Inverter single machine electromechanical modeling parameter identification method and device

Abstract

The application discloses an inverter unit electromechanical modeling parameter identification method and device, wherein the inverter unit electromechanical modeling parameter identification method comprises the following steps: obtaining measured data or semi-physical test data of fault ride-through of a target type inverter; processing the measured data or semi-physical test data according to a preset voltage threshold to generate a ride-through data matrix; establishing a corresponding control strategy according to the ride-through data matrix to generate a to-be-identified control parameter vector; constructing a least square method objective function to fit a predicted value under the control strategy with an actual output value to obtain an initial control parameter vector; introducing an elastic network regularization term into the least square method objective function for optimization; and solving the least square method objective function after iterative optimization by using a proximal gradient descent method to output a final control parameter vector. The modeling parameter is identified by the optimized least square method, and the identification accuracy of the key control parameter is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of photovoltaic inverters, in particular to an inverter single-machine electromechanical modeling parameter identification method and device. BACKGROUND

[0002] The photovoltaic power station needs to carry out power quality testing in the process of grid connection, including active / reactive power control capability testing, primary frequency modulation and inertia response testing, inverter and SVG (static var generator) performance testing, fault ride-through capability verification, and power station whole-station simulation modeling, etc. The fault ride-through capability verification needs to compare the measured data or semi-physical data of the inverter single-machine with the electromechanical model data, and the power station whole-station simulation modeling needs to build the electromechanical model of the whole photovoltaic power station on the basis of single-machine modeling.

[0003] As the confidentiality requirements of manufacturers on control strategies and parameters are gradually increasing, the accurate modeling of inverter single-machines is also gradually increasing in difficulty, which affects the authenticity of the comparison between the measured data or semi-physical data of the fault ride-through and the modeling parameters. The existing common scheme is based on the measured data or semi-physical data of the fault ride-through, uses the least square method to complete the identification of the control logic parameters of the inverter during the ride-through, and then uses the identified control logic parameters to complete the construction of the fault ride-through model of the inverter single-machine and compare it with the measured data or semi-physical data of the fault ride-through, to verify whether the error results meet the requirements of the relevant standards of photovoltaic systems. However, the common scheme has the following deficiencies: the least square method and some of its optimization algorithms still have the problems of overfitting or insufficient analysis capability for specific "ill-conditioned" data, and therefore, there is an urgent need for a method that can improve the accuracy of single-machine electromechanical modeling parameter identification. SUMMARY

[0004] Embodiments of the present application provide an inverter single-machine electromechanical modeling parameter identification method and device to improve the accuracy of modeling parameter identification and the capability of processing specific data.

[0005] To solve the above technical problems, embodiments of the present application disclose the following technical solutions:

[0006] In a first aspect, the application provides an inverter single machine electromechanical modeling parameter identification method, comprising: obtaining measured data or semi-physical test data of fault ride-through of a target type inverter; processing the measured data or semi-physical test data according to a preset voltage threshold to generate a ride-through data matrix; establishing a corresponding control strategy according to the ride-through data matrix to generate a to-be-identified control parameter vector; constructing a least squares objective function to fit the predicted value under the control strategy with the real output value to obtain an initial control parameter vector; introducing an elastic network regularization term into the least squares objective function for optimization; solving the least squares objective function after iterative optimization by using a proximal gradient descent method to output a final control parameter vector.

[0007] Further, in the above method, the measured data or semi-physical test data includes low-voltage ride-through data and high-voltage ride-through data, the preset voltage threshold includes a low-voltage ride-through threshold and a high-voltage ride-through threshold, and the ride-through data matrix includes a low-voltage ride-through data matrix and a high-voltage ride-through data matrix.

[0008] In the above further scheme, preferably, the low-voltage ride-through threshold is preset as 0.9 p.u., and the high-voltage ride-through threshold is preset as 1.1 p.u.

[0009] Preferably, the low-voltage ride-through data includes three-phase symmetric faults and two-phase asymmetric faults, and the positive sequence voltage during the fault is 0 p.u., 0.2 p.u., 0.35 p.u., 0.5 p.u., and 0.75 p.u., respectively; and the high-voltage ride-through data includes three-phase symmetric faults and two-phase asymmetric faults, and the positive sequence voltage during the fault is 1.2 p.u., 1.25 p.u., and 1.30 p.u., respectively.

[0010] Further, the measured data or semi-physical test data is stored in a six-column matrix form, including time labels, positive sequence voltages, active powers, reactive powers, active currents, and reactive currents, respectively.

[0011] Further, the elastic network regularization term includes L1 regularization and L2 regularization, and the weight ratio of the L1 regularization and the L2 regularization is dynamically adjusted according to the voltage ride-through working condition.

[0012] Still further, the proximal gradient descent method includes the following steps: initializing the control parameter vector, calculating the fixed term gradient, applying a soft threshold function to the L1 regularization part, performing spherical projection on the L2 regularization part, updating the parameter vector and performing termination judgment.

[0013] Further, the data acquisition frequency of the measured data or semi-physical test data is not less than 1000 Hz, and all are in per unit format.

[0014] In a second aspect, the application provides an inverter single machine electromechanical modeling parameter identification device, comprising: a data acquisition module, configured to acquire fault ride-through measured data or semi-physical test data of a photovoltaic inverter; a data processing module, configured to process the measured data according to a preset voltage threshold to generate a ride-through data matrix; a control strategy module, configured to construct a corresponding control strategy according to the ride-through data matrix to generate a to-be-identified control parameter vector; a parameter fitting module, configured to construct a least square method objective function and fit a predicted value and an actual output value to generate an initial control parameter vector; and a parameter optimization module, configured to introduce an elastic network regularization term into the least square method objective function and perform iterative optimization through a proximal gradient descent method to output a final control parameter vector.

[0015] Further, the parameter optimization module comprises a soft threshold operation unit for processing L1 regularization and a spherical projection unit for processing L2 regularization.

[0016] The above technical solutions have at least the following advantages or beneficial effects: by introducing an elastic network optimized least square method into inverter single machine electromechanical modeling parameter identification, overfitting and ill-conditioned data sensitivity problems prone to occurring in traditional least square methods can be effectively avoided, and accurate identification of key control parameters can be achieved.

[0017] In the above technical solutions, by distinguishing high-voltage and low-voltage ride-through conditions and dynamically adjusting the regularization ratio, the accuracy and stability of parameter identification are ensured, the adaptability of data under different fault ride-through conditions is improved, reliable data support is provided for whole-station modeling and grid-connected verification of photovoltaic power stations, and the process, convenience and high robustness of the modeling parameter identification method are improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A basic flowchart of the modeling parameter identification method provided by the application is shown in the figure.

[0019] Figure 2 An example proximal gradient descent solution flowchart provided by the application is shown in the figure.

[0020] Figure 3 A least square method low-voltage ride-through parameter identification result provided by the application is shown in the figure, wherein Figure 3 (a) is a voltage drop degree-reactive power scatter plot during fault ride-through, Figure 3 (b) is a voltage drop degree-active power scatter plot during fault ride-through.

[0021] Figure 4 An optimized low-voltage ride-through parameter identification result provided by the application is shown in the figure, wherein Figure 4 (a) is a voltage drop degree-reactive power scatter plot during fault ride-through, Figure 4(b) is a voltage dip degree-active power scatter plot during fault ride through;

[0022] Figure 5 The least square method high voltage ride through parameter identification result figure provided for the present application, wherein Figure 5 (a) is a voltage dip degree-reactive power scatter plot during fault ride through, Figure 5 (b) is a voltage dip degree-active power scatter plot during fault ride through;

[0023] Figure 6 The optimized high voltage ride through parameter identification result figure provided for the present application, wherein Figure 6 (a) is a voltage dip degree-reactive power scatter plot during fault ride through, Figure 6 (b) is a voltage dip degree-active power scatter plot during fault ride through. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical scheme and beneficial effects of the present application clearer and more understandable, the present application is further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described in the present specification are only for the purpose of explaining the present application, and are not intended to limit the present application. It should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits and methods are omitted to avoid unnecessary details that hinder the description of the present application.

[0025] In the description of the present application, it should be noted that, unless otherwise explicitly limited, the terms "comprise", "include" and the like should be broadly understood as meaning that the listed elements or steps can be partial or total, and can also include other elements or steps not listed; the terms "based on" and "according to" should be understood as "based at least partially on" and "according to at least partially"; the terms "step" and "process" can be executed in sequence, or can be executed in parallel or in different order under certain conditions. Those skilled in the art can understand the specific meaning of the above terms in the method of the present application according to the specific application scenario.

[0026] In the description of the present application, it should also be noted that "semi-physical testing" refers to obtaining the operating data of the inverter under specific working conditions through HIL (Hardware-in-the-Loop) testing or a combination of partial real devices and simulation platforms.

[0027] To address the problems of inaccurate control parameter acquisition, overfitting susceptibility of traditional least squares methods, and sensitivity to ill-conditioned data in existing inverter single-unit electromechanical modeling, this application proposes a parameter identification method for inverter single-unit electromechanical modeling using elastic network optimization of least squares. This method acquires and processes measured data of the inverter under fault ride-through conditions, establishes a control strategy and generates a vector of control parameters to be identified, combines least squares fitting with elastic network regularization optimization, and iteratively solves the problem using the proximal gradient descent method to ultimately obtain control parameters with higher accuracy and stronger robustness. The specific steps and implementation process of this invention are described in detail below with reference to the accompanying drawings.

[0028] Figure 1 A schematic diagram illustrating the basic process of the modeling parameter identification method provided in this application. Figure 1 As shown, this application provides an exemplary method for identifying electromechanical modeling parameters of a single inverter, including the following steps: S1: Obtaining measured data or semi-physical test data of the fault ride-through of the target model inverter; S2: Processing the measured data or semi-physical test data according to a preset voltage threshold to generate a ride-through data matrix; S3: Establishing a corresponding control strategy based on the ride-through data matrix to generate a vector of control parameters to be identified; S4: Constructing a least squares objective function, fitting the predicted value under the control strategy with the actual output value to obtain an initial control parameter vector; S5: Introducing an elastic network regularization term into the least squares objective function for optimization; S6: Solving and iteratively optimizing the least squares objective function using the near-end gradient descent method, and outputting the final control parameter vector.

[0029] This application provides an explanation of the above method steps, using the following data format and fault conditions as examples: The measured or semi-physical test data for the target model inverter fault includes low-voltage ride-through data and high-voltage ride-through data, both stored in a six-column matrix format. Each column of the matrix represents a time stamp, positive-sequence voltage, active power, reactive power, active current, and reactive current. Preset voltage thresholds include low-voltage ride-through thresholds and high-voltage ride-through thresholds. The ride-through data matrix includes a low-voltage ride-through data matrix and a high-voltage ride-through data matrix. The data acquisition frequency of the measured or semi-physical test data is no less than 1000Hz, and all data are in per-unit format. The low-voltage ride-through data includes three-phase symmetrical faults and two-phase asymmetrical faults, with positive-sequence voltages of 0 pu, 0.2 pu, 0.35 pu, 0.5 pu, and 0.75 pu during the fault period, respectively. The high-voltage ride-through data includes three-phase symmetrical faults and two-phase asymmetrical faults, with positive-sequence voltages of 1.2 pu, 1.25 pu, and 1.30 pu during the fault period, respectively.

[0030] In step S1 above, taking the three-phase 0 pu crossing the original data matrix A as the acquired measured data or semi-physical test data as an example, we can have:

[0031] A= ,

[0032] The matrix consists of a first column containing time stamps, a second column containing positive-sequence voltage data, a third column containing active power data, a fourth column containing reactive power data, a fifth column containing active current data, and a sixth column containing reactive current data; there are a total of n sampling points.

[0033] In step S2 above, a low voltage ride-through threshold is generally preset. The voltage is 0.9 pu, and the preset high voltage ride-through threshold is [not specified]. The value is 1.1 PU, which can be adjusted according to actual conditions. An example low-voltage data matrix. The format is as follows:

[0034] The meaning of each row of data is as follows, depending on the different crossing conditions: The per-unit value of the steady-state positive sequence voltage before the three-phase symmetrical 0 pu crossover. The per-unit value of steady-state active power before three-phase symmetry 0 pu crossover. The per-unit value of steady-state reactive power before three-phase symmetric 0 pu crossover; This represents the per-unit value of the steady-state positive sequence voltage during the three-phase symmetrical 0 pu crossing process. This represents the per-unit value of steady-state active power during the three-phase symmetrical 0 pu crossing process. This is the per-unit value of steady-state reactive power during the three-phase symmetrical 0 pu ride-through process, and so on; The per-unit value of the steady-state positive sequence voltage before the two-phase 0 pu crossover. This represents the per-unit value of the steady-state active power before the two-phase 0 pu crossover. The per-unit value of steady-state reactive power before the two-phase 0 pu crossover; This represents the per-unit value of the steady-state positive sequence voltage during the two-phase 0 pu ride-through process. This represents the per-unit value of steady-state active power during the two-phase 0 pu ride-through process. This represents the per-unit value of steady-state reactive power during a two-phase 0 pu ride-through, and so on. An exemplary high-speed ride-through data matrix. The format is as follows:

[0035] The meaning of each row of data is as follows, according to different crossing conditions: The per-unit value of the steady-state positive sequence voltage before the three-phase symmetrical 1.2 PU crossover. the steady-state active power per unit for three-phase symmetry before 1.2 p.u. ride-through, the steady-state reactive power per unit for three-phase symmetry before 1.2 p.u. ride-through; the steady-state positive-sequence voltage per unit for three-phase symmetry during 1.2 p.u. ride-through, the steady-state active power per unit for three-phase symmetry during 1.2 p.u. ride-through, the steady-state reactive power per unit for three-phase symmetry during 1.2 p.u. ride-through, and so on. the steady-state positive-sequence voltage per unit for two-phase before 1.2 p.u. ride-through, the steady-state active power per unit for two-phase before 1.2 p.u. ride-through, the steady-state reactive power per unit for two-phase before 1.2 p.u. ride-through; the steady-state positive-sequence voltage per unit for two-phase during 1.2 p.u. ride-through, the steady-state active power per unit for two-phase during 1.2 p.u. ride-through, the steady-state reactive power per unit for two-phase during 1.2 p.u. ride-through, and so on.

[0036] The present specification also provides a description of the calculation of the above ride-through data matrix. In any of the above low-ride-through data matrix , first, the positive-sequence voltage values in the second column of matrix A in step S1 are sequentially compared with the low-voltage ride-through threshold , and the first and last positive-sequence voltage values less than the low-voltage ride-through threshold are found and recorded as and , respectively. , and then to are determined as the positive-sequence voltage before ride-through, to are determined as the positive-sequence voltage during ride-through, to are determined as the positive-sequence voltage after ride-through; to are determined as the active power before ride-through, to are determined as the active power during ride-through, to are determined as the active power after ride-through, to are determined as the reactive power before ride-through, to are determined as the reactive power during ride-through, to For the low-pass, the initial search interval length is set as , if , and , , , then record , , , respectively , , ; if , and the above conditions are met, then reduce the initial search interval length until , , , respectively , , are recorded; in the above formula, is the deviation setting value; the initial search interval length is set as , if , and , , are met, then record , , , respectively , , ; if , and the above conditions are met, then reduce the initial search interval length until , , , respectively , , are recorded; in the above formula, is the deviation setting value; the low-pass data matrix is calculated in the same way as the rest of the rows, which will not be repeated here; the high-pass data matrix is calculated in the same way as the low-pass, which will not be repeated here.

[0037] The present application provides the following exemplary control strategy according to the above step S3, and generates the to-be-identified control parameter vector according to the exemplary control strategy.

[0038] Taking the low-pass as an example, the corresponding control strategy is set as

[0039] ;

[0040] .

[0041] In the above formula, is the steady-state reactive current value during fault ride-through, is the reactive control voltage coefficient, is the reactive control current coefficient, is the steady-state positive sequence voltage value during fault ride-through, is the steady-state value of the reactive current before fault ride-through; is the reactive current deviation value during fault ride-through; is the steady-state active current value during fault ride-through, is the active control voltage coefficient, is the active control current coefficient, is the steady-state positive sequence voltage value during fault ride-through, is the steady-state value of the active current before fault ride-through; is the active current deviation value during fault ride-through. Taking three-phase symmetric 0 p.u. ride-through as an example, in the above objective function ; ; ; ; Through the corresponding low-voltage ride-through control strategy, a set of to-be-identified control parameter vectors under each working condition of low-voltage ride-through are identified, which include the reactive control voltage coefficient , the reactive control current coefficient , the reactive current deviation value during fault ride-through ; the active control voltage coefficient , the active control current coefficient , and the active current deviation value during fault ride-through .

[0042] Taking high-voltage ride-through as an example, the corresponding control strategy is set as:

[0043] ;

[0044] .

[0045] In the above formula, is the steady-state reactive current value during fault ride-through, is the reactive control voltage coefficient, is the reactive control current coefficient, is the steady-state positive sequence voltage value during fault ride-through, is the steady-state value of the reactive current before fault ride-through; is the reactive current deviation value during fault ride-through; is the steady-state active current value during fault ride-through, is the active control voltage coefficient, is the active control current coefficient, is the steady-state positive sequence voltage value during fault ride-through, is the steady-state active current value before fault ride-through; is the active current deviation value during fault ride-through. Taking the three-phase symmetric 1.20 p.u. ride-through as an example, at this time ; ; ; ; . Through the corresponding high penetration control strategy, a set of to-be-identified control parameter vectors under each working condition of high voltage ride-through are identified, which include the reactive control voltage coefficient , the reactive control current coefficient , the reactive current deviation value during fault ride-through ; the active control voltage coefficient , the active control current coefficient , and the active current deviation value during fault ride-through .

[0046] The present application provides the following exemplary least squares objective function according to the above step S4, and obtains the initial control parameter vector.

[0047] Taking the above exemplary low penetration and high penetration control strategies as examples, the least squares objective function, input, and output are set as follows:

[0048] ;

[0049] ,

[0050] wherein, is the real output vector corresponding to the low penetration control strategy obtained in step S3, and has:

[0051] ,

[0052] wherein are the steady-state given values of the reactive and active currents during low voltage fault ride-through under three-phase symmetric fault and the steady-state given values of the reactive and active currents during low voltage fault ride-through under two-phase fault working condition, respectively.

[0053] According to step S3, it can be obtained that:

[0054] ;

[0055] ;

[0056] ;

[0057] .

[0058] The input vector can be represented as:

[0059] ,

[0060] In the above formula These are the steady-state values ​​of positive sequence voltage, reactive current, and active current during low-voltage fault ride-through under three-phase symmetrical fault conditions, and the steady-state values ​​of positive sequence voltage, reactive current, and active current before fault ride-through under two-phase fault conditions.

[0061] Based on step S3, we can conclude that:

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] .

[0068] The control parameter vector for low-voltage ride-through can be expressed as:

[0069] ,

[0070] The corresponding set of reactive power control voltage coefficients under various low-voltage ride-through conditions in step S3. Reactive power control current coefficient Reactive current deviation during fault ride-through Active control voltage coefficient Active control current coefficient Active current deviation during fault ride-through .

[0071] This is the actual output vector for low-voltage crossover. For a set of control parameter vectors for low voltage ride-through Below, the predicted fitted value vector Compared with the true output value vector The least squares error; To minimize the least squares error mentioned above, the control parameter vector for this low-voltage ride-through... The initial low-altitude penetration control parameters identified;

[0072] Similarly, For the true output vector corresponding to the high-altitude penetration control strategy obtained in step S3, we have:

[0073] ,

[0074] in These are the steady-state setpoints of reactive and active currents during high-voltage fault ride-through under different operating conditions of three-phase symmetry and two-phase faults, respectively.

[0075] Based on step S3, we can conclude that:

[0076] ;

[0077] ;

[0078] ;

[0079] .

[0080] The input vector can be represented as:

[0081]

[0082] ,in These represent the steady-state values ​​of positive sequence voltage during high-voltage fault ride-through under different operating conditions of three-phase symmetry and two-phase faults, respectively, and the steady-state values ​​of reactive and active current before fault ride-through under different operating conditions of three-phase symmetry and two-phase faults.

[0083] Based on step S3, we can conclude that:

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] .

[0090] The control parameter vector for low-voltage ride-through can be expressed as:

[0091] This corresponds to a set of reactive power control voltage coefficients under various high-voltage ride-through conditions in step S3. Reactive power control current coefficient Reactive current deviation during fault ride-through Active control voltage coefficient Active control current coefficient Active current deviation during fault ride-through .

[0092] This is the actual output vector for high voltage ride-through; For a set of control parameter vectors for high voltage ride-through Below, the predicted fitted value vector Compared with the true output value vector The least squares error; To minimize the least squares error mentioned above, the control coefficient vector for this high-voltage ride-through is... These are the identified initial height-penetration control parameters.

[0093] This application provides an exemplary process for introducing regularization terms into a resilient network based on step S5 described above.

[0094] Taking the least squares objective function in step S4 above as an example, the following exemplary elastic network is used for optimization:

[0095] ;

[0096] ,

[0097] In the above formula, , These represent the overall regularization strength of the parameter identification functions for low-voltage ride-through and high-voltage ride-through, respectively. , These are the weight ratio adjustment coefficients for L1 regularization and L2 regularization of the low-voltage ride-through and high-voltage ride-through parameter identification functions, respectively. , These are the L1 regularization penalty terms for the low-voltage ride-through and high-voltage ride-through parameter identification functions, respectively. and These are the L2 regularization penalty terms for the low-voltage ride-through and high-voltage ride-through parameter identification functions, respectively.

[0098] This application provides an exemplary proximal gradient descent method for solving the exemplary optimized least squares objective function based on step S6 above. Figure 2 This is an exemplary flowchart of the proximal gradient descent solution provided in this application. Combined with... Figure 2 The exemplary proximal gradient descent solution steps in this embodiment include:

[0099] S601: Initialize the first group and Set the learning rate (step size) 𝛼.

[0100] S602: Set the method for calculating the gradient of fixed terms for low-pass and high-pass penetration using least squares, as follows:

[0101] ;

[0102] ,

[0103] In the above formula, , They are respectively , The transpose of .

[0104] S603: Set the gradients of the regularization terms for low-voltage ride-through and high-voltage ride-through, where the L1 regularization part and the L2 regularization part are shown below, respectively.

[0105] Low-cut L1 section: ;

[0106] Low-cut L2 section: ;

[0107] High penetration L1 section: ;

[0108] High penetration of L2 section: ;

[0109] In the above formula, This is a basic function used to determine the positive or negative value of a number.

[0110] S604: Performs proximal operations on L1 and L2 regularization. For low-penetration and high-penetration L1 regularization, a soft threshold function is used, as detailed below.

[0111] ;

[0112] ,

[0113] In the above formula, Indicates projection. This is the input to the proximal operator, used to handle the non-differentiable part; for low-pass and high-pass L2 regularization, it is projected onto the sphere, as follows:

[0114] ;

[0115] .

[0116] S605: Update and The specific formula is as follows:

[0117] ;

[0118] ,

[0119] In the above formula, and This represents the result of the h-th iteration of low-voltage ride-through and high-voltage ride-through parameters. and This represents the result of the (h+1)th iteration of low-voltage ride-through and high-voltage ride-through parameters.

[0120] S606: Perform termination determination, specifically: for low-voltage ride-through, if... This indicates that the iteration is complete, and the final control parameter vector is output. This is the final parameter; otherwise, continue iterating according to S605; similarly, for high-voltage ride-through, if... This indicates that the iteration is complete, and the final control parameter vector is output. This is the final parameter; otherwise, continue iterating according to S605. Wherein, Allowed deviation to terminate the iteration.

[0121] To illustrate the effectiveness of the inverter single-unit electromechanical modeling parameter identification method provided in this application in identifying modeling parameters, this application provides the results after performing the above exemplary steps.

[0122] Figure 3 The image provided in this application shows the results of least squares method low voltage ride-through parameter identification. Figure 3 - (a) is a scatter plot of voltage drop-reactive power distribution during fault ride-through. Figure 3 - (b) is a scatter plot of voltage drop during fault ride-through and active power. Figure 4 The optimized low-voltage ride-through parameter identification result diagram provided in this application is shown in the figure. Figure 4 - (a) is a scatter plot of voltage drop-reactive power distribution during fault ride-through. Figure 4 - (b) is a scatter plot of voltage drop during fault ride-through and active power. Figure 5 The image provided in this application shows the results of the least squares method for high voltage ride-through parameter identification. Figure 5 - (a) is a scatter plot of voltage drop-reactive power distribution during fault ride-through. Figure 5 - (b) is a scatter plot of voltage drop during fault ride-through and active power. Figure 6 The optimized high-voltage ride-through parameter identification result diagram provided in this application is shown in the figure. Figure 6- (a) is a scatter plot of voltage drop-reactive power distribution during fault ride-through. Figure 6 - (b) is a scatter plot of voltage drop during fault ride-through and active power.

[0123] Combination Figure 3 and Figure 4 Explanation. For example... Figure 3 As shown, the time-per-unit scatter plot display of the inverter's reactive / active power identification results under low-voltage ride-through conditions using the traditional least squares method shows a large fitting deviation in some time periods, indicating significant overfitting or fluctuations; for example... Figure 4 As shown, the optimized method provided in this application is used for low voltage ride-through parameter identification, in conjunction with... Figure 3 Under the same working conditions and coordinate system, the optimized identification scatter points are closer to the actual measured data than... Figure 3 The deviation was significantly reduced, indicating that the recognition accuracy and stability were significantly improved after the introduction of elastic network optimization.

[0124] Combination Figure 5 and Figure 6 Explanation. For example... Figure 5 As shown, the traditional least squares method was used to identify high-voltage ride-through parameters. The scatter plots of the results show that, under high-voltage ride-through conditions, the reactive / active power identification results have a significant fitting error compared to the measured values, especially during voltage surges, where the model predictions deviate considerably from the measured values. Figure 6 As shown, the optimized method provided in this application is used for high voltage ride-through parameter identification, in conjunction with... Figure 5 Under the same high-voltage ride-through conditions, the optimized identification results are in high agreement with the measured data, the error is significantly reduced, and the fitting process is smoother, indicating that the method provided in this application can still maintain good robustness when processing ill-conditioned high-voltage ride-through data.

[0125] This application also provides a device for identifying electromechanical modeling parameters of a single inverter unit. The device includes: a data acquisition module for acquiring fault ride-through measured data or semi-physical test data of a photovoltaic inverter; a data processing module for processing the measured data according to a preset voltage threshold to generate a ride-through data matrix; a control strategy module for constructing a corresponding control strategy based on the ride-through data matrix to generate a vector of control parameters to be identified; a parameter fitting module for constructing a least-squares objective function and fitting predicted values ​​with actual output values ​​to generate an initial control parameter vector; and a parameter optimization module for introducing an elastic network regularization term into the least-squares objective function and iteratively optimizing it using a proximal gradient descent method to output the final control parameter vector. Further, the parameter optimization module includes a soft thresholding unit for processing L1 regularization and a spherical projection unit for processing L2 regularization.

[0126] In summary, this application's embodiments, by introducing the least squares method of elastic network optimization and combining iterative near-end gradient descent, achieve high-precision identification of inverter single-unit fault ride-through control parameters. This method can effectively overcome the problems of overfitting and sensitivity to ill-conditioned data in traditional least squares methods, ensuring the accuracy and stability of parameter results. The corresponding device can realize the rapid implementation and execution of this method, improving the process of photovoltaic power plant whole-site modeling and grid-connected verification, making it more streamlined, convenient, and robust.

[0127] The above steps are provided only to help understand the method, structure, and core ideas of this application. It should be noted that the embodiments described in this application are only for illustrating the principle and effects of the method and do not constitute a limitation on the scope of protection of this application. Those skilled in the art can make various equivalent substitutions and improvements to the execution order, combination of steps, or implementation means without departing from the essence of the method of this application, and all such equivalent solutions should fall within the scope of protection of this application.

Claims

1. A method for identifying electromechanical modeling parameters of a single inverter unit, characterized in that, include: Obtain measured or semi-physical test data of the fault ride-through of the target inverter model; The measured data or semi-physical test data are processed according to the preset voltage threshold to generate a cross-through data matrix; Based on the traversal data matrix, a corresponding control strategy is established, and a vector of control parameters to be identified is generated. Construct a least squares objective function, fit the predicted value under the control strategy with the actual output value, and obtain the initial control parameter vector; The least squares objective function is optimized by introducing an elastic network regularization term, which includes L1 regularization and L2 regularization based on low voltage and high voltage ride-through data, and the weight ratio of L1 regularization and L2 regularization is dynamically adjusted according to the voltage ride-through condition. The least squares objective function is solved and iteratively optimized using the proximal gradient descent method, and the final control parameter vector is output.

2. The method as described in claim 1, characterized in that, The measured data or semi-physical test data includes low voltage ride-through data and high voltage ride-through data; The preset voltage thresholds include a low voltage ride-through threshold and a high voltage ride-through threshold; The crossing data matrix includes a low-crossing data matrix and a high-crossing data matrix.

3. The method as described in claim 2, characterized in that, The low voltage ride-through threshold is preset to 0.9 pu, and the high voltage ride-through threshold is preset to 1.1 pu.

4. The method as described in claim 2, characterized in that, The low-voltage ride-through data includes three-phase symmetrical faults and two-phase asymmetrical faults, with positive sequence voltages of 0 pu, 0.2 pu, 0.35 pu, 0.5 pu, and 0.75 pu during the fault periods, respectively. The high-voltage ride-through data includes three-phase symmetrical faults and two-phase asymmetrical faults, with positive sequence voltages of 1.2 pu, 1.25 pu, and 1.30 pu during the fault periods, respectively.

5. The method according to any one of claims 1-4, characterized in that, The measured data or semi-physical test data is stored in a six-column matrix, namely time tag, positive sequence voltage, active power, reactive power, active current and reactive current.

6. The method as described in claim 1, characterized in that, The proximal gradient descent method includes the following steps: initializing the control parameter vector, calculating the gradient of the fixed term, applying a soft threshold function to the L1 regularization part, performing spherical projection on the L2 regularization part, updating the parameter vector, and making a termination decision.

7. The method according to any one of claims 1-4, characterized in that, The data acquisition frequency of the measured data or semi-physical test data is no less than 1000Hz, and all data are in per-unit format.

8. A device for identifying electromechanical modeling parameters of a single inverter unit, characterized in that, include: The data acquisition module is used to acquire fault ride-through measured data or semi-physical test data of the photovoltaic inverter; The data processing module is used to process the measured data according to a preset voltage threshold and generate a cross-crossing data matrix; The control strategy module is used to construct a corresponding control strategy based on the traversal data matrix and generate a vector of control parameters to be identified. The parameter fitting module is used to construct the least squares objective function and fit the predicted values ​​with the actual output values ​​to generate the initial control parameter vector. The parameter optimization module is used to introduce an elastic network regularization term into the least squares objective function. The elastic network regularization term includes L1 regularization and L2 regularization based on low voltage and high voltage ride-through data. The weight ratio of L1 regularization and L2 regularization is dynamically adjusted according to the voltage ride-through conditions. The module is iteratively optimized using the near-end gradient descent method to output the final control parameter vector.

9. The apparatus as claimed in claim 8, characterized in that, The parameter optimization module includes a soft thresholding unit for processing L1 regularization and a spherical projection unit for processing L2 regularization.

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