix; based on the local inertia support capability index of each network node, determining the network area with an index value lower than a preset index threshold as an inertia-weak area, and predicting the dynamic evolution trend of the inertia-weak area based on a hybrid automata model to obtain a prediction result; finally, based on the prediction result, establishing a first mapping relationship between the inertia support capability of the inertia-weak area and the commutation failure risk of the high-voltage DC converter station, and evaluating the stability margin of the system based on the first mapping relationship. In this way, the problem that traditional methods cannot identify and assess the spatiotemporal heterogeneity of inertia can be solved, and the accurate identification and risk assessment of areas with weak local inertia support capacity can be achieved.

[0089] In another aspect, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the methods provided above for evaluating the local inertia support capability of AC / DC power systems for new energy transmission. The method includes: first, establishing a parameterized differential-algebraic equation model including synchronous generators and converter interface new energy power generation equipment based on acquired real-time operating data and network topology data of the power system; then, deriving the inertia distribution matrix of the power system using a generalized energy framework based on the differential-algebraic equation model, and calculating the local inertia support capability index of each network node based on the inertia distribution matrix; determining network regions with index values ​​below a preset index threshold as inertia-weak regions based on the local inertia support capability index of each network node, and predicting the dynamic evolution trend of the inertia-weak regions based on a hybrid automata model to obtain prediction results; finally, establishing a first mapping relationship between the inertia support capability of the inertia-weak regions and the commutation failure risk of the high-voltage DC converter station based on the prediction results, and evaluating the stability margin of the system based on the first mapping relationship. In this way, the problem that traditional methods cannot identify and assess the spatiotemporal heterogeneity of inertia can be solved, and the accurate identification and risk assessment of areas with weak local inertia support capacity can be achieved.

[0090] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0091] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for evaluating the local inertia support capability of AC / DC power systems for transmitting new energy sources, characterized in that, include: Based on the real-time operation data and network topology data of the power system, a parameterized differential-algebraic equation model of new energy power generation equipment including synchronous generators and converter interfaces is established. Based on the differential-algebraic equation model, the inertia distribution matrix of the power system is derived using the generalized energy framework, and the local inertia support capability index of each network node is calculated based on the inertia distribution matrix. Based on the local inertia support capability index of each network node, network areas with index values ​​lower than the preset index threshold are identified as inertia-weak areas. The dynamic evolution trend of the inertia-weak areas is predicted based on the hybrid automata model to obtain the prediction results. Based on the prediction results, a first mapping relationship is established between the inertia support capacity of the weak inertia region and the commutation failure risk of the high-voltage DC converter station, and the stability margin of the system is evaluated based on the first mapping relationship.

2. The method according to claim 1, characterized in that, Based on the acquired real-time operating data and network topology data of the power system, a parameterized differential-algebraic equation model is established for new energy power generation equipment including synchronous generators and converter interfaces, including: Based on the acquired real-time operating data and network topology data of the power system, a system of differential equations and algebraic equations are constructed. The system of differential equations is used to describe the internal physical dynamics or control dynamics of the synchronous generator and converter interface equipment. The differential state variables of the synchronous generator include at least one of the following: rotor angle, rotor angular velocity, and transient electromotive force. The differential state variables of the converter interface resources include at least one of the following: phase-locked loop output phase, integral term of active / reactive power control loop, and DC side capacitor voltage. The system of algebraic equations is used to describe the network topology constraints and the power balance relationship of the system. The key parameters of the system are introduced into the differential equation system and the algebraic equation system in vector form to obtain a parameterized differential-algebraic equation model.

3. The method according to claim 1, characterized in that, The process of deriving the inertia distribution matrix of the power system using the generalized energy framework based on the differential-algebraic equation model, and calculating the local inertia support capability index of each network node based on the inertia distribution matrix, includes: Based on the differential-algebraic equation model, the physical rotational kinetic energy of the synchronous generator and the electromagnetic energy provided by the converter interface resources through virtual inertia control are given a unified mathematical description. The system dynamic equation is constructed by using the linearized system power flow equation and combining it with the generator sway equation. Based on the system dynamic equations, a second mapping relationship between the angular acceleration vector of all network nodes and the power disturbance vector of nodes is obtained. Based on the second mapping relationship, the inertia distribution matrix is ​​defined as the inverse matrix of the mapping matrix between the angular acceleration vector of nodes and the power disturbance vector. The diagonal elements of the inertia distribution matrix are determined as the local inertia support capability index of the corresponding network node, and the off-diagonal elements of the inertia distribution matrix are interpreted as coefficients characterizing the inertia support coupling relationship between different nodes.

4. The method according to claim 3, characterized in that, The formula for calculating the local inertia support capacity index is as follows: in, For nodes k Local inertia, For the inertia of other synchronous generator nodes, IBR For converter interface resources, The virtual inertia of the converter interface resources. and The inertia penetration coefficient is inversely proportional to the electrical distance between nodes.

5. The method according to claim 1, characterized in that, The prediction of the dynamic evolution trend of the inertia-weak region based on the hybrid automata model yields the following prediction results: Define a discrete state set and a continuous state variable space for a hybrid automaton; the discrete state set includes at least one of the following: normal operation state, current limiting protection action state, and energy reserve depletion state; the continuous state variable space includes at least one of the following: converter DC capacitor voltage, wind turbine rotor speed, energy storage system state of charge, and converter output current. Based on the set of discrete states, a set of continuous dynamic equations corresponding to each discrete state is established, and guarding conditions that trigger discrete state transitions are set; different discrete states correspond to different virtual inertia parameters; the guarding conditions include current over-limit conditions and energy reserve lower limit conditions. Based on the hybrid automata model, the jump time points of the virtual inertia parameters and the location migration trend of the weak inertia region in the power grid are simulated and predicted to obtain the prediction results.

6. The method according to claim 5, characterized in that, The rules for determining the values ​​of virtual inertia parameters under different discrete states include: Under normal operating conditions, the virtual inertia parameter is the design value; under current limiting mode, the virtual inertia parameter is zero; under energy depletion mode, the virtual inertia parameter is zero and does not respond to frequency support requests.

7. The method according to claim 1, characterized in that, Based on the prediction results, establishing a first mapping relationship between the inertia support capacity of the weak inertia region and the commutation failure risk of the high-voltage DC converter station includes: Based on the prediction results, the influence of power disturbance in the weak inertia region on the AC voltage phase angle is analyzed, and a correlation model between the local frequency change rate and the power angle fluctuation is established. Based on the aforementioned correlation model, the impact of AC voltage phase angle fluctuation on inverter turn-off angle is calculated, and a first mapping relationship between turn-off angle margin and local inertia support capability index is established. Among them, when the turn-off angle margin is lower than the preset safety threshold, the commutation failure risk level is determined to be high risk.

8. The method according to claim 1, characterized in that, The evaluation of the system's stability margin based on the first mapping relationship includes: Based on the first mapping relationship, the upper limit of the local frequency change rate of the weak inertia region is calculated, and the upper limit of the local frequency change rate is compared with the system protection setting to obtain the comparison result; If the comparison result indicates that the upper limit of the local frequency change rate exceeds the system protection setting, a warning signal indicating insufficient frequency stability margin is output.

9. The method according to claim 1, characterized in that, The method further includes: Within a preset time interval, calculate the local inertia support capacity index of each node, and generate a visualization map of the spatiotemporal distribution of inertia based on the calculation results. In the spatiotemporal distribution visualization map, weak areas where the local inertia support capacity index is lower than the preset index threshold are identified and given early warning.

10. A device for evaluating the local inertia support capability of a renewable energy-exporting AC / DC power system, characterized in that, The device includes: The data acquisition module is used to establish a parameterized differential-algebraic equation model of new energy power generation equipment, including synchronous generators and converter interfaces, based on the acquired real-time operation data and network topology data of the power system. The inertia distribution calculation module is used to derive the inertia distribution matrix of the power system based on the differential-algebraic equation model using the generalized energy framework, and to calculate the local inertia support capability index of each network node based on the inertia distribution matrix. The inertia-weak region identification module is used to identify network regions with inertia-weak regions based on the local inertia support capability index of each network node, and to predict the dynamic evolution trend of the inertia-weak regions based on the hybrid automata model to obtain the prediction result. The risk assessment module is used to establish a first mapping relationship between the inertia support capacity of the weak inertia region and the commutation failure risk of the high-voltage DC converter station based on the prediction results, and to assess the stability margin of the system based on the first mapping relationship.

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