Power system inertial parameter real-time evaluation method and system based on recursive system identification

Abstract

The invention relates to an electric power system inertial parameter real-time evaluation method and system based on recursive system identification, and the method comprises the following steps: carrying out measurement by using a phasor measurement unit installed on a power generation bus, and collecting active power and bus frequency measurement values in real time; the collected data are preprocessed; calculating the rotor speed change of the synchronous generator or the internal frequency change of the converter interface generator through an estimation algorithm of the rotor speed; constructing an adaptive infinite input response filter model through a recursive least square equation error algorithm, and obtaining a z-domain transfer function through the filter model; and converting the z-domain transfer function into an s-domain transfer function, and extracting a mechanical time constant, a damping coefficient and a droop gain of the generator, namely the inertial parameters of the power system to be evaluated. Based on an advanced recursive algorithm and real-time measurement data, the inertia of the generator in the power system is estimated in real time, the method can adapt to dynamic changes of the system, and a more accurate evaluation result is provided.

Description

Technical Field

[0001] The present invention relates to a stability analysis and control technology of an electric power system, and in particular to a generator inertia evaluation method and system based on recursive system identification in an electric power system. Background Art

[0002] The stability of power systems depends on many factors, among which the inertial response provided by synchronous generators (SGs) is one of the key ones. This inertial response is the first line of defense for power systems facing power imbalances, helping to limit the rate of frequency change and the minimum frequency, thereby maintaining system operation within a safe range and avoiding accidental triggering of protection systems. However, with the gradual integration of converter interface generators (CIGs) in power systems, the traditional inertia of these systems is decreasing, bringing unprecedented challenges to the operation and control of power systems.

[0003] In the past few decades, converter interface generators have gradually replaced synchronous generators, resulting in a reduction in system inertia. In order to safely increase the proportion of non-synchronous generation in the power system, converter interface generators need to provide simulated inertia and other services such as primary frequency control (PFC), damping and reactive power control. However, the control parameters of converter interface generators may change over time, which may expose the power system to security issues if the control services are not provided.

[0004] Traditionally, power system operators (TSOs) rely on models and assumptions to estimate system inertia. However, with the addition of converter-interfaced generators, these models and assumptions may no longer be accurate. Therefore, developing an online parameter estimation method that can accurately estimate the inertia parameters of synchronous generators and converter-interfaced generators is crucial for the stable operation of modern power systems.

[0005] Existing inertia estimation methods can be divided into two main categories: model-based methods and data-driven methods. Model-based methods rely on a deep understanding of system dynamics and require accurate system parameters and models. However, these methods may be limited by the accuracy and availability of models in practical applications. Data-driven methods use historical data to predict system behavior, but they may require a large amount of data collection and complex data processing techniques. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a real-time evaluation method for inertia parameters of an electric power system based on recursive system identification and a system thereof. The method is based on advanced recursive algorithms and real-time measurement data to achieve real-time estimation of the inertia of generators in the electric power system, can adapt to dynamic changes in the system, and provide more accurate evaluation results.

[0007] A real-time evaluation method for inertia parameters of a power system based on recursive system identification comprises the following steps:

[0008] S1. Use the phasor measurement unit installed on the power generation bus to measure and collect the real-time measurement values ​​of active power and bus frequency to provide raw data for subsequent signal processing and system identification;

[0009] S2, preprocessing the data collected in step S1, including unit conversion, detrending and filtering, to improve the accuracy of subsequent evaluation;

[0010] S3, based on the data preprocessed in step S2, calculating the rotor speed change of the synchronous generator or the internal frequency change of the converter interface generator by using a rotor speed estimation algorithm;

[0011] S4, constructing an adaptive infinite input response filter model by a recursive least squares equation error algorithm, and obtaining a z-domain transfer function from the filter model;

[0012] S5. Convert the obtained z-domain transfer function into an s-domain transfer function, extract the mechanical time constant, damping coefficient and droop gain of the generator, which are the inertia parameters of the power system to be evaluated.

[0013] As a further preferred embodiment, the estimation algorithm of the rotor speed in S3 is:

[0014] Δω g =(D T D) -1 D T Δω B =D + Δω B (1)

[0015] Among them, Δω g is the generator internal frequency vector to be estimated, Δω B is the bus frequency measurement vector, D is the constructed frequency allocation matrix, D + is its left inverse matrix;

[0016] Calculate Δω using the above formula g , for conventional synchronous generators, it directly represents the change in rotor speed; for converter interface generators, it reflects the internal frequency change under the effect of the virtual inertia they provide.

[0017] As a further preferred embodiment, in S4, the step of constructing the filter model is as follows:

[0018] (a) Define an information vector Φ(k), which contains the current and past measured values ​​of the input signal, i.e., the active power change, and the output signal, i.e., the rotor speed change. The specific expression of Φ(k) is as follows:

[0019] Φ(k)=[d(k-1)...d(kn d )x(k)x(k-1)...x(kn x )] T (2)

[0020] Where k is the iteration index, vector d represents the measured output signal, x is the input signal vector, and n d and n x are the denominator and numerator orders of the transfer function, respectively, and T represents the matrix transpose.

[0021] (b) defining a parameter vector θ(k) whose elements correspond to the parameters of the filter transfer function; the parameter vector θ(k) is defined as follows:

[0022]

[0023] where a 1 ,...,a nd and b 0 ,...,b nx are the denominator and numerator coefficients of the z-domain transfer function, respectively, and T represents the matrix transpose;

[0024] The equation error e(k) represents the difference between the actual measured value and the predicted value and is defined as the difference between the measured output d(k) and the product of the transposed parameter vector θ(k) and the information vector Φ(k):

[0025] e(k)=d(k)-θ(k) T Φ(k). (4)

[0026] (c) Take the following least squares function as the minimization objective function:

[0027]

[0028] Among them, λ is the forgetting factor, which is used to reduce the weight of past information when updating the coefficients. The range is usually selected to be 0<λ<1, e(i) represents the error at the i-th moment, d(i) represents the actual measurement value at the i-th moment, θ(k) is the parameter vector, and Φ(i) represents the information vector at the i-th moment.

[0029] (d) Using the recursive least squares error algorithm to adjust the parameter vector θ(k) so that the calculated output signal y(k) = θ(k) TΦ(k) matches the measured output d(k). In each k iterations, the steps to update the parameter vector θ(k) include:

[0030] Calculate the error of the equation at the current moment:

[0031] e(k)=d(k)-y(k) (6)

[0032] It is used to update the real-time error between the actual measured output d(k) and the parameter-calculated output y(k) in each iteration;

[0033] Update the covariance matrix S De (k+1):

[0034]

[0035] Among them, λ is the forgetting factor, Φ(k) is the information vector, and T represents the matrix transpose;

[0036] Update parameter vector θ(k+1):

[0037] θ(k+1)=θ(k)+S De (k+1)Φ(k)e(k) (8)

[0038] Among them, S DE Yes (n d +n x , n d +n x )-dimensional coefficient-error-vector covariance matrix, S De (k+1) is the covariance matrix, Φ(k) is the information vector, and e(k) is the equation error.

[0039] Through the above steps, the filter model can effectively estimate the z-domain transfer function from the measured data.

[0040] As a further preferred embodiment, the specific steps of S5 include:

[0041] (a) Constructing the s-domain transfer function. By performing Laplace transform on the well-known differential equation for the rotor speed dynamics of a synchronous generator, the s-domain transfer function of the synchronous generator can be derived.

[0042]

[0043] where Δω g Represents the synchronous generator rotor speed change, ΔP g Represents the change in electromagnetic power provided by the generator, M g is the mechanical time constant of the synchronous generator, D is the machine damping coefficient;

[0044] (b) Considering the general converter interface generator adopts virtual synchronous machine network control scheme, using Laplace transform, the following s-domain transfer function can be obtained;

[0045]

[0046] Among them, Δω CIG Represents the internal frequency change, ΔP CIG Represents the change in electromagnetic power provided by the generator, G represents the inverse of the droop gain, M CIG is the virtual mechanical time constant provided by the converter interface generator;

[0047] (c) Since all s-domain transfer functions have corresponding z-domain counterparts, by comparing formulas (9) and (10) with the general z-domain transfer function form, the corresponding s-domain transfer function parameters can be obtained;

[0048]

[0049] Wherein, formula (11) is the s-domain transfer function parameter applicable to the synchronous generator, and formula (12) is the s-domain transfer function parameter applicable to the converter interface generator; M g is the mechanical time constant of the synchronous generator, D is the machine damping coefficient, T S is the sampling period, M CIG is the virtual mechanical time constant of the converter interface generator, G represents the inverse of the droop gain, b 0 and a 1 are coefficients determined using a recursive least squares equation error algorithm.

[0050] A generator inertia evaluation system based on recursive system identification, comprising:

[0051] Data acquisition module: Through the phasor measurement unit installed on the power generation bus, the active power and bus frequency data of the power system are collected in real time to provide raw data for subsequent analysis and estimation;

[0052] Signal preprocessing module: performs necessary preprocessing on the collected signals, including filtering, detrending, normalization, etc., to improve the data quality and prepare the data for further analysis;

[0053] Model Independent Estimator Module: Provides a rotor speed estimate that is independent of the specific generator model and is applicable to different types of generators, including synchronous generators and converter interface generators, providing key dynamic parameters for system identification;

[0054] Recursive system identification algorithm module; implements the recursive least squares equation error algorithm and recursively updates system parameter estimates to adapt to real-time dynamic changes in the power system;

[0055] Parameter real-time update conversion module: converts the z-domain transfer parameters obtained by the least squares equation error algorithm into s-domain transfer parameters, and updates these parameter estimates in real time to obtain system dynamic parameters with physical significance, ensuring the accuracy and real-time performance of system analysis.

[0056] The present invention has the following advantages:

[0057] 1. This method can quickly and accurately estimate the inertia parameters of synchronous generators and converter interface generators through advanced recursive algorithms and real-time measurement data. Compared with traditional inertia estimation methods, the method of the present invention does not rely on specific system models, can adapt to system dynamic changes, and provide more accurate estimation results. It can provide reliable estimation results even when the power system undergoes rapid changes or unbalanced events, and can better understand and control the dynamic behavior of the power grid, ensuring the stable and safe operation of the power grid.

[0058] 2. This method can process environmental measurement data under normal operating conditions and signal measurement data after power imbalance events, and is suitable for real-time monitoring and control applications. g or M CIG It reflects the response speed of the generator to power imbalance. For traditional synchronous generators, M g The larger the value, the greater the inertia of the generator, the slower the frequency change, which helps maintain the stability of the system. CIG Reflects the level of virtual inertia it provides. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flow chart of the present invention;

[0060] Figure 2 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0061] The specific implementation modes of the present invention are described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention.

[0062] like Figure 1 As shown, the present invention relates to a real-time evaluation method for inertia parameters of a power system based on recursive system identification, comprising the following steps:

[0063] S1. Use the phasor measurement unit (PMU) installed on the power generation bus to measure and collect the real-time measurement values ​​of active power and bus frequency to provide raw data for subsequent signal processing and system identification;

[0064] S2, preprocessing the data collected in step S1, including unit conversion, detrending and filtering, to improve the accuracy of subsequent evaluation;

[0065] S3, based on the data preprocessed in step S2, calculating the rotor speed change of the synchronous generator (SGs) or the internal frequency change of the converter interface generator (CIGs) by a rotor speed estimation algorithm;

[0066] The estimation algorithm of rotor speed is:

[0067] Δω g =(D T D) -1 D T Δω B =D + Δω B (1)

[0068] Among them, Δω g is the generator internal frequency vector to be estimated, Δω B is the bus frequency measurement vector, D is the constructed frequency allocation matrix, D + is its left inverse matrix;

[0069] Calculate Δω using the above formula g , which directly represents the change in rotor speed for conventional synchronous generators (SGs) and reflects the change in internal frequency due to the virtual inertia they provide for converter interface generators (CIGs).

[0070] S4, constructing an adaptive infinite input response filter model by a recursive least squares equation error algorithm, and obtaining a z-domain transfer function from the filter model;

[0071] The steps of constructing the filter model are as follows:

[0072] (a) Define an information vector Φ(k), which contains the current and past measured values ​​of the input signal, i.e., the active power change, and the output signal, i.e., the rotor speed change. The specific expression of Φ(k) is as follows:

[0073] Φ(k)=[d(k-1)...d(kn d )x(k)x(k-1)...x(kn x )] T (2)

[0074] Where k is the iteration index, vector d represents the measured output signal, x is the input signal vector, and n d and n xare the denominator and numerator orders of the transfer function, respectively, and T represents the matrix transpose.

[0075] (b) defining a parameter vector θ(k) whose elements correspond to the parameters of the filter transfer function; the parameter vector θ(k) is defined as follows:

[0076]

[0077] where a 1 ,...,a nd and b 0 ,...,b nx are the denominator and numerator coefficients of the z-domain transfer function, respectively, and T represents the matrix transpose;

[0078] The equation error e(k) represents the difference between the actual measured value and the predicted value and is defined as the difference between the measured output d(k) and the product of the transposed parameter vector θ(k) and the information vector Φ(k):

[0079] e(k)=d(k)-θ(k) T Φ(k). (4)

[0080] (c) Take the following least squares function as the minimization objective function:

[0081]

[0082] Among them, λ is the forgetting factor, which is used to reduce the weight of past information when updating the coefficients. The range is usually selected to be 0<λ<1, e(i) represents the error at the i-th moment, d(i) represents the actual measurement value at the i-th moment, θ(k) is the parameter vector, and Φ(i) represents the information vector at the i-th moment.

[0083] (d) Using the recursive least squares error algorithm to adjust the parameter vector θ(k) so that the calculated output signal y(k) = θ(k) T Φ(k) matches the measured output d(k). In each k iterations, the steps to update the parameter vector θ(k) include:

[0084] Calculate the error of the equation at the current moment:

[0085] e(k)=d(k)-y(k) (6)

[0086] It is used to update the real-time error between the actual measured output d(k) and the parameter-calculated output y(k) in each iteration;

[0087] Update the covariance matrix S De (k+1):

[0088]

[0089] Among them, λ is the forgetting factor, Φ(k) is the information vector, and T represents the matrix transpose;

[0090] Update parameter vector θ(k+1):

[0091] θ(k+1)=θ(k)+S De (k+1)Φ(k)e(k) (8)

[0092] Among them, S DE Yes (n d +n x , n d +n x )-dimensional coefficient-error-vector covariance matrix, S De (k+1) is the covariance matrix, Φ(k) is the information vector, and e(k) is the equation error.

[0093] Through the above steps, the z-domain transfer function can be effectively estimated from the measured data through the filter model.

[0094] S5. Convert the obtained z-domain transfer function into an s-domain transfer function, extract the mechanical time constant, damping coefficient and droop gain of the generator, which are the inertia parameters of the power system to be evaluated. The specific steps include:

[0095] (a) Constructing the s-domain transfer function. By performing Laplace transform on the well-known differential equation for the rotor speed dynamics of a synchronous generator, the s-domain transfer function of the synchronous generator can be derived.

[0096]

[0097] where Δω g Represents the synchronous generator rotor speed change, ΔP g Represents the change in electromagnetic power provided by the generator, M g is the mechanical time constant of the synchronous generator, D is the machine damping coefficient;

[0098] (b) Considering the general converter interface generator adopts virtual synchronous machine network control scheme, using Laplace transform, the following s-domain transfer function can be obtained;

[0099]

[0100] Among them, Δω CIG Represents the internal frequency change, ΔP CIG Represents the change in electromagnetic power provided by the generator, G represents the inverse of the droop gain, M CIG is the virtual mechanical time constant provided by the converter interface generator;

[0101] (c) Since all s-domain transfer functions have corresponding z-domain counterparts, by comparing formulas (9) and (10) with the general z-domain transfer function form, the corresponding s-domain transfer function parameters can be obtained;

[0102]

[0103] Wherein, formula (11) is the s-domain transfer function parameter applicable to the synchronous generator, and formula (12) is the s-domain transfer function parameter applicable to the converter interface generator; M g is the mechanical time constant of the synchronous generator, D is the machine damping coefficient, T S is the sampling period, M CIG is the virtual mechanical time constant of the converter interface generator, G represents the inverse of the droop gain, b 0 and a 1 are coefficients determined using a recursive least squares equation error algorithm.

[0104] The method can process environmental measurement data under normal operating conditions and signal measurement data after power imbalance events, and is suitable for real-time monitoring and control applications. g or M CIG It reflects the response speed of the generator to power imbalance. For traditional synchronous generators, M g The larger the value, the greater the inertia of the generator, the slower the frequency change, which helps maintain the stability of the system. CIG Reflects the level of virtual inertia it provides.

[0105] like Figure 2 As shown, the present invention relates to a real-time evaluation system for inertia parameters of a power system based on recursive system identification, which specifically includes:

[0106] Data acquisition module: Through the phasor measurement unit installed on the power generation bus, the active power and bus frequency data of the power system are collected in real time to provide raw data for subsequent analysis and estimation.

[0107] Signal preprocessing module; performs necessary preprocessing on the collected signals, including filtering, detrending, normalization, etc., to improve the data quality and prepare the data for further analysis.

[0108] Model Independent Estimator Module: Provides a rotor speed estimate that is independent of the specific generator model and is applicable to different types of generators, including synchronous generators and converter interface generators, providing key dynamic parameters for system identification.

[0109] Recursive system identification algorithm module; implements the recursive least squares equation error algorithm and recursively updates system parameter estimates to adapt to real-time dynamic changes in the power system.

[0110] Parameter real-time update conversion module: converts the z-domain transfer parameters obtained by the least squares equation error algorithm into s-domain transfer parameters, and updates these parameter estimates in real time to obtain system dynamic parameters with physical significance, ensuring the accuracy and real-time performance of system analysis.

[0111] This method can quickly and accurately estimate the inertia parameters of synchronous generators and converter interface generators through advanced recursive algorithms and real-time measurement data. Compared with traditional inertia estimation methods, the method of the present invention does not rely on specific system models, can adapt to system dynamic changes, and provide more accurate estimation results. It can provide reliable estimation results even when the power system experiences rapid changes or unbalanced events, and can better understand and control the dynamic behavior of the power grid, ensuring the stable and safe operation of the power grid.

[0112] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A real-time evaluation method for inertia parameters of power system based on recursive system identification, characterized in that The steps are as follows: S1. Use the phasor measurement unit installed on the power generation bus to measure and collect the real-time measurement values ​​of active power and bus frequency to provide raw data for subsequent signal processing and system identification; S2, preprocessing the data collected in S1, including unit conversion, detrending and filtering, to improve the accuracy of subsequent evaluation; S3, based on the data preprocessed in S2, calculating the rotor speed change of the synchronous generator or the internal frequency change of the converter interface generator through a rotor speed estimation algorithm; S4, constructing an adaptive infinite input response filter model by a recursive least squares equation error algorithm, and obtaining a z-domain transfer function from the filter model; S5. Convert the obtained z-domain transfer function into an s-domain transfer function, extract the mechanical time constant, damping coefficient and droop gain of the generator, which are the inertia parameters of the power system to be evaluated.

2. The method for real-time evaluation of inertia parameters of a power system based on recursive system identification according to claim 1 is characterized in that: The estimation algorithm of the rotor speed in S3 is: Give g =(D T D) -1 D T Give B =D + Give B (1) Among them, Δω g is the generator internal frequency vector to be estimated, Δω B is the bus frequency measurement vector, D is the constructed frequency allocation matrix, D + is its left inverse matrix; Calculate Δω using the above formula g , for conventional synchronous generators, it directly represents the change in rotor speed; for converter interface generators, it reflects the internal frequency change under the effect of the virtual inertia they provide.

3. The method for real-time evaluation of inertia parameters of a power system based on recursive system identification according to claim 1 is characterized in that: in S4, the step of constructing the filter model is as follows: (a) Define an information vector Φ(k), which contains the current and past measured values ​​of the input signal, i.e., the active power change, and the output signal, i.e., the rotor speed change. The specific expression of Φ(k) is as follows: Φ(k)=[d(k-1)...d(k-n d )x(k)x(k-1)...x(k-n x )] T (2) in, k is the iteration index, vector d represents the measured output signal, x is the input signal vector, n d and n x are the denominator and numerator orders of the transfer function, respectively, and T represents the matrix transpose. (b) defining a parameter vector θ(k) whose elements correspond to the parameters of the filter transfer function; the parameter vector θ(k) is defined as follows: where a1,...,a nd and b0,...,b nx are the denominator and numerator coefficients of the z-domain transfer function, respectively, and T represents the matrix transpose; The equation error e(k) represents the difference between the actual measured value and the predicted value and is defined as the difference between the measured output d(k) and the product of the transposed parameter vector θ(k) and the information vector Φ(k): e(k)=d(k)-θ(k) T Φ(k). (4) (c) Take the following least squares function as the minimization objective function: Among them, λ is the forgetting factor, which is used to reduce the weight of past information when updating the coefficients. The range is usually selected to be 0<λ<1, e(i) represents the error at the i-th moment, d(i) represents the actual measurement value at the i-th moment, θ(k) is the parameter vector, and Φ(i) represents the information vector at the i-th moment. (d) Using the recursive least squares error algorithm to adjust the parameter vector θ(k) so that the calculated output signal y(k) = θ(k) T Φ(k) matches the measured output d(k). In each k iterations, the steps to update the parameter vector θ(k) include: Calculate the error of the equation at the current moment: e(k)=d(k)-y(k) (6) It is used to update the real-time error between the actual measured output d(k) and the parameter-calculated output y(k) in each iteration; Update the covariance matrix S De (k+1): Among them, λ is the forgetting factor, Φ(k) is the information vector, and T represents the matrix transpose; Update parameter vector θ(k+1): θ(k+1)=θ(k)+S De (k+1)Φ(k)e(k) (8) Among them, S DE Yes (n d +n x , n d +n x )-dimensional coefficient-error-vector covariance matrix, S De (k+1) is the covariance matrix, Φ(k) is the information vector, and e(k) is the equation error. Through the above steps, the filter model can effectively estimate the z-domain transfer function from the measured data.

4. The method for real-time evaluation of inertia parameters of a power system based on recursive system identification according to claim 1 is characterized in that: The specific steps of S5 include: (a) Constructing the s-domain transfer function. By performing Laplace transform on the well-known differential equation for the rotor speed dynamics of a synchronous generator, the s-domain transfer function of the synchronous generator can be derived. where Δω g Represents the synchronous generator rotor speed change, ΔP g Represents the change in electromagnetic power provided by the generator, M g is the mechanical time constant of the synchronous generator, D is the machine damping coefficient; (b) Considering the general converter interface generator adopts virtual synchronous machine network control scheme, using Laplace transform, the following s-domain transfer function can be obtained; Among them, Δω CIG Represents the internal frequency change, ΔP CIG represents the change in electromagnetic power provided by the generator, G represents the inverse of the droop gain, M CIG is the virtual mechanical time constant provided by the converter interface generator; (c) Since all s-domain transfer functions have corresponding z-domain counterparts, by comparing formulas (9) and (10) with the general z-domain transfer function form, the corresponding s-domain transfer function parameters can be obtained; Wherein, formula (11) is the s-domain transfer function parameter applicable to the synchronous generator, and formula (12) is the s-domain transfer function parameter applicable to the converter interface generator; M g is the mechanical time constant of the synchronous generator, D is the machine damping coefficient, T S is the sampling period, M CIG is the virtual mechanical time constant of the converter interface generator, G represents the inverse of the droop gain, and b0 and a1 are coefficients determined using a recursive least squares equation error algorithm.

5. A generator inertia evaluation system based on recursive system identification, applied to a real-time evaluation method for inertia parameters of a power system based on recursive system identification as claimed in any one of claims 1 to 4, characterized in that include: Data acquisition module; Through the phasor measurement unit installed on the power generation bus, the active power and bus frequency data of the power system are collected in real time to provide raw data for subsequent analysis and estimation; Signal preprocessing module; Perform necessary preprocessing on the collected signals, including filtering, detrending, normalization, etc., to improve data quality and prepare data for further analysis; Model Independent Estimator Module: Provides a rotor speed estimate that is independent of the specific generator model and is applicable to different types of generators, including synchronous generators and converter interface generators, providing key dynamic parameters for system identification; Recursive system identification algorithm module; Implement a recursive least squares equation error algorithm to recursively update system parameter estimates to adapt to real-time dynamic changes in the power system; Parameters real-time update conversion module; The z-domain transfer parameters obtained by the least squares equation error algorithm are converted into s-domain transfer parameters, and these parameter estimates are updated in real time to obtain system dynamic parameters with physical significance, ensuring the accuracy and real-time performance of system analysis.

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