A method for identifying subsynchronous oscillation based on multi-point low-frequency measurement data

Abstract

The application discloses a kind of sub-synchronous oscillation identification methods based on multi-point low-frequency measurement data, comprising: 1, according to the layout of synchronous phasor measurement device PMU, obtain the voltage measurement data of multi-point PMU;2, according to the voltage expression under the sub-synchronous oscillation of new energy power system, determine the parameter to be identified and parameter search interval, establish the objective function of parameter identification;3, the preliminary optimization result of parameter is obtained by using particle swarm optimization PSO algorithm;4, the preliminary optimization result of PSO is adjusted using sequential quadratic programming method, and the parameter identification result is obtained;5, repeat step 3 and step 4 several times, the identification result of each time is substituted into the objective function, and the identification result corresponding to the minimum value of the objective function value is the final parameter identification result.The application can accurately estimate the sub-synchronous oscillation of power system, provide a new choice for oscillation monitoring, and reduce the huge investment brought by the sampling upgrade of existing PMU.

Description

Technical Field

[0001] This invention belongs to the field of stability analysis of new energy power systems, and in particular relates to a method for identifying subsynchronous oscillations based on multi-point low-frequency measurement data. Background Technology

[0002] With the continuous grid connection of new energy power generation, the risk of subsynchronous oscillation in the power system is increasing, which seriously threatens the safe and stable operation of the power system. It may not only trigger the disconnection of new energy units, but may also cause the linkage oscillation of nearby thermal power units, seriously affecting the safe transmission capacity of new energy power generation.

[0003] Currently, with the widespread deployment of synchronous phasor devices, oscillation pattern identification based on PMU data is beneficial for locating oscillation sources and taking effective measures to suppress oscillations. However, existing PMUs are typically used for low-frequency oscillation monitoring, and their sampling rates cannot meet the monitoring requirements of subsynchronous oscillations. To monitor subsynchronous oscillations in new energy power systems, it is necessary to increase the data sampling frequency of existing PMU measurement devices; however, due to the large number of PMU devices across the entire network, upgrading the entire network with PMUs is costly.

[0004] Therefore, there is an urgent need to find a new method for monitoring subsynchronous oscillations that can obtain accurate information on subsynchronous oscillation modes without requiring a large investment in a comprehensive upgrade of the PMU, thereby helping to take effective prevention and control measures and improve the safety and stability of the new power system. Summary of the Invention

[0005] Objective: To address the shortcomings and deficiencies of existing technologies, this invention proposes a subsynchronous oscillation mode identification method based on low-frequency measurement data from multiple PMUs. By collecting measurement information from multiple PMUs, a step-by-step identification method is employed to identify subsynchronous oscillation modes. Results show that the proposed method can accurately estimate subsynchronous oscillations in power systems, providing a new option for oscillation monitoring while reducing the substantial investment required for upgrading existing PMU sampling systems.

[0006] Technical solution: The present invention provides a subsynchronous oscillation identification method based on multi-point low-frequency measurement data, comprising the following steps:

[0007] Step 1: Based on the layout of the synchronous phasor measurement unit (PMU), obtain voltage measurement data from multiple PMUs.

[0008] Step 2: Based on the voltage expression under subsynchronous oscillation of the new energy power system, determine the parameters to be identified and the parameter search interval, and establish the objective function for parameter identification;

[0009] Step 3: Use the Particle Swarm Optimization (PSO) algorithm to obtain preliminary optimization results for the parameters;

[0010] Step 4: Use the sequential quadratic programming method to adjust the preliminary optimization results of PSO and obtain the parameter identification results;

[0011] Step 5: Repeat steps 3 and 4 several times, substituting each identification result into the objective function, and using the identification result corresponding to the minimum value of the objective function as the final parameter identification result.

[0012] Furthermore, in step 1, the voltage measurement data of the multi-point PMU is reported at a frequency of 50-60 points per second.

[0013] Furthermore, step 2 specifically involves: taking two signals as an example, the voltage expression under subsynchronous oscillation of the power system is as follows:

[0014] (1)

[0015] in, and These represent the voltages of the PMU at measurement point 1 and measurement point 2, respectively. and They represent voltages respectively. and The amplitude of the fundamental frequency component; and They represent voltages respectively. and The phase of the fundamental frequency component; and They represent voltages respectively. and The amplitude of the subsynchronous component; and They represent voltages respectively. and The phase of the subsynchronous component; The fundamental frequency of the system; and Here, t represents the damping and frequency of the subsynchronous oscillation mode, respectively, and t represents time in seconds. The amplitude of the subsynchronous oscillation mode is expressed exponentially over time. attenuation.

[0016] Let the resistance between points 1 and 2 of the PMU be... The inductance is Then the voltage equation between the two points can be expressed as:

[0017] (2)

[0018] In the formula, and These are the fundamental frequency voltage phasors for PMU deployment points 1 and 2, respectively. and The voltage phasors for subsynchronous oscillation modes at PMU points 1 and 2 are respectively. and These are the current phasors of the fundamental frequency component current and the subsynchronous oscillation component of the line between points 1 and 2, respectively; j represents the imaginary unit.

[0019] According to equation (2), we can obtain:

[0020] (3)

[0021] in, This indicates that the phase of the subsynchronous oscillation mode at PMU point 2 is closely related to the subsynchronous oscillation frequency. Resistance between points 1 and 2 With inductance Closely related;

[0022] Taking equation (1) as an example, let two PMU measurement signals be used for subsynchronous oscillation information identification. Since the fundamental frequency of the power system is known, it is 50Hz. Let the parameter vector to be identified be β, which specifically includes:

[0023] (4)

[0024] According to equation (3). according to , ,resistance and inductors Since it is obtained, it is not included in the parameters to be identified;

[0025] Express the voltage sampling per-unit value in equation (1), and give the search interval for each parameter to be identified based on engineering experience. .in and These are the lower and upper bounds of the parameter vector β, respectively.

[0026] The objective function and constraints for parameter identification are:

[0027] (5)

[0028] Where J is the objective function value; M is the number of signals, i represents the i-th signal; N is the number of sampling points of the signal, n represents the n-th sampling point; This represents the voltage at the nth sampling point of the i-th signal; Substituting the identified parameter vector β into equation (1) yields the voltage estimate of the nth sampling point of the i-th signal.

[0029] Furthermore, in step 3, the number of particles in the Particle Swarm Optimization (PSO) algorithm is set to 300, the maximum number of iterations is 200, and the parameter identification results are obtained.

[0030] Furthermore, step 4 specifically involves using a sequential quadratic programming method, taking the PSO optimization result as the initial value, and iteratively approximating the optimal solution of the original nonlinear optimization problem to re-identify the PSO optimization result.

[0031] The sequential quadratic programming method approximates the solution by decomposing the original nonlinear optimization problem into a series of quadratic programming (QP) subproblems. Each QP subproblem is constructed and solved at the current iteration point to update the iteration point. Based on the objective function (5), the Lagrangian function is constructed:

[0032] (6)

[0033] Where μ and λ are the Lagrange multiplier vectors of the inequality constraints, respectively; the subscript k indicates the k-th inequality, and K indicates the number of parameter vectors β.

[0034] At a given point Linearizing equation (6) and approximating the Lagrange function L with a quadratic polynomial, we obtain a quadratic programming subproblem of the following form:

[0035] (7)

[0036] in, , L represents the second derivative of the Lagrange function L with respect to β; T represents the transpose. Indicates the search direction. The optimal solution of equation (7) Let be the search direction of the variable in equation (5) during the h-th iteration of β.

[0037] (8)

[0038] in, The search step size is determined by the sequential quadratic programming algorithm based on the value function. The convergence condition is checked according to equation (8). If it is met, the iteration ends; otherwise, it returns to equation (6) for the next iteration.

[0039] Furthermore, step 5 specifically involves repeating steps 3 and 4 10 times, substituting the identification results of each iteration into the objective function, and using the identification result corresponding to the minimum value of the objective function as the final parameter identification result.

[0040] The results of parameter identification 10 times Substituting the objective function into equation (3), the objective function value under 10 identification results is calculated. Select The minimum value in, its corresponding This is the final identification result of the parameters.

[0041] A subsynchronous oscillation identification system based on multi-point low-frequency measurement data includes:

[0042] Data acquisition module: Collects voltage waveforms at each node based on PMU data deployed at power system sites;

[0043] Modeling module: Establish the objective function for parameter identification under multiple signals;

[0044] Analysis module: Based on the power flow equations of the power system, analyze the phase relationship between multiple PMU signals. Using one of the signals as a reference, derive the relationship between the phase of the other signals and the reference signal and the oscillation frequency.

[0045] Optimization module: A step-by-step method is used to optimize parameters and obtain parameter identification results for subsynchronous oscillations. First, the PSO method is used to obtain preliminary parameter estimates. Then, the PSO parameter estimation results are fine-tuned based on the sequence quadratic programming method to obtain parameter identification results.

[0046] Model validation module: This module identifies the oscillation mode parameters multiple times and selects the parameters corresponding to the minimum value of the objective function. The final identification result is used as a parameter.

[0047] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method of the present invention.

[0048] The present invention also discloses a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the method of the present invention.

[0049] The present invention also discloses a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method of the present invention.

[0050] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: This invention derives the initial phase relationship between the voltage fundamental frequency component and the subsynchronous oscillation component at different PMU locations by constructing power system power flow equations, thereby determining the parameters of the mode to be identified; it proposes a step-by-step subsynchronous oscillation mode identification method, firstly using the PSO method for preliminary identification of subsynchronous oscillation mode parameters, and then using a sequential quadratic programming method to fine-tune the PSO identification parameters, thereby obtaining the identification results of the oscillation mode parameters; a multi-round identification method is adopted, using the parameters corresponding to the minimum value of the objective function of the multi-round parameter identification as the final mode identification result. Compared with existing methods, this invention does not require increasing the sampling frequency of existing PMU devices, significantly reducing the huge investment brought about by PMU upgrades; at the same time, the step-by-step parameter identification method and multi-round identification approach improve the identification accuracy of subsynchronous oscillation modes, which is beneficial for the subsequent location and stability assessment of subsynchronous oscillation sources in new power systems. Attached Figure Description

[0051] Figure 1 This is a flowchart of the method of the present invention;

[0052] Figure 2 Collect data for PMU;

[0053] Figure 3 The reconstructed signal and the original sampling points are based on the parameter identification results. Detailed Implementation

[0054] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0055] Example 1: As Figure 1 As shown, the method of the present invention includes the following steps:

[0056] Step 1: Based on the PMU (Power Controller Unit) layout of the synchronous phasor measurement device, obtain voltage measurement data from multiple PMUs. See [link / details]. Figure 2 As shown.

[0057] Step 2: Based on the voltage expression under subsynchronous oscillation of the new energy power system, determine the parameters to be identified and the parameter search interval, and establish the objective function for parameter identification;

[0058] Taking two signals as an example, the voltage expression under subsynchronous oscillation of the power system is as follows:

[0059] (1)

[0060] in, and These represent the voltages of the PMU at measurement point 1 and measurement point 2, respectively. and They represent voltages respectively. and The amplitude of the fundamental frequency component; and They represent voltages respectively. and The phase of the fundamental frequency component; and They represent voltages respectively. and The amplitude of the subsynchronous component; and They represent voltages respectively. and The phase of the subsynchronous component; The fundamental frequency of the system; and These represent the damping and frequency of the subsynchronous oscillation mode, respectively. t represents time, in seconds. The amplitude of the subsynchronous oscillation mode is expressed exponentially over time. attenuation.

[0061] Let the resistance between points 1 and 2 of the PMU be... The inductance is Then the voltage equation between the two points can be expressed as:

[0062] (2)

[0063] In the formula, and These are the fundamental frequency voltage phasors for PMU deployment points 1 and 2, respectively. and The voltage phasors for subsynchronous oscillation modes at PMU points 1 and 2 are respectively. and These are the current phasors of the fundamental frequency component current and the subsynchronous oscillation component of the line between points 1 and 2, respectively; j represents the imaginary unit.

[0064] According to equation (2), we can obtain:

[0065] (3)

[0066] in, This indicates that the phase of the subsynchronous oscillation mode at PMU point 2 is closely related to the subsynchronous oscillation frequency. Resistance between points 1 and 2 With inductance Closely related.

[0067] Taking equation (1) as an example, suppose two PMU measurement signals are used for subsynchronous oscillation information identification. Since the fundamental frequency of the power system is known, it is 50Hz. Let the parameter vector to be identified be β, specifically including:

[0068] (4)

[0069] According to equation (3). According to , ,resistance and inductors Therefore, it is not included in the parameters to be identified.

[0070] Express the voltage sampling per-unit value in equation (1), and give the search interval for each parameter to be identified based on engineering experience. .in and These are the lower and upper bounds of the parameter vector β, respectively;

[0071] The objective function and constraints for parameter identification are:

[0072] (5)

[0073] Where J is the objective function value; M is the number of signals, i represents the i-th signal; N is the number of sampling points of the signal, n represents the n-th sampling point; This represents the voltage at the nth sampling point of the i-th signal; Substituting the identified parameter vector β into equation (1) yields the voltage estimate of the nth sampling point of the i-th signal.

[0074] Step 3: Use the Particle Swarm Optimization (PSO) algorithm to obtain preliminary parameter estimates. The number of particles is set to 300, the maximum number of iterations is 200, and the parameter identification results are obtained.

[0075] Step 4: Use the sequential quadratic programming method to fine-tune the PSO optimization results and obtain the parameter identification results.

[0076] While the Particle Swarm Optimization (PSO) algorithm boasts advantages such as strong global search capability, fast convergence speed, and insensitivity to initial values, it is prone to getting trapped in local optima. To further improve the accuracy of subsynchronous oscillation mode identification, this invention further employs a sequential quadratic programming method. Using the PSO optimization result as the initial value, the PSO optimization result is re-identified by iteratively approximating the optimal solution of the original nonlinear optimization problem.

[0077] The sequential quadratic programming method approximates the solution by decomposing the original nonlinear optimization problem into a series of quadratic programming (QP) subproblems. Each QP subproblem is constructed and solved at the current iteration point to update the iteration point. Based on the objective function (5), the Lagrangian function is constructed:

[0078] (6)

[0079] Where μ and λ are the Lagrange multiplier vectors of the inequality constraints, respectively; the subscript k indicates the k-th inequality, and K indicates the number of parameter vectors β.

[0080] At a given point Linearizing equation (6) and approximating the Lagrange function L with a quadratic polynomial, we obtain a quadratic programming subproblem of the following form:

[0081] (7)

[0082] in, , L represents the second derivative of the Lagrange function L with respect to β; T represents the transpose. Indicates the search direction. The optimal solution of equation (7) Let be the search direction of the variable in equation (5) during the h-th iteration of β.

[0083] (8)

[0084] in, The search step size is determined by the sequential quadratic programming algorithm based on the value function. The convergence condition is checked according to equation (8). If it is met, the iteration ends; otherwise, it returns to equation (6) for the next iteration.

[0085] Steps 5, 3, and 4 are repeated 10 times. The identification results of each step are substituted into the objective function. The identification result corresponding to the minimum value of the objective function is taken as the final parameter identification result, as shown in Table 1.

[0086] Table 1. Parameter Search Range and Identification Results

[0087]

[0088] The objective function value is calculated based on the parameter identification results in Table 1, and the result is J. min =7.5795×10 -11 As can be seen from Table 1, the identification results of the system oscillation mode parameters are close to the actual values, verifying the effectiveness and feasibility of the method proposed in this invention.

[0089] Based on the parameter identification results in Table 1, the voltage disturbance trajectory of the reconstructed system is shown below. Figure 3 As shown; Figure 3 The sampling points of the original two voltage signals are also provided. It can be seen that the signal reconstruction result matches the PMU acquisition result quite well.

[0090] Example 2:

[0091] A subsynchronous oscillation identification system based on multi-point low-frequency measurement data includes:

[0092] Data acquisition module: Collects voltage waveforms at each node based on PMU data deployed at power system sites;

[0093] Modeling module: Establish the objective function for parameter identification under multiple signals;

[0094] Analysis module: Based on the power flow equations of the power system, analyze the phase relationship between multiple PMU signals. Using one of the signals as a reference, derive the relationship between the phase of the other signals and the reference signal and the oscillation frequency.

[0095] Optimization Module: A step-by-step approach is used to optimize parameters and obtain parameter identification results for subsynchronous oscillations. First, the PSO method is used to obtain preliminary parameter estimates. Then, the PSO parameter estimates are fine-tuned based on a sequence quadratic programming method to obtain the parameter identification results.

[0096] Model validation module: This module identifies the oscillation mode parameters multiple times and selects the parameters corresponding to the minimum value of the objective function. The final identification result is used as a parameter.

[0097] For specific implementation schemes of each module of the system of the present invention, please refer to the specific implementation process of the method described in Embodiment 1.

[0098] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] The above description is only a preferred embodiment of the present invention. It should be noted that the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for identifying subsynchronous oscillations based on multi-point low-frequency measurement data, characterized in that, Includes the following steps: Step 1: Based on the layout of the synchronous phasor measurement unit (PMU), obtain voltage measurement data from multiple PMUs. Step 2: Based on the voltage expression under subsynchronous oscillation of the new energy power system, determine the parameters to be identified and the parameter search interval, and establish the objective function for parameter identification; Step 3: Use the Particle Swarm Optimization (PSO) algorithm to obtain preliminary optimization results for the parameters; Step 4: Use the sequential quadratic programming method to adjust the preliminary optimization results of PSO and obtain the parameter identification results; Step 5: Repeat steps 3 and 4 several times, substituting each identification result into the objective function, and using the identification result corresponding to the minimum value of the objective function as the final parameter identification result.

2. The subsynchronous oscillation identification method based on multi-point low-frequency measurement data according to claim 1, characterized in that, In step 1, the voltage measurement data of the multi-point PMU is reported at a frequency of 50-60 points per second.

3. The subsynchronous oscillation identification method based on multi-point low-frequency measurement data according to claim 1, characterized in that, Step 2 specifically involves: taking two signals as an example, the voltage expression under subsynchronous oscillation of the power system is as follows: (1) in, and These represent the voltages of the PMU at measurement point 1 and measurement point 2, respectively. and They represent voltages respectively. and The amplitude of the fundamental frequency component; and They represent voltages respectively. and The phase of the fundamental frequency component; and They represent voltages respectively. and The amplitude of the subsynchronous component; and They represent voltages respectively. and The phase of the subsynchronous component; The fundamental frequency of the system; and , respectively, represent the damping and frequency of the subsynchronous oscillation mode; t represents time; The amplitude of the subsynchronous oscillation mode is expressed exponentially over time. attenuation; Let the resistance between points 1 and 2 of the PMU be... The inductance is The voltage equation between the two points is then expressed as: (2) In the formula, and These are the fundamental frequency voltage phasors for PMU deployment points 1 and 2, respectively. and The voltage phasors for subsynchronous oscillation modes at PMU points 1 and 2 are respectively. and These are the current phasors of the fundamental frequency component current and the subsynchronous oscillation component of the line between points 1 and 2, respectively; j represents the imaginary unit. According to equation (2), we can obtain: (3) in, This indicates that the phase of the subsynchronous oscillation mode at PMU point 2 is closely related to the subsynchronous oscillation frequency. Resistance between points 1 and 2 With inductance Closely related; Taking equation (1) as an example, let two PMU measurement signals be used for subsynchronous oscillation information identification. Since the fundamental frequency of the power system is known, it is 50Hz. Let the parameter vector to be identified be β, which specifically includes: (4) According to equation (3). according to , ,resistance and inductors Since it is obtained, it is not included in the parameters to be identified; Express the voltage sampling per-unit value in equation (1), and give the search interval for each parameter to be identified based on engineering experience. ,in and These are the lower and upper bounds of the parameter vector β, respectively; The objective function and constraints for parameter identification are: (5) Where J is the objective function value; M is the number of signals, i represents the i-th signal; N is the number of sampling points of the signal, n represents the n-th sampling point; This represents the voltage at the nth sampling point of the i-th signal; Substituting the identified parameter vector β into equation (1) yields the voltage estimate of the nth sampling point of the i-th signal.

4. The subsynchronous oscillation identification method based on multi-point low-frequency measurement data according to claim 1, characterized in that, In step 3, the number of particles in the Particle Swarm Optimization (PSO) algorithm is set to 300, the maximum number of iterations is 200, and the parameter identification results are obtained.

5. The subsynchronous oscillation identification method based on multi-point low-frequency measurement data according to claim 3, characterized in that, Step 4 specifically involves using a sequential quadratic programming method, taking the PSO optimization result as the initial value, and iteratively approximating the optimal solution of the original nonlinear optimization problem to re-identify the PSO optimization result. The sequential quadratic programming method approximates the solution by decomposing the original nonlinear optimization problem into a series of quadratic programming (QP) subproblems; each QP subproblem is constructed and solved at the current iteration point to update the iteration point; the Lagrangian function is constructed according to the objective function (5): (6) Where μ and λ are the Lagrange multiplier vectors of the inequality constraints, respectively; the subscript k indicates the k-th inequality, and K indicates the number of parameter vectors β; At a given point Linearizing equation (6) and approximating the Lagrange function L with a quadratic polynomial, we obtain a quadratic programming subproblem of the following form: (7) in, , L represents the second derivative of the Lagrange function L with respect to β; T represents the transpose. Indicates the search direction; the optimal solution of equation (7) Let be the search direction of the variable in equation (5) in the h-th iteration process; (8) in, The search step size is determined by the sequential quadratic programming algorithm based on the value function; the convergence condition is judged according to equation (8). If it is satisfied, the iteration ends; if it is not satisfied, the iteration returns to equation (6) for the next iteration.

6. The subsynchronous oscillation identification method based on multi-point low-frequency measurement data according to claim 3, characterized in that, Step 5 specifically involves repeating steps 3 and 4 10 times, substituting the identification results of each step into the objective function, and using the identification result corresponding to the minimum value of the objective function as the final parameter identification result. The results of parameter identification 10 times Substituting the objective function into equation (3), the objective function value under 10 identification results is calculated. ; Select The minimum value in, its corresponding This is the final identification result of the parameters.

7. A subsynchronous oscillation identification system based on multi-point low-frequency measurement data, used to implement the method as described in claim 1, characterized in that, include: Data acquisition module: Collects voltage waveforms at each node based on PMU data deployed at power system sites; Modeling module: Establish the objective function for parameter identification under multiple signals; Analysis module: Based on the power flow equations of the power system, analyze the phase relationship between multiple PMU signals. Using one of the signals as a reference, derive the relationship between the phase of the other signals and the reference signal and the oscillation frequency. Optimization module: A step-by-step method is used to optimize parameters and obtain parameter identification results for subsynchronous oscillations. First, the PSO method is used to obtain preliminary parameter estimates. Then, the PSO parameter estimation results are fine-tuned based on the sequence quadratic programming method to obtain parameter identification results. Model validation module: This module identifies the oscillation mode parameters multiple times and selects the parameters corresponding to the minimum value of the objective function. The final identification result is used as a parameter.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method of claim 1.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 1.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 1.

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