Model identification and damping control method and system for network-forming converter

Abstract

The invention discloses a model identification and damping control method and system for a network-forming converter, and belongs to the technical field of power systems. The method comprises the following steps: injecting a pre-generated test signal sequence into an active power reference instruction of a network-forming converter to excite a system to generate a dynamic response; response data of the system under excitation of the test signal sequence is collected, and the response data and the test signal sequence form an evolution type time sequence data set; based on the evolution type time sequence data set, an equivalent linear model for describing system dynamics is calculated through a Koopman operator identification method; calculating to obtain a state feedback gain matrix by solving a linear quadratic optimal control problem; generating a damping control signal based on the state feedback gain matrix and system state data acquired in real time, and superposing the damping control signal to a control instruction of the network-forming converter; according to the method, the risk of performance reduction and even instability caused by model mismatch in a traditional method is overcome.

Description

A model identification and damping control method and system for a grid-type converter Technical Field

[0001] This invention relates to a model identification and damping control method and system for grid-type converters, belonging to the field of power system technology. Background Technology

[0002] With the rapid development of "high-voltage and high-efficiency" power systems, the dynamic characteristics of power grids have undergone fundamental changes. The large-scale grid connection of distributed energy sources such as wind and solar power, and the widespread application of power electronic equipment such as converters and flexible AC transmission devices, while enhancing system flexibility, have also brought unprecedented stability challenges. Among these, low-frequency oscillations have become increasingly prominent, becoming a key factor restricting power grid transmission capacity and threatening the safe and stable operation of the system. Traditional damping control strategies, such as power system stabilizers, are mostly designed based on linearization theory. These methods typically approximate the nonlinear system with a linearization at a specific operating point and design the controller based on this approximation.

[0003] However, under the "high-voltage and high-efficiency" background, the power system exhibits stronger nonlinearity, time-varying nature, and uncertainty, and its operating conditions are complex and varied. Fixed parameter controllers designed based on local linearization models will experience a significant decrease in control performance when the system operating point deviates from the design conditions, and may even produce negative damping effects, failing to effectively suppress broadband oscillations.

[0004] The challenges faced by existing damping controllers are mainly reflected in three aspects: First, they are highly dependent on accurate system mathematical models. Accurate modeling is extremely difficult for new types of power grids containing a large number of power electronic devices, and parameter perturbations are frequent. Second, their adaptability is insufficient. Traditional linear control methods struggle to cope with nonlinear dynamic characteristic changes caused by system topology changes and load fluctuations. Third, their suppression effect on broadband oscillations is limited. Novel broadband oscillation modes such as subsynchronous oscillations and hypersynchronous oscillations are coupled with traditional electromechanical oscillations, requiring controllers to provide damping across the entire frequency band. Although advanced methods such as intelligent control and adaptive control have been introduced to improve control performance, problems such as computational complexity, poor real-time performance, or the need for large amounts of training data still exist in practical applications. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a model identification and damping control method and system for grid-type converters, which overcomes the risk of performance degradation or even instability caused by model mismatch in traditional methods.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] In a first aspect, the present invention provides a model identification and damping control method for a grid-type converter, comprising:

[0008] A pre-generated test signal sequence is injected into the active power reference command of the grid-type converter to excite the system to generate a dynamic response;

[0009] Collect system response data under test signal sequence excitation, and combine the response data with the test signal sequence to form an evolutionary time series dataset;

[0010] Based on an evolutionary time series dataset, an equivalent linear model describing the system dynamics is calculated using the Koopman operator identification method.

[0011] Based on the equivalent linear model, the state feedback gain matrix is ​​calculated by solving the linear quadratic optimal control problem;

[0012] Based on the state feedback gain matrix and the real-time acquired system state data, a damping control signal is generated and superimposed on the control command of the grid-type converter.

[0013] Furthermore, the step of injecting the pre-generated test signal sequence into the active power reference command of the grid-type converter includes:

[0014] Generate a composite test sequence consisting of a unit step signal, a pulse signal, and a set of sinusoidal sweep signals with frequencies ranging from 0.1 Hz to 1000 Hz;

[0015] The composite test sequence is subjected to amplitude limiting and delayed triggering processing to obtain a safety test signal sequence;

[0016] The safety test signal sequence is injected into the active power reference command of the grid-type converter.

[0017] Furthermore, the step of collecting the system's response data under the excitation of the test signal sequence, and constructing the response data and the test signal sequence into an evolutionary time series dataset, includes:

[0018] The system response data is collected under the excitation of a safety test signal sequence. The response data includes at least one of generator speed deviation, power angle deviation, active power, reactive power, and bus voltage.

[0019] The collected response data is subjected to noise reduction filtering, outlier removal and data standardization to obtain preprocessed measurement data;

[0020] The preprocessed measurement data and the security test signal sequence are aligned and segmented in chronological order to construct an evolutionary time series dataset, which includes a first measurement dataset, a second measurement dataset, and an input signal dataset.

[0021] Furthermore, the equivalent linear model describing the system dynamics, calculated using the Koopman operator identification method based on the evolutionary time series dataset, includes:

[0022] The first measurement dataset and the input signal dataset are combined into a first data matrix;

[0023] Use the second measurement dataset as the second data matrix;

[0024] Based on the first data matrix and the second data matrix, the initial state space matrix and the input matrix are obtained through pseudo-inverse calculation;

[0025] For different types of test signals, the corresponding state space matrix and input matrix are calculated respectively, and all matrices are fused by arithmetic averaging to obtain the fused equivalent linear model, which includes the average state space matrix and the average input matrix.

[0026] Furthermore, the state feedback gain matrix is ​​calculated by solving a linear quadratic optimal control problem based on the equivalent linear model, including:

[0027] Based on the average state-space matrix and the average input matrix, a performance index for a linear quadratic regulator is constructed.

[0028] The optimal state feedback gain matrix is ​​obtained by solving the algebraic Riccati equation corresponding to the performance index.

[0029] Furthermore, the step of generating a damping control signal based on the state feedback gain matrix and the real-time acquired system state data includes:

[0030] Real-time acquisition of system status data from grid-type converters;

[0031] The system state data is multiplied by the optimal state feedback gain matrix to generate a damping control signal;

[0032] The damping control signal is superimposed on the original active power control command of the grid-type converter to suppress wideband oscillations.

[0033] Secondly, the present invention provides a model identification and damping control system for a grid-type converter, used to implement the model identification and damping control method for the grid-type converter described in any one of the preceding claims, comprising:

[0034] The test signal generation module is used to inject a pre-generated test signal sequence into the active power reference command of the grid-type converter to excite the system to generate a dynamic response.

[0035] A dataset construction module is used to collect system response data under test signal sequence excitation, and to combine the response data with the test signal sequence to form an evolutionary time series dataset.

[0036] The first calculation module is used to calculate an equivalent linear model describing the dynamics of the system based on an evolutionary time series dataset using the Koopman operator identification method.

[0037] The second calculation module is used to calculate the state feedback gain matrix by solving the linear quadratic optimal control problem based on the equivalent linear model.

[0038] The control module is used to generate a damping control signal based on the state feedback gain matrix and the real-time acquired system state data, and to superimpose the damping control signal into the control command of the grid-type converter.

[0039] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0040] Fourthly, the present invention provides an electronic device, comprising:

[0041] Memory, used to store computer programs / instructions;

[0042] A processor for executing the computer program / instructions to implement the steps of any of the methods described above.

[0043] Fifthly, the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0044] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0045] 1. This invention provides a model identification and damping control method and system for grid-type converters. It does not rely on any prior precise mechanistic model. By actively injecting designable test signals into the system and collecting response data, it directly identifies the equivalent linear model characterizing the system's dynamics from the data and designs the controller accordingly. This data-driven nature enables it to automatically adapt to complex operating conditions such as changes in system parameters and topology, fundamentally overcoming the performance degradation or even instability risks caused by model mismatch in traditional methods.

[0046] 2. This invention uses model identification technology based on the Koopman operator to map the inherent nonlinear oscillation dynamics of the power system into a globally effective linear state-space model over a wide frequency band. This transforms the originally complex nonlinear control problem into a linear optimal control problem that can be solved precisely, laying a mathematical foundation for designing a theoretically rigorous and high-performance damping controller. Attached Figure Description

[0047] Figure 1 is a schematic diagram of the model identification and damping control system test technology flow of the grid converter provided in the embodiment of the present invention. Detailed Implementation

[0048] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0049] Example 1: This example introduces a model identification and damping control method for a grid-type converter, including:

[0050] A pre-generated test signal sequence is injected into the active power reference command of the grid-type converter to excite the system to generate a dynamic response;

[0051] Collect system response data under test signal sequence excitation, and combine the response data with the test signal sequence to form an evolutionary time series dataset;

[0052] Based on an evolutionary time series dataset, an equivalent linear model describing the system dynamics is calculated using the Koopman operator identification method.

[0053] Based on the equivalent linear model, the state feedback gain matrix is ​​calculated by solving the linear quadratic optimal control problem;

[0054] Based on the state feedback gain matrix and the real-time acquired system state data, a damping control signal is generated and superimposed on the control command of the grid-type converter.

[0055] The model identification and damping control method for grid-type converters provided in this embodiment involves the following steps in its application:

[0056] Step 1: Injection of multiple types of test signal sequences;

[0057] The core of this step is to safely inject a set of test signals that can fully stimulate the broadband dynamic characteristics of the power system into the active power reference command of the grid-type converter.

[0058] Generate test sequence: Generate and inject three types of test signals in sequence:

[0059] First, a unit step signal is injected to test the system's step response.

[0060] Secondly, a pulse signal is injected to stimulate the system's broadband frequency characteristics.

[0061] Finally, a set of sinusoidal sweep signals, with a frequency range from 0.1 Hz to 1000.0 Hz, is injected to precisely excite the system's oscillation modes at different frequencies.

[0062] Safety limiting and delayed triggering: All generated test signals must pass through a safety protection module before output. This module first delays the signal output time by a preset trigger time to ensure the system is in a stable state before injecting disturbances. Simultaneously, this module limits the amplitude of all signals within a preset safety range to avoid impacting the stable operation of the actual power system.

[0063] Step 2: Constructing the system response dataset;

[0064] This step is responsible for collecting the system's response to the test signal and building a dataset for model identification.

[0065] Data Acquisition and Preprocessing: Key output response quantities of the system during the synchronous acquisition of test signal injection are mainly included: generator speed deviation, power angle deviation, active power, reactive power, and bus voltage. Subsequently, the acquired raw data are preprocessed, including noise reduction filtering, outlier removal, and data standardization, to eliminate measurement noise and dimensional differences.

[0066] Dataset Construction: The preprocessed measurement data and the input test signal data are organized in chronological order. Specifically, two measurement data matrices are constructed: one containing all data from time 1 to time b-1, and the other containing all data from time 2 to time b; simultaneously, an input data matrix is ​​constructed, containing the test signal data from time 1 to time b-1.

[0067] Step 3: Koopman linear model identification;

[0068] This step directly identifies an equivalent linear model from the data that can globally describe the nonlinear dynamics of the power system.

[0069] Construct the identification data matrix: Combine the measurement data matrix obtained in the second step with the input data matrix into a new data matrix.

[0070] State-space model identification: Based on this data matrix, the system's state-space matrix (A matrix) and input matrix (B matrix) are directly identified through numerical calculations (such as the least squares method). This model can linearly describe the evolution of the system's measured signal from one time moment to the next.

[0071] Model fusion: Since multiple types of test signals were injected in the first step, each type of signal will generate an independent model (A_i, B_i). In order to obtain a more robust global model, the model matrices identified from all different types of signals are arithmetically averaged to finally obtain a fused average system model (A_avg, B_avg).

[0072] Step 4: Design of the optimal damping controller;

[0073] This step involves designing an optimal state feedback controller to suppress system oscillations based on the identified average system model.

[0074] Design goal: The design goal of the controller is to minimize a comprehensive performance index that takes into account both the system's state error and control energy consumption.

[0075] Calculate the feedback gain: The optimal state feedback gain matrix (L) is calculated by solving an algebraic Riccati equation.

[0076] Control signal generation: The damping controller generates an additional active power control signal in real time by multiplying the estimated system state with the calculated optimal feedback gain matrix (L), and superimposes this signal into the original control command of the grid-type converter, thereby effectively suppressing broadband oscillations in the power system.

[0077] The following description, in conjunction with a preferred embodiment, illustrates the content involved in the above embodiments.

[0078] This embodiment provides a model identification and damping control system and method for a grid-type converter. The specific flowchart is shown in Figure 1, and includes the following steps:

[0079] S1, Test Input Signal Generation Module:

[0080] The core function of this module is to generate and inject a set of test signal sequences that can fully stimulate the dynamic characteristics of the power system over a wide frequency band, providing a rich and high-quality data foundation for subsequent model identification.

[0081] This module sequentially injects a test sequence consisting of multiple signal types into the active power reference command of the grid-type converter. This sequence includes:

[0082] Unit step signal;

[0083] ;

[0084] in, Represents a unit step input signal. The value represents the amplitude of a unit step signal, and t represents time. The time of input of the step signal represents the unit.

[0085] Pulse signal; ;

[0086] in, Represents the pulse input signal. Represents the value of the pulse signal. This represents the moment when the pulse signal is input.

[0087] A set of sinusoidal signals with frequencies covering 0.1-1000.0Hz:

[0088] ;

[0089] in, Represents a sinusoidal input signal. represents the signal amplitude, and f represents the frequency of the sinusoidal input signal.

[0090] The module integrates amplitude limiting and delay triggering mechanisms to ensure that all disturbance signals remain within preset safety limits, thus avoiding impacts on system stability. ;

[0091] in, The final output signal is L, which is the preset input signal amplitude limit. min represents calculating the minimum value, and max represents calculating the maximum value. u is the input test signal. , or , To delay the trigger time.

[0092] S2, Dataset Construction Module: This module is responsible for constructing a dataset from the system response data stimulated by module S1, and for building a dataset for identification by the Koopman model.

[0093] Data Acquisition and Preprocessing: The module synchronously acquires key output response quantities of the system, including generator speed deviation, power angle deviation, active power, reactive power, and bus voltage. The acquired raw data undergoes noise reduction filtering, outlier removal, and data standardization to eliminate the influence of dimensional differences and measurement noise.

[0094] Dataset assembly: After preprocessing, the measurement data and input dataset are further segmented:

[0095]

[0096] ;

[0097] Where 'a' represents the number of measurement data types, 'b' represents the time length of the measurement data, 'r' represents the number of input signals, and 'M' represents the measurement dataset from time 1 to time b-1. represents the measurement dataset from time 2 to time b, and U represents the input signal dataset from time 1 to time b-1.

[0098] S3, Model Identification Module: Responsible for directly identifying from the data the equivalent model that can globally linearize and describe the nonlinear oscillation dynamics of the power system.

[0099] Measurement dataset Together with the input signal dataset U, they form a new matrix:

[0100] ;

[0101] in, It is a newly constructed model identification data matrix.

[0102] The evolution law of the system's measurement signal satisfies the following formula:

[0103] ;

[0104] Where A and B are the state space matrix and input matrix to be identified, and C is the system model matrix composed of A and B, C=[AB].

[0105] The system model can be calculated as

[0106] ;

[0107] in, Find the pseudo-inverse of a matrix.

[0108] Considering the various types of input signals, namely the unit step, impulse, and sinusoidal input signals in S1, each type of signal will generate a set of equivalent models A and B. Therefore, the model matrices obtained for different input signals... and Calculate the arithmetic mean of each, and obtain the fused system model. and (i.e., the mean of A and B):

[0109] ;

[0110] this The model serves as the controlled object model for subsequent controller design.

[0111] S4, Damping Controller Module:

[0112] Based on the system model obtained by fusion Design an optimal state feedback controller. The damping controller module needs to minimize the performance metrics:

[0113] ;

[0114] This index can be minimized by designing the feedback gain matrix L. The optimal feedback gain matrix L is calculated as follows:

[0115] ;

[0116] Where P is the intermediate matrix, calculated as follows:

[0117] ;

[0118] The matrix P is calculated using this formula, and then the optimal feedback gain matrix can be solved.

[0119] The final damping controller form is as follows .

[0120] The beneficial effects of this embodiment are as follows:

[0121] This embodiment provides a model identification and damping control method and system for a grid-type converter. It does not rely on any prior precise mechanistic model. By actively injecting designable test signals into the system and collecting response data, it directly identifies the equivalent linear model characterizing the system's dynamics from the data and designs the controller accordingly. This data-driven nature enables it to automatically adapt to complex operating conditions such as changes in system parameters and topology, fundamentally overcoming the performance degradation or even instability risks caused by model mismatch in traditional methods.

[0122] This embodiment uses model identification technology based on the Koopman operator to map the inherent nonlinear oscillation dynamics of the power system into a globally effective linear state-space model over a wide frequency band. This transforms the originally complex nonlinear control problem into a linear optimal control problem that can be solved precisely, laying a mathematical foundation for designing a theoretically rigorous and high-performance damping controller.

[0123] Example 2: This example provides a model identification and damping control system for a grid-type converter, including:

[0124] The test signal generation module is used to inject a pre-generated test signal sequence into the active power reference command of the grid-type converter to excite the system to generate a dynamic response.

[0125] A dataset construction module is used to collect system response data under test signal sequence excitation, and to combine the response data with the test signal sequence to form an evolutionary time series dataset.

[0126] The first calculation module is used to calculate an equivalent linear model describing the dynamics of the system based on an evolutionary time series dataset using the Koopman operator identification method.

[0127] The second calculation module is used to calculate the state feedback gain matrix by solving the linear quadratic optimal control problem based on the equivalent linear model.

[0128] The control module is used to generate a damping control signal based on the state feedback gain matrix and the real-time acquired system state data, and to superimpose the damping control signal into the control command of the grid-type converter.

[0129] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0130] Example 3: This example provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described in Example 1.

[0131] Example 4: This example provides an electronic device, including:

[0132] Memory, used to store computer programs / instructions;

[0133] A processor for executing the computer program / instructions to implement the steps of any of the methods described in Embodiment 1.

[0134] Example 5: This example provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the method described in any one of Examples 1.

[0135] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0136] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure 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.

[0137] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as 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, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0138] 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 that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0139] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims.

Claims

1. A method for model identification and damping control of a grid-type converter, characterized in that, include: A pre-generated test signal sequence is injected into the active power reference command of the grid-type converter to excite the system to generate a dynamic response; Collect system response data under test signal sequence excitation, and combine the response data with the test signal sequence to form an evolutionary time series dataset; Based on the evolutionary time series dataset, an equivalent linear model describing the system dynamics is calculated using the Koopman operator identification method. According to the equivalent linear model, the state feedback gain matrix is ​​calculated by solving the linear quadratic optimal control problem. Based on the state feedback gain matrix and the real-time acquired system state data, a damping control signal is generated and superimposed on the control command of the grid-type converter.

2. The model identification and damping control method for a grid-type converter according to claim 1, characterized in that, The step of injecting a pre-generated test signal sequence into the active power reference command of the grid-type converter includes: generating a composite test sequence consisting of a unit step signal, a pulse signal, and a set of sinusoidal sweep signals with frequencies covering 0.1Hz to 1000Hz; performing amplitude limiting and delay triggering processing on the composite test sequence to obtain a safety test signal sequence; and injecting the safety test signal sequence into the active power reference command of the grid-type converter.

3. The model identification and damping control method for a grid-type converter according to claim 2, characterized in that, The process of acquiring system response data under test signal sequence excitation and constructing an evolutionary time series dataset with the test signal sequence includes: acquiring system response data under safety test signal sequence excitation, wherein the response data includes at least one of generator speed deviation, power angle deviation, active power, reactive power, and bus voltage; performing noise reduction filtering, outlier removal, and data standardization on the acquired response data to obtain preprocessed measurement data; and aligning and segmenting the preprocessed measurement data with the safety test signal sequence in chronological order to construct an evolutionary time series dataset, wherein the dataset includes a first measurement dataset, a second measurement dataset, and an input signal dataset.

4. The model identification and damping control method for a grid-type converter according to claim 3, characterized in that, The method of calculating an equivalent linear model describing the system dynamics based on an evolutionary time series dataset using the Koopman operator identification method includes: combining the first measurement dataset and the input signal dataset into a first data matrix; using the second measurement dataset as a second data matrix; obtaining an initial state space matrix and an input matrix through pseudo-inverse calculation based on the first data matrix and the second data matrix; calculating the corresponding state space matrix and input matrix for different types of test signals, and performing arithmetic averaging and fusion of all matrices to obtain a fused equivalent linear model, wherein the equivalent linear model includes an average state space matrix and an average input matrix.

5. The model identification and damping control method for a grid-type converter according to claim 4, characterized in that, The process of calculating the state feedback gain matrix by solving a linear quadratic optimal control problem based on an equivalent linear model includes: constructing a linear quadratic regulator performance index based on the average state space matrix and the average input matrix; and calculating the optimal state feedback gain matrix by solving the algebraic Riccati equation corresponding to the performance index.

6. The model identification and damping control method for a grid-type converter according to claim 5, characterized in that, The step of generating a damping control signal based on the state feedback gain matrix and the real-time acquired system state data includes: real-time acquisition of system state data of the grid-type converter; multiplying the system state data with the optimal state feedback gain matrix to generate a damping control signal; and superimposing the damping control signal onto the original active power control command of the grid-type converter to achieve broadband oscillation suppression.

7. A model identification and damping control system for a grid-type converter, characterized in that, include: The test signal generation module is used to inject a pre-generated test signal sequence into the active power reference command of the grid-type converter to excite the system to generate a dynamic response. The system comprises a dataset construction module for collecting system response data under test signal sequence excitation and constructing an evolutionary time series dataset from the response data and the test signal sequence; a first calculation module for calculating an equivalent linear model describing the system dynamics based on the evolutionary time series dataset using the Koopman operator identification method; a second calculation module for calculating the state feedback gain matrix by solving a linear quadratic optimal control problem based on the equivalent linear model; and a control module for generating a damping control signal based on the state feedback gain matrix and the real-time collected system state data, and superimposing the damping control signal onto the control command of the grid-type converter.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-6.

9. An electronic device, characterized in that, include: Memory, used to store computer programs / instructions; A processor for executing the computer program / instructions to implement the steps of the method according to any one of claims 1-6.

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 described in any one of claims 1-6.

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