Power system real-time partition inertia estimation method, system, device and storage medium

Abstract

The present application relates to the technical field of power system, especially to a kind of power system real-time partition inertia estimation method, system, equipment and storage medium, method includes: the dynamic response data of each node frequency of power system is measured, it is preprocessed, for each node in region, the dynamic time warping DTW index between its frequency data is calculated, and regional division is realized based on this;All generators in each region are equivalent, and the equivalent unit power is represented by internal unit power aggregation, and the equivalent unit frequency is represented by the frequency of regional inertia center;For each region, a regression model is constructed based on the rotor motion equation, and the equivalent inertia of the region is solved using the design of time-varying gain and integrator based on fixed time convergence. Based on the time series measurement data of the bus frequency of each node of the power system, the generator terminal power change and other time series measurement data, the real-time partition and regional inertia level estimation of the power system is realized to ensure the safe and stable operation of the power grid.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, system, device and storage medium for real-time partitioned inertia estimation of power systems. Background Technology

[0002] Inertia, as a crucial indicator of power system frequency stability, primarily reflects the rate of change of frequency (RoCoF) after active power disturbances. With the integration of a high proportion of renewable energy units, system inertia is characterized by low inertia. When a low-inertia power system experiences an active power disturbance, RoCoF triggers frequency protection devices more quickly and easily, exacerbating frequency security risks. Therefore, assessing system inertia is beneficial for ensuring system frequency security.

[0003] Based on different spatial dimensions, the inertia assessment of power systems can be divided into system-level, regional, and nodal inertia assessments. Real-world power systems are generally composed of interconnected multiple regions. Furthermore, with the increasing penetration of new energy sources, the uneven distribution of power system inertia is becoming increasingly apparent. Simultaneously, the dynamic frequency response of power systems exhibits regional characteristics. Sensing and monitoring the inertia levels of each region can provide a basis for formulating refined inertia control strategies, making regional inertia assessment a key research direction in the stability analysis of power systems with a high proportion of new energy sources.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention 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, device, and storage medium for real-time partitioned inertia estimation of power systems, thereby improving the precision and stability of power system inertia distribution in power system control decisions.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a real-time partitioned inertia estimation method for power systems, comprising the following steps: The frequency dynamic response data of each node in the power system is measured, the frequency dynamic response data is preprocessed, the dynamic time warping (DTW) index between the frequency dynamic response data of each node in the region is calculated, and the region is divided using the k-medoids algorithm based on the dynamic time warping (DTW) index. All generators in each region are equivalent to a single unit. The power of the equivalent unit in a region is represented by the aggregated power of the internal units, and the frequency of the equivalent unit is represented by the regional inertial center frequency. For the units after equivalent processing in each region, a regression model is constructed based on the rotor motion equation, and the equivalent inertia of the region is solved by using time-varying gain and integrator design based on fixed-time convergence.

[0007] Furthermore, the calculation of the Dynamic Time Warping (DTW) index between the frequency dynamic response data for each node within the region includes: ; In the formula, f p and f q These are the discrete frequency time series of nodes p and q, respectively. f represents a discrete-time series p and f q The dynamic time warping (DTW) distance between them; S is the optimal matching path, and is the minimum Euclidean distance between sampling points along the optimal matching path; Represents the first [number]th ... Point pair The Euclidean distance between them; This indicates a pair of points in two sequences that form the optimal matching path; The DTW matrix of each node in the calculation system is used. When the DTW distance index between the frequencies of the nodes is less than the set value, their frequency similarity is high, and these nodes are divided into the same region.

[0008] Furthermore, the method of using the k-medoids algorithm to partition the region includes the following steps: S11, Calculate the DTW matrix for the system node frequencies; S12 specifies the number of partitions, k. S13, Cluster center initialization: Select k nodes as initial cluster centers; S14, assign the node to the cluster center with the smallest distance from DTW; S15, Update cluster center: Select the node with the smallest total DTW distance within the cluster; S16. Repeat steps S14 and S15 until the cluster center no longer changes, and output the partitioning results.

[0009] Furthermore, the inertial center frequency of the region is calculated by the following formula: ; In the formula, f CoI H is the region's equivalent inertial center frequency; gi and f i These represent the inertial constants and node frequencies of each generator within the region.

[0010] Furthermore, the construction of regression models for each region based on the rotor motion equations includes: ; In the formula, y(t) is the system output; θ is the vector of regressors; θ is the vector of unknown parameters.

[0011] Furthermore, the method for solving the equivalent inertia of the region based on fixed-time convergence using time-varying gain and integrator includes the following steps: The regression model is filtered on both sides to construct auxiliary filter variables, which include the filtered output Y(t) and the filtered regression matrix Φ(t). The filtered output Y(t) and the regression matrix Φ(t) are defined as follows: ; In the formula, This represents a time-varying gain function used to drive the filter variable to converge within a fixed time period; The auxiliary filter variable represents the state of the system output y(t) after passing through the time-varying filter; The auxiliary filter variable is a vector of regressors. The state after passing through the same time-varying filter; Represents the regressor vector; For time-varying gain function Integrate over the interval [0, T]: ; In the formula, T represents the preset convergence time, and integral divergence means... As it approaches T, it tends to 0, thus compressing the tracking process within an infinite time into a finite time T, achieving the core requirement of fixed-time convergence; Construct an algebraic relation, and the filtered variables satisfy the following algebraic equation: ; An estimator is a tool used to calculate unknown parameters from filtered algebraic relations. If Φ(t) is full rank, the algorithm or formula for updating the parameters of the estimator is as follows: ; The time-varying gain is designed as follows: ; In the formula, The proportional gain coefficient represents the time-varying gain and controls how fast the gain increases. The power exponent represents the variable gain, controlling the steepness of the gain increase near a preset time endpoint; when hour, ,and This makes the estimator in Always be restrained.

[0012] Furthermore, the rotor motion equation is: ; In the formula, H represents the equivalent inertia of the region, and f is the inertial center frequency of the region. Indicates the change in equivalent unit power; Indicates time; The following is defined during the construction of the regression model: .

[0013] Furthermore, in the construction of the auxiliary filter variables, the filter parameters are calculated using the trapezoidal integral method: ; In the formula, Δt represents the data sampling time, k represents the current time, and k+1 represents the next time. This represents the filtered output value at time k; This represents the filtered output value at the discrete time k+1; This represents the time-varying gain value at time k; This represents the original output value at time k; This represents the time-varying gain value at time k+1; This represents the original output value at time k+1; This represents the filtered regression value at time k; This represents the value of the filtered regression at time k+1; This represents the regression vector value at time k; This represents the regression vector value at time k+1.

[0014] The present invention also includes a real-time partitioned inertia estimation system for power systems, using the method described above, wherein the system comprises: The regional division unit is used to measure the frequency dynamic response data of each node in the power system, preprocess it, calculate the dynamic time warping (DTW) index between the frequency data of each node in the region, and use the k-medoids algorithm to divide the region based on this. The regional equivalent unit is used to make all generators in each region equivalent. The power of the equivalent unit is represented by the aggregated power of the internal units, and the frequency of the equivalent unit is represented by the regional inertial center frequency. The inertia solving unit is used to construct a regression model for each region based on the rotor motion equation, and to solve for the equivalent inertia of the region by using time-varying gain and integrator design based on fixed-time convergence.

[0015] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0016] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0017] The beneficial effects of this invention are as follows: By calculating the dynamic time warping (DTW) index between the frequency data of each node in the region, the k-medoids algorithm is used to divide the region based on this. Then, all generators in each region are equivalent. Finally, based on fixed-time convergence, the equivalent inertia of the region is solved using the design of time-varying gain and integrator. In view of the increasingly obvious uneven distribution of power grid inertia due to the development of new energy sources, and the accompanying decrease in inertia level, the invention realizes real-time partitioning and regional inertia level estimation of the power system under high proportion of new energy access based on time series measurement data such as the bus frequency and generator terminal power change of each node in the power system, so as to ensure the safe and stable operation of the power grid. 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 This is a flowchart of the method in Example 1; Figure 2 This is a schematic diagram of the system structure in Example 1; Figure 3 This is a flowchart illustrating the method in Example 2; Figure 4 This is a diagram showing the topology and region division of the IEEE-39 node system in Example 2; Figure 5 This is a graph showing the frequency response curves of each node in the IEEE-39 node system in Example 2. The node frequency curves in the same region are represented by the same color. Figure 6 This is a region partitioning diagram of the improved IEEE-39 node system in Example 2.

[0020] Figure 7 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0021] 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. Example 1:

[0022] like Figure 1 As shown: A method for real-time partitioned inertia estimation in a power system, comprising the following steps: The dynamic frequency response data of each node in the power system is measured, preprocessed, and the dynamic time warping (DTW) index between the frequency data of each node in the region is calculated. Based on this, the k-medoids algorithm is used to divide the region. All generators in each region are equivalent, and the power of the equivalent unit is represented by the aggregated power of the internal units, while the frequency of the equivalent unit is represented by the regional inertial center frequency. For each region, a regression model is constructed based on the rotor motion equation. The equivalent inertia of the region is solved by using time-varying gain and integrator design based on fixed-time convergence.

[0023] By calculating the Dynamic Time Warping (DTW) index between the frequency data of each node within a region, the k-medoids algorithm is used to divide the region. Then, all generators in each region are equivalent. Finally, based on fixed-time convergence, the equivalent inertia of the region is solved using the design of time-varying gain and integrator. In view of the increasingly obvious uneven distribution of power grid inertia due to the development of new energy sources, and the accompanying decrease in inertia level, the system realizes real-time partitioning of the power system and estimation of regional inertia levels based on time series measurement data such as the bus frequency and generator terminal power changes of each node in the power system, so as to ensure the safe and stable operation of the power grid.

[0024] In this embodiment, the Dynamic Time Warping (DTW) index between the frequency data of each node within the region is calculated, including: ; In the formula, f p and f q Let p and q be the discrete frequency time series of nodes p and q, respectively; S is the optimal matching path, and q is the minimum Euclidean distance between sampling points along the optimal matching path. The DTW matrix of each node in the calculation system is used. When the DTW distance index between the frequencies of the nodes is less than the set value, their frequency similarity is high, and these nodes are divided into the same region.

[0025] The k-medoids algorithm is used to partition regions, including the following steps: S11, Calculate the DTW matrix for the system node frequencies; S12 specifies the number of partitions, k. S13, Cluster center initialization: Select k nodes as initial cluster centers; S14, assign the node to the cluster center with the smallest distance from DTW; S15, Update cluster center: Select the node with the smallest total DTW distance within the cluster; S16, Repeat steps S14 and S15 until the cluster center no longer changes, and output the partitioning results; In the implementation process, the number of regions k can be determined by the elbow rule.

[0026] The regional inertial center frequency is calculated by the following formula: ; In the formula, f CoI H is the region's equivalent inertial center frequency; gi and f i These represent the inertial constants and node frequencies of each generator within the region.

[0027] As a preferred embodiment of the above, a regression model is constructed for each region based on the rotor motion equation, including: ; In the formula, y(t) is the system output; θ is the vector of regressors; θ is the vector of unknown parameters.

[0028] In this embodiment, the equivalent inertia of the region is solved using a time-varying gain and integrator design based on fixed-time convergence, including the following steps: Construct auxiliary filtering variables to filter both sides of the regression model, and define the filtered output Y(t) and regression matrix Φ(t) as follows: ; In the formula, α(t)>0 is a time-varying gain function that converges over a fixed time interval, and its integral over the interval [0,T] diverges. ; In the formula, T represents that the gain tends to infinity within a preset time T, reducing the influence of the initial error; The time-varying gain is designed as follows: ; when hour, ,and This makes the estimator in Always be mindful of your surroundings; Construct an algebraic relation, and the filtered variables satisfy the following algebraic equation: ; If Φ(t) is full rank, the parameters of the estimator are updated as follows: .

[0029] As a preferred embodiment of the above, the rotor motion equation is: ; In the formula, H represents the equivalent inertia of the region, and f is the inertial center frequency of the region. Indicates the change in equivalent unit power; Defined during the construction of the regression model: .

[0030] In constructing the auxiliary filter variables, the filter parameters are calculated using the trapezoidal integral method: ; In the formula, Δt represents the data sampling time, k represents the current time, and k+1 represents the next time.

[0031] like Figure 2 As shown, this embodiment also includes a real-time partitioned inertia estimation system for power systems, using the method described above. The system includes: The regional division unit is used to measure the frequency dynamic response data of each node in the power system, preprocess it, calculate the dynamic time warping (DTW) index between the frequency data of each node in the region, and use the k-medoids algorithm to divide the region based on this. The regional equivalent unit is used to make all generators in each region equivalent. The power of the equivalent unit is represented by the aggregated power of the internal units, and the frequency of the equivalent unit is represented by the regional inertial center frequency. The inertia solving unit is used to construct a regression model for each region based on the rotor motion equation, and to solve for the equivalent inertia of the region by using time-varying gain and integrator design based on fixed-time convergence. Example 2:

[0032] like Figure 3 As shown, the real-time partitioned inertia estimation method for power systems in this embodiment includes the following steps: S1. Measure the frequency dynamic response data of each node in the power system, perform preprocessing such as normalization, calculate the DTW index between the frequency data of each node in the region, and use the k-medoids algorithm to divide the region based on this. S2, equivalent to all generators in each region, the equivalent unit power is represented by the aggregated power of the internal units, and the equivalent unit frequency is represented by the regional inertial center frequency; S3, for each region, a model is constructed based on the rotor motion equation, and the equivalent inertia of the region is solved by using time-varying gain and integrator design based on fixed-time convergence. In S1, the DTW distance, used as a similarity evaluation index for the dynamic frequency response of nodes, can be expressed as: ; In the formula, f p and f q Let be the discrete frequency time series of nodes p and q, respectively; S is the optimal matching path, and is the minimum Euclidean distance between sampling points along the optimal matching path. When the DTW distance index between node frequencies is small, their frequency similarity is high, and these nodes can be classified into the same region. Therefore, the DTW matrix of each node in the system can be calculated. To further implement the above technical solution, the k-medoids algorithm is used in S1 to perform the partitioning steps as follows: S11, Calculate the DTW matrix for the system node frequencies; S12 specifies the number of partitions, k. S13, Cluster center initialization: Select k nodes as initial cluster centers; S14, assign the node to the cluster center with the smallest distance from DTW; S15, Update cluster center: Select the node with the smallest total DTW distance within the cluster; S16. Repeat steps S14 and S15 until the cluster center no longer changes, and output the partitioning results.

[0033] In the implementation process, the number of regions k can be determined by the elbow rule.

[0034] The formula for calculating the regional inertial center frequency in S2 is as follows: ; In the formula, f CoI H is the region's equivalent inertial center frequency; gi and f i These represent the inertial constants and node frequencies of each generator within the region.

[0035] To further implement the above technical solution, the specific content of S3 includes: S31, Constructing a regression model based on the rotor motion equation: ; In the formula, y(t) is the system output; The vector represents the regressors (composed of system inputs / outputs); θ represents the vector of unknown parameters. S32, construct auxiliary filtering variables to filter both sides of the regression equation. Define the filtered output Y(t) and the regression matrix Φ(t) as follows: ; Where α(t)>0 is a time-varying gain function, and its key characteristic for achieving fixed-time convergence is that its integral over the interval [0,T] diverges: ; In the formula, T represents the gain tending to infinity within a preset time T, thereby reducing the influence of the initial error.

[0036] The time-varying gain can be designed as follows: ; when hour, and This forces the estimator to... It converges precisely at all times.

[0037] S33, Construct an algebraic relationship. The filtered variables satisfy the following algebraic equation: ; S34, if Φ(t) is full rank, the parameters of the design estimator are updated as follows: ; To further implement the above technical solution, the rotor motion equation in S31 can be expressed as: ; In the formula, H represents the equivalent inertia of the region, and f is the inertial center frequency of the region. Indicates the change in equivalent unit power; Defined during model building: ; To further implement the above technical solution, the filtering parameters in S32 can be calculated using the trapezoidal integral method: ; In the formula, Δt represents the data sampling time, k represents the current time, and k+1 represents the next time.

[0038] The invention will be further illustrated by the following specific experiments: Based on the above implementation steps, this example applies the proposed system partitioning based on the DTW distance index and the regional inertia solution method designed using time-varying gain and integrator to the IEEE-39 node system. Its system topology is as follows: Figure 4As shown, this method enables power system zoning and regional inertia calculation. Simulation verification was performed in DIgSILENT with a system rated frequency of 50Hz. At t=1s, a load increase of 103MW was set at bus 28. Zoning was implemented using frequency data from high-voltage nodes other than the generator terminals.

[0039] The system frequency response curve is as follows Figure 5 As shown in the figure, the frequency response curves of each node from t=0 to 5s are presented. It can be seen from the figure that the frequency dynamic response curves of each node in this test system differ significantly in both value and shape, exhibiting obvious spatial distribution characteristics. To estimate its regional equivalent inertia, the system first needs to be partitioned. The partitioning method results are as follows... Figure 4 As shown, the system is divided into four regions. Region 1 contains only a single generator G1, Region 2 contains generators G8, G9, and G10, Region 3 contains generators G2 and G3, and the remaining four generators are assigned to Region 4.

[0040] Figure 4 In the system frequency response curve diagram, the node frequency curves in the same region are represented by the same color. The diagram shows that the node frequencies in each region are similar and can be grouped into the same region. In region 2, there are two curves with fluctuation amplitudes that deviate significantly from the cluster, corresponding to nodes 28 and 29. Analysis indicates that the disturbance occurs at node 28, so the frequency fluctuations of this node and its neighboring nodes will be greater. However, it is still classified into the region with the highest frequency fluctuation similarity, making the partitioning result reasonable. Therefore, the proposed partitioning method is effective.

[0041] Based on the above partitioning, the inertia of each region is estimated by applying the proposed regional inertia solution method. The inertia estimation results for each region are shown in Table 1.

[0042] Table 1. Estimation results of regional inertia for the IEEE 39-node system According to the estimation results in Table 1, the absolute value of the relative error of the results for each region is less than 4%, which shows high accuracy and enables the estimation of regional inertia.

[0043] An improved IEEE 39-node model was obtained by replacing generators G6, G7, and G9 with wind turbines, and the simulation settings remained the same as described above. The wind turbines were doubly-fed induction motors using virtual inertia integrated control. The partitioning results are as follows: Figure 6 As shown: Regions 1 and 3 are single generators 1 and 9 respectively, Region 2 includes generators G2, G3, G8 and G9, and the remaining 4 generators are assigned to Region 4.

[0044] Based on the above partitioning, Table 2 presents the regional inertia estimation results for the improved IEEE 39-node system.

[0045] Table 2. Results of improved regional inertia estimation for the IEEE 39-node system The results in the table show that the error is below 4.5% after the integration of new energy sources. This indicates that the method presented in this paper is also applicable to the estimation of the equivalent inertia of power systems containing virtual inertia of new energy sources.

[0046] Please see Figure 7 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0047] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0048] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0049] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0050] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0051] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0052] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0053] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0054] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0055] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0056] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for real-time partitioned inertia estimation in a power system, characterized in that, Includes the following steps: The frequency dynamic response data of each node in the power system is measured, the frequency dynamic response data is preprocessed, the dynamic time warping (DTW) index between the frequency dynamic response data of each node in the region is calculated, and the region is divided using the k-medoids algorithm based on the dynamic time warping (DTW) index. All generators in each region are equivalent to a single unit. The power of the equivalent unit in a region is represented by the aggregated power of the internal units, and the frequency of the equivalent unit is represented by the regional inertial center frequency. For the units after equivalent processing in each region, a regression model is constructed based on the rotor motion equation, and the equivalent inertia of the region is solved by using time-varying gain and integrator design based on fixed-time convergence.

2. The real-time partitioned inertia estimation method for power systems according to claim 1, characterized in that, The calculation of the Dynamic Time Warping (DTW) index between the frequency dynamic response data for each node within the region includes: ； In the formula, f p and f q These are the discrete frequency time series of nodes p and q, respectively. f represents a discrete-time series p and f q The dynamic time warping (DTW) distance between them; S is the optimal matching path, and is the minimum Euclidean distance between sampling points along the optimal matching path; Represents the first [number]th ... Point pair The Euclidean distance between them; This indicates a pair of points in two sequences that form the optimal matching path; The DTW matrix of each node in the calculation system is used. When the DTW distance index between the frequencies of the nodes is less than the set value, their frequency similarity is high, and these nodes are divided into the same region.

3. The real-time partitioned inertia estimation method for power systems according to claim 2, characterized in that, The method of using the k-medoids algorithm to partition regions includes the following steps: S11, Calculate the DTW matrix for the system node frequencies; S12 specifies the number of partitions, k. S13, Cluster center initialization: Select k nodes as initial cluster centers; S14, assign the node to the cluster center with the smallest distance from DTW; S15, Update cluster center: Select the node with the smallest total DTW distance within the cluster; S16. Repeat steps S14 and S15 until the cluster center no longer changes, and output the partitioning results.

4. The real-time partitioned inertia estimation method for power systems according to claim 1, characterized in that, The inertial center frequency of the region is calculated by the following formula: ； In the formula, f CoI H is the region's equivalent inertial center frequency; gi and f i These represent the inertial constants and node frequencies of each generator within the region.

5. The real-time partitioned inertia estimation method for power systems according to claim 1, characterized in that, The regression model constructed for each region based on the rotor motion equation includes: ； In the formula, y(t) is the system output; θ is the vector of regressors; θ is the vector of unknown parameters.

6. The real-time partitioned inertia estimation method for power systems according to claim 5, characterized in that, The method for solving the equivalent inertia of a region based on fixed-time convergence using time-varying gain and integrator includes the following steps: The regression model is filtered on both sides to construct auxiliary filter variables, which include the filtered output Y(t) and the filtered regression matrix Φ(t). The filtered output Y(t) and the regression matrix Φ(t) are defined as follows: ； In the formula, This represents a time-varying gain function used to drive the filter variable to converge within a fixed time period; The auxiliary filter variable represents the state of the system output y(t) after passing through the time-varying filter; The auxiliary filter variable is a vector of regressors. The state after passing through the same time-varying filter; Represents the regressor vector; For time-varying gain function Integrate over the interval [0, T]: ； In the formula, T represents the preset convergence time, and integral divergence means... As it approaches T, it tends to 0, thus compressing the tracking process within an infinite time into a finite time T, achieving the core requirement of fixed-time convergence; Construct an algebraic relation, and the filtered variables satisfy the following algebraic equation: ； An estimator is a tool used to calculate unknown parameters from filtered algebraic relations. If Φ(t) is full rank, the algorithm or formula for updating the parameters of the estimator is as follows: ； The time-varying gain is designed as follows: ； In the formula, The proportional gain coefficient represents the time-varying gain and controls how fast the gain increases. The power exponent represents the variable gain, controlling the steepness of the gain increase near a preset time endpoint; when hour, ,and This makes the estimator in Always be restrained.

7. The real-time partitioned inertia estimation method for power systems according to claim 6, characterized in that, The rotor motion equation is: ； In the formula, H represents the equivalent inertia of the region, and f is the inertial center frequency of the region. Indicates the change in equivalent unit power; Indicates time; The following is defined during the construction of the regression model: 。 8. The real-time partitioned inertia estimation method for power systems according to claim 6, characterized in that, In the construction of the auxiliary filter variables, the filter parameters are calculated using the trapezoidal integral method: ； In the formula, Δt represents the data sampling time, k represents the current time, and k+1 represents the next time. This represents the filtered output value at time k; This represents the filtered output value at the discrete time k+1; This represents the time-varying gain value at time k; This represents the original output value at time k; This represents the time-varying gain value at time k+1; This represents the original output value at time k+1; This represents the value of the filtered regression at time k; This represents the value of the filtered regression at time k+1; This represents the regression vector value at time k; This represents the regression vector value at time k+1.

9. A real-time regional inertia estimation system for a power system, characterized in that, Using the method of any one of claims 1 to 8, the system comprises: The regional division unit is used to measure the frequency dynamic response data of each node in the power system, preprocess it, calculate the dynamic time warping (DTW) index between the frequency data of each node in the region, and use the k-medoids algorithm to divide the region based on this. The regional equivalent unit is used to make all generators in each region equivalent. The power of the equivalent unit is represented by the aggregated power of the internal units, and the frequency of the equivalent unit is represented by the regional inertial center frequency. The inertia solving unit is used to construct a regression model for each region based on the rotor motion equation, and to solve for the equivalent inertia of the region by using time-varying gain and integrator design based on fixed-time convergence.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-8.

11. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-8.

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