Dynamic grouping method and device for heterogeneous direct current delivery system

Abstract

The invention provides a dynamic grouping method and device for a heterogeneous direct current delivery system, and relates to the technical field of dynamic analysis and control of a power system, and the method comprises the steps: deducing a linear time-varying variation equation which is satisfied by the trajectory sensitivity of a system state trajectory to a system parameter vector based on a parameterized differential-algebraic equation model; calculating the track sensitivity of each device to a system parameter vector, projecting the track sensitivity to a common connection point corresponding to each device, and constructing a unified sensitivity feature vector of each device; calculating the dynamic similarity between the devices based on the unified sensitivity feature vector, and constructing a mixed similarity matrix in combination with the electrical distance similarity between the devices; and feature decomposition and feature vector clustering are carried out based on the mixed similarity matrix, and dynamic clustering of heterogeneous equipment is realized. According to the dynamic grouping method provided by the invention, unified grouping of heterogeneous equipment such as a synchronous machine and a converter can be realized in a heterogeneous power system containing high-proportion new energy and direct-current power transmission.

Description

Technical Field

[0001] This application relates to the field of dynamic analysis and control technology of power systems, and in particular to a dynamic grouping method and apparatus for heterogeneous DC transmission systems. Background Technology

[0002] The penetration rate of inverter-based resources (IBR), represented by wind power and photovoltaics, in the power system has risen sharply, especially in areas rich in new energy resources. Large-scale transmission of new energy to load centers through high-voltage direct current transmission systems has become the mainstream mode, forming a multi-type heterogeneous power system that includes synchronous generators (thermal power), wind power converters, photovoltaic inverters, energy storage converters, and DC converter stations.

[0003] Dynamic grouping of power systems is the foundation for transient stability analysis and emergency control strategy design. Grouping methods in related technologies are based on slow coherency theory, the core idea of ​​which is that after a large disturbance, synchronous generators with similar rotor angular trajectories are grouped into the same group.

[0004] However, the aforementioned clustering methods in related technologies can no longer accurately reflect the differences in the dynamic characteristics of equipment in heterogeneous power systems that include a high proportion of new energy sources and DC transmission. Summary of the Invention

[0005] The purpose of this application is to provide a dynamic grouping method and apparatus for heterogeneous DC transmission systems, which can realize the unified grouping of heterogeneous equipment such as synchronous machines and converters in heterogeneous power systems that include a high proportion of new energy sources and DC transmission.

[0006] This application provides a dynamic clustering method for heterogeneous DC transmission systems, including: Based on the acquired real-time system operation data and network topology data, a parameterized differential-algebraic equation model including synchronous machines and new energy converters is established. This model incorporates new energy penetration rate and DC transmission power as system parameter vectors. Based on this model, a linear time-varying variational equation is derived to satisfy the trajectory sensitivity of the system state trajectory to the system parameter vector. Then, based on this equation, the trajectory sensitivity of each device to the system parameter vector is calculated. The calculated trajectory sensitivity of each device is projected onto its corresponding common connection point to construct a unified sensitivity feature vector for each device. The dynamic similarity between any two devices is calculated based on their unified sensitivity feature vectors, and combined with the electrical distance similarity, a hybrid similarity matrix indexed by the devices is constructed. A normalized Laplace matrix is ​​constructed based on the hybrid similarity matrix, and dynamic grouping of heterogeneous devices is achieved through eigenvalue decomposition and eigenvector clustering of the normalized Laplace matrix.

[0007] Optionally, the step of establishing a parameterized differential-algebraic equation model including a synchronous machine and a renewable energy converter based on the acquired real-time operating data and network topology data of the power system includes: constructing a system of differential equations and a system of algebraic equations based on the acquired real-time operating data and network topology data of the power system; the system of differential equations is used to describe the physical or control dynamics of the synchronous machine and the renewable energy converter; the differential state variables of the synchronous machine include at least one of the following: rotor angle, rotor angular velocity, and transient electromotive force; the differential state variables of the renewable energy converter include at least one of the following: phase-locked loop phase, active and reactive power control integral term, and DC capacitor voltage; the system of algebraic equations is used to describe network topology constraints and power balance equations; and the system parameter vector including renewable energy penetration rate and DC transmission power is introduced into the system of differential equations and the system of algebraic equations to obtain the parameterized differential-algebraic equation model.

[0008] Optionally, deriving the linear time-varying variational equation satisfied by the trajectory sensitivity of the system state trajectory to the system parameter vector based on the parameterized differential-algebraic equation model includes: defining the trajectory sensitivity function as the partial derivative of the system state trajectory with respect to the system parameter vector; taking the total derivative of the parameterized differential-algebraic equation model with respect to the system parameter vector to obtain a linear time-varying variational equation containing the system state Jacobian matrix, the algebraic variable Jacobian matrix, and the direct driving terms of the parameters.

[0009] Optionally, the step of calculating the trajectory sensitivity of each device to the system parameter vector based on the linear time-varying variational equation includes: detecting discontinuous events during system operation; calculating the jump condition based on the Jacobian matrix of the discrete event reset mapping at the time of the discontinuous event, and resetting the sensitivity state based on the calculated jump condition.

[0010] Optionally, the step of projecting the calculated trajectory sensitivity of each device onto the common connection point corresponding to each device to construct a unified sensitivity feature vector includes: extracting the voltage amplitude sensitivity sequence and the frequency sensitivity sequence at the common connection point of each device within a preset time window; performing scale normalization processing on the extracted voltage sensitivity sequence and frequency sensitivity sequence using a weighted normalization coefficient; and concatenating the normalized voltage sensitivity sequence and frequency sensitivity sequence to obtain a unified sensitivity feature vector.

[0011] Optionally, the step of calculating the dynamic similarity between any two devices based on the unified sensitivity feature vector of each device includes: calculating the cosine similarity between any two devices based on the unified sensitivity feature vector of the devices; converting the cosine similarity into dynamic similarity weights through a Gaussian kernel function to obtain the dynamic similarity between any two devices.

[0012] Optionally, the step of constructing a hybrid similarity matrix indexed by devices by combining the electrical distance similarity between devices includes: calculating the electrical distance between devices based on the node impedance matrix, and converting the electrical distance into electrical distance similarity weights through a Gaussian kernel function; the node impedance matrix includes: the self-impedance of the nodes and the mutual impedance between the nodes; and weighting and fusing the dynamic similarity weights and the electrical distance similarity weights through an adjustment factor to generate a hybrid similarity matrix indexed by devices.

[0013] Optionally, the step of constructing a normalized Laplacian matrix based on the hybrid similarity matrix and performing eigenvalue decomposition and eigenvector clustering on the normalized Laplacian matrix to achieve dynamic grouping of heterogeneous devices includes: calculating a degree matrix based on the hybrid similarity matrix indexed by devices, and constructing a normalized Laplacian matrix based on the degree matrix and the hybrid similarity matrix; performing eigenvalue decomposition on the normalized Laplacian matrix, selecting the eigenvectors corresponding to the K smallest non-zero eigenvalues ​​to form an eigenvector matrix; normalizing each row of the eigenvector matrix, and using the K-Means clustering algorithm to cluster the normalized eigenvectors to obtain the grouping results of heterogeneous devices.

[0014] This application also provides a dynamic grouping device for a heterogeneous DC transmission system, comprising: The model building module is used to establish a parameterized differential-algebraic equation model, including synchronous machines and new energy converters, based on the acquired real-time system operation data and network topology data. The parameterized differential-algebraic equation model incorporates new energy penetration rate and DC transmission power as system parameter vectors. The sensitivity calculation module is used to derive the linear time-varying variational equation satisfied by the trajectory sensitivity of the system state trajectory to the system parameter vector based on the parameterized differential-algebraic equation model, and calculate the trajectory sensitivity of each device to the system parameter vector based on the linear time-varying variational equation. The feature extraction module is used to project the calculated trajectory sensitivity of each device to the corresponding common connection point of each device, constructing a unified sensitivity feature vector for each device. The similarity calculation module is used to calculate the dynamic similarity between any two devices based on the unified sensitivity feature vector of each device, and combine it with the electrical distance similarity between devices to construct a hybrid similarity matrix indexed by the devices. The device clustering module is used to construct a normalized Laplace matrix based on the hybrid similarity matrix, and achieve dynamic clustering of heterogeneous devices by performing feature decomposition and feature vector clustering on the normalized Laplace matrix.

[0015] Optionally, the model building module is specifically used to construct a system of differential equations and algebraic equations based on the acquired real-time operating data and network topology data of the power system. The differential equations are used to describe the physical or control dynamics of the synchronous machine and the renewable energy converter. The differential state variables of the synchronous machine include at least one of the following: rotor angle, rotor angular velocity, and transient electromotive force. The differential state variables of the renewable energy converter include at least one of the following: phase-locked loop phase, active and reactive power control integral term, and DC capacitor voltage. The algebraic equations are used to describe the network topology constraints and power balance equations. The model building module is also specifically used to introduce the system parameter vector containing the renewable energy penetration rate and DC transmission power into the differential equations and algebraic equations to obtain a parameterized differential-algebraic equation model.

[0016] Optionally, the sensitivity calculation module is specifically used to define the trajectory sensitivity function as the partial derivative of the system state trajectory with respect to the system parameter vector; the sensitivity calculation module is also specifically used to calculate the total derivative of the parameterized differential-algebraic equation model with respect to the system parameter vector, to obtain a linear time-varying variational equation containing the system state Jacobian matrix, the algebraic variable Jacobian matrix, and the direct driving terms of the parameters.

[0017] Optionally, the sensitivity calculation module is specifically used to detect discontinuous events during system operation; the sensitivity calculation module is also specifically used to calculate the transition condition based on the Jacobian matrix of the discrete event reset mapping at the time of the occurrence of the discontinuous event, and to reset the sensitivity state based on the calculated transition condition.

[0018] Optionally, the feature extraction module is specifically used to extract the voltage amplitude sensitivity sequence and the frequency sensitivity sequence at the common connection point of each device within a preset time window; the feature extraction module is further used to perform scale normalization processing on the extracted voltage sensitivity sequence and frequency sensitivity sequence using a weighted normalization coefficient; the feature extraction module is further used to concatenate the normalized voltage sensitivity sequence and frequency sensitivity sequence to obtain a unified sensitivity feature vector.

[0019] Optionally, the similarity calculation module is specifically used to calculate the cosine similarity between any two devices based on the unified sensitivity feature vector of the devices; the similarity calculation module is also specifically used to convert the cosine similarity into dynamic similarity weights through a Gaussian kernel function to obtain the dynamic similarity between any two devices.

[0020] Optionally, the similarity calculation module is further configured to calculate the electrical distance between devices based on the node impedance matrix, and convert the electrical distance into electrical distance similarity weights through a Gaussian kernel function; the node impedance matrix includes: the self-impedance of the nodes and the mutual impedance between the nodes; the similarity calculation module is further configured to perform weighted fusion of dynamic similarity weights and electrical distance similarity weights through adjustment factors to generate a hybrid similarity matrix indexed by the devices.

[0021] Optionally, the device clustering module is specifically used to calculate a degree matrix based on a mixed similarity matrix indexed by devices, and to construct a normalized Laplacian matrix based on the degree matrix and the mixed similarity matrix; the device clustering module is further used to perform eigenvalue decomposition on the normalized Laplacian matrix, select the eigenvectors corresponding to the first K smallest non-zero eigenvalues ​​to form an eigenvector matrix; the device clustering module is further used to normalize each row of the eigenvector matrix, and to cluster the normalized eigenvectors using the K-Means clustering algorithm to obtain the clustering results of heterogeneous devices.

[0022] This application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the dynamic clustering method for heterogeneous DC transmission systems as described above.

[0023] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the dynamic clustering method for any of the heterogeneous DC transmission systems described above.

[0024] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the dynamic clustering method for heterogeneous DC transmission systems as described above.

[0025] The dynamic clustering method and apparatus for heterogeneous DC transmission systems provided in this application firstly establishes a parameterized differential-algebraic equation model including synchronous machines and new energy converters based on the acquired real-time operation data and network topology data of the power system. This model incorporates the new energy penetration rate and DC transmission power as system parameter vectors. Then, based on the parameterized differential-algebraic equation model, a linear time-varying variational equation is derived to satisfy the trajectory sensitivity of the system state trajectory to the system parameter vector. Based on this linear time-varying variational equation, the trajectory sensitivity of each device to the system parameter vector is calculated. The calculated trajectory sensitivity of each device is projected to the common connection point corresponding to each device, constructing a unified sensitivity feature vector for each device. Based on the unified sensitivity feature vector of each device, the dynamic similarity between any two devices is calculated, and combined with the electrical distance similarity between devices, a hybrid similarity matrix indexed by the devices is constructed. Finally, a normalized Laplace matrix is ​​constructed based on the hybrid similarity matrix, and dynamic clustering of heterogeneous devices is achieved by performing eigenvalue decomposition and eigenvector clustering on the normalized Laplace matrix. In this way, it is possible to achieve unified grouping of heterogeneous equipment such as synchronous machines and converters in heterogeneous power systems that include a high proportion of new energy sources and DC transmission. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a topology diagram of the heterogeneous DC power transmission system provided in this application; Figure 2 This is a flowchart illustrating the dynamic clustering method for heterogeneous DC transmission systems provided in this application. Figure 3 This is a schematic diagram of the structure of the dynamic grouping device of the heterogeneous DC transmission system provided in this application; Figure 4 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship. All actions involving the acquisition of signal information or data in this application are performed in accordance with the relevant data protection laws and policies of the country where the application is located and with authorization from the owner of the relevant device.

[0030] like Figure 1 The diagram shown is a topology diagram of a heterogeneous DC power transmission system provided in an embodiment of this application. SG is a synchronous generator, VSC is a wind power converter, INV is a photovoltaic inverter, PCS is an energy storage converter, PCC is a point of common coupling, and HVDC is a high-voltage DC transmission system.

[0031] For the aforementioned heterogeneous DC power transmission systems, the clustering methods in related technologies have the following limitations: 1. New energy converters do not have a physical rotor. Their dynamic characteristics are determined by control systems such as phase-locked loop, power outer loop, and current inner loop, and cannot be directly applied to the grouping criteria based on rotor angle.

[0032] 2. The high proportion of new energy access changes the dynamic coupling characteristics of the system, and the traditional modal analysis assumptions based on synchronous machines are no longer valid.

[0033] 3. The rapid power modulation capability of the DC system and the volatility of new energy sources are superimposed, causing the system operating point to change frequently. Static clustering results are difficult to reflect the differences in equipment characteristics during dynamic operation.

[0034] To address the aforementioned technical problems in related technologies, this application provides a dynamic clustering method for heterogeneous DC transmission systems. This method enables unified clustering of heterogeneous equipment such as synchronous machines and converters in heterogeneous power systems containing a high proportion of new energy and DC transmission. Instead of relying on a single physical state quantity (such as rotor angle), this method quantifies the dynamic response sensitivity of equipment at the point of common coupling to changes in key system parameters, constructs a unified feature space, and, combined with electrical distance constraints, utilizes spectral theory to transform nonlinear dynamic coupling into a low-dimensional geometric clustering problem.

[0035] The dynamic clustering method for heterogeneous DC transmission systems provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0036] like Figure 2 As shown in the embodiment of this application, a dynamic clustering method for a heterogeneous DC transmission system is provided. This method may include the following steps 201 to 205: Step 201: Based on the acquired real-time operation data of the power system and network topology data, establish a parameterized differential-algebraic equation model that includes synchronous machines and new energy converters.

[0037] The parameterized differential-algebraic equation model incorporates new energy penetration rate and DC transmission power as system parameter vectors.

[0038] It should be noted that the real-time operation data of the power system and the network topology data obtained in step 201 above can also be used for calculations in subsequent steps.

[0039] For example, after obtaining the real-time operation data and network topology data of the heterogeneous power system proposed in the embodiments of this application, they can be used for subsequent steps such as model construction and sensitivity calculation.

[0040] Specifically, step 201 above may also include the following steps 201a1 and 201a2: Step 201a1: Based on the acquired real-time operation data of the power system and network topology data, construct the system's differential equation system and algebraic equation system.

[0041] The differential equations are used to describe the physical or control dynamics of the synchronous machine and the renewable energy converter. The differential state variables of the synchronous machine include at least one of the following: rotor angle, rotor angular velocity, and transient electromotive force; the differential state variables of the renewable energy converter include at least one of the following: phase-locked loop phase, active and reactive power control integral term, and DC capacitor voltage; the algebraic equations are used to describe the network topology constraints and power balance equations.

[0042] Step 201a2: Introduce the system parameter vector containing the new energy penetration rate and DC transmission power into the differential equation system and algebraic equation system to obtain the parameterized differential-algebraic equation model.

[0043] For example, in the embodiments of this application, the dynamic behavior of the power system including a synchronous machine, a wind-solar-storage converter, and a DC system is described by the following set of parameterized differential-algebraic equations: in, This is a vector of differential state variables. For a synchronous generator, this includes the rotor angle. Rotor angular velocity Transient potential etc.; for converter interface resources, including phase-locked loop phases Active / reactive control integral term, DC capacitor voltage wait. This is an algebraic variable vector, primarily referring to the voltage amplitudes at each node of the network. V and phase angle It follows the power balance equation. This is a vector of key system parameters, representing the system's structure and composition. For example, , Indicates wind power penetration rate, Indicates DC transmission power. This indicates the penetration rate of solar power generation.

[0044] For example, the above It is a system of differential equations used to describe the physical or control dynamics (i.e., physical dynamics or control dynamics) inside the device. It is a system of algebraic equations used to describe network topology constraints and generator stator equations.

[0045] Step 202: Based on the parameterized differential-algebraic equation model, derive the linear time-varying variational equation satisfied by the trajectory sensitivity of the system state trajectory to the system parameter vector, and calculate the trajectory sensitivity of each device to the system parameter vector based on the linear time-varying variational equation.

[0046] For example, after the parametric differential-algebraic equation model is constructed, the linear time-varying variational equation satisfied by the trajectory sensitivity of the system state trajectory to the system parameter vector can be derived using the parametric differential-algebraic equation model.

[0047] Specifically, step 202 above may also include the following steps 202a1 and 202a2: Step 202a1: Define the trajectory sensitivity function as the partial derivative of the system state trajectory with respect to the system parameter vector.

[0048] Step 202a2: Take the total derivative of the parameterized differential-algebraic equation model with respect to the system parameter vector to obtain a linear time-varying variational equation containing the system state Jacobian matrix, the algebraic variable Jacobian matrix, and the parameter direct driving terms.

[0049] For example, to quantify the degree of deformation of the system state trajectory when parameters change slightly, a trajectory sensitivity function is defined: For example, regarding the parameterized differential-algebraic equation system with respect to By taking the total derivative and using the chain rule, we obtain the linear time-varying variational equation: in, Let be the system state Jacobian matrix, which varies with time because it is calculated along the original trajectory. It reflects the coupling strength between the internal states of the system. , , These are the Jacobian matrices of the system with respect to algebraic variables and the algebraic equations with respect to state variables, respectively. This is a direct driver of the parameters, reflecting the direct impact of parameter changes on the physical equations.

[0050] For example, initial conditions are set: if If these are physical parameters, then the initial state remains unaffected. If initial runpoint settings are included, then It is determined by the sensitivity of the initial power flow equations.

[0051] Step 203: Project the calculated trajectory sensitivity of each device onto the common connection point of each device to construct a unified sensitivity feature vector for each device.

[0052] For example, to address the problem that synchronous machines (with rotors) and converters (without rotors) cannot be directly compared, all dynamic characteristics are projected onto a common connection point.

[0053] Specifically, step 203 above may also include steps 203a1 to 203a3: Step 203a2: Within a preset time window, extract the voltage amplitude sensitivity sequence and the frequency sensitivity sequence at the common connection point of each device.

[0054] Step 203a2: The extracted voltage sensitivity sequence and frequency sensitivity sequence are scaled and normalized using weighted normalization coefficients.

[0055] Step 203a3: Concatenate the normalized voltage sensitivity sequence and frequency sensitivity sequence to obtain a unified sensitivity feature vector.

[0056] For example, for the first i The device, within the time window [0, T Within [the range], extract the voltage amplitude at its point of common coupling. and frequency For parameters Sensitivity, constructing feature vectors : in, These are the sampling points for the simulation time step; This is a voltage trajectory sensitivity sequence, reflecting changes in reactive power / voltage support characteristics; This is a frequency trajectory sensitivity sequence, reflecting changes in active / frequency response characteristics; This is the weighting normalization coefficient, used to balance the order-of-magnitude difference between voltage and frequency; it is typically taken as... .

[0057] In one possible implementation, step 203 above may also include the following steps 203b1 and 203b2: Step 203b1: Detect discontinuous events during system operation.

[0058] Step 203b2: At the moment when discontinuous events occur, calculate the transition condition based on the Jacobian matrix of the discrete event reset mapping, and reset the sensitivity state based on the calculated transition condition.

[0059] For example, at the moment of a fault, the system may experience a discontinuity (such as when the limiter activates). At this time, the sensitivity state... A transition will occur. A transition condition must be introduced: in, This is the Jacobian matrix of the discrete event reset mapping. Ignoring this term will cause the sensitivity calculation to diverge.

[0060] Step 204: Calculate the dynamic similarity between any two devices based on the unified sensitivity feature vector of each device, and combine it with the electrical distance similarity between devices to construct a hybrid similarity matrix indexed by the devices.

[0061] Specifically, the dynamic similarity calculation step in step 204 above may further include the following steps 204a1 and 204a2: Step 204a1: Calculate the cosine similarity between any two devices based on the unified sensitivity feature vector of the devices.

[0062] Step 204a2: Convert the cosine similarity into dynamic similarity weights using a Gaussian kernel function to obtain the dynamic similarity between any two devices.

[0063] For example, to ensure that the clustering results satisfy both dynamic consistency and network topology constraints, a device is defined. i and j The total similarity between them is .

[0064] For example, when calculating similarity, the focus is on whether the waveform shapes of the responses are consistent, rather than the absolute amplitude. Dynamic similarity based on cosine similarity can be calculated through the following steps: in, The cosine similarity has a value range of [-1, 1], where 1 represents perfect positive correlation (homology), -1 represents out of phase (opposition), and 0 represents orthogonality (irrelevance). The dynamic kernel bandwidth controls the rate at which similarity decays.

[0065] Specifically, step 204 above, the step of constructing the hybrid similarity matrix indexed by device, may further include the following steps 204c1 and 204c2: Step 204c1: Calculate the electrical distance between devices based on the node impedance matrix, and convert the electrical distance into electrical distance similarity weights using a Gaussian kernel function.

[0066] The node impedance matrix includes: the self-impedance of each node and the mutual impedance between nodes. It should be noted that, in this embodiment, the node-related information is determined based on network topology data.

[0067] Step 204c2: Weight the dynamic similarity weight and electrical distance similarity weight by adjusting the factor to generate a hybrid similarity matrix indexed by the device.

[0068] For example, the electrical distance similarity based on the impedance matrix can be calculated through the following steps: Using the node impedance matrix Define electrical distance: in, and For nodes i and nodes j The self-impedance (i.e., Thevenin equivalent impedance). For nodes i and nodes j The mutual impedance (i.e., transfer impedance) between them. Electrical distance, in physical terms, is the distance between nodes. i and nodes j Electrical distance is the voltage drop required to transfer a unit of electrical energy between two nodes.

[0069] For example, after obtaining the dynamic similarity and electrical distance similarity mentioned above, similarity fusion can be performed: Among them, the regulating factor It is usually set to 0.6~0.8, focusing on dynamic characteristics.

[0070] Step 205: Construct a normalized Laplacian matrix based on the hybrid similarity matrix, and achieve dynamic grouping of heterogeneous devices by performing eigenvalue decomposition and eigenvector clustering on the normalized Laplacian matrix.

[0071] For example, spectral clustering utilizes the characteristic spectral properties of the Laplacian matrix in graph theory to transform complex high-dimensional non-convex clustering problems into simple clustering problems in low-dimensional spaces.

[0072] Specifically, step 205 above may also include steps 205a1 to 205a3: Step 205a1: Calculate the degree matrix based on the mixed similarity matrix indexed by device, and construct the normalized Laplacian matrix based on the degree matrix and the mixed similarity matrix.

[0073] Step 205a2: Perform eigenvalue decomposition on the normalized Laplace matrix, select the eigenvectors corresponding to the first K smallest non-zero eigenvalues, and form an eigenvector matrix.

[0074] Step 205a3: Normalize each row of the feature vector matrix and use the K-Means clustering algorithm to cluster the normalized feature vectors to obtain the clustering results of heterogeneous devices.

[0075] For example, first calculate the degree matrix D, which is a diagonal matrix: Next, construct the normalized Laplace matrix. : Where I is The identity matrix, It is the inverse square root of the degree matrix. The eigenvalues ​​reflect the connectivity of the graph. The closer the eigenvalue is to 0, the smaller the weight of the edge cut by the corresponding partitioning method.

[0076] For example, after obtaining the normalized Laplace matrix, one can... Perform eigenvalue decomposition: Sort the eigenvalues ​​in ascending order: Select the eigenvectors corresponding to the first K smallest non-zero eigenvalues. Construct the feature matrix Normalizing each row of U to unit length yields matrix Y: in, Representation of the characteristic matrix The Middle i Line 1 j Each row of Y is considered as a point in a K-dimensional space. Using the K-means clustering algorithm, the final cluster labels can be obtained. For example, the following is an algorithm complexity analysis of the dynamic clustering method for heterogeneous DC transmission systems provided in the embodiments of this application: Sensitivity calculation: The computational cost of solving the variational equation is approximately the same as that of solving the original differential-algebraic equation system. times, of which, The number of parameters. Since the Jacobian matrix has already been calculated during the solution process, it can be directly reused. Through LU decomposition and reuse techniques, the actual computation time is only increased by approximately [amount missing]. .

[0077] Eigenvalue decomposition: For N A system with n nodes has a complexity of O(n). However, in power systems, the values ​​are typically in the thousands, and the matrix is ​​sparse. The Lanczos algorithm can reduce this to a smaller value. .

[0078] The dynamic clustering method for heterogeneous DC transmission systems provided in this application has the following technical advantages: 1. Theoretical breakthrough: By using variational equations, the concept of "homology" is extended from "rotor sway consistency" to "parameter sensitivity consistency", solving the definition problem of rotorless converters.

[0079] 2. Methodological innovation: By integrating dynamic waveform similarity and static electrical distance, spectral mapping ensures the consistency of the clustering results in terms of physical topology connectivity and control characteristics.

[0080] 3. Practical value: The generated feature vector contains predictive information about the equipment's response to key disturbances such as DC blocking and new energy fluctuations, providing accurate grouping of controlled objects for subsequent broadband oscillation suppression and emergency control strategies.

[0081] The dynamic clustering method for heterogeneous DC transmission systems provided in this application first establishes a parameterized differential-algebraic equation model including synchronous machines and new energy converters based on the acquired real-time power system operation data and network topology data. This model incorporates new energy penetration rate and DC transmission power as system parameter vectors. Then, based on the parameterized differential-algebraic equation model, a linear time-varying variational equation is derived to satisfy the trajectory sensitivity of the system state trajectory to the system parameter vector. Based on this linear time-varying variational equation, the trajectory sensitivity of each device to the system parameter vector is calculated. The calculated trajectory sensitivity of each device is projected to its corresponding common connection point to construct a unified sensitivity feature vector for each device. The dynamic similarity between any two devices is calculated based on their unified sensitivity feature vectors, and combined with the electrical distance similarity between devices, a hybrid similarity matrix indexed by the devices is constructed. Finally, a normalized Laplace matrix is ​​constructed based on the hybrid similarity matrix, and dynamic clustering of heterogeneous devices is achieved by performing eigenvalue decomposition and eigenvector clustering on the normalized Laplace matrix. In this way, it is possible to achieve unified grouping of heterogeneous equipment such as synchronous machines and converters in heterogeneous power systems that include a high proportion of new energy sources and DC transmission.

[0082] It should be noted that the dynamic grouping method for heterogeneous DC transmission systems provided in this application can be executed by a dynamic grouping device for the heterogeneous DC transmission system, or by a control module within that dynamic grouping device for executing the dynamic grouping method. This application uses the example of a dynamic grouping device for the heterogeneous DC transmission system executing the dynamic grouping method to illustrate the dynamic grouping device for the heterogeneous DC transmission system provided in this application.

[0083] It should be noted that, in the embodiments of this application, the dynamic clustering methods for heterogeneous DC transmission systems shown in the accompanying drawings are all illustrated using one accompanying drawing from an embodiment of this application as an example. In specific implementation, the dynamic clustering methods for heterogeneous DC transmission systems shown in the accompanying drawings of the above methods can also be implemented in conjunction with any other accompanying drawings illustrated in the above embodiments, which will not be elaborated here.

[0084] The dynamic grouping device for the heterogeneous DC transmission system provided in this application is described below. The dynamic grouping method for the heterogeneous DC transmission system described below can be referred to in correspondence with the method described above.

[0085] Figure 3 This is a schematic diagram of the structure of the dynamic grouping device of the heterogeneous DC transmission system provided in the embodiments of this application, as shown below. Figure 3 As shown, it specifically includes: The model construction module 301 is used to establish a parameterized differential-algebraic equation model including a synchronous machine and a new energy converter based on the acquired real-time system operation data and network topology data. The parameterized differential-algebraic equation model introduces the new energy penetration rate and DC transmission power as system parameter vectors. The sensitivity calculation module 302 is used to derive the linear time-varying variational equation satisfied by the trajectory sensitivity of the system state trajectory to the system parameter vector based on the parameterized differential-algebraic equation model, and to calculate the trajectory sensitivity of each device to the system parameter vector based on the linear time-varying variational equation. Feature extraction... Module 303 is used to project the calculated trajectory sensitivity of each device onto the common connection point corresponding to each device to construct a unified sensitivity feature vector for each device; Module 304 is used to calculate the dynamic similarity between any two devices based on the unified sensitivity feature vector of each device, and combine it with the electrical distance similarity between devices to construct a hybrid similarity matrix indexed by the device; Module 305 is used to construct a normalized Laplacian matrix based on the hybrid similarity matrix, and realize the dynamic grouping of heterogeneous devices by performing feature decomposition and feature vector clustering on the normalized Laplacian matrix.

[0086] Optionally, the model building module 301 is specifically used to construct a system of differential equations and algebraic equations based on the acquired real-time operating data and network topology data of the power system. The differential equations are used to describe the physical or control dynamics of the synchronous machine and the renewable energy converter. The differential state variables of the synchronous machine include at least one of the following: rotor angle, rotor angular velocity, and transient electromotive force. The differential state variables of the renewable energy converter include at least one of the following: phase-locked loop phase, active and reactive power control integral term, and DC capacitor voltage. The algebraic equations are used to describe the network topology constraints and power balance equations. The model building module 301 is also specifically used to introduce the system parameter vector containing the renewable energy penetration rate and DC transmission power into the differential equations and algebraic equations to obtain a parameterized differential-algebraic equation model.

[0087] Optionally, the sensitivity calculation module 302 is specifically used to define the trajectory sensitivity function as the partial derivative of the system state trajectory with respect to the system parameter vector; the sensitivity calculation module 302 is also specifically used to calculate the total derivative of the parameterized differential-algebraic equation model with respect to the system parameter vector, to obtain a linear time-varying variational equation containing the system state Jacobian matrix, the algebraic variable Jacobian matrix, and the parameter direct driving terms.

[0088] Optionally, the sensitivity calculation module 302 is specifically used to detect discontinuous events during system operation; the sensitivity calculation module 302 is also specifically used to calculate the transition condition based on the Jacobian matrix of the discrete event reset mapping at the time of the occurrence of the discontinuous event, and to reset the sensitivity state based on the calculated transition condition.

[0089] Optionally, the feature extraction module 303 is specifically used to extract the voltage amplitude sensitivity sequence and the frequency sensitivity sequence at the common connection point of each device within a preset time window; the feature extraction module 303 is further used to perform scale normalization processing on the extracted voltage sensitivity sequence and frequency sensitivity sequence using a weighted normalization coefficient; the feature extraction module 303 is further used to concatenate the normalized voltage sensitivity sequence and frequency sensitivity sequence to obtain a unified sensitivity feature vector.

[0090] Optionally, the similarity calculation module 304 is specifically used to calculate the cosine similarity between any two devices based on the unified sensitivity feature vector of the devices; the similarity calculation module 304 is also specifically used to convert the cosine similarity into dynamic similarity weights through a Gaussian kernel function to obtain the dynamic similarity between any two devices.

[0091] Optionally, the similarity calculation module 304 is further configured to calculate the electrical distance between devices based on the node impedance matrix, and convert the electrical distance into electrical distance similarity weights through a Gaussian kernel function; the node impedance matrix includes: the self-impedance of the nodes and the mutual impedance between the nodes; the similarity calculation module 304 is further configured to perform weighted fusion of dynamic similarity weights and electrical distance similarity weights through adjustment factors to generate a hybrid similarity matrix indexed by the devices.

[0092] Optionally, the device clustering module 305 is specifically used to calculate a degree matrix based on a mixed similarity matrix indexed by devices, and to construct a normalized Laplacian matrix based on the degree matrix and the mixed similarity matrix; the device clustering module 305 is further used to perform eigenvalue decomposition on the normalized Laplacian matrix, select the eigenvectors corresponding to the first K smallest non-zero eigenvalues ​​to form an eigenvector matrix; the device clustering module 305 is further used to normalize each row of the eigenvector matrix, and to cluster the normalized eigenvectors using the K-Means clustering algorithm to obtain the clustering results of heterogeneous devices.

[0093] The dynamic clustering device for heterogeneous DC transmission systems provided in this application first establishes a parameterized differential-algebraic equation model, including synchronous machines and new energy converters, based on the acquired real-time operation data and network topology data of the power system. This model incorporates the new energy penetration rate and DC transmission power as system parameter vectors. Then, based on the parameterized differential-algebraic equation model, a linear time-varying variational equation is derived to satisfy the trajectory sensitivity of the system state trajectory to the system parameter vector. Based on this linear time-varying variational equation, the trajectory sensitivity of each device to the system parameter vector is calculated. The calculated trajectory sensitivity of each device is projected to the common connection point corresponding to each device, constructing a unified sensitivity feature vector for each device. Based on the unified sensitivity feature vector of each device, the dynamic similarity between any two devices is calculated, and combined with the electrical distance similarity between devices, a hybrid similarity matrix indexed by the devices is constructed. Finally, a normalized Laplace matrix is ​​constructed based on the hybrid similarity matrix, and dynamic clustering of heterogeneous devices is achieved by performing eigenvalue decomposition and eigenvector clustering on the normalized Laplace matrix. In this way, it is possible to achieve unified grouping of heterogeneous equipment such as synchronous machines and converters in heterogeneous power systems that include a high proportion of new energy sources and DC transmission.

[0094] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a dynamic clustering method for heterogeneous DC transmission systems. This method includes: first, establishing a parameterized differential-algebraic equation model including synchronous machines and new energy converters based on the acquired real-time operating data and network topology data of the power system; the parameterized differential-algebraic equation model introduces the new energy penetration rate and DC transmission power as system parameter vectors; then, based on the parameterized differential-algebraic equation model, deriving the linear time-varying variational equation satisfied by the trajectory sensitivity of the system state trajectory to the system parameter vector, and based on the linear time-varying variational equation model, deriving the linear time-varying variational equation satisfied by the system state trajectory sensitivity to the system parameter vector, and deriving the linear time-varying variational equation. A time-varying variational equation is used to calculate the trajectory sensitivity of each device to the system parameter vector. The calculated trajectory sensitivity of each device is projected onto its corresponding common connection point to construct a unified sensitivity feature vector for each device. Based on the unified sensitivity feature vector, the dynamic similarity between any two devices is calculated, and combined with the electrical distance similarity between devices, a hybrid similarity matrix indexed by the devices is constructed. Finally, a normalized Laplace matrix is ​​constructed based on the hybrid similarity matrix, and dynamic grouping of heterogeneous devices is achieved by performing eigenvalue decomposition and eigenvector clustering on the normalized Laplace matrix. In this way, unified grouping of heterogeneous devices such as synchronous machines and converters can be achieved in heterogeneous power systems containing a high proportion of new energy and DC transmission.

[0095] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] On the other hand, this application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the dynamic clustering method for heterogeneous DC transmission systems provided by the above methods. This method includes: first, establishing a parameterized differential-algebraic equation model including synchronous machines and new energy converters based on the acquired real-time operating data of the power system and network topology data; the parameterized differential-algebraic equation model introduces the new energy penetration rate and DC transmission power as system parameter vectors; then, deriving the system state based on the parameterized differential-algebraic equation model. The trajectory sensitivity of the trajectory to the system parameter vector is satisfied by a linear time-varying variational equation. Based on this equation, the trajectory sensitivity of each device to the system parameter vector is calculated. The calculated trajectory sensitivity of each device is projected onto the common connection point corresponding to each device to construct a unified sensitivity feature vector for each device. The dynamic similarity between any two devices is calculated based on the unified sensitivity feature vector, and combined with the electrical distance similarity between devices, a hybrid similarity matrix indexed by the devices is constructed. Finally, a normalized Laplace matrix is ​​constructed based on the hybrid similarity matrix, and dynamic grouping of heterogeneous devices is achieved by performing eigenvalue decomposition and eigenvector clustering on the normalized Laplace matrix. In this way, unified grouping of heterogeneous devices such as synchronous machines and converters can be achieved in heterogeneous power systems containing a high proportion of new energy and DC transmission.

[0097] Furthermore, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the dynamic clustering methods for the heterogeneous DC transmission systems provided above. The method includes: first, establishing a parameterized differential-algebraic equation model including synchronous machines and renewable energy converters based on acquired real-time power system operation data and network topology data; the parameterized differential-algebraic equation model introducing renewable energy penetration rate and DC transmission power as system parameter vectors; and then, based on the parameterized differential-algebraic equation model, deriving the trajectory sensitivity of the system state trajectory to the system parameter vectors. The linear time-varying variational equation is used to calculate the trajectory sensitivity of each device to the system parameter vector. The calculated trajectory sensitivity of each device is projected onto its corresponding common connection point to construct a unified sensitivity feature vector for each device. Based on the unified sensitivity feature vector, the dynamic similarity between any two devices is calculated, and combined with the electrical distance similarity between devices, a hybrid similarity matrix indexed by the devices is constructed. Finally, a normalized Laplace matrix is ​​constructed based on the hybrid similarity matrix, and dynamic grouping of heterogeneous devices is achieved by performing eigenvalue decomposition and eigenvector clustering on the normalized Laplace matrix. In this way, unified grouping of heterogeneous devices such as synchronous machines and converters can be achieved in heterogeneous power systems containing a high proportion of new energy and DC transmission.

[0098] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0099] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A dynamic clustering method for a heterogeneous DC transmission system, characterized in that, include: Based on the acquired real-time operation data of the power system and network topology data, a parameterized differential-algebraic equation model including synchronous machines and new energy converters is established. The parameterized differential-algebraic equation model introduces new energy penetration rate and DC transmission power as system parameter vectors; Based on the parameterized differential-algebraic equation model, the linear time-varying variational equation satisfied by the trajectory sensitivity of the system state trajectory to the system parameter vector is derived, and based on the linear time-varying variational equation, the trajectory sensitivity of each device to the system parameter vector is calculated. The calculated trajectory sensitivity of each device is projected onto the common connection point of each device to construct a unified sensitivity feature vector for each device. The dynamic similarity between any two devices is calculated based on the unified sensitivity feature vector of each device, and combined with the electrical distance similarity between devices, a hybrid similarity matrix indexed by the devices is constructed. A normalized Laplacian matrix is ​​constructed based on a hybrid similarity matrix, and dynamic grouping of heterogeneous devices is achieved by performing eigenvalue decomposition and eigenvector clustering on the normalized Laplacian matrix.

2. The method according to claim 1, characterized in that, Based on the acquired real-time operating data and network topology data of the power system, a parameterized differential-algebraic equation model including synchronous machines and new energy converters is established, including: Based on the acquired real-time power system operation data and network topology data, a system of differential equations and algebraic equations are constructed. The differential equations describe the physical or control dynamics of the synchronous machine and the renewable energy converter. The differential state variables of the synchronous machine include at least one of the following: rotor angle, rotor angular velocity, and transient electromotive force. The differential state variables of the renewable energy converter include at least one of the following: phase-locked loop phase, active and reactive power control integral term, and DC capacitor voltage. The algebraic equations describe the network topology constraints and power balance equations. By introducing the system parameter vector, which includes the penetration rate of new energy sources and DC transmission power, into the differential equation system and the algebraic equation system, a parameterized differential-algebraic equation model is obtained.

3. The method according to claim 1, characterized in that, The derivation of the linear time-varying variational equation satisfied by the trajectory sensitivity of the system state trajectory to the system parameter vector based on the parameterized differential-algebraic equation model includes: The trajectory sensitivity function is defined as the partial derivative of the system state trajectory with respect to the system parameter vector; Taking the total derivative of the parameterized differential-algebraic equation model with respect to the system parameter vector yields a linear time-varying variational equation containing the system state Jacobian matrix, the algebraic variable Jacobian matrix, and the direct driving terms of the parameters.

4. The method according to claim 1 or 3, characterized in that, The calculation of the trajectory sensitivity of each device to the system parameter vector based on the linear time-varying variational equation includes: Detect discontinuous events during the operation of the detection system; At the moment when discontinuous events occur, the transition condition is calculated based on the Jacobian matrix of the discrete event reset mapping, and the sensitivity state is reset based on the calculated transition condition.

5. The method according to claim 1, characterized in that, The step of projecting the calculated trajectory sensitivity of each device onto the common connection point corresponding to each device to construct a unified sensitivity feature vector includes: Within a preset time window, extract the voltage amplitude sensitivity sequence and the frequency sensitivity sequence at the common connection point of each device. The extracted voltage sensitivity sequence and frequency sensitivity sequence were scaled using weighted normalization coefficients. The normalized voltage sensitivity sequence and frequency sensitivity sequence are concatenated to obtain a unified sensitivity feature vector.

6. The method according to claim 1, characterized in that, The calculation of dynamic similarity between any two devices based on the unified sensitivity feature vector of each device includes: Based on the unified sensitivity feature vector of the devices, calculate the cosine similarity between any two devices; The cosine similarity is converted into dynamic similarity weights using a Gaussian kernel function to obtain the dynamic similarity between any two devices.

7. The method according to claim 6, characterized in that, The method of combining electrical distance similarity between devices to construct a hybrid similarity matrix indexed by devices includes: The electrical distance between devices is calculated based on the node impedance matrix, and the electrical distance is converted into electrical distance similarity weights using a Gaussian kernel function; the node impedance matrix includes: the self-impedance of the nodes and the mutual impedance between the nodes; By adjusting the dynamic similarity weight and electrical distance similarity weight, a hybrid similarity matrix indexed by devices is generated.

8. The method according to claim 1, characterized in that, The process of constructing a normalized Laplacian matrix based on a hybrid similarity matrix, and then performing eigenvalue decomposition and eigenvector clustering on the normalized Laplacian matrix to achieve dynamic grouping of heterogeneous devices includes: The degree matrix is ​​calculated based on the hybrid similarity matrix indexed by device, and a normalized Laplacian matrix is ​​constructed based on the degree matrix and the hybrid similarity matrix. Perform eigenvalue decomposition on the normalized Laplacian matrix, and select the eigenvectors corresponding to the first K smallest non-zero eigenvalues ​​to form an eigenvector matrix. Each row of the eigenvector matrix is ​​normalized, and the K-Means clustering algorithm is used to cluster the normalized eigenvectors to obtain the clustering results of heterogeneous devices.

9. A dynamic grouping device for a heterogeneous DC transmission system, characterized in that, The device includes: The model building module is used to establish a parameterized differential-algebraic equation model containing synchronous machines and new energy converters based on the acquired real-time system operation data and network topology data; the parameterized differential-algebraic equation model introduces the new energy penetration rate and DC transmission power as system parameter vectors; The sensitivity calculation module is used to derive the linear time-varying variational equation satisfied by the trajectory sensitivity of the system state trajectory to the system parameter vector based on the parameterized differential-algebraic equation model, and to calculate the trajectory sensitivity of each device to the system parameter vector based on the linear time-varying variational equation. The feature extraction module is used to project the calculated trajectory sensitivity of each device onto the common connection point of each device to construct a unified sensitivity feature vector for each device. The similarity calculation module is used to calculate the dynamic similarity between any two devices based on the unified sensitivity feature vector of each device, and combine the electrical distance similarity between devices to construct a hybrid similarity matrix indexed by the devices. The device clustering module is used to construct a normalized Laplacian matrix based on the hybrid similarity matrix, and to achieve dynamic clustering of heterogeneous devices by performing feature decomposition and feature vector clustering on the normalized Laplacian matrix.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the dynamic clustering method for a heterogeneous DC transmission system as described in any one of claims 1 to 8.

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