Stable region determination method, device and medium for converter cluster

Abstract

Embodiments of the present application provide a kind of converter cluster stable region determination method, equipment and medium.Method includes: obtaining the system parameter of converter cluster, according to system parameter, establish the equivalent phase angle dynamic model of network type and follow network type converter, and then construct phase angle synchronization model;Determine inertia weighted phase angle center, and calculate relative phase angle with phase angle center as benchmark;Based on relative phase angle, construct energy function, establish synchronization stability criterion using the derivative of energy function with respect to time;Generate a plurality of state points in phase angle-frequency state space, respectively solve the equation of phase angle synchronization model to each state point;Judgment result is obtained by means of synchronization stability criterion;According to state point and judgment result, train state discrimination model;The state point to be judged is input into the trained model, and the judgment result of each state point is determined to determine the stable region of converter cluster.The present application can realize the quick and accurate discrimination of converter cluster stable region.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of power grid control technology, and in particular to a method, device and medium for determining the stable region of a converter cluster. Background Technology

[0002] With the large-scale integration of new energy power generation, energy storage systems, and flexible loads, grid-connected equipment in power systems is gradually shifting from traditional synchronous generators to voltage source converters and current source converters. With a large number of converters operating in parallel, the synchronous stability problem of the power system exhibits characteristics of multiple nodes, multiple controls, multiple time scales, and strong nonlinearity. Existing analytical methods for converter cluster stability suffer from inaccurate modeling and low computational efficiency, making it difficult to meet the need for rapid online assessment of stability regions. Summary of the Invention

[0003] This invention provides a method, device, and medium for determining the stable region of a converter cluster, which enables rapid and accurate identification of the stable region of a converter cluster.

[0004] In a first aspect, the method for determining the stable region of a converter cluster provided in embodiments of the present invention includes: Obtain the system parameters of the converter cluster; Based on the system parameters, equivalent phase angle dynamic models of grid-type converters and grid-connected converters are established respectively. A phase angle synchronization model is then constructed based on the equivalent phase angle dynamic models of grid-type converters and grid-connected converters. The centroid coordinate transformation is performed on the phase angle synchronization model to determine the inertia-weighted phase angle center of the converter cluster, and the relative phase angle of each converter node is calculated with the inertia-weighted phase angle center as the reference. Construct an energy function based on the relative phase angle, and establish a synchronization stability criterion using the derivative of the energy function with respect to time; Multiple state points are generated in the state space composed of the phase angle and frequency variables of the converter cluster. The dynamic equation of the phase angle synchronization model is solved for each state point. The synchronization stability criterion is used to determine whether each state point is stable. Sample data is constructed based on each state point and the judgment results of each state point. A state discrimination model is trained based on sample data; The state points to be judged are input into the trained state discrimination model to obtain the discrimination results of each state point, and the stable region of the converter cluster is determined based on the discrimination results of multiple state points.

[0005] Secondly, the converter cluster stability region determination device provided in the embodiments of the present invention includes: The acquisition module is used to acquire system parameters of the converter cluster; The model building module is used to build the equivalent phase angle dynamic model of the grid-type converter and the equivalent phase angle dynamic model of the grid-following converter according to the system parameters, and to build the phase angle synchronization model based on the equivalent phase angle dynamic model of the grid-type converter and the equivalent phase angle dynamic model of the grid-following converter. The phase angle calculation module is used to perform centroid coordinate transformation on the phase angle synchronization model, determine the inertia-weighted phase angle center of the converter cluster, and calculate the relative phase angle of each converter node with the inertia-weighted phase angle center as the reference. The criterion establishment module is used to construct an energy function based on the relative phase angle and to establish a synchronization stability criterion using the derivative of the energy function with respect to time. The sample construction module is used to generate multiple state points in the state space composed of the phase angle variables and frequency variables of the converter cluster, solve the dynamic equation of the phase angle synchronization model for each state point, and use the synchronization stability criterion to determine whether each state point is stable. Based on each state point and the judgment results of each state point, sample data is constructed. The training module is used to train the state discrimination model based on sample data; The determination module is used to input the state points to be judged into the trained state discrimination model, obtain the discrimination results of each state point, and determine the stable region of the converter cluster based on the discrimination results of multiple state points.

[0006] Thirdly, the electronic device provided in the embodiments of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for determining the stable region of a converter cluster as in any embodiment of the present invention.

[0007] Fourthly, the computer-readable storage medium provided in the embodiments of the present invention stores computer instructions thereon, the computer instructions being used to cause a processor to execute a method for determining the stable region of a converter cluster as described in any embodiment of the present invention.

[0008] Fifthly, the computer program product provided in the embodiments of the present invention includes a computer program that, when executed by a processor, implements the method for determining the stable region of a converter cluster as described in any embodiment of the present invention.

[0009] In this embodiment of the invention, based on system parameters, equivalent phase angle dynamic models of grid-type converters and grid-following converters are established respectively. A phase angle synchronization model is then constructed based on these models, enabling a more accurate description of the contribution of grid-type converters to the frequency support, phase angle recovery, and synchronization stability of the power grid system. Equivalent modeling of the phase angle synchronization oscillation state of converter clusters of grid-type and grid-following converters is performed, thereby accurately characterizing the grid-type converter... The stable region of a converter cluster in a hybrid grid-connected scenario is determined. The inertia-weighted phase angle center of the converter cluster is identified, and the relative phase angle of each converter node is calculated using this center as a reference. An energy function is constructed based on the relative phase angle, and a synchronization stability criterion is established using the time derivative of the energy function. This ensures that the energy function only reflects the oscillation dynamics of each converter relative to the cluster's phase angle center, thus making the determination of the converter cluster's stable state more accurate. The stability region is determined by the phase angle and frequency variables of the converter cluster. Multiple state points are generated in space, and the dynamic equations of the phase angle synchronization model are solved for each state point. The stability criterion is used to determine whether each state point is stable. Sample data is constructed based on each state point and the judgment results. This method can strictly determine the stability of each state point using a phase angle synchronization model with a clear physical mechanism and a synchronization stability criterion. This fundamentally ensures that the sample labels truly reflect the physical laws of the real phase angle synchronization evolution and energy dissipation of the converter cluster, thus guaranteeing the accuracy of the training sample data. It provides high-confidence supervision information for the training of the state discrimination model and effectively improves the reliability of the subsequent determination of the stability region. The state discrimination model is trained based on the sample data. The state points to be judged are input into the trained state discrimination model to obtain the discrimination results of each state point. The stable region of the converter cluster is determined based on the discrimination results of multiple state points. The state discrimination model can replace time-domain simulation by training the state discrimination model and quickly output the stability discrimination results corresponding to the state points. It is more suitable for online real-time stability assessment of converter clusters and helps to ensure the safe and stable operation of converter clusters. Attached Figure Description

[0010] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of a process for determining the stable region of a converter cluster provided in an embodiment of the present invention; Figure 2This is another flowchart illustrating the determination of the stable region of a converter cluster provided in this embodiment of the invention; Figure 3 This is a schematic diagram of a device for determining the stable region of a converter cluster provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0013] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0014] Figure 1 This is a flowchart illustrating a method for determining the stable region of a converter cluster according to an embodiment of the present invention. This method is applicable to scenarios involving online identification of the stable region of a converter cluster. The method can be executed by a device for determining the stable region of a converter cluster, as provided in this embodiment. This device can be implemented using software and / or hardware. In one specific embodiment, this device can be integrated into an electronic device, such as a computer, server, or industrial control computer. The following embodiment uses the integration of the device for determining the stable region of a converter cluster into an electronic device as an example. (See also...) Figure 1 The method for determining the stable region of the converter cluster in this embodiment may include the following steps: Step 101: Obtain the system parameters of the converter cluster.

[0015] A converter cluster is a group of electrically connected converters within a grid-connected unit of a power grid system. Specifically, the multiple converters in the cluster are coordinated and scheduled through a unified control strategy. The grid-connected unit can be a renewable energy power plant, energy storage power station, or microgrid, etc., which connects to the power grid system through a grid connection point to supply electrical energy to the grid.

[0016] The converter cluster integrates both grid-connected and grid-linked converters. Grid-linked converters typically rely on phase-locked loops (PLLs) to obtain grid synchronization phase and inject active and reactive power through current control. Grid-connected converters, on the other hand, actively establish voltage amplitude, frequency, and phase angle through virtual synchronous machine control, droop control, and power synchronization control, providing inertia, damping, and voltage support to the grid system.

[0017] The system parameters of a converter cluster are a set of parameters describing the overall electrical characteristics and operating status of the cluster. Specifically, the system parameters may include the grid topology of the converter cluster, the line impedance between each converter node in the cluster, the type and control method of each converter node, voltage amplitude, active power reference value, and operating constraints. The control parameters of the converter cluster can be obtained from its control system.

[0018] Step 102: Based on the system parameters, establish the equivalent phase angle dynamic model of the grid-type converter and the equivalent phase angle dynamic model of the grid-following converter, respectively, and construct the phase angle synchronization model based on the equivalent phase angle dynamic model of the grid-type converter and the equivalent phase angle dynamic model of the grid-following converter.

[0019] The equivalent phase angle dynamic model is a computational model used to model the dynamic changes in the output voltage phase angle of a converter as the output active power changes.

[0020] Specifically, grid-type converters actively establish the AC side output voltage phase angle through virtual synchronous machine control, droop control, or power synchronization control. The dynamic phase angle model of a grid-type converter can be expressed as: ;in, Let i be the output voltage phase angle of the i-th grid-connected converter. Let be the angular frequency of the i-th grid-connected converter. Let be the rate of change of the angular frequency of the i-th grid-type converter. Let be the equivalent virtual inertia of the i-th grid-connected converter. The equivalent virtual damping or droop damping of the i-th grid-connected converter. Let be the virtual stiffness coefficient of the i-th grid-type converter. The inertia-weighted phase angle center of the converter cluster. This is the reference value for the active power of the i-th grid-connected converter. Let be the active power output of the i-th grid-type converter.

[0021] The phase angle of the AC output voltage of a grid-connected converter is usually obtained by a phase-locked loop (PLL). The grid-connected converter itself does not actively establish the output voltage phase angle, but rather operates following the phase of the grid voltage. Therefore, the equivalent phase angle dynamic model of a grid-connected converter can be expressed as: ;in, Let be the equivalent inertia of the i-th grid-connected converter. Since the inertial characteristics of the grid-connected converter are not significant, Usually, it is set to 0. This represents the equivalent damping coefficient formed by the phase-locked loop and power control circuit of the i-th grid-connected converter. It represents the equivalent recovery coefficient during the phase angle following process of the phase-locked loop of the i-th grid converter.

[0022] By combining the equivalent phase angle dynamic models of grid-connected and grid-connected converters, a phase angle synchronization model for the converter cluster can be obtained. This phase angle synchronization model uniformly models the dynamic variation characteristics of the output voltage phase angle of both grid-connected and grid-connected converters.

[0023] Specifically, the phase synchronization model can be expressed as: ;in, Let represent the equivalent inertia coefficient of the i-th converter node. This represents the rate of change of the angular frequency of the i-th converter node. Let represent the equivalent damping coefficient of the i-th converter node. This represents the angular frequency of the i-th converter node. Let represent the equivalent stiffness coefficient of the i-th converter node. This represents the output voltage phase angle of the i-th converter node. This indicates the center of the inertia-weighted phase angle of the converter cluster. This represents the active power reference value of the i-th converter node. This represents the output active power of the i-th converter node. The equivalent inertia coefficient represents the converter node's ability to provide inertial support; the equivalent damping coefficient represents the converter node's ability to suppress angular frequency oscillations; and the equivalent stiffness coefficient represents the converter's ability to recover from phase angle deviations. If the converter node is a grid-type converter, the equivalent inertia coefficient is the virtual inertia, the equivalent damping coefficient is the equivalent virtual damping or droop damping, and the equivalent stiffness coefficient is the virtual stiffness coefficient. If the converter node is a grid-following converter, the equivalent inertia coefficient is the equivalent inertia, the equivalent damping coefficient is the equivalent damping coefficient formed by the phase-locked loop and power control circuit, and the equivalent stiffness coefficient is the equivalent recovery coefficient during the phase-locked loop phase angle following process.

[0024] Step 103: Perform centroid coordinate transformation on the phase angle synchronization model to determine the inertia-weighted phase angle center of the converter cluster, and calculate the relative phase angle of each converter node using the inertia-weighted phase angle center as a reference.

[0025] The inertia-weighted phase angle center is the equivalent reference phase angle obtained by weighting the output voltage phase angles of all converter nodes within the converter cluster according to their equivalent inertia. The inertia-weighted phase angle center characterizes the overall motion trend of the converter cluster dominated by inertia. The relative phase angle is the deviation of the output voltage phase angle of a converter node from the cluster's inertia-weighted phase angle center. Specifically, the relative phase angle of a converter node can be obtained by subtracting its output voltage phase angle from the inertia-weighted phase angle center.

[0026] Specifically, the inertia-weighted phase angle center of the converter cluster is determined, and the relative phase angle of each converter node is calculated using this center as a reference. This can be understood as transforming the output voltage phase angle of each converter node from an absolute phase angle to a relative phase angle based on the inertia-weighted phase angle center. For example, the inertia-weighted phase angle center of the converter cluster... According to the formula Determined; among them, This represents the output voltage phase angle of the i-th converter node. represents the equivalent inertia coefficient of the i-th converter node, and N represents the total number of converter nodes in the converter cluster.

[0027] Step 104: Construct an energy function based on the relative phase angle, and use the derivative of the energy function with respect to time to establish a synchronous stability criterion.

[0028] The energy function is a mapping function that determines the total energy of the converter cluster based on the relative phase angles of each converter node in the converter cluster. Specifically, the equivalent kinetic energy function of the converter cluster can be constructed based on the equivalent inertia coefficient and relative phase angle change rate of each converter node; the local recovery potential energy function of the converter cluster can be constructed based on the equivalent damping coefficient and relative phase angle of each converter node; the network coupling potential energy function of the converter cluster can be constructed based on the relative phase angle of each converter node, the coupling strength between each converter node and the first converter node corresponding to each converter node, and the line phase lag value between each converter node and the first converter node corresponding to each converter node; the network coupling potential energy function is obtained by subtracting the network coupling potential energy function from the sum of the equivalent kinetic energy function and the local recovery potential energy function; wherein, the first converter node corresponding to the converter node is another converter node that precedes the current converter node in the preset grid connection topology.

[0029] The synchronous stability criterion is a rule used to determine whether each converter node in a converter cluster can eventually reach a stable state, starting from a time point corresponding to a certain state point. If the total energy of the converter cluster gradually decreases over time and converges to a minimum value, it can be determined that the converter cluster has reached a synchronous stable state; if the total energy of the converter cluster gradually increases over time or continues to oscillate and cannot converge, it can be determined that the converter cluster cannot reach a synchronous stable state.

[0030] Specifically, the derivative of the energy function with respect to time reflects the direction and magnitude of the change in the total energy of the converter cluster over time. The trend of total energy change in the converter cluster can be determined by analyzing the derivative of the energy function with respect to time, and based on this trend, it can be determined whether the converter cluster can reach a stable state. For example, if the derivative of the energy function with respect to time is negative within a preset time period, and its absolute value gradually decreases until it approaches 0, then the converter cluster can be determined to have reached a synchronous stable state. If the derivative of the energy function with respect to time is positive, or its absolute value oscillates continuously and cannot stabilize at 0, then the converter cluster cannot be determined to have reached a synchronous stable state.

[0031] Step 105: Generate multiple state points in the state space composed of the phase angle variables and frequency variables of the converter cluster. Solve the dynamic equation of the phase angle synchronization model for each state point, and use the synchronization stability criterion to determine whether each state point is stable. Construct sample data based on each state point and the judgment results of each state point.

[0032] Phase angle variable is a physical quantity describing the rotational position of the converter's output voltage vector in space. The phase angle variable can be the phase angle deviation, which is the difference between the converter's output voltage phase angle and the angle the converter rotates through at its rated synchronous phase angle frequency. Frequency variable is a physical quantity describing how fast the converter's output voltage vector rotates. The frequency variable can be the frequency deviation, which is the difference between the converter's instantaneous angular frequency and its rated synchronous angular frequency.

[0033] Within the range of values ​​for phase angle and frequency variables, all combinations of variable values ​​constitute the state space. If the converter cluster includes N converters, the state space is a 2N-dimensional space. A state point in the state space represents an initial state of the converter cluster. A state point can be formed by the initial phase angle deviation and initial frequency deviation of each converter node in the converter cluster.

[0034] Specifically, starting from a state point of the converter cluster, the state changes of the converter cluster can be simulated time-by-time based on the phase angle synchronization model. Specifically, a target time period can be set. At each time point within the target time period, by solving the phase angle synchronization model, the output voltage phase angle of each converter node and the inertia-weighted center phase angle of the converter cluster are obtained. Then, based on the output voltage phase angle and the inertia-weighted center phase angle, the relative phase angles of the converter nodes are obtained. Next, the relative phase angles of each converter node are substituted into the energy function to obtain the total energy of the converter cluster. The derivative of the energy function with respect to time is then calculated. Based on the derivative value, it can be determined whether the converter cluster can reach a stable state within the target time period starting from that state point. If it can, the state point is considered stable; otherwise, the state point is considered unstable.

[0035] A training sample can be constructed based on a state point of the converter cluster and the corresponding stability judgment result. Multiple training samples corresponding to various state points can form a training dataset, which is then used to train the state discrimination model. Here, the state point serves as the input to the state discrimination model, and the stability judgment result serves as the supervised learning label for the model's output.

[0036] Step 106: Train the state discrimination model based on sample data.

[0037] The state discrimination model is a data-driven computational model used to determine whether a converter cluster can reach a stable state starting from an input state point.

[0038] Specifically, the state discrimination model takes the state points of the converter cluster as input and the stability discrimination result corresponding to that state point as output. Through supervised learning, it fits the nonlinear mapping relationship between the state points and the stability judgment results from the sample data. The trained state discrimination model can quickly evaluate the stability judgment result of any given state point.

[0039] For example, the state discrimination model can be a multilayer perceptron neural network. The training dataset can be represented as follows: ,in, Let be the sample size of the training dataset; the k-th sample can be represented as The model input is , For the state points of a converter cluster, it can be represented as: Where N represents the total number of converter nodes in the converter cluster. This represents the phase angle deviation of the i-th converter node. This represents the frequency deviation of the i-th converter node; the model output is... , This indicates the result of the judgment on whether the converter cluster can reach a steady state. The value of can range from [0,1], where 0 indicates that the converter cluster eventually becomes unstable, and 1 indicates that the converter cluster eventually reaches a stable state. The loss function during model training can be the cross-entropy loss function.

[0040] In this embodiment, multiple state points are generated, and the dynamic equations of the phase angle synchronization model are solved for each state point. Sample data is constructed based on each state point and the judgment results of each state point. The state discrimination model is trained based on the sample data. The trained state discrimination model can replace time-domain simulation and quickly output the stability discrimination results corresponding to the state points, which enhances the real-time performance of stable region determination and is more suitable for online evaluation scenarios.

[0041] Step 107: Input the state points to be judged into the trained state discrimination model to obtain the discrimination results of each state point, and determine the stable region of the converter cluster based on the discrimination results of multiple state points.

[0042] Specifically, within the high-dimensional state space comprised of the phase angle and frequency variables of the converter cluster, a two-dimensional cross-section is selected as the state plane. The state plane is then uniformly sampled in a gridded manner to obtain multiple state points to be judged. These multiple state points are input into a trained state discrimination model to obtain the discrimination result corresponding to each state point. Based on the position of the state point in the state plane and its corresponding discrimination result, the stable region of the converter cluster is determined. State points within the stable region are judged as "stable," while state points within the unstable region are judged as "instable." The boundary between these two regions is the synchronous stability domain boundary of the converter cluster. The synchronous stability domain boundary can be used for online evaluation of the stability margin of the converter cluster under current system parameters.

[0043] The stable region of a converter cluster can also be used for safety early warning. Specifically, the control system of the converter cluster can monitor the phase angle deviation and frequency deviation of each converter in real time, thereby obtaining the distance of the real-time state point from the boundary of the stable region, and thus issuing safety early warnings based on the distance of the real-time state point from the boundary of the stable region. Determining the stable region of the converter cluster can also be used for optimizing the control parameters of the converter cluster. Specifically, the control parameters of the converter cluster can be changed, and the changes in the stable and unstable regions can be observed to determine the optimal set of control parameters.

[0044] In this embodiment, based on system parameters, equivalent phase angle dynamic models of the grid-type converter and the grid-following converter are established respectively. A phase angle synchronization model is then constructed based on these models, enabling a more accurate description of the contribution of the grid-type converter to the power grid system's frequency support, phase angle recovery, and synchronization stability. Equivalent modeling of the phase angle synchronization oscillation state of the converter clusters of both grid-type and grid-following converters is performed, thus accurately characterizing the phase angle synchronization oscillations of the grid-type and grid-following converters. The stable region of a converter cluster in a hybrid grid-type converter access scenario is determined. The inertia-weighted phase angle center of the converter cluster is identified, and the relative phase angle of each converter node is calculated using this center as a reference. An energy function is constructed based on the relative phase angles, and a synchronization stability criterion is established using the time derivative of the energy function. This ensures that the energy function only reflects the oscillation dynamics of each converter relative to the cluster's phase angle center, thus making the determination of the converter cluster's stable state more accurate. The state space is defined by the phase angle and frequency variables of the converter cluster. Multiple state points are generated within a given time period. The dynamic equations of the phase angle synchronization model are solved for each state point, and the stability of each state point is determined using the synchronization stability criterion. Sample data is constructed based on each state point and the determination results. This method can rigorously determine the stability of each state point using a phase angle synchronization model with a clear physical mechanism and the synchronization stability criterion. This fundamentally ensures that the sample labels truly reflect the physical laws of the real phase angle synchronization evolution and energy dissipation of the converter cluster, thereby guaranteeing the accuracy of the training sample data and providing high-confidence supervision information for the training of the state discrimination model. This effectively improves the reliability of the subsequent determination of the stability region. The state discrimination model is trained based on the sample data. The state points to be judged are input into the trained state discrimination model to obtain the discrimination results of each state point. The stable region of the converter cluster is determined based on the discrimination results of multiple state points. This method can replace time-domain simulation by training the state discrimination model and quickly output the stability discrimination results corresponding to the state points. It is more suitable for online real-time stability assessment of converter clusters and helps to ensure the safe and stable operation of converter clusters.

[0045] The following is combined Figure 2 The method for determining the stable region of a converter cluster provided in this embodiment of the invention is further explained. Figure 2 This is another flowchart illustrating the method for determining the stable region of a converter cluster provided in this embodiment of the invention. (See also...) Figure 2 The method for determining the stable region of the converter cluster in this embodiment may include the following steps: Step 201: Obtain the system parameters of the converter cluster.

[0046] Optionally, the system parameters of the converter cluster include the equivalent inertia coefficient, equivalent damping coefficient, equivalent stiffness coefficient, active power reference value and voltage amplitude of each converter node in the converter cluster, the preset grid connection topology sequence of each converter node, and the equivalent admittance between any two converter nodes.

[0047] Step 202: Based on the system parameters, establish the equivalent phase angle dynamic model of the grid-type converter and the equivalent phase angle dynamic model of the grid-following converter, respectively, and construct the phase angle synchronization model based on the equivalent phase angle dynamic model of the grid-type converter and the equivalent phase angle dynamic model of the grid-following converter.

[0048] Optionally, the phase synchronization model is: ;in, Let represent the equivalent inertia coefficient of the i-th converter node. This represents the rate of change of the angular frequency of the i-th converter node. Let represent the equivalent damping coefficient of the i-th converter node. This represents the angular frequency of the i-th converter node. Let represent the equivalent stiffness coefficient of the i-th converter node. This represents the output voltage phase angle of the i-th converter node. This indicates the center of the inertia-weighted phase angle of the converter cluster. This represents the active power reference value of the i-th converter node. This represents the output active power of the i-th converter node.

[0049] Optionally, the system parameters of the converter cluster also include the voltage amplitude and equivalent admittance of each converter node. The formula for calculating the output active power of the i-th converter node is as follows: Where N represents the total number of converter nodes in the converter cluster. This represents the output voltage phase angle of the j-th converter node. This represents the coupling strength between the i-th converter node and the j-th converter node. Let represent the line phase lag value between the i-th converter node and the j-th converter node, and sin() represent the sine function. The formula for calculating the coupling strength between the i-th converter node and the j-th converter node is: ;in, This indicates that the voltage amplitude is based on the voltage magnitude of the i-th converter node. This represents the voltage amplitude at the j-th converter node. This represents the equivalent admittance between converter node i and converter node j.

[0050] Step 203: Perform centroid coordinate transformation on the phase angle synchronization model to determine the inertia-weighted phase angle center of the converter cluster, and calculate the relative phase angle of each converter node using the inertia-weighted phase angle center as a reference.

[0051] Optionally, it can be based on the formula Determine the inertia-weighted phase angle center of the converter cluster; where, This indicates the center of the inertia-weighted phase angle of the converter cluster. This represents the output voltage phase angle of the i-th converter node. represents the equivalent inertia coefficient of the i-th converter node, and N represents the total number of converter nodes in the converter cluster.

[0052] Step 204: Construct the equivalent kinetic energy function of the converter cluster based on the equivalent inertia coefficient of each converter node and the relative phase angle change rate of each converter node in the converter cluster.

[0053] The equivalent kinetic energy function is used to model the mapping relationship between the relative phase angle change rate of a converter node and the kinetic energy stored in the node. The relative phase angle change rate is the derivative of the relative phase angle of the node with respect to time.

[0054] Optionally, the equivalent kinetic energy function is: ;in, This represents the equivalent kinetic energy of the converter cluster. This represents the rate of change of the relative phase angle. N This represents the total number of converter nodes in the converter cluster. Let represent the equivalent inertia coefficient of the i-th converter node. It represents the square of the relative phase angle change rate of the i-th converter node.

[0055] Step 205: Construct the local recovery potential energy function of the converter cluster based on the equivalent damping coefficient and relative phase angle of each converter node.

[0056] The local recovery potential energy function is used to model the mapping relationship between the relative phase angle of a converter node and the potential energy required for the node to recover to its equilibrium point.

[0057] Optionally, the local recovery potential function is: ;in, This represents the local recovery potential energy of the converter cluster. Indicates the relative phase angle. Let represent the equivalent damping coefficient of the i-th converter node. It represents the square of the relative phase angle of the i-th converter node.

[0058] Step 206: Construct the network coupling potential energy function of the converter cluster based on the relative phase angle of each converter node, the coupling strength between each converter node and the first converter node corresponding to each converter node, and the line phase lag value between each converter node and the first converter node corresponding to each converter node; wherein, the preset grid connection topology order of the first converter node corresponding to the converter node is before the converter node.

[0059] The network coupling potential energy function is used to model the mapping relationship between the difference in relative phase angles between different converter nodes and the potential energy accumulated in the transmission lines between nodes.

[0060] Optionally, the network coupling potential function is: ;in, This represents the network coupling potential energy of the converter cluster. This represents the coupling strength between the i-th converter node and the j-th converter node. This represents the relative phase angle of the j-th converter node. Let represent the line phase lag value between the i-th converter node and the j-th converter node, and cos(.) represent the cosine function. Here, represents the coupling strength between the i-th converter node and the j-th converter node. The calculation formula is ;in, This indicates that the voltage amplitude is based on the voltage magnitude of the i-th converter node. This represents the voltage amplitude at the j-th converter node. This represents the equivalent admittance between converter node i and converter node j.

[0061] Step 207: Construct the energy function based on the equivalent kinetic energy function, the local recovery potential energy function, and the network coupling potential energy function.

[0062] Specifically, the energy function can be ,in, Represents the equivalent kinetic energy function. This represents the locally recovered potential energy function. This represents the network coupling potential function.

[0063] Step 208: Establish a synchronous stability criterion using the derivative of the energy function with respect to time.

[0064] Step 209: Generate multiple state points in the state space consisting of the initial phase angle deviation and initial frequency deviation of each converter node in the converter cluster.

[0065] Step 210: For each converter node in the converter cluster, determine the current angular frequency based on the angular frequency of the converter node at the previous moment and the rate of change of the angular frequency at the previous moment; determine the current output voltage phase angle based on the output voltage phase angle of the converter node at the previous moment and the angular frequency at the previous moment.

[0066] The angular frequency of the converter node at the initial moment is determined based on the initial frequency deviation of the converter node; the output voltage phase angle of the converter node at the initial moment is determined based on the initial phase angle deviation of the converter node.

[0067] Specifically, for each state point, simulation can be performed within the target time period based on the synchronous phase angle model. The target time period is a continuous simulation time window starting from the initial time. The target time period includes multiple sampling times, with the initial time being the time corresponding to the state point. By observing the derivative of the total energy of the converter cluster with respect to time within the target time period, and combining this with the synchronous stability criterion, it can be ultimately determined whether the converter cluster can reach a stable state within the target time period.

[0068] The initial frequency deviation of a converter node is the frequency deviation corresponding to a state point, and the initial phase angle deviation of the converter is the phase angle deviation corresponding to a state point. At the initial moment, the angular frequency at the initial moment is determined based on the initial frequency deviation of each converter node, and the output voltage phase angle is determined based on the initial phase angle deviation of each converter node. The angular frequency change rate at the initial moment can be initialized to 0. At each sampling moment after the initial moment, for each converter node, firstly, the angular frequency, angular frequency change rate, and output voltage phase angle of the previous moment are obtained; then, the angular frequency change value is determined by integrating the angular frequency change rate of the previous moment with time, and the angular frequency change value of the previous moment is accumulated to obtain the angular frequency of the current moment; the phase angle change value is determined by integrating the angular frequency of the previous moment with time, and the output voltage phase angle of the previous moment is accumulated to obtain the output voltage phase angle of the current moment.

[0069] Step 211: Perform centroid coordinate transformation on the phase angle synchronization model based on the output voltage phase angle of each converter node at the current moment to determine the inertia-weighted phase angle center of the converter cluster at the current moment.

[0070] Specifically, at each moment, after calculating the rate of change of angular frequency and the output voltage phase angle of all nodes in the converter cluster, a centroid coordinate transformation can be performed on the phase angle synchronization model, i.e., the inertia-weighted phase angle center of the converter cluster can be updated. The current angular frequency and output voltage phase angle of each converter node can be substituted into the calculation formula in step 203 to calculate the inertia-weighted phase angle center of the converter cluster at the current moment.

[0071] Step 212: For each converter node in the converter cluster, solve the phase angle synchronization model based on the current angular frequency of the converter node, the current output voltage phase angle, and the current inertia-weighted phase angle center of the converter cluster to obtain the current angular frequency change rate of the converter node.

[0072] Specifically, after obtaining the angular frequency and output voltage phase angle of each converter node at the current moment, as well as the inertia-weighted phase angle center of the converter cluster, the above physical quantities can be substituted into the phase angle synchronization model constructed in step 202 to solve for the rate of change of the angular frequency of the converter node at the current moment.

[0073] Step 213: Return to step 210 until all moments within the target time period are generated, including the inertia-weighted phase angle center of the converter cluster and the output voltage phase angle of each converter node.

[0074] Specifically, after completing the phase angle synchronization state simulation of each converter node at the current moment, return to step 210 to enter the simulation of the next moment, until the simulation of all moments within the target time period is completed, and generate the inertia-weighted phase angle center of the converter cluster and the output voltage phase angle of each converter node at all moments within the target time period.

[0075] Step 214: For each moment within the target time period, calculate the relative phase angle of each converter node using the inertia-weighted phase angle center of the converter cluster at the current moment as the reference benchmark, and solve the energy function value based on the relative phase angle of each converter node.

[0076] Specifically, after completing the simulation of all times within the target time period, the inertia-weighted phase angle center of the converter cluster and the output voltage phase angle of each converter node can be obtained for all recorded times within the target time period, and then the relative phase angle can be calculated moment by moment. Alternatively, after completing the simulation of each time moment, the inertia-weighted phase angle center and output voltage phase angle at the current time can be obtained, and the relative phase angle at the current time can be calculated. At each time moment, for each converter node, the output voltage phase angle at the current time is subtracted from the inertia-weighted phase angle center at the current time to obtain the relative phase angle of that converter node at the current time. After obtaining the relative phase angles of each target node at the current moment, the rate of change of the relative phase angle is determined, and the rate of change of the relative phase angle of each node at the current moment is substituted into the equivalent energy function in step 204 to obtain the equivalent energy function value of the converter cluster at the current moment; then, the relative phase angles of each converter node at the current moment are substituted into the local recovery potential energy function in step 205 to obtain the local recovery potential energy function value of the converter cluster at the current moment; the relative phase angles of each converter node at the current moment are substituted into the network coupling potential energy function in step 206 to obtain the network coupling potential energy function value of the converter cluster at the current moment; finally, the kinetic energy function value, the local recovery potential energy function value, and the network coupling potential energy function value are substituted into the formula in step 207 to obtain the energy function value.

[0077] Step 215: Combine the derivative of the energy function value with respect to time at each moment with the synchronization stability criterion to determine whether the state point is stable.

[0078] Specifically, a predetermined number of sampling times can be selected at the end of the target time period, and the derivative of the energy function value with respect to time at each sampling time can be calculated. This derivative is then combined with a synchronization stability criterion to determine whether the state point is stable. For example, if the derivative of the energy function value with respect to time at each sampling time is negative and its absolute value gradually decreases until it approaches 0, it can be determined that the converter cluster has reached a synchronous stable state; if it is positive, or the absolute value oscillates continuously and cannot stabilize at 0, it can be determined that the converter cluster has not reached a synchronous stable state.

[0079] Step 216: Construct sample data based on each state point and the judgment results of each state point.

[0080] Step 217: Train the state discrimination model based on sample data.

[0081] Step 218: Input the state points to be judged into the trained state discrimination model to obtain the discrimination results of each state point, and determine the stable region of the converter cluster based on the discrimination results of multiple state points.

[0082] In this embodiment, based on system parameters, equivalent phase angle dynamic models for grid-connected converters and grid-connected converters are established respectively. A phase angle synchronization model is then constructed based on these models, enabling a more accurate description of the contribution of grid-connected converters to the frequency support, phase angle recovery, and synchronization stability of the power grid system. Equivalent modeling of the phase angle synchronization oscillation state of converter clusters with both grid-connected and grid-connected converters is performed, thus accurately characterizing the stable region of the converter cluster in a mixed grid-connected and grid-connected converter access scenario. The inertia-weighted phase angle center of the converter cluster is determined, and calculations are performed using the inertia-weighted phase angle center as a reference. The relative phase angles of the converter nodes ensure that the energy function reflects only the oscillation dynamics of each converter relative to the center of the cluster phase angle. The equivalent kinetic energy function of the converter cluster is constructed based on the equivalent inertia coefficients of each converter node and the rate of change of the relative phase angles of each converter node in the converter cluster. The local restoring potential energy function of the converter cluster is constructed based on the equivalent damping coefficients of each converter node and the relative phase angles. The network coupling potential energy function of the converter cluster is constructed based on the relative phase angles of each converter node, the coupling strength between each converter node and its corresponding first converter node, and the line phase lag value between each converter node and its corresponding first converter node. Based on the equivalent kinetic energy function and the local restoring potential energy function... An energy function is constructed using the energy function and the network coupling potential energy function. A synchronization stability criterion is established using the time derivative of this energy function. This comprehensively considers the combined effects of the converter's virtual inertia support, damping dissipation, and network impedance on the phase synchronization process, thus more accurately describing the actual energy conversion and dissipation path of the converter cluster and making the determination of the converter cluster's stable state more accurate. Multiple state points are generated within the state space composed of the phase angle and frequency variables of the converter cluster, consisting of the initial phase angle deviation and initial frequency deviation of each converter node in the cluster. For each converter node in the cluster, the current time is determined based on the angular frequency and rate of change of the angular frequency at the previous moment. The angular frequency of the converter node is determined; the output voltage phase angle at the current moment is determined based on the output voltage phase angle of the converter node at the previous moment and the angular frequency at the previous moment; the centroid coordinate transformation of the phase angle synchronization model is performed based on the output voltage phase angle of each converter node at the current moment to determine the inertia-weighted phase angle center of the converter cluster at the current moment; for each converter node in the converter cluster, the phase angle synchronization model is solved based on the angular frequency of the converter node at the current moment, the output voltage phase angle at the current moment, and the inertia-weighted phase angle center of the converter cluster at the current moment to obtain the angular frequency change rate of the converter node at the current moment; the inertia-weighted phase angle center of the converter cluster and the output voltage phase angle of each converter node at all moments within the target time period are generated;For each moment within the target time period, the relative phase angle of each converter node is calculated using the inertia-weighted phase angle center of the converter cluster at the current moment as a reference. The energy function value is then calculated based on the relative phase angle of each converter node. The stability of the state point is determined based on the derivative of the energy function value with respect to time at each sampling moment. This method accurately tracks the synchronous evolution of the converter cluster's phase angle and energy dissipation over time, improving the interpretability of the judgment results. Sample data is constructed based on each state point and its judgment results. A state discrimination model is trained based on the sample data. The state points to be judged are input into the trained state discrimination model to obtain the discrimination results for each state point. The stable region of the converter cluster is determined based on the discrimination results of multiple state points. This method can replace time-domain simulation by training the state discrimination model, quickly outputting the stability discrimination results corresponding to the state points. It is more suitable for online real-time stability assessment of converter clusters, which is beneficial for ensuring the safe and stable operation of the converter cluster.

[0083] Figure 3 This is a schematic diagram of a stable region determination device for a converter cluster provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the device includes: Module 301 is used to acquire system parameters of the converter cluster; The model building module 302 is used to build the equivalent phase angle dynamic model of the grid-type converter and the equivalent phase angle dynamic model of the grid-connected converter according to the system parameters, and to build the phase angle synchronization model based on the equivalent phase angle dynamic model of the grid-type converter and the equivalent phase angle dynamic model of the grid-connected converter. The phase angle calculation module 303 is used to perform centroid coordinate transformation on the phase angle synchronization model, determine the inertia-weighted phase angle center of the converter cluster, and calculate the relative phase angle of each converter node with the inertia-weighted phase angle center as a reference. The criterion establishment module 304 is used to construct an energy function based on the relative phase angle and to establish a synchronization stability criterion using the derivative of the energy function with respect to time. The sample construction module 305 is used to generate multiple state points in the state space composed of the phase angle variables and frequency variables of the converter cluster, solve the dynamic equation of the phase angle synchronization model for each state point, and use the synchronization stability criterion to determine whether each state point is stable, and construct sample data based on each state point and the judgment result of each state point. Training module 306 is used to train a state discrimination model based on sample data; The determination module 307 is used to input the state points to be judged into the trained state discrimination model, obtain the discrimination results of each state point, and determine the stable region of the converter cluster based on the discrimination results of multiple state points.

[0084] In one embodiment, the system parameters of the converter cluster include the equivalent inertia coefficient, equivalent damping coefficient, equivalent stiffness coefficient, and active power reference value of each converter node in the converter cluster; the phase angle synchronization model is as follows: ;in, Let represent the equivalent inertia coefficient of the i-th converter node. This represents the rate of change of the angular frequency of the i-th converter node. Let represent the equivalent damping coefficient of the i-th converter node. This represents the angular frequency of the i-th converter node. Let represent the equivalent stiffness coefficient of the i-th converter node. This represents the output voltage phase angle of the i-th converter node. This indicates the center of the inertia-weighted phase angle of the converter cluster. This represents the active power reference value of the i-th converter node. This represents the output active power of the i-th converter node.

[0085] In one embodiment, the system parameters of the converter cluster also include the voltage amplitude of each converter node and the equivalent admittance between any two converter nodes; the formula for calculating the output active power of the i-th converter node is as follows: Where N represents the total number of converter nodes in the converter cluster. This represents the output voltage phase angle of the j-th converter node. This represents the coupling strength between the i-th converter node and the j-th converter node. Let represent the line phase lag value between the i-th converter node and the j-th converter node, and sin() represent the sine function; where the formula for calculating the coupling strength between the i-th converter node and the j-th converter node is: ;in, This indicates that the voltage amplitude is based on the voltage magnitude of the i-th converter node. This represents the voltage amplitude at the j-th converter node. This represents the equivalent admittance between converter node i and converter node j.

[0086] In one embodiment, the phase angle calculation module 303 performs a centroid coordinate transformation on the phase angle synchronization model to determine the inertia-weighted phase angle center of the converter cluster, including: according to the formula Determine the inertia-weighted phase angle center of the converter cluster; where, This indicates the center of the inertia-weighted phase angle of the converter cluster. This represents the output voltage phase angle of the i-th converter node. represents the equivalent inertia coefficient of the i-th converter node, and N represents the total number of converter nodes in the converter cluster.

[0087] In one embodiment, the system parameters of the converter cluster include the equivalent inertia coefficient, equivalent damping coefficient, and preset grid connection topology sequence of each converter node in the converter cluster; the criterion establishment module 304 constructs an energy function based on the relative phase angle, including: The equivalent kinetic energy function of the converter cluster is constructed based on the equivalent inertia coefficient and relative phase angle change rate of each converter node. The local recovery potential energy function of the converter cluster is constructed based on the equivalent damping coefficient and relative phase angle of each converter node. The network coupling potential energy function of the converter cluster is constructed based on the relative phase angle of each converter node, the coupling strength between each converter node and the first converter node corresponding to each converter node, and the line phase lag value between each converter node and the first converter node corresponding to each converter node; the preset grid connection topology order of the first converter node corresponding to the converter node is before the converter node. The energy function is constructed based on the equivalent kinetic energy function, the local recovery potential energy function, and the network coupling potential energy function.

[0088] In one embodiment, the equivalent kinetic energy function is: ;in, This represents the equivalent kinetic energy of the converter cluster. This represents the relative phase angle change rate, and N represents the total number of converter nodes in the converter cluster. Let represent the equivalent inertia coefficient of the i-th converter node. This represents the square of the relative phase angle change rate of the i-th converter node; The local recovery potential function is ;in, This represents the local recovery potential energy of the converter cluster. Indicates the relative phase angle. Let represent the equivalent damping coefficient of the i-th converter node. This represents the square of the relative phase angle of the i-th converter node; The network coupling potential function is ;in, This represents the network coupling potential energy of the converter cluster. This represents the coupling strength between the i-th converter node and the j-th converter node. This represents the relative phase angle of the j-th converter node. represents the line phase lag value between the i-th converter node and the j-th converter node, and cos(.) represents the cosine function.

[0089] In one embodiment, the state points include the initial phase angle deviation and initial frequency deviation of each converter node in the converter cluster; the sample construction module 305 solves the dynamic equations of the phase angle synchronization model for each state point, including: For each converter node in the converter cluster, the current angular frequency is determined based on the angular frequency of the converter node at the previous moment and the rate of change of the angular frequency at the previous moment; the current output voltage phase angle is determined based on the output voltage phase angle of the converter node at the previous moment and the angular frequency at the previous moment; wherein, the initial angular frequency of the converter node is determined based on the initial frequency deviation of the converter node; the initial output voltage phase angle of the converter node is determined based on the initial phase angle deviation of the converter node; Based on the current output voltage phase angle of each converter node, the centroid coordinate transformation is performed on the phase angle synchronization model to determine the inertia-weighted phase angle center of the converter cluster at the current moment. For each converter node in the converter cluster, the phase angle synchronization model is solved based on the current angular frequency of the converter node, the current output voltage phase angle, and the current inertia-weighted phase angle center of the converter cluster to obtain the current angular frequency change rate of the converter node. Repeat the above steps until all moments within the target time period are generated, including the inertia-weighted phase angle center of the converter cluster and the output voltage phase angle of each converter node.

[0090] In one embodiment, the sample construction module 305 uses a synchronization stability criterion to determine whether a state point is stable, including: For each moment within the target time period, the relative phase angle of each converter node is calculated with the inertia-weighted phase angle center of the converter cluster at the current moment as the reference benchmark, and the energy function value is solved based on the relative phase angle of each converter node. The derivative of the energy function value with respect to time at each moment is combined with the synchronization stability criterion to determine whether the state point is stable.

[0091] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0092] The apparatus of this invention establishes equivalent phase angle dynamic models for both grid-type and grid-following converters based on system parameters. It then constructs a phase angle synchronization model based on these models. This allows for a more accurate description of the contribution of grid-type converters to the frequency support, phase angle recovery, and synchronization stability of the power grid system. Furthermore, it provides equivalent modeling of the phase angle synchronization oscillation state of the converter clusters of both grid-type and grid-following converters, thereby precisely characterizing the grid-connected converters. This study investigates the stable region of converter clusters in mixed-access scenarios involving both modular and grid-connected converters. It determines the inertia-weighted phase angle center of the converter cluster and calculates the relative phase angles of each converter node using this center as a reference. An energy function is constructed based on the relative phase angles, and a synchronization stability criterion is established using the time derivative of this energy function. This ensures that the energy function reflects only the oscillation dynamics of each converter relative to the cluster's phase angle center, thus making the determination of the converter cluster's stable state more accurate. The study also considers the stability region of the converter cluster as composed of phase angle and frequency variables. Multiple state points are generated in the state space, and the dynamic equations of the phase angle synchronization model are solved for each state point. The stability criterion is used to determine whether each state point is stable. Sample data is constructed based on each state point and the judgment results. This method can strictly determine the stability of each state point using a phase angle synchronization model with a clear physical mechanism and a synchronization stability criterion. This fundamentally ensures that the sample labels truly reflect the physical laws of the real phase angle synchronization evolution and energy dissipation of the converter cluster, thus guaranteeing the accuracy of the training sample data. It provides high-confidence supervision information for the training of the state discrimination model and effectively improves the reliability of the subsequent determination of the stability region. The state discrimination model is trained based on the sample data. The state points to be judged are input into the trained state discrimination model to obtain the discrimination results of each state point. The stable region of the converter cluster is determined based on the discrimination results of multiple state points. The state discrimination model can replace time-domain simulation by training the state discrimination model and quickly output the stability discrimination results corresponding to the state points. It is more suitable for online real-time stability assessment of converter clusters and helps to ensure the safe and stable operation of converter clusters.

[0093] The following is for reference. Figure 4 It shows a schematic diagram of the structure of a computer system 400 suitable for implementing an electronic device according to embodiments of the present invention. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0094] like Figure 4As shown, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from storage section 408 into Random Access Memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the computer system 400. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0095] The following components are connected to I / O interface 405: input section 406 including keyboard, mouse, etc.; output section 407 including cathode ray tube, liquid crystal display, etc., and speakers, etc.; storage section 408 including hard disk, etc.; and communication section 409 including network interface card, such as modem, etc. Communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I / O interface 405 as needed. Removable media 411, such as disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 410 as needed so that computer programs read from them can be installed into storage section 408 as needed.

[0096] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the functions defined above in the system of this invention.

[0097] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, etc., or any suitable combination thereof.

[0098] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0099] The modules and / or units described in the embodiments of the present invention can be implemented in software or hardware. The described modules and / or units can also be housed in a processor; for example, a processor can be described as including an acquisition module, a model building module, a phase angle calculation module, a criterion establishment module, a sample construction module, a training module, and a determination module. The names of these modules do not necessarily constitute a limitation on the module itself.

[0100] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include: Obtain the system parameters of the converter cluster; based on the system parameters, establish equivalent phase angle dynamic models for both the grid-type and grid-following converters, and construct a phase angle synchronization model based on these models; perform centroid coordinate transformation on the phase angle synchronization model to determine the inertia-weighted phase angle center of the converter cluster, and calculate the relative phase angle of each converter node using the inertia-weighted phase angle center as a reference; construct an energy function based on the relative phase angle, and use the energy function to adjust for time... The derivative is used to establish a synchronization stability criterion; multiple state points are generated in the state space composed of the phase angle and frequency variables of the converter cluster; the dynamic equation of the phase angle synchronization model is solved for each state point; the synchronization stability criterion is used to determine whether each state point is stable; sample data is constructed based on each state point and the judgment results of each state point; a state discrimination model is trained based on the sample data; the state points to be judged are input into the trained state discrimination model to obtain the discrimination results of each state point; and the stable region of the converter cluster is determined based on the discrimination results of multiple state points.

[0101] The technical solution of this invention establishes equivalent phase angle dynamic models for grid-type converters and grid-connected converters based on system parameters. Then, it constructs a phase angle synchronization model based on these models. This allows for a more accurate description of the contribution of grid-type converters to the frequency support, phase angle recovery, and synchronization stability of the power grid system. It also provides equivalent modeling of the phase angle synchronization oscillation state of converter clusters for both grid-type and grid-connected converters, thereby precisely characterizing... The stable region of a converter cluster in a mixed grid-type and follow-grid-type converter access scenario is determined. The inertia-weighted phase angle center of the converter cluster is identified, and the relative phase angle of each converter node is calculated using this center as a reference. An energy function is constructed based on the relative phase angle, and a synchronization stability criterion is established using the time derivative of the energy function. This ensures that the energy function only reflects the oscillation dynamics of each converter relative to the cluster's phase angle center, thus making the determination of the converter cluster's stable state more accurate. The stability region is determined by the phase angle and frequency variables of the converter cluster. Multiple state points are generated in the state space. The dynamic equations of the phase angle synchronization model are solved for each state point, and the stability of each state point is determined using the synchronization stability criterion. Sample data is constructed based on each state point and the judgment results. This method can strictly determine the stability of each state point using a phase angle synchronization model with a clear physical mechanism and the synchronization stability criterion. This fundamentally ensures that the sample labels truly reflect the physical laws of the real phase angle synchronization evolution and energy dissipation of the converter cluster, thus guaranteeing the accuracy of the training sample data. It provides high-confidence supervision information for the training of the state discrimination model and effectively improves the reliability of the subsequent determination of the stability region. The state discrimination model is trained based on the sample data. The state points to be judged are input into the trained state discrimination model to obtain the discrimination results of each state point. The stable region of the converter cluster is determined based on the discrimination results of multiple state points. This method can replace time-domain simulation by training the state discrimination model and quickly output the stability discrimination results corresponding to the state points. It is more suitable for online real-time stability assessment of converter clusters and helps to ensure the safe and stable operation of converter clusters.

[0102] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the method for determining the stable region of a converter cluster as provided in any embodiment of this invention.

[0103] In the implementation of a computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​as well as conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including local area networks (LANs) or wide area networks (WANs), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0105] It should be noted that the collection, use, storage, sharing, and transfer of user personal information involved in the technical solution of this invention all comply with the provisions of relevant laws and regulations, and require notification to the user and obtaining the user's consent or authorization. Where applicable, user personal information has undergone de-identification and / or anonymization and / or encryption technical processing. In addition, a corresponding operation entry is provided for the user to choose to agree to or reject the automated decision result; if the user chooses to reject, the process proceeds to the expert decision-making process.

[0106] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining the stable region of a converter cluster, characterized in that, The method includes: Obtain the system parameters of the converter cluster; Based on the system parameters, equivalent phase angle dynamic models of the grid-type converter and the grid-following converter are established respectively, and a phase angle synchronization model is constructed based on the equivalent phase angle dynamic models of the grid-type converter and the grid-following converter. The centroid coordinate transformation is performed on the phase angle synchronization model to determine the inertia-weighted phase angle center of the converter cluster, and the relative phase angle of each converter node is calculated with the inertia-weighted phase angle center as a reference. An energy function is constructed based on the relative phase angle, and a synchronization stability criterion is established using the derivative of the energy function with respect to time. Multiple state points are generated in the state space composed of the phase angle variables and frequency variables of the converter cluster. The dynamic equation of the phase angle synchronization model is solved for each state point. The synchronization stability criterion is used to determine whether each state point is stable. Sample data is constructed based on each state point and the judgment results of each state point. A state discrimination model is trained based on the sample data; The state points to be judged are input into the trained state discrimination model to obtain the discrimination results of each state point, and the stable region of the converter cluster is determined based on the discrimination results of multiple state points.

2. The method according to claim 1, characterized in that, The system parameters of the converter cluster include the equivalent inertia coefficient, equivalent damping coefficient, equivalent stiffness coefficient, and active power reference value of each converter node in the converter cluster. The phase angle synchronization model is as follows: ;in, Let represent the equivalent inertia coefficient of the i-th converter node. This represents the rate of change of the angular frequency of the i-th converter node. Let represent the equivalent damping coefficient of the i-th converter node. This represents the angular frequency of the i-th converter node. Let represent the equivalent stiffness coefficient of the i-th converter node. This represents the output voltage phase angle of the i-th converter node. This indicates the center of the inertia-weighted phase angle of the converter cluster. This represents the active power reference value of the i-th converter node. This represents the output active power of the i-th converter node.

3. The method according to claim 2, characterized in that, The system parameters of the converter cluster also include the voltage amplitude of each converter node and the equivalent admittance between any two converter nodes; The formula for calculating the output active power of the i-th converter node is as follows: Where N represents the total number of converter nodes in the converter cluster. This represents the output voltage phase angle of the j-th converter node. This represents the coupling strength between the i-th converter node and the j-th converter node. This represents the line phase lag value between the i-th converter node and the j-th converter node, and sin() represents the sine function; The formula for calculating the coupling strength between the i-th converter node and the j-th converter node is as follows: ;in, This indicates that the voltage amplitude is based on the voltage magnitude of the i-th converter node. This represents the voltage amplitude at the j-th converter node. This represents the equivalent admittance between converter node i and converter node j.

4. The method according to claim 1, characterized in that, The step of performing centroid coordinate transformation on the phase angle synchronization model to determine the inertia-weighted phase angle center of the converter cluster includes: According to the formula Determine the inertia-weighted phase angle center of the converter cluster; where, This indicates the center of the inertia-weighted phase angle of the converter cluster. This represents the output voltage phase angle of the i-th converter node. represents the equivalent inertia coefficient of the i-th converter node, and N represents the total number of converter nodes in the converter cluster.

5. The method according to claim 1, characterized in that, The system parameters of the converter cluster include the equivalent inertia coefficient, equivalent damping coefficient, and preset grid connection topology sequence of each converter node in the converter cluster. The construction of the energy function based on the relative phase angle includes: The equivalent kinetic energy function of the converter cluster is constructed based on the equivalent inertia coefficient of each converter node and the relative phase angle change rate. The local recovery potential energy function of the converter cluster is constructed based on the equivalent damping coefficient of each converter node and the relative phase angle. The network coupling potential energy function of the converter cluster is constructed based on the relative phase angle of each converter node, the coupling strength between each converter node and the first converter node corresponding to each converter node, and the line phase lag value between each converter node and the first converter node corresponding to each converter node; the preset grid connection topology order of the first converter node corresponding to the converter node is before the converter node. The energy function is constructed based on the equivalent kinetic energy function, the local recovery potential energy function, and the network coupling potential energy function.

6. The method according to claim 5, characterized in that: The equivalent kinetic energy function is: ;in, This represents the equivalent kinetic energy of the converter cluster. This represents the relative phase angle change rate, and N represents the total number of converter nodes in the converter cluster. Let represent the equivalent inertia coefficient of the i-th converter node. This represents the square of the relative phase angle change rate of the i-th converter node; The local recovery potential energy function is: ;in, This represents the local recovery potential energy of the converter cluster. Indicates the relative phase angle. Let represent the equivalent damping coefficient of the i-th converter node. This represents the square of the relative phase angle of the i-th converter node; The network coupling potential function is: ;in, This represents the network coupling potential energy of the converter cluster. This represents the coupling strength between the i-th converter node and the j-th converter node. This represents the relative phase angle of the j-th converter node. represents the line phase lag value between the i-th converter node and the j-th converter node, and cos(.) represents the cosine function.

7. The method according to claim 1, characterized in that, The state points include the initial phase angle deviation and initial frequency deviation of each converter node in the converter cluster; For any given state point, solving the dynamic equations of the phase synchronization model includes: For each converter node in the converter cluster, the current angular frequency is determined based on the angular frequency of the converter node at the previous moment and the rate of change of the angular frequency at the previous moment; the current output voltage phase angle is determined based on the output voltage phase angle of the converter node at the previous moment and the angular frequency at the previous moment; wherein, the initial angular frequency of the converter node is determined based on the initial frequency deviation of the converter node; the initial output voltage phase angle of the converter node is determined based on the initial phase angle deviation of the converter node; Based on the current output voltage phase angle of each converter node, the centroid coordinate transformation is performed on the phase angle synchronization model to determine the inertia-weighted phase angle center of the converter cluster at the current moment. For each converter node in the converter cluster, the phase angle synchronization model is solved based on the current angular frequency of the converter node, the current output voltage phase angle, and the current inertia-weighted phase angle center of the converter cluster to obtain the current angular frequency change rate of the converter node. Repeat the above steps until all moments within the target time period are generated, including the inertia-weighted phase angle center of the converter cluster and the output voltage phase angle of each converter node.

8. The method according to claim 7, characterized in that, For any given state point, determining whether the state point is stable using the synchronization stability criterion includes: For each moment within the target time period, the relative phase angle of each converter node is calculated with the inertia-weighted phase angle center of the converter cluster at the current moment as a reference, and the energy function value is solved based on the relative phase angle of each converter node. The state point is determined to be stable by combining the derivative of the energy function value with respect to time at each moment with the synchronous stability criterion.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the program to implement the method for determining the stable region of the converter cluster as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for determining the stable region of a converter cluster as described in any one of claims 1 to 8.

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