A new energy grid-connected network equipment group clustering reduction method, system and medium

Abstract

The present application relates to the technical field of power system modeling, and more particularly to a new energy grid-connected network equipment group clustering reduction method, system and medium, the method comprising: obtaining parameters of multiple types of network equipment and grid-connected network in a new energy base grid-connected system; performing network dynamic isomorphism processing on the multiple types of network equipment to obtain a standard network outer ring model; identifying an approximately isomorphic network equipment group based on the standard network outer ring model and the grid-connected network coupling relationship; constructing a virtual isomorphic network equipment group according to the approximately isomorphic network equipment group and establishing a group clustering dynamic model; performing characteristic transformation on the network matrix to decompose the group clustering dynamic model into single-machine equivalent subsystems; selecting external interaction equivalent subsystems and accessing the grid-connected network with a reserved topology to generate a structure-preserving reduced model. Through the present application, the problem that the prior art is difficult to reduce the model dimension while uniformly retaining the dominant dynamics of multiple types of network equipment and their group clustering coupling relationship with the grid-connected network is effectively solved.

Description

Technical Field

[0001] This invention relates to the field of power system modeling technology, and in particular to a method, system and medium for reducing the order of new energy grid-connected equipment clusters. Background Technology

[0002] With the large-scale development of new energy bases and the continuous advancement of long-distance transmission projects, a large number of power electronic devices in new energy grid-connected systems are connected to the power grid through collector lines, busbars, step-up transformers, and transmission lines. Grid-type control equipment can provide voltage and frequency support at AC ports and plays a crucial role in grid-connected systems with a high proportion of new energy. Due to differences in capacity level, control parameters, operating conditions, and connection locations among different devices, the dynamic characteristics of the grid-connected system depend not only on the control performance of individual devices but also on the network topology and line electrical parameters within the site.

[0003] Existing technologies typically employ methods such as detailed modeling of each unit, capacity-weighted equivalence, parameter aggregation, impedance aggregation, or homology grouping to establish models of renewable energy power plants or grid-connected systems. While detailed modeling of each unit can comprehensively reflect the connections between equipment and the network, the model has high dimensionality and computational complexity, which is not conducive to engineering analysis and parameter tuning. Capacity-weighted equivalence and parameter aggregation methods usually focus on averaging equipment capacity or control parameters, making it difficult to accurately reflect the dynamic similarity relationships between different devices. Impedance aggregation and homology grouping methods primarily serve equivalent impedance calculation or cluster partitioning, making it difficult to form a reduced-order dynamic model that takes into account both equipment dynamics and network structure. Therefore, existing equivalent modeling techniques for renewable energy grid-connected systems still face the challenge of preserving the dominant dynamics of grid-connected equipment and their coupling relationship with the grid network while reducing model dimensionality.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method, system, and medium for reducing the order of grid-connected new energy equipment clusters, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for reducing the order of grid-connected new energy equipment clusters, the method comprising: Obtain basic parameters of various types of grid-connected equipment and grid-connected networks in the grid-connected system of the new energy base; The network dynamic isomorphism processing is performed on the various types of network-building devices to obtain a standard network outer ring model under a unified state coordinate system. Based on the coupling relationship between the standard network outer ring model and the grid-connected network, a group of approximately homogeneous network devices is identified; A virtual isomorphic network device group is constructed based on the approximately isomorphic network device group, and a clustering dynamic model of the virtual isomorphic network device group is established. The network matrix in the cluster dynamic model is subjected to feature transformation, and the cluster dynamic model is decomposed into multiple single-machine equivalent subsystems. From the multiple equivalent subsystems of the single-machine type, the equivalent subsystems for external interaction are selected, and the equivalent subsystems for external interaction are connected to the grid-connected network that retains the topology to generate a structure-preserving reduced-order model.

[0007] Furthermore, the basic parameters include the physical type, rated capacity, operating point, outer loop control parameters, virtual impedance parameters, and access nodes of the various types of grid-connected equipment, as well as the collector line parameters, collection bus parameters, step-up transformer parameters, transmission line parameters, and grid connection point parameters of the grid-connected network; the grid-connected equipment includes one or more of grid-connected direct-drive wind turbines, grid-connected energy storage, and grid-connected static var generators.

[0008] Furthermore, the network dynamic isomorphism processing includes: The outer ring state variables of the network construction devices of various types are rearranged according to a preset state order to obtain a unified set of state variables. The outer loop control parameters of the network construction equipment of various types are normalized according to the rated capacity, per-unit benchmark and control parameter benchmark to obtain the normalized outer loop dominant parameters. The missing outer rings of the network structure devices of various types are filled by zero-gain placeholders, first-order equivalent placeholders or zero-state placeholders to obtain the standard outer ring model of the network structure. Occupied states with no effective dynamic contribution are removed from the calculation of effective dynamic distance and contribution weight in the approximate isomorphism identification. For grid-type static var generators, the dynamics of the grid-type outer loop corresponding to reactive power-voltage, voltage amplitude control, and virtual impedance are retained, and zero-gain or zero-state occupation is adopted for the active power-frequency link.

[0009] Further, identifying the group of near-homogeneous network devices includes: Based on the state structure of the standard network outer ring model, the normalized outer ring dominant parameters, and the network coupling relationship between the operating point and the access node, a candidate device group is formed. Calculate the comprehensive distance between devices within the candidate device group based on the normalized outer ring dominant parameters and the network electrical distance between the operating point and the access node; Using the dynamic center, parameter center, operating point center, and network coupling strength center within the candidate device group as references, calculate the comprehensive deviation index of the candidate device group; When the comprehensive deviation index does not exceed the first preset threshold, the candidate device group is determined as the approximately homogeneous network device group; When the comprehensive deviation index exceeds the first preset threshold but does not exceed the second preset threshold, a secondary clustering is performed on the candidate device group; When the comprehensive deviation index exceeds the second preset threshold, the network equipment that exceeds the limit will be retained as a single-element equipment group.

[0010] Furthermore, constructing the virtual homogeneous networking device group includes: Select representative devices, typical devices, or dynamic centers from the group of near-isomorphic network devices as templates; Based on the number of devices, access nodes, and network clustering pattern of the approximately homogeneous network device group, establish the corresponding virtual homogeneous network device group; The devices within the virtual isomorphic network device group are dynamically configured as the standard network outer ring model corresponding to the template; The virtual homogeneous network device group is used as the object for establishing the cluster dynamic model; When the approximately homogeneous network device group is a completely homogeneous network device group, the strictly equivalent single-machine equivalent subsystem is obtained through the virtual homogeneous network device group; When the group of near-homogeneous network devices is a group of near-homogeneous network devices, the near-equivalent single-machine equivalent subsystem is obtained through the virtual homogeneous network device group.

[0011] Furthermore, the aforementioned clustering dynamic model is established, including: Based on the standard network outer ring model corresponding to the template, determine the state matrix, input matrix, and output matrix of the common standard outer ring dynamics; Based on the access nodes of the virtual homogeneous network device group and the basic parameters of the grid-connected network, construct a network reactance matrix or a clustered network matrix; The network feedback relationship within the group is determined based on the network reactance matrix or the cluster network matrix, the port variable selection matrix or coordinate transformation matrix, the voltage deviation at the common grid connection point, and the distribution vector or distribution matrix of the external voltage at each device port within the group. The cluster dynamic model is established based on the outer ring dynamics of the common standard, the feedback relationship of the intra-cluster network, and the coupling term of the external grid-connected network.

[0012] Furthermore, the cluster dynamic model is decomposed into multiple equivalent subsystems of the single-machine type, including: The network reactance matrix or the clustered network matrix is ​​used as the network matrix; The network matrix is ​​subjected to eigenvalue decomposition or similarity transformation to obtain the eigenvalues ​​and eigenvectors of the network matrix; Construct a state similarity transformation matrix based on the feature vectors; The cluster dynamic model is subjected to state similarity transformation based on the state similarity transformation matrix to obtain multiple equivalent subsystems of the single-machine type. The dynamic matrix of each of the single-machine equivalent subsystems is set as a matrix determined by the common standard outer loop dynamics and the corresponding eigenvalues; The coupling strength between each of the single-unit equivalent subsystems and the external grid-connected network is set to the corresponding external coupling coefficient.

[0013] Further, generating the structure-preserving reduced-order model includes: The dominant index is calculated based on the external coupling coefficient, damping characteristics, and grid connection point response contribution of each of the single-unit equivalent subsystems. The single-machine equivalent subsystems are ranked according to the aforementioned dominant indicators; Based on a preset coupling threshold and a preset cumulative contribution rate threshold, the external interaction equivalent subsystem is determined from multiple single-machine equivalent subsystems; The template, the capacity normalized weight or node weight in the network matrix, and the number of single-machine equivalent subsystems retained in the structure-preserving reduced-order model are determined based on the cluster dynamic contribution weight. Connect the external interaction equivalent subsystem to the grid-connected network with the preserved topology; In the grid-connected network with the retained topology, the topology of collector lines, collector buses, step-up transformers, transmission lines, and grid connection points is retained; Based on the state of the external interaction equivalent subsystem and the retained grid-connected network state, the state of the structure-preserving reduced-order model is generated; The output of the structure-preserving reduced-order model is generated based on the grid connection point voltage, active power, reactive power, or observation deviation.

[0014] A clustering and order reduction system for new energy grid-connected equipment, the system comprising: The basic parameter acquisition module acquires the basic parameters of various types of grid-connected equipment and grid-connected networks in the grid-connected system of the new energy base. The dynamic isomorphism processing module performs dynamic isomorphism processing on multiple types of network construction devices to obtain a standard network outer ring model under unified state coordinates. The homogeneous device identification module identifies groups of devices in a nearly homogeneous network based on the coupling relationship between the standard network outer ring model and the grid-connected network. The virtual clustering modeling module constructs a virtual isomorphic network device cluster based on an approximately isomorphic network device cluster, and establishes a clustering dynamic model of the virtual isomorphic network device cluster. The matrix eigenvalue decomposition module performs eigenvalue transformation on the network matrix in the cluster dynamic model, decomposing the cluster dynamic model into multiple single-machine equivalent subsystems. The interactive filtering and generation module filters out external interactive equivalent subsystems from multiple stand-alone equivalent subsystems, and connects the external interactive equivalent subsystems to the grid-connected network that preserves the topology, generating a structure-preserving reduced-order model.

[0015] A computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, can implement the aforementioned method for clustering and reducing the order of new energy grid-connected equipment.

[0016] The technical solution of this invention can achieve the following technical effects: By acquiring the parameters of various types of grid-connected equipment and grid-connected networks in new energy grid-connected systems, the grid-connected equipment is dynamically homogenized and approximately homogenous equipment groups are identified. Furthermore, a virtual homogenous equipment group and a cluster dynamic model are constructed. Then, a single-machine equivalent subsystem is generated through network matrix feature transformation, and an external interactive equivalent subsystem is selected. Finally, a structure-preserving reduced-order model that retains the grid-connected network topology is formed. This effectively solves the problem in the existing equivalent modeling of new energy grid-connected systems that it is difficult to uniformly retain the dominant dynamics of various types of grid-connected equipment and their cluster coupling relationship with the grid-connected network while reducing the model dimension.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A schematic diagram of the grid connection system structure for a new energy base; Figure 2 A schematic diagram illustrating the dynamic isomorphic transformation of multiple types of network-building devices; Figure 3 This is a schematic diagram illustrating the identification and difference processing of near-homogeneous network device groups. Figure 4 A schematic diagram of the full-order state-space model of an approximately homogeneous network of devices; Figure 5 A schematic diagram illustrating the characteristic transformation of the network reactance matrix and the generation of the equivalent subsystem for a single machine. Figure 6 A schematic diagram of the equivalent subsystem selection for external interaction and the grid-connected system order reduction model; Figure 7 The image shows a comparison of time-domain simulations of the reduced-order model and the full-order model. Detailed Implementation

[0020] 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.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] Example 1; like Figure 1 As shown, this application provides a method for reducing the order of grid-connected new energy equipment clusters, the method including: Obtain basic parameters of various types of grid-connected equipment and grid-connected networks in the grid-connected system of the new energy base; Dynamic isomorphism processing of multiple types of network-building devices is performed to obtain a standard network outer ring model under unified state coordinates; Based on the coupling relationship between the standard network outer ring model and the grid-connected network, identify approximately homogeneous network device groups; A virtual homogeneous network device cluster is constructed based on the approximately homogeneous network device cluster, and a clustering dynamic model of the virtual homogeneous network device cluster is established. The network matrix in the cluster dynamic model is transformed by feature transformation, and the cluster dynamic model is decomposed into multiple single-machine equivalent subsystems; From multiple stand-alone equivalent subsystems, we select the external interaction equivalent subsystems and connect them to the grid-connected network that preserves the topology to generate a structure-preserving reduced-order model.

[0023] Specifically, this implementation method is aimed at the grid connection system of new energy bases. The grid connection system of new energy bases includes multiple types of grid-building equipment and grid connection networks. The multiple types of grid-building equipment include one or more of grid-building wind turbines, grid-building energy storage devices and grid-building reactive power support equipment. The grid connection network includes collection lines, collection busbars, step-up transformers, transmission lines and grid connection points. First, the basic parameters of various types of grid-connected equipment and grid-connected networks in the new energy base grid-connected system are obtained. The basic parameters include equipment physical type, rated capacity, operating point, grid outer loop control parameters, virtual impedance parameters, access nodes, line impedance, bus connection relationship, transformer parameters, transmission line parameters, and grid connection point parameters. After obtaining the basic parameters, the different equipment capacity benchmarks and electrical quantity benchmarks are converted to a unified per-unit benchmark, and the network coupling relationship between equipment access nodes and grid connection points is formed according to the grid connection network connection relationship. Secondly, dynamic isomorphism processing is performed on various types of network-building equipment; the outer-loop state variables of each network-building equipment are organized according to a unified state sequence, so that the phase angle, frequency, active power measurement, reactive power measurement, voltage amplitude, voltage outer loop, and virtual impedance related states are entered into a unified state coordinate; the outer-loop control parameters are normalized according to the capacity benchmark and control parameter benchmark; for missing outer-loop states, they are filled in according to the zero-gain occupancy, first-order equivalent occupancy, or zero-state occupancy method; after isomorphism processing, each network-building equipment can be represented as a standard network outer-loop model under a unified state coordinate; Secondly, based on the coupling relationship between the standard network outer ring model and the grid-connected network, approximately isomorphic network equipment groups are identified. Specifically, candidate equipment groups are formed according to the state structure of the standard network outer ring model, the normalized outer ring dominant parameters, and the network coupling relationship between the operating point and the access node. Dynamic deviation, parameter deviation, operating point deviation, and network coupling deviation are calculated for each device in the candidate equipment group, and a comprehensive deviation index is formed based on the above deviations. When the comprehensive deviation index meets the preset isomorphic conditions, the candidate equipment group is determined as an approximately isomorphic network equipment group. When the comprehensive deviation index does not meet the preset isomorphic conditions, the candidate equipment group is further divided, or devices with deviations exceeding the limit are retained as single-element equipment groups. Then, a virtual isomorphic network device group is constructed based on the approximately isomorphic network device group, and a cluster dynamic model of the virtual isomorphic network device group is established. Specifically, representative devices, typical devices, or dynamic centers are selected from the approximately isomorphic network device group as templates, keeping the number of devices, access nodes, and network clustering pattern of the original device group unchanged. The dynamics of the devices within the group are unified into the standard network outer ring model corresponding to the template, forming a virtual isomorphic network device group. Based on the standard network outer ring model of the template device, the common outer ring dynamics are determined, and a network reactance matrix or clustering network matrix is ​​constructed by combining the device access nodes and grid-connected network parameters. Then, the network feedback relationship and the external grid-connected network coupling terms are incorporated into the common outer ring dynamics to obtain the cluster dynamic model of the virtual isomorphic network device group. Next, eigenvalue transformation is performed on the network matrix in the cluster dynamic model to decompose the cluster dynamic model into multiple single-machine equivalent subsystems. Specifically, eigenvalue decomposition is performed on the network reactance matrix or the cluster network matrix to obtain the eigenvalues ​​and eigenvectors of the network matrix. A state similarity transformation matrix is ​​constructed based on the eigenvectors, and the stacked state in the cluster dynamic model is converted into the cluster modal state using the state similarity transformation matrix. After the transformation, the cluster dynamic model is decomposed into multiple single-machine equivalent subsystems, and each single-machine equivalent subsystem is jointly determined by the common standard network outer ring dynamics and the corresponding eigenvalues ​​of the network matrix. Finally, an external interaction equivalent subsystem is selected from multiple single-unit equivalent subsystems and connected to the grid-connected network with preserved topology to generate a structure-preserving reduced-order model. Specifically, based on the coupling strength, damping characteristics, and grid connection point response contribution of each single-unit equivalent subsystem and the external grid-connected network, dominant indices are calculated, and single-unit equivalent subsystems that meet the preservation conditions are identified as external interaction equivalent subsystems. The external interaction equivalent subsystems are connected to the grid-connected network with preserved topology, and the main topological connections of collector lines, collection buses, step-up transformers, transmission lines, and grid connection points are preserved. The structure-preserving reduced-order model is composed of the state of the external interaction equivalent subsystem and the preserved network state. This structure-preserving reduced-order model can be used for small-disturbance stability analysis, broadband oscillation risk assessment, grid equipment parameter tuning, and grid connection scheme evaluation of new energy base grid-connected systems.

[0024] The technical solution of this invention obtains the parameters of multiple types of grid-connected equipment and grid-connected networks in a new energy grid-connected system, performs dynamic isomorphic processing on the grid-connected equipment and identifies approximately isomorphic equipment groups, further constructs virtual isomorphic equipment groups and cluster dynamic models, generates single-machine equivalent subsystems through network matrix feature transformation and filters external interactive equivalent subsystems, and finally forms a structure-preserving reduced-order model that retains the grid-connected network topology. This effectively solves the problem in existing equivalent modeling of new energy grid-connected systems that it is difficult to uniformly retain the dominant dynamics of multiple types of grid-connected equipment and their cluster coupling relationship with the grid-connected network while reducing the model dimension.

[0025] Furthermore, the basic parameters include the physical type, rated capacity, operating point, outer loop control parameters, virtual impedance parameters, and access nodes of various types of grid-connected equipment, as well as the parameters of the collection lines, collection buses, step-up transformers, transmission lines, and grid connection points of the grid-connected network; the grid-connected equipment includes one or more of the following: grid-connected direct-drive wind turbines, grid-connected energy storage, and grid-connected static var generators.

[0026] Furthermore, the dynamic isomorphism processing of the network includes: The outer ring state variables of multiple types of network construction devices are rearranged according to a preset state order to obtain a unified set of state variables. The outer loop control parameters of various types of network-building equipment are normalized according to rated capacity, per-unit benchmark and control parameter benchmark to obtain normalized outer loop dominant parameters. For missing network outer rings of various types of network equipment, zero-gain occupancy, first-order equivalent occupancy, or zero-state occupancy are used to fill in the missing network outer rings to obtain a standard network outer ring model. Occupied states with no effective dynamic contribution are removed from the calculation of effective dynamic distance and contribution weight in the approximate isomorphism identification. For grid-type static var generators, the dynamics of the grid-type outer loop corresponding to reactive power-voltage, voltage amplitude control, and virtual impedance are retained, and zero-gain or zero-state occupation is adopted for the active power-frequency link.

[0027] As a preferred embodiment of the above, such as Figure 1 As shown, the grid-connected system of the new energy base has a total of N grid-connected devices, denoted as the i-th device. Collect or read the physical type and rated capacity of each device. Active operating point Reactive power operating point Voltage operating point The system acquires the outer loop control parameters, virtual impedance parameters, and access nodes; simultaneously, it acquires the network parameters of the collector lines, collecting busbars, step-up transformers, transmission lines, and grid connection points to form the node admittance matrix, branch impedance set, or network reactance matrix of the grid-connected network.

[0028] like Figure 2As shown, a dynamic isomorphism transformation is performed on different network-connected devices. This transformation does not require different physical devices to have completely identical internal structures. Instead, while preserving the dominant dynamics of the outer loop, it rearranges the phase angle, frequency, power measurement, voltage amplitude, and virtual impedance-related states into a unified order, and normalizes the control parameters under different capacity and per-unit bases. Missing outer loops are filled according to defined rules: when a missing state does not participate in the active or reactive power dynamics of the port, a zero-gain placeholder is set and the dynamic contribution of that state is set to zero; when a missing outer loop can be approximated by port measurement filtering or execution, a first-order equivalent placeholder is used; when it only maintains the consistency of state dimension without effective dynamic contribution, a zero-state placeholder is used, and the state is removed from the similarity distance and contribution weight.

[0029] After isomorphic transformation, the standard network outer loop state variables of the i-th network-building device are defined as follows: (1) in, This is the phase angle deviation. For frequency deviation, This refers to the active power measurement state deviation. This refers to the reactive power measurement state deviation. This refers to the voltage amplitude or the deviation of the outer voltage loop. This refers to the virtual impedance equivalent state deviation. Let be the standard network outer loop state variables of the i-th device, composed of the above variables. For network-type static var generators that mainly participate in voltage and reactive power support, the active-frequency related zero-state occupancy is retained, and the dynamic contribution of this occupancy state is not included in subsequent approximate isomorphism determination or contribution calculation.

[0030] The standard network outer ring model for the i-th device is written as: (2) (3) (4) (5) (6) In the formula, This is the first derivative of the phase angle deviation with respect to time. This is virtual inertia; This is the first derivative of the frequency deviation with respect to time. For virtual damping; The deviation of the active power reference value of the i-th grid-connected device; and The filtering time constants for active power and reactive power measurements are respectively. This is the first derivative of the active power measurement state deviation with respect to time. The first derivative of the reactive power measurement state deviation with respect to time; and For the active and reactive power deviation of the AC port; The outer loop time constant of the voltage; This is the first derivative of the voltage amplitude or the voltage outer loop state deviation with respect to time. This is the reactive power-voltage regulation coefficient; The voltage amplitude reference value deviation of the i-th grid-connected device represents the disturbance of the outer voltage loop setpoint relative to the operating point. For droop control, power synchronization control, or grid-connected control with virtual impedance, this is achieved through a state rearrangement matrix. and parameter mapping The dominant dynamics of its outer ring are expressed as a subset or extended form of equations (1) to (6); If the original device outer loop state vector is used If we represent the internal state of the device controller, then the isomorphism transformation can be written as: (7) in, This is the state rearrangement and scaling transformation matrix; This is the translation amount of the running point; These are the original outer loop parameters of the equipment; To unify the outer loop parameter vector of the network; For parameter normalization and equivalent mapping relationships; the unified outer ring parameter vector includes , , , , , It also includes the reactive power-voltage regulation coefficient of the i-th grid-connected device. The virtual impedance equivalent resistance parameters of the i-th network device The virtual impedance equivalent reactance parameters of the i-th network equipment When a parameter corresponds to an invalid placeholder state, that parameter will not participate in distance calculation and contribution weight calculation.

[0031] Furthermore, identifying groups of devices in a nearly homogeneous network includes: Based on the state structure of the standard network outer ring model, the normalized outer ring dominant parameters, and the network coupling relationship between the operating point and the access node, a candidate device group is formed. Based on the normalized outer ring dominant parameters, the network electrical distance between the operating point and the access node, calculate the comprehensive distance between devices in the candidate device group; Using the dynamic center, parameter center, operating point center, and network coupling strength center within the candidate device group as references, calculate the comprehensive deviation index of the candidate device group; When the comprehensive deviation index does not exceed the first preset threshold, the candidate device group is determined as an approximately homogeneous network device group; When the comprehensive deviation index exceeds the first preset threshold but does not exceed the second preset threshold, a secondary grouping is performed on the candidate device group; When the comprehensive deviation index exceeds the second preset threshold, the network equipment that exceeds the limit will be retained as a single-element equipment group.

[0032] As a preferred embodiment of the above, such as Figure 3 As shown, this implementation identifies approximately isomorphic network equipment groups based on the standardized outer ring model of the network after isomorphism. Unlike simply classifying equipment according to physical type, this implementation uses the standard outer ring state structure, normalized dominant parameters, operating points, and network coupling relationships as the criteria. For network-type direct-drive wind turbines, network-type energy storage, and network-type static var generators with different physical types but similar outer ring dynamics, they can be classified into the same approximately isomorphic network equipment group. For equipment with the same physical type but significantly different outer ring parameters or significantly different network coupling, they can be classified into different equipment groups. For the i-th network device, the normalized outer loop dominant parameter vector is defined as: (8) in, , , , , , , , , These are the reference values ​​for the numerators in the corresponding fractions, used to eliminate scale differences between different capacity levels, different control parameter dimensions, and different equipment types; The comprehensive distance between the i-th device and the j-th device is defined as: (9) in, Let be the combined distance between the i-th device and the j-th device; To unify the outer loop parameter vector of the network, Let L2 be the distance between the outer ring parameter vectors of the unified network structure of device i and device j. and These are the active operating points of the i-th and j-th devices, respectively; and These are the reactive power operating points of the i-th and j-th devices, respectively. and These are the rated capacity or uniform per-unit capacity benchmarks for the i-th and j-th devices, respectively. The normalized network electrical distance or network coupling distance between device access nodes; Let be the equivalent reactance distance along the collector network between the i-th device access node and the j-th device access node; Let be the equivalent reactance distance along the collector network between the p-th device access node and the q-th device access node; To prevent small positive numbers with a denominator of zero; , , , are non-negative coefficients and This implementation method takes , , , When network coupling data is lacking, take... The remaining coefficients are then renormalized; when the standard outer ring structure is consistent or can be completed to be consistent, , A comprehensive distance threshold is used to determine whether two devices meet the condition of approximately isomorphic partitioning; and At that time, the corresponding devices will be grouped into the same approximately homogeneous network device group. ,in The normalized network electrical distance threshold; where Take a value between 0.10 and 0.15. Take a value between 0.10 and 0.20.

[0033] For the g-th approximately isomorphic network device group Suppose it contains The device is equipped with a set of devices. This implementation method adopts a defined hierarchical processing rule. First, the device deviation index is calculated based on the dynamic center within the group. Second, the processing method is determined according to the range of the deviation index: devices that meet the small disturbance condition pass the verification; devices that exceed the small disturbance condition but do not exceed the secondary grouping threshold participate in the secondary grouping; devices that exceed the secondary grouping threshold are retained as single-element device groups. In this embodiment, the weights of the group's dynamic center and network nodes are determined according to the following auxiliary weights: (10) in, For the i-th device in the g-th approximately homogeneous network device group The auxiliary weights within the group are used to form the dynamic center within the group, the capacity normalization or node weight correction of the network reactance matrix, and to determine the number of equivalent subsystems for external interaction that are subsequently retained. and For the rated capacity or capacity under a uniform per-unit standard of the i-th and k-th devices, For the group The sum of the internal equipment capacities; and The network coupling strengths of the i-th and k-th devices, respectively, can be determined by the reciprocal of the equivalent reactance from the access node to the grid connection point, i.e. , For equivalent reactance, For the group The sum of the coupling strengths of the internal networks; and Let be the normalized outer loop dominant parameter vectors of the i-th and k-th devices, respectively. The parameter center within the group; It is a norm 2. This is used to convert the distance between the outer loop parameters of the device and the center of the parameters within the group into dynamic similarity weights. The smaller the distance, the greater the weight of this item. , , As a non-negative coefficient, this implementation method takes... and take , , ; Specifically, for the i-th device, we define matrix deviation index, dominant parameter deviation index, operating point deviation index, and network coupling deviation index: (11) (12) (13) (14) In the formula, Let be the deviation index of the state matrix of the i-th device relative to the dynamic center matrix within the group. Let be the dominant parameter deviation index of the i-th device relative to the parameter center within the group. Let be the outer loop state matrix of the i-th device after dynamic isomorphism in the network. The dynamic center matrix within the group, As the parameter center within the group, As the center of the network coupling strength within the group, , , and To prevent small positive numbers with a denominator of zero; Let be the operating point deviation index of the i-th device; These are the active operating point, reactive operating point, rated capacity or uniform per-unit capacity, and port voltage amplitude operating point of the i-th device, respectively. These are the active power center, reactive power center, capacity center, and voltage center within the g-th approximately homogeneous network equipment group, respectively. Taken as the sum of the device capacities within the group or as a capacity-weighted benchmark; where It can be obtained by weighting the devices in the group according to their capacity or auxiliary weights: ; In the formula, The weighting coefficient is the weighting coefficient of the k-th device within the g-th approximately homogeneous network device group; in this implementation, the first threshold is used. The value is between 0.05 and 0.10, and the second threshold is taken. The value ranges from 0.15 to 0.20. The first preset threshold is used to determine whether the group of devices satisfies the condition of near isomorphism under small disturbances. The second preset threshold is used to determine whether secondary clustering or single-element retention is required. ;when When the g-th device group is deemed to satisfy the approximate isomorphism condition, it is considered an approximate isomorphic network device group, and the dynamic center matrix within the group is used as the basis for its classification. and corresponding , Construct a common standard outer ring model for a virtual homogeneous network of devices; when At that time, the overall distance between devices is recalculated within the group, and secondary grouping is performed to obtain several candidate subgroups, after which consistency verification is performed again; when In this case, the over-limited equipment is retained as a single-element equipment group; thus, the near-homogeneous network equipment group can retain the necessary dynamic differences within the equipment group while maintaining the applicability of the cluster dynamic effect.

[0034] Furthermore, constructing a virtual homogeneous network device group includes: Select representative equipment, typical equipment, or dynamic centers from the approximately homogeneous network equipment group as templates; Based on the number of devices, access nodes, and network clustering patterns of the approximately homogeneous network device group, establish a corresponding virtual homogeneous network device group; Dynamically set the devices in the virtual homogeneous network device group to the standard network outer ring model corresponding to the template; The virtual homogeneous network device group is used as the object for establishing the cluster dynamic model; When the near-homogeneous network device group is a completely homogeneous network device group, a strictly equivalent single-machine equivalent subsystem is obtained through the virtual homogeneous network device group; When the approximately homogeneous network device group is approximately homogeneous, an approximately equivalent single-machine equivalent subsystem can be obtained through the virtual homogeneous network device group.

[0035] Furthermore, establishing a dynamic model of clustering includes: Based on the standard network outer ring model corresponding to the template, determine the state matrix, input matrix, and output matrix of the common standard outer ring dynamics; Based on the access nodes of the virtual homogeneous network device group and the basic parameters of the grid-connected network, construct the network reactance matrix or the clustered network matrix; The network feedback relationship within the group is determined based on the network reactance matrix or cluster network matrix, the port variable selection matrix or coordinate transformation matrix, the voltage deviation at the common grid connection point, and the distribution vector or distribution matrix of the external voltage at each device port within the group. A cluster dynamic model is established based on the dynamics of the outer ring of the public standard, the feedback relationship of the network within the cluster, and the coupling term of the external grid-connected network.

[0036] Furthermore, the cluster dynamic model is decomposed into multiple standalone equivalent subsystems, including: Use the network reactance matrix or clustered network matrix as the network matrix; Eigenvalues ​​and eigenvectors of the network matrix are obtained by performing eigenvalue decomposition or similarity transformation on the network matrix. Construct a state similarity transformation matrix based on the eigenvectors; Based on the state similarity transformation matrix, the cluster dynamic model is subjected to state similarity transformation to obtain multiple single-machine equivalent subsystems; The dynamic matrix of each stand-alone equivalent subsystem is set as a matrix determined by the common standard outer loop dynamics and the corresponding eigenvalues; Set the coupling strength between each stand-alone equivalent subsystem and the external grid-connected network to the corresponding external coupling coefficient.

[0037] As a preferred embodiment of the above, such as Figure 4 As shown, for the first A group of nearly isomorphic network devices, with Let represent the outer loop state variables of the network for the i-th device. This indicates the port voltage deviation or network feedback quantity. This represents the current, power, or equivalent output injected into the network by the device; the dynamic outer loop of the group's common standard is written as: (15) (16) in, The first derivative of the outer loop state variables with respect to time; , , Let the state matrix, input matrix, and output matrix be the dynamic center within the group; The equipment is stacked as follows: (17) The network feedback relationship within the group is represented as follows: (18) in, This is the stacking vector of port voltage deviations or network feedback quantities of each device within the group. The stacking vector of current, power, or equivalent output injected into the network by each device in the group; is the network reactance matrix corresponding to the g-th device group or the equivalent clustered network matrix; E is the port variable selection matrix or coordinate transformation matrix. Voltage deviation at the common grid connection point, the busbar, or the external grid connection network; This represents the distribution vector or matrix of external voltage across the ports of each device within the group; when the external voltage is applied to each device in the same port configuration, Can be written as ,in External voltage allocation vector at the device level. This is the identity matrix corresponding to the dimension of the port voltage disturbance of a single device; Substituting the network feedback relationship shown in equation (18) into equations (15) and (16), we can obtain the full-order clustering dynamic model of the approximately homogeneous network device group: (19) (20) in, For the reason E And the cluster dynamic feedback matrix obtained by combining network feedback relationships, ; The identity matrix corresponding to the dimension of the port voltage disturbance of a single device can be output by each device in the group. Vector allocation based on external voltage We get the weighted average. For the Kronecker product; in equation (19) Reflecting the dynamic characteristics of the equipment, This demonstrates the impact of network clustering patterns on dynamic feedback. like Figure 5 As shown, for network reactance matrix or clustered network matrix Perform eigenvalue decomposition or similarity transformation: (twenty one) (twenty two) in, This represents the i-th eigenvalue of the network reactance matrix; Let be the transformation matrix composed of eigenvectors; define the transformed state: (twenty three) in, This is the transformed cluster mode state vector. Let be the identity matrix corresponding to the state dimension of a single device; substituting equations (21) to (23) into equations (19) and (20), the full-order approximate isomorphic multi-machine model is decomposed into There are several single-machine equivalent subsystems. The i-th single-machine equivalent subsystem is written as: (twenty four) (25) (26) Equations (24) and (25) show that the i-th single-machine equivalent subsystem can be interpreted as: a single subsystem with a common network outer ring dynamic The template device, via the i-th eigenvalue of the network reactance matrix The equivalent network feedback connection is used to access the external grid-connected network. It reflects the network topology and parameters of the device group, i.e., the clustering pattern; This reflects the dynamics of the outer ring of the common network of the equipment group; This reflects the degree of coupling between the equivalent subsystem and the external grid-connected network; This represents the deviation of the equivalent injection quantity of the i-th single-unit equivalent subsystem to the external grid-connected network; therefore, each single-unit equivalent subsystem collectively reflects the coupling influence of the equipment's dynamic characteristics and clustering pattern.

[0038] Furthermore, generative structure-preserving reduced-order models include: The dominant index is calculated based on the external coupling coefficient, damping characteristics, and grid connection point response contribution of each single-unit equivalent subsystem. The equivalent subsystems of multiple stand-alone models are ranked according to the dominant indicators; Based on preset coupling thresholds and preset cumulative contribution rate thresholds, external interaction equivalent subsystems are determined from multiple stand-alone equivalent subsystems. The template, capacity normalization weights or node weights in the network matrix, and the number of single-machine equivalent subsystems retained in the structure-preserving reduced-order model are determined based on the cluster dynamic contribution weights. Connect the equivalent subsystem for external interaction to the grid-connected network that retains the topology; In a grid-connected network that preserves the topology, the topology of collector lines, collector buses, step-up transformers, transmission lines, and grid connection points is retained. Based on the state of the equivalent subsystem for external interaction and the state of the retained grid-connected network, generate the state of the structure-preserving reduced-order model; The output of the structure-preserving reduced-order model is generated based on the grid connection point voltage, active power, reactive power, or observation deviation.

[0039] As a preferred embodiment of the above, such as Figure 6 As shown, after obtaining After identifying the equivalent subsystems for each single-unit type, the equivalent subsystems for external interaction are identified based on coupling coefficients, damping characteristics, and grid connection point response contributions; for The equivalent subsystem is determined to primarily reflect the internal dynamics of the group; for Furthermore, the dominant index of the i-th single-unit equivalent subsystem in the g-th approximately homogeneous network device group The equivalent subsystem whose cumulative contribution rate enters the retention set is determined to be the external interaction equivalent subsystem reflecting the interaction between the equipment group and the external grid-connected network; among which, a preset coupling threshold is included. Pick to ; Define the screening criteria for equivalent subsystems in external interaction: (27) in, This is the dominant index of the i-th equivalent subsystem; Let be the coupling coefficient between the i-th stand-alone equivalent subsystem and the external grid-connected network; The damping ratio; This refers to the participation matrix or sensitivity of the equivalent subsystem output to the grid connection point. , , These are the weighting coefficients. It is a small positive number; this implementation method takes and take , , ;Will Sorting subsystems from largest to smallest, retaining those with a cumulative contribution rate of 90% or higher as equivalent subsystems for external interaction, and then further refining the candidate equivalent subsystems according to... Sort by largest to smallest and calculate contribution rate: (28) in, The normalized contribution rate of the i-th candidate external interaction equivalent subsystem in the g-th approximately homogeneous network device group; It is the dominant index of the k-th candidate external interaction equivalent subsystem in the g-th approximately homogeneous network device group; To meet The set of candidate equivalent subsystems for external interaction; For the final retained set; The preset cumulative contribution rate threshold; In special cases where long-distance transmission lines are transmitted or the network within a group has a relatively small impact, if the network reactance matrix has only one dominant mode that is significantly coupled with the external grid-connected network, then only the single-unit equivalent subsystem corresponding to that mode is retained as the reduced-order model of the approximately isomorphic network equipment group; for general network configurations, multiple external interactive equivalent subsystems are retained according to the rules of dominant indicators and cumulative contribution rates. The equivalent subsystems corresponding to the external interactions of each approximately isomorphic network equipment group are connected to the grid-connected network with the preserved topology, forming a reduced-order model of the new energy base grid-connected system with the structure preserved. The preserved topology includes collection lines, collection buses, step-up transformers, transmission lines, and grid connection points. If the engineering requires further reduction of the network dimension, the internal branches of the same equipment group can be equivalent, but the main electrical coupling relationship of the equipment group to the external grid-connected network and the external interaction ports should be preserved. Before order reduction, the small-disturbance state-space model of the original grid-connected system is represented as: (29) (30) in, It consists of the unified outer ring state and network state of all network-building devices. for The first derivative with respect to time; For disturbance input; Let A represent the deviations in grid connection point voltage, active power, reactive power, or other observations; and let A, B, C, and D represent the small disturbance state matrix, input matrix, output matrix, and direct transfer matrix of the original grid-connected system, respectively. After screening through equivalent subsystems of approximately isomorphic grid-connected equipment groups and external interactions, the reduced-order model is expressed as: (31) (32) in, It consists of the equivalent subsystem states of external interactions of each group of nearly homogeneous networked devices and the retained grid-connected network states. for The first derivative with respect to time; The output of the reduced-order model includes... Corresponding grid connection point voltage, active power, reactive power, or other observation deviations; , , , It is obtained by reassembling the state matrix of the retained external interaction equivalent subsystem, its external injection relationship, and the retained network topology; if a device is not suitable to be incorporated into other device groups due to the special dynamics of the outer ring or the special location of the access network, the device is retained as a single-element device group. The reduced-order model obtained in this implementation can be used for small-disturbance stability analysis, broadband oscillation risk assessment, grid equipment parameter tuning, and grid connection scheme evaluation of new energy base grid-connected systems. Since each external interactive equivalent subsystem in the model can be traced back to the dynamics of the common outer ring within the group, the eigenvalue of the network reactance matrix, and the external coupling coefficient, engineers can identify the dominant dynamic source based on the reduced-order model and adjust the control parameters of the grid equipment or the grid connection network access scheme.

[0040] Example 2; Based on the same inventive concept as the method for reducing the order of new energy grid-connected equipment clusters in the foregoing embodiments, the present invention also provides a system for reducing the order of new energy grid-connected equipment clusters, the system comprising: The basic parameter acquisition module acquires the basic parameters of various types of grid-connected equipment and grid-connected networks in the grid-connected system of the new energy base. The dynamic isomorphism processing module performs dynamic isomorphism processing on multiple types of network construction devices to obtain a standard network outer ring model under unified state coordinates. The homogeneous device identification module identifies groups of devices in a nearly homogeneous network based on the coupling relationship between the standard network outer ring model and the grid-connected network. The virtual clustering modeling module constructs a virtual isomorphic network device cluster based on an approximately isomorphic network device cluster, and establishes a clustering dynamic model of the virtual isomorphic network device cluster. The matrix eigenvalue decomposition module performs eigenvalue transformation on the network matrix in the cluster dynamic model, decomposing the cluster dynamic model into multiple single-machine equivalent subsystems. The interactive filtering and generation module filters out external interactive equivalent subsystems from multiple stand-alone equivalent subsystems, and connects the external interactive equivalent subsystems to the grid-connected network that preserves the topology, generating a structure-preserving reduced-order model.

[0041] The system described above in this invention can effectively realize a method for reducing the order of grid-connected new energy equipment clusters, and the technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0042] Example 3; Based on the same inventive concept as the method for reducing the order of new energy grid-connected equipment clusters in the foregoing embodiments, the present invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, can realize the method for reducing the order of new energy grid-connected equipment clusters.

[0043] Example 4; Based on the same inventive concept as the method for reducing the order of grid-connected new energy equipment in the aforementioned embodiments, in one embodiment, a grid-connected system consisting of 15 units (including 10 grid-connected direct-drive wind turbines, 3 grid-connected energy storage units, and 2 grid-connected static var generators) is used as a case study; all 15 units are controlled by a grid-connected virtual synchronous machine. Each unit is connected to the grid connection point via a line, and the line reactance... , The resistance of the transmission line is When implementing clustered dynamic equivalent order reduction, the line resistance is relatively small compared to the line reactance, so the line resistance is ignored in the construction of the network reactance matrix and the calculation of the equivalent subsystem. The steady-state output active power of each grid-type direct-drive wind turbine is taken as 1.0 pu, and the power factor is taken as 1; the grid-type energy storage is treated as a bidirectional power support device, with its steady-state active power taken as 0.6 pu and reactive power taken as 0; the grid-type static var generator mainly participates in reactive power-voltage support, and its active power-frequency link adopts zero-state occupation, only incorporating the effective grid outer loop such as reactive power-voltage outer loop, voltage amplitude control, and virtual impedance into the isomorphic model.

[0044] First, a dynamic isomorphic transformation was performed on the 15 grid-connected devices. For grid-connected direct-drive wind turbines and grid-connected energy storage, the outer loop states, including phase angle, frequency, active power measurement, reactive power measurement, and voltage amplitude, were retained. For grid-connected static var generators, their reactive power-voltage outer loop and virtual impedance-related dynamics were retained, and zero-gain or zero-state occupancy was adopted for components that do not participate in active power-frequency support. After state variable rearrangement, capacity benchmark normalization, and control parameter normalization, each device was represented as a standard grid outer loop model under a unified state coordinate system.

[0045] Then, candidate device groups are formed based on the standard outer loop state structure, normalized outer loop dominant parameters, power / voltage operating points, and network coupling relationships between device access nodes, and the candidate device groups are subjected to approximate isomorphism verification. Calculations show that the 15 devices are divided into three approximately isomorphic network device groups: the first group... Includes 10 grid-type direct-drive wind turbines, the second group Includes 3 grid-connected energy storage units, the third group It includes two network-type static var generators. The intra-group consistency deviation of the three device groups does not exceed a first preset threshold; therefore, they are each checked for approximate isomorphism and proceed to subsequent virtual isomorphic modeling. For each device group, an intra-group dynamic center is selected as a template to construct the corresponding virtual isomorphic network device group. The virtual isomorphic network device group maintains the original device group's number of devices, access nodes, and network clustering pattern, but the intra-group device dynamics all adopt the corresponding dynamic center model.

[0046] Furthermore, network reactance matrices are constructed for the three virtual isomorphic network equipment groups. Since line resistance is ignored in this embodiment, network feedback relationships are represented by the network reactance matrix. Eigenvalue decomposition is performed on the network reactance matrix of each equipment group, and state similarity transformations are constructed using its eigenvalues ​​and eigenvectors, thereby decomposing the corresponding full-order cluster dynamic model into several single-unit equivalent subsystems. For each equipment group, the equivalent subsystem with weak external coupling mainly reflects the internal dynamics within the group; the equivalent subsystem with significant coupling to the external grid-connected network reflects the dominant influence of the equipment group on the grid-connected point dynamics. Since each equipment group in this embodiment is fed out via long lines, and the influence of the network within the group relative to the external transmission channel is relatively small, an externally interactive equivalent subsystem is selected as the reduced-order model for each approximately isomorphic network equipment group. Thus, the original outer-loop dynamic model of the 15 network equipment units is reduced to 3 externally interactive equivalent subsystems, which are then reassembled with the retained gathering bus, step-up transformer, transmission line, and grid-connected point topology to form a structurally preserved reduced-order model of the new energy base grid-connected system.

[0047] Table 1. Comparison of typical oscillation modes between the reduced-order model and the full-order model.

[0048] Table 1 above presents the comparison results of typical oscillation modes of the reduced-order model and the full-order model in this embodiment. The results show that there is consistency in the dominant oscillation mode. The corresponding modes in the full-order model exhibit multiplicity, indicating that the cluster system formed by multiple approximately isomorphic devices can be decomposed into several single-machine equivalent subsystems through the characteristic transformation of the network reactance matrix. As can be seen from the table, the modes in the reduced-order model can cover the main oscillation modes in the full-order model, indicating that the constructed equivalent subsystem can characterize the dominant dynamic characteristics and cluster morphology influence of the device group.

[0049] like Figure 7 As shown, a comparison of the dynamic responses of the full-order model, the reduced-order model, and the expanded-capacity model proposed in this embodiment after a disturbance is presented. In the simulation, a voltage disturbance at the grid connection point is applied at 0.5s, and the active power response of unit 1 and the entire wind farm is observed. Figure 7 It is evident that the reduced-order model can track the dynamic response of the full-order model well in the early stage of disturbance and within the dominant oscillation frequency band, indicating that the proposed cluster dynamic equivalent reduced-order method can preserve the dominant external interactive dynamics of the grid-connected system of new energy bases. Although the capacity expansion model can reflect steady-state capacity changes, it differs significantly from the full-order model in terms of oscillation amplitude and phase changes, indicating that conventional equivalent methods such as capacity expansion or parameter weighting are insufficient to accurately characterize the cluster dynamic effect.

[0050] In summary, this embodiment demonstrates that for a new energy base grid-connected system composed of multiple types of grid-connected equipment, including grid-connected direct-drive wind turbines, grid-connected energy storage, and grid-connected static var generators, this invention can first form an equipment cluster through dynamic isomorphic transformation and approximate isomorphic verification of the grid, and then obtain an equivalent subsystem for external interaction based on the virtual isomorphic grid-connected equipment cluster and the characteristic transformation of the network reactance matrix. Compared with the full-order model that models each unit individually, this method significantly reduces the model order; compared with the ordinary capacity-weighted expansion model, this method can retain the coupling effect between the dynamic characteristics of the outer loop of the equipment and the network clustering pattern, and is therefore more suitable for small-disturbance stability analysis, broadband oscillation risk assessment, and grid-connected equipment parameter tuning of new energy base grid-connected systems.

[0051] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for reducing the order of grid-connected new energy equipment clusters, characterized in that, The method includes: Obtain basic parameters of various types of grid-connected equipment and grid-connected networks in the grid-connected system of the new energy base; The network dynamic isomorphism processing is performed on the various types of network-building devices to obtain a standard network outer ring model under a unified state coordinate system. Based on the coupling relationship between the standard network outer ring model and the grid-connected network, a group of approximately homogeneous network devices is identified; A virtual isomorphic network device group is constructed based on the approximately isomorphic network device group, and a clustering dynamic model of the virtual isomorphic network device group is established. The network matrix in the cluster dynamic model is subjected to feature transformation, and the cluster dynamic model is decomposed into multiple single-machine equivalent subsystems. From the multiple equivalent subsystems of the single-machine type, the equivalent subsystems for external interaction are selected, and the equivalent subsystems for external interaction are connected to the grid-connected network that retains the topology to generate a structure-preserving reduced-order model.

2. The method for reducing the order of new energy grid-connected equipment clusters according to claim 1, characterized in that, The basic parameters include the physical type, rated capacity, operating point, outer loop control parameters, virtual impedance parameters, and access nodes of the various types of grid-connected equipment, as well as the collector line parameters, collection bus parameters, step-up transformer parameters, transmission line parameters, and grid connection point parameters of the grid-connected network; the grid-connected equipment includes one or more of the following: grid-connected direct-drive wind turbines, grid-connected energy storage, and grid-connected static var generators.

3. The method for reducing the order of new energy grid-connected equipment clusters according to claim 1, characterized in that, The network dynamic isomorphism processing includes: The outer ring state variables of the network construction devices of various types are rearranged according to a preset state order to obtain a unified set of state variables. The outer loop control parameters of the network construction equipment of various types are normalized according to the rated capacity, per-unit benchmark and control parameter benchmark to obtain the normalized outer loop dominant parameters. The missing outer rings of the network structure devices of various types are filled by zero-gain placeholders, first-order equivalent placeholders or zero-state placeholders to obtain the standard outer ring model of the network structure. Occupied states with no effective dynamic contribution are removed from the calculation of effective dynamic distance and contribution weight in the approximate isomorphism identification. For grid-type static var generators, the dynamics of the grid-type outer loop corresponding to reactive power-voltage, voltage amplitude control, and virtual impedance are retained, and zero-gain or zero-state occupation is adopted for the active power-frequency link.

4. The method for reducing the order of new energy grid-connected equipment clusters according to claim 1, characterized in that, Identifying the group of near-homogeneous network devices includes: Based on the state structure of the standard network outer ring model, the normalized outer ring dominant parameters, and the network coupling relationship between the operating point and the access node, a candidate device group is formed. Calculate the comprehensive distance between devices within the candidate device group based on the normalized outer ring dominant parameters and the network electrical distance between the operating point and the access node; Using the dynamic center, parameter center, operating point center, and network coupling strength center within the candidate device group as references, calculate the comprehensive deviation index of the candidate device group; When the comprehensive deviation index does not exceed the first preset threshold, the candidate device group is determined as the approximately homogeneous network device group; When the comprehensive deviation index exceeds the first preset threshold but does not exceed the second preset threshold, a secondary clustering is performed on the candidate device group; When the comprehensive deviation index exceeds the second preset threshold, the network equipment that exceeds the limit will be retained as a single-element equipment group.

5. The method for reducing the order of new energy grid-connected equipment clusters according to claim 1, characterized in that, Constructing the virtual homogeneous networking device group includes: Select representative devices, typical devices, or dynamic centers from the group of near-isomorphic network devices as templates; Based on the number of devices, access nodes, and network clustering pattern of the approximately homogeneous network device group, establish the corresponding virtual homogeneous network device group; The devices within the virtual isomorphic network device group are dynamically configured as the standard network outer ring model corresponding to the template; The virtual homogeneous network device group is used as the object for establishing the cluster dynamic model; When the approximately homogeneous network device group is a completely homogeneous network device group, the strictly equivalent single-machine equivalent subsystem is obtained through the virtual homogeneous network device group; When the group of near-homogeneous network devices is a group of near-homogeneous network devices, the near-equivalent single-machine equivalent subsystem is obtained through the virtual homogeneous network device group.

6. The method for reducing the order of new energy grid-connected equipment clusters according to claim 5, characterized in that, Establishing the aforementioned cluster dynamic model includes: Based on the standard network outer ring model corresponding to the template, determine the state matrix, input matrix, and output matrix of the common standard outer ring dynamics; Based on the access nodes of the virtual homogeneous network device group and the basic parameters of the grid-connected network, construct a network reactance matrix or a clustered network matrix; The network feedback relationship within the group is determined based on the network reactance matrix or the cluster network matrix, the port variable selection matrix or coordinate transformation matrix, the voltage deviation at the common grid connection point, and the distribution vector or distribution matrix of the external voltage at each device port within the group. The cluster dynamic model is established based on the outer ring dynamics of the common standard, the feedback relationship of the intra-cluster network, and the coupling term of the external grid-connected network.

7. The method for reducing the order of new energy grid-connected equipment clusters according to claim 6, characterized in that, The cluster dynamic model is decomposed into multiple equivalent subsystems of the single-machine type, including: The network reactance matrix or the clustered network matrix is ​​used as the network matrix; The network matrix is ​​subjected to eigenvalue decomposition or similarity transformation to obtain the eigenvalues ​​and eigenvectors of the network matrix; Construct a state similarity transformation matrix based on the feature vectors; The cluster dynamic model is subjected to state similarity transformation based on the state similarity transformation matrix to obtain multiple equivalent subsystems of the single-machine type. The dynamic matrix of each of the single-machine equivalent subsystems is set as a matrix determined by the common standard outer loop dynamics and the corresponding eigenvalues; The coupling strength between each of the single-unit equivalent subsystems and the external grid-connected network is set to the corresponding external coupling coefficient.

8. The method for reducing the order of new energy grid-connected equipment clusters according to claim 7, characterized in that, Generating the structure-preserving reduced-order model includes: The dominant index is calculated based on the external coupling coefficient, damping characteristics, and grid connection point response contribution of each of the single-unit equivalent subsystems. The single-machine equivalent subsystems are ranked according to the aforementioned dominant indicators; Based on a preset coupling threshold and a preset cumulative contribution rate threshold, the external interaction equivalent subsystem is determined from multiple single-machine equivalent subsystems; The template, the capacity normalized weight or node weight in the network matrix, and the number of single-machine equivalent subsystems retained in the structure-preserving reduced-order model are determined based on the cluster dynamic contribution weight. Connect the external interaction equivalent subsystem to the grid-connected network with the preserved topology; In the grid-connected network with the retained topology, the topology of collector lines, collector buses, step-up transformers, transmission lines, and grid connection points is retained; Based on the state of the external interaction equivalent subsystem and the retained grid-connected network state, the state of the structure-preserving reduced-order model is generated; The output of the structure-preserving reduced-order model is generated based on the grid connection point voltage, active power, reactive power, or observation deviation.

9. A clustering and order reduction system for new energy grid-connected equipment, characterized in that, The system includes: The basic parameter acquisition module acquires the basic parameters of various types of grid-connected equipment and grid-connected networks in the grid-connected system of the new energy base. The dynamic isomorphism processing module performs dynamic isomorphism processing on multiple types of network construction devices to obtain a standard network outer ring model under unified state coordinates. The homogeneous device identification module identifies groups of devices in a nearly homogeneous network based on the coupling relationship between the standard network outer ring model and the grid-connected network. The virtual clustering modeling module constructs a virtual isomorphic network device cluster based on an approximately isomorphic network device cluster, and establishes a clustering dynamic model of the virtual isomorphic network device cluster. The matrix eigenvalue decomposition module performs eigenvalue transformation on the network matrix in the cluster dynamic model, decomposing the cluster dynamic model into multiple single-machine equivalent subsystems. The interactive filtering and generation module filters out external interactive equivalent subsystems from multiple stand-alone equivalent subsystems, and connects the external interactive equivalent subsystems to the grid-connected network that retains the topology, generating a structure-preserving reduced-order model.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which includes program instructions that, when executed by a processor, can implement the method for reducing the order of new energy grid-connected equipment clusters as described in any one of claims 1-8.

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