Parameter Estimation Method and Related Equipment for an LCL-Type Grid-Connected Converter

Abstract

An embodiment of the present invention discloses a parameter estimation method and related devices for an LCL grid-connected converter. The method includes: constructing a mathematical model of the LCL grid-connected converter based on Kirchhoff's theorem and the Pade approximation transformation method, and obtaining the voltage and current models of the LCL grid-connected converter; constructing an auxiliary matrix based on the voltage and current signals, and obtaining parameter estimation error information based on the auxiliary matrix; constructing a parameter estimation error leakage term with a sliding mode form based on the parameter estimation error information, and determining a finite-time convergence adaptive rate according to the parameter estimation error leakage term to obtain the parameter estimation result of the LCL grid-connected converter. Aiming at the uncertainty problems of the parameters of the LCL filter of the grid-connected converter and the control delay, the resonance peak and the corresponding phase information of the LCL filter can be accurately estimated, and better effects are achieved in terms of the parameter convergence rate and the parameter convergence accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of grid-connected converter management, and particularly relates to a method for parameter estimation of an LCL-type grid-connected converter and related equipment. Background Art

[0002] During the construction of a new power system, more and more new energy sources are connected to the power grid. When new energy is grid-connected, grid-connected converters are mostly used, and high-frequency PWM modulation is adopted, resulting in a large number of high-order harmonic currents entering the power grid and affecting the grid-connected power quality and grid-connected stability of new energy power generation equipment. In addition, these high-order current harmonics will also interfere with the normal operation of electromagnetic sensitive equipment in the power grid. Therefore, it is necessary to select a suitable filter to filter the high-order harmonics output by the AC side of the grid-connected converter. Compared with the traditional L-type filter, the high-order LCL-type filter is widely used in grid-connected converter equipment due to its better suppression ability for high-frequency harmonics. Especially in high-power grid-connected scenarios, the LCL-type filter has a smaller volume and lower cost. However, the LCL-type filter is a third-order resonant circuit and has an inherent resonance problem. The resonance phenomenon caused by using the LCL-type filter can be improved by reasonably designing the parameters of the LCL-type filter. However, due to factors such as processing technology, manufacturing errors, and environmental changes, the LCL-type filter has the problem of parameter uncertainty. In practice, there is also inevitably control delay, which makes the actual parameter value deviate from the designed value. However, most current grid-connected converter controls adopt control strategies based on models and design parameters, resulting in the parameters of the LCL filter and control delay directly or indirectly affecting the control effect.

[0003] Accurately obtaining the parameter and control delay information of the LCL-type filter plays a very important role in improving the grid-connected performance and enhancing the grid-connected stability. Currently, the mainstream methods can be divided into two categories: parameter identification methods based on the frequency domain and parameter estimation methods based on models. The parameter identification method based on the frequency domain does not require an accurate mathematical model. Only by collecting the input excitation signal and the corresponding response signal can the information such as the parameters of the LCL-type filter be identified. However, this type of method requires injecting harmonic voltage or current excitation into the power grid through an additional hardware circuit, which increases the cost, and injecting harmonic excitation into the power grid will affect the power quality of the power grid and the grid-connected performance of the equipment. The identification method based on the model does not have the above problems. After determining the model structure, parameter estimation methods such as neural networks and extended Kalman filters can be used to obtain the model parameters. However, these parameter estimation methods have problems such as large computational complexity, difficulty in online real-time operation, slow convergence of parameter estimation, and difficulty in debugging. Summary of the Invention

[0004] In view of this, the present invention provides a method for estimating parameters of an LCL grid-connected converter and related devices, which is used to solve the problems of high cost and large calculation amount in obtaining the parameters of the LCL filter and the control delay information in the prior art.

[0005] To achieve one or part or all of the above purposes or other purposes, the present invention proposes a method for estimating parameters of an LCL grid-connected converter, including: constructing a mathematical model of the LCL grid-connected converter based on Kirchhoff's theorem and the Pade approximation transformation method, and obtaining the voltage and current models of the LCL grid-connected converter;

[0006] Constructing an auxiliary matrix based on the voltage and current signals, and obtaining parameter estimation error information based on the auxiliary matrix;

[0007] Constructing a parameter estimation error leakage term with a sliding mode form based on the parameter estimation error information, and determining a finite-time convergence adaptive rate according to the parameter estimation error leakage term to obtain the parameter estimation result of the LCL grid-connected converter.

[0008] Optionally, the step of constructing a mathematical model of the LCL grid-connected converter based on Kirchhoff's theorem and the Pade approximation transformation method includes:

[0009] Establishing a current-voltage equation of the LCL grid-connected converter including digital control delay based on Kirchhoff's theorem and the Pade approximation transformation method;

[0010] Converting the current-voltage equation by separately analyzing the three phases of the LCL grid-connected converter and defining state variables to obtain the state space expression of the LCL grid-connected converter;

[0011] Simplifying the state space expression into the form of a regression matrix multiplied by an unknown parameter matrix to obtain the target mathematical model of the LCL grid-connected converter.

[0012] Optionally, the step of constructing an auxiliary matrix based on the voltage and current signals includes:

[0013] Performing a filtering operation on the voltage and current signals, and correcting the state variables based on the filtering parameters as:

[0014]

[0015] where the state variable and k is a filtering parameter;

[0016] Determining the functional relationship between the corrected state variable and the unknown parameter matrix based on the corrected state variable and the target mathematical model as:

[0017]

[0018] Construct the auxiliary matrix based on the function relationship and the corrected state variables.

[0019] Optionally, the step of constructing the auxiliary matrix based on the function relationship and the corrected state variables includes:

[0020] Construct a first auxiliary matrix P ∈ R based on the function relationship and the corrected state variables 4×4 , a second auxiliary matrix H ∈ R 4 and an auxiliary vector N ∈ R 4×1 , specifically:

[0021]

[0022]

[0023] where l is the forgetting factor, the first auxiliary matrix P ∈ R 4×4 and the auxiliary vector N ∈ R 4×1 are bounded, is the estimated value of the unknown parameter, is the parameter estimation error.

[0024] Optionally, the step of obtaining the parameter estimation error information based on the auxiliary matrix includes:

[0025] Convert the first auxiliary matrix P ∈ R 4×4 and the auxiliary vector N ∈ R 4×1 to:

[0026]

[0027] Based on the converted first auxiliary matrix P ∈ R 4×4 , the converted auxiliary vector N ∈ R 4×1 and the second auxiliary matrix H ∈ R 4 , obtain the parameter estimation error information through algebraic calculation.

[0028] Optionally, the step of constructing a parameter estimation error leakage term with a sliding mode form based on the parameter estimation error information and determining a finite-time convergence adaptive rate includes:

[0029] The finite-time convergence adaptive rate is:

[0030]

[0031] where Γ1 > 0, Γ1 ∈ R4×4 is the gain matrix.

[0032] Optionally, the step of obtaining the parameter estimation result of the LCL grid-connected converter includes:

[0033] Determine the parameter estimation result of the LCL grid-connected converter based on the finite-time convergence adaptive rate and the mathematical model of the LCL grid-connected converter.

[0034] In a second aspect, the present application provides a parameter estimation system for an LCL grid-connected converter, the system includes:

[0035] A model construction module, configured to construct a mathematical model of the LCL grid-connected converter based on Kirchhoff's theorem and the Pade approximation transformation method, and obtain the voltage and current models of the LCL grid-connected converter;

[0036] A solution module, configured to construct an auxiliary matrix based on the voltage and current signals, and obtain parameter estimation error information based on the auxiliary matrix;

[0037] An estimation module, configured to construct a parameter estimation error leakage term with a sliding mode form based on the parameter estimation error information, and determine a finite-time convergence adaptive rate according to the parameter estimation error leakage term, to obtain the parameter estimation result of the LCL grid-connected converter.

[0038] In a third aspect, the present application provides an electronic device, including: a processor, a memory, and a bus, the memory stores machine-readable instructions executable by the processor, when the electronic device runs, the processor communicates with the memory through the bus, and when the machine-readable instructions are executed by the processor, the steps of the parameter estimation method of the LCL grid-connected converter as described above are executed.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the steps of the parameter estimation method of the LCL grid-connected converter as described above are executed.

[0040] Implementing the embodiments of the present invention will have the following beneficial effects:

[0041] Construct a mathematical model of the LCL - type grid - connected converter by using Kirchhoff's theorem and the Pade approximation transformation method, and obtain the voltage - current model of the LCL - type grid - connected converter; construct an auxiliary matrix based on the voltage - current signals, and obtain parameter estimation error information based on the auxiliary matrix; construct a parameter estimation error leakage term with a sliding - mode form based on the parameter estimation error information, and determine a finite - time convergence adaptive rate according to the parameter estimation error leakage term to obtain the parameter prediction result of the LCL - type grid - connected converter. Aiming at the uncertainty problems of the parameters of the LCL - type filter of the grid - connected converter and the control delay, the resonant peak and the corresponding phase information of the LCL filter can be accurately estimated, and better effects are achieved in terms of the parameter convergence rate and the parameter convergence accuracy. Description of the Drawings

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following - described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0043] Wherein:

[0044] Figure 1 is a flowchart of a method for predicting the parameters of an LCL - type grid - connected converter provided by an embodiment of the present application;

[0045] Figure 2 is a schematic structural diagram of a system for predicting the parameters of an LCL - type grid - connected converter provided by an embodiment of the present application;

[0046] Figure 3 is a schematic structural diagram of a system for predicting the parameters of an LCL - type grid - connected converter provided by an embodiment of the present application;

[0047] Figure 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

[0048] Figure 5 is a schematic structural diagram of a storage medium provided by an embodiment of the present application;

[0049] Figure 6 is a comparison diagram of the parameter estimation results obtained by the method for predicting the parameters of an LCL - type grid - connected converter provided by an embodiment of the present application and the prior art;

[0050] Figure 7 is a comparison diagram of the amplitude - phase frequency characteristic identification effects obtained by the method for predicting the parameters of an LCL - type grid - connected converter provided by an embodiment of the present application and the prior art. Detailed Embodiments

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] As Figure 1 shown, the embodiment of the present application provides a parameter estimation method for an LCL grid-connected converter, including:

[0053] S101. Construct a mathematical model of the LCL grid-connected converter based on Kirchhoff's theorem and the Pade approximation transformation method, and obtain the voltage and current models of the LCL grid-connected converter;

[0054] S102. Construct an auxiliary matrix based on the voltage and current signals, and obtain parameter estimation error information based on the auxiliary matrix;

[0055] S103. Construct a parameter estimation error leakage term with a sliding mode form based on the parameter estimation error information, and determine a finite-time convergence adaptive rate according to the parameter estimation error leakage term to obtain the parameter estimation result of the LCL grid-connected converter.

[0056] By constructing a mathematical model of the LCL grid-connected converter based on Kirchhoff's theorem and the Pade approximation transformation method, and obtaining the voltage and current models of the LCL grid-connected converter; constructing an auxiliary matrix based on the voltage and current signals, and obtaining parameter estimation error information based on the auxiliary matrix; constructing a parameter estimation error leakage term with a sliding mode form based on the parameter estimation error information, and determining a finite-time convergence adaptive rate according to the parameter estimation error leakage term to obtain the parameter estimation result of the LCL grid-connected converter. For the uncertainty problem of the parameters of the LCL filter of the grid-connected converter and the control delay, the resonance peak and the corresponding phase information of the LCL filter can be accurately estimated, and better effects are achieved in terms of the parameter convergence rate and the parameter convergence accuracy.

[0057] In a possible implementation manner, the step of constructing a mathematical model of the LCL grid-connected converter based on Kirchhoff's theorem and the Pade approximation transformation method includes:

[0058] Establish a current-voltage equation of the LCL grid-connected converter including digital control delay based on Kirchhoff's theorem and the Pade approximation transformation method;

[0059] By separately analyzing the three phases of the LCL - type grid - connected converter and defining state variables, the current - voltage equation is transformed to obtain the state - space expression of the LCL - type grid - connected converter;

[0060] The state - space expression is simplified into the form of a regression matrix multiplied by an unknown parameter matrix to obtain the target mathematical model of the LCL - type grid - connected converter.

[0061] Exemplarily, based on Kirchhoff's theorem and Pade approximation transformation, the current - voltage equation of the LCL - type grid - connected converter with digital - control delay is established to online estimate L i 、C f 、L g and τ, and the current - voltage equation is as follows:

[0062]

[0063] For the convenience of analysis and representation, the three - phase ABC of the LCL - type grid - connected converter is separately analyzed and variables are defined and Then the state - space expression is:

[0064]

[0065] where A ∈ R 4×4 is the extended system matrix, C ∈ R 1×4 is the extended system output matrix, B ref ∈ R 4×1 is the extended system input matrix, B ga ∈ R4×1 is the matrix related to the grid - side voltage, ||δ|| ≤ ε δ , ε δ > 0 is the unknown bounded external disturbance. The matrices in the above formula are as follows:

[0066]

[0067] C = [0 0 1 0]

[0068] For the convenience of analysis, the system (2) is simplified into the form of a regression matrix multiplied by an unknown parameter matrix, as follows:

[0069]

[0070] where, Θ = [1 / L i , 1 / C f , 1 / L g , - 2 / (3τ)] T ∈ R 4×1 is the unknown parameter to be estimated, is the regression matrix.

[0071] In a possible implementation, the step of constructing the auxiliary matrix based on the voltage-current signal includes:

[0072] Perform a filtering operation on the voltage-current signal, and correct the state variable based on the filtering parameter as:

[0073]

[0074] where the state variable and k is the filtering parameter;

[0075] Determine the functional relationship between the corrected state variable and the unknown parameter matrix based on the corrected state variable and the target mathematical model as:

[0076]

[0077] Construct the auxiliary matrix based on the functional relationship and the corrected state variable.

[0078] Exemplarily, in order to avoid directly measuring and and to weaken the influence of high-frequency noise (such as measurement noise) on parameter estimation, a first-order low-pass filtering operation is performed on both sides of formula (4), and the filtered state variable x f , Φ f (x f , u f ) and δ f are defined as follows:

[0079]

[0080] k > 0 is the filtering parameter, where δ f is for analysis use.

[0081] Substitute formula (5) into formula (4), and we can get:

[0082]

[0083] In a possible implementation, the step of constructing the auxiliary matrix based on the functional relationship and the corrected state variable includes:

[0084] Construct a first auxiliary matrix P ∈ R 4×4 , a second auxiliary matrix H ∈ R 4 and an auxiliary vector N ∈ R 4×1 , specifically:

[0085]

[0086]

[0087] where \(l\) is the forgetting factor, the first auxiliary matrix \(P\in\mathbb{R}\) 4×4 and the auxiliary vector \(N\in\mathbb{R}\) 4×1 are bounded, is the estimated value of the unknown parameter, is the parameter estimation error.

[0088] In a possible implementation manner, the step of obtaining the parameter estimation error information based on the auxiliary matrix includes:

[0089] By solving the differential equation, the first auxiliary matrix \(P\in\mathbb{R}\) 4×4 and the auxiliary vector \(N\in\mathbb{R}\) 4×1 are converted to:

[0090]

[0091] Based on the converted first auxiliary matrix \(P\in\mathbb{R}\) 4×4 , the converted auxiliary vector \(N\in\mathbb{R}\) 4×1 and the second auxiliary matrix \(H\in\mathbb{R}\) 4 , the parameter estimation error information is obtained through algebraic calculation.

[0092] Exemplarily, in order to be able to extract the parameter estimation error information through simple algebraic calculation, the following auxiliary matrix \(P\in\mathbb{R}\) 4×4 and auxiliary vector \(N\in\mathbb{R}\) 4×1 are designed as follows:

[0093]

[0094] where the purpose of designing \(l\gt0\) is to ensure that the auxiliary matrices \(P\) and \(N\) are bounded. By solving the differential equation (7), we get:

[0095]

[0096] In order to extract the parameter estimation error, another auxiliary vector \(H\in\mathbb{R}\) 4 is further designed as follows:

[0097]

[0098] where is the estimated value of the unknown parameter, is the parameter estimation error.

[0099] In a possible implementation, the step of constructing a parameter estimation error leakage term in the form of a sliding mode based on the parameter estimation error information and determining a finite-time convergence adaptive rate according to the parameter estimation error leakage term includes:

[0100] The finite-time convergence adaptive rate is:

[0101]

[0102] where Γ1 > 0, Γ1 ∈ R 4×4 is the gain matrix.

[0103] Exemplarily, in order to enable the parameter estimation to converge quickly and stably, a finite-time convergence adaptive law for the sliding mode leakage term is designed as follows:

[0104]

[0105] where Γ1 > 0, Γ1 ∈ R 4×4 is the gain matrix.

[0106] In a possible implementation, the step of obtaining the parameter prediction result of the LCL-type grid-connected converter includes:

[0107] Determine the parameter prediction result of the LCL-type grid-connected converter based on the finite-time convergence adaptive rate and the mathematical model of the LCL-type grid-connected converter.

[0108] Exemplarily, using the adaptive law formula (10) can ensure that formula (2) satisfies the estimated parameter is bounded and the parameter estimation error can converge to a compact set near zero, that is satisfies uniformly ultimately bounded in finite time.

[0109] Exemplarily, the convergence rate of the parameter estimation error depends on the gain matrix Γ1. Generally, a large gain Γ1 can accelerate the parameter convergence speed. However, when the gain is too large, it will cause oscillations in the parameter estimation. The forgetting factor l in formula (8) is to weaken the influence of the initial conditions on the parameter estimation. The parameter should not be too large. The filtering parameter k in formula (5) is the bandwidth of the filter. It should be noted that when selecting the forgetting factor parameter and the filter parameter, a trade-off needs to be made between the parameter estimation convergence rate and the convergence robustness.

[0110] In a possible implementation, the proposed method is implemented and verified in the MATLAB environment. Table 1 shows the power parameters to be estimated and the relevant parameters used in the simulation. Among them, the DC power supply u dc is a constant; the sampling frequency is f s= 10 kHz; the switching frequency of the converter is half of the sampling frequency, i.e., f PWM = 5 kHz.

[0111] Table 1 Simulation system parameters

[0112]

[0113] In order to compare with the traditional gradient parameter estimation method, the same structure and parameters are selected for the current loop controller in the simulation, that is, the control input is consistent during the parameter estimation process.

[0114] The specific parameters used in the parameter estimation simulation based on finite-time convergence are: gain matrix Γ1 = 5diag(50, 5×10 6 , 5, 1), filtering coefficient k = 0.01, forgetting factor l = 0.2.

[0115] The specific parameters used in the traditional gradient-based parameter estimation simulation are: r2 = 5diag(400, 5×10 6 , 6, 1) and λ = 5diag(30, 5×10 3 , 50, 20).

[0116] Figure 6 The parameter estimation performance of the method described in the embodiment of the present application and the traditional gradient-based parameter estimation method is given, and the final estimation results are given in Table 2. From Figure 6 and Table 2, it can be seen that the method described in the embodiment of the present application has better effects in terms of parameter convergence rate and parameter convergence accuracy.

[0117] Table 2 Parameter setting values and estimation effects

[0118]

[0119] Figure 7 The amplitude-phase frequency characteristic diagrams of the estimated parameters obtained by the method described in the embodiment of the present application and the traditional parameter estimation algorithm corresponding to the simulation reference value model are given. From Figure 7 it can be seen that the method described in the embodiment of the present application can accurately estimate the resonance peak and the corresponding phase information of the LCL filter.

[0120] In a possible implementation manner, as Figure 2 shown, the present application provides a parameter prediction system for an LCL-type grid-connected converter, and the system includes:

[0121] A model construction module 201, configured to construct a mathematical model of the LCL-type grid-connected converter based on Kirchhoff's theorem and the Pade approximation transformation method, and obtain the voltage and current signals of the LCL-type grid-connected converter;

[0122] The solution module 202 is configured to construct an auxiliary matrix based on the voltage and current signals, and obtain parameter estimation error information based on the auxiliary matrix;

[0123] The prediction module 203 is configured to construct a parameter estimation error leakage term in the form of a sliding mode based on the parameter estimation error information, and determine a finite-time convergence adaptive rate according to the parameter estimation error leakage term, so as to obtain a parameter prediction result of the LCL-type grid-connected converter.

[0124] Exemplarily, as Figure 3 shown, for the parameter prediction system of the LCL-type grid-connected converter, u dc is the DC voltage of the grid-connected converter. The LCL filter is connected in series to the grid-connected system to filter out the high-frequency harmonics introduced by the switching action of the grid-connected converter. The current loop controller is used to control the current injected by the converter into the grid, and the digital control delay G delay and the external unknown disturbance d(t) are also taken into consideration. The parameter estimator uses the measured electrical signals and as inputs to estimate the electrical parameters L i , C f , L g and τ of the LCL-type grid-connected converter. Among them, is the output current of the converter, is the output voltage of the converter, is the capacitor voltage of the LCL filter, is the grid-side current, is the command voltage of the converter, is the grid-side voltage. L i is the converter-side inductor of the LCL filter, C f is the capacitor of the LCL filter, L g is the grid-side inductor of the LCL filter (including the leakage inductance on the transformer side), and τ is the control delay.

[0125] In a possible implementation manner, as Figure 4As shown in the figure, an embodiment of the present application provides an electronic device 300, including: a memory 310, a processor 320, and a computer program 311 stored on the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, the following steps are implemented: obtaining a user service request, where the service request includes service scenario information and task information; constructing a basic behavior graph knowledge base based on the service scenario information; matching a preset task execution process in a preset template library according to the scenario information and the task information; evaluating the matched preset task execution process according to the basic behavior graph knowledge base and a preset analysis and decision-making model to obtain a scheduling execution strategy, and completing the scheduling of the robot based on the scheduling execution strategy.

[0126] In a possible implementation manner, as Figure 5 As shown in the figure, an embodiment of the present application provides a computer-readable storage medium 400, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented: obtaining a user service request, where the service request includes service scenario information and task information; constructing a basic behavior graph knowledge base based on the service scenario information; matching a preset task execution process in a preset template library according to the scenario information and the task information; evaluating the matched preset task execution process according to the basic behavior graph knowledge base and a preset analysis and decision-making model to obtain a scheduling execution strategy, and completing the scheduling of the robot based on the scheduling execution strategy.

[0127] The computer storage medium of the embodiments of the present invention may be any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium may be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, apparatus, or device.

[0128] A computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0129] The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.

[0130] The computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The above programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0131] Those of ordinary skill in the art should understand that the above-mentioned modules or steps of the present invention can be implemented using a general-purpose computing device. They can be concentrated on a single computing device or distributed over a network composed of multiple computing devices. Optionally, they can be implemented using program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0132] Note that the above is only the preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described above. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments only. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

[0133] What is disclosed above is only the preferred embodiment of the present invention. Of course, the scope of the rights of the present invention cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A parameter estimation method for an LCL-type grid-connected converter, characterized in that, Including: Construct a mathematical model of the LCL grid-connected converter based on Kirchhoff's theorem and the Pade approximation transformation method, and obtain the voltage and current models of the LCL grid-connected converter; Construct an auxiliary matrix based on the voltage and current signals, and obtain parameter estimation error information based on the auxiliary matrix; Construct a parameter estimation error leakage term with a sliding mode form based on the parameter estimation error information, and determine a finite-time convergence adaptive rate according to the parameter estimation error leakage term to obtain the parameter prediction result of the LCL grid-connected converter; Among them, the steps of constructing the mathematical model of the LCL grid-connected converter based on Kirchhoff's theorem and the Pade approximation transformation method include: Establish a current-voltage equation of the LCL grid-connected converter including digital control delay based on Kirchhoff's theorem and the Pade approximation transformation method; Convert the current-voltage equation by separately analyzing the three phases of the LCL grid-connected converter and defining state variables to obtain the state space expression of the LCL grid-connected converter; Simplify the state space expression into the form of a regression matrix multiplied by an unknown parameter matrix to obtain the target mathematical model of the LCL grid-connected converter.

2. The parameter estimation method of the LCL-type grid-connected converter according to claim 1, characterized in that The steps of constructing the auxiliary matrix based on the voltage and current signals include: Perform a filtering operation on the voltage and current signals, and correct the state variables based on the filtering parameters as: ； Among them, the state variables include , is a filtering parameter, , and is the corrected state variable; Determine the functional relationship between the corrected state variables and the unknown parameter matrix based on the corrected state variables and the target mathematical model as: ； Construct the auxiliary matrix based on the functional relationship and the corrected state variables.

3. The parameter estimation method of the LCL-type grid-connected converter according to claim 2, characterized in that, The steps of constructing the auxiliary matrix based on the functional relationship and the corrected state variables include: Construct a first auxiliary matrix based on the functional relationship and the corrected state variables , a second auxiliary matrix and an auxiliary vector , specifically: wherein, is a forgetting factor, the first auxiliary matrix and the auxiliary vector are bounded, is an estimated value of an unknown parameter, is a parameter estimation error.

4. The parameter estimation method of the LCL-type grid-connected converter according to claim 3, wherein, The steps of obtaining parameter estimation error information based on the auxiliary matrix include: Convert the first auxiliary matrix and the auxiliary vector into: Based on the transformed first auxiliary matrix , the transformed auxiliary vector and the second auxiliary matrix , the parameter estimation error information is obtained through algebraic calculation.

5. The parameter estimation method of the LCL-type grid-connected converter according to claim 1, characterized in that, The steps of constructing a parameter estimation error leakage term with a sliding mode form based on the parameter estimation error information and determining a finite-time convergence adaptive rate according to the parameter estimation error leakage term include: The finite-time convergence adaptive rate is: Among them, , is the gain matrix, P is the first auxiliary matrix, and H is the second auxiliary matrix.

6. The parameter estimation method of the LCL-type grid-connected converter according to claim 5, characterized in that The steps of obtaining the parameter prediction result of the LCL grid-connected converter include: Determine the parameter prediction result of the LCL grid-connected converter based on the finite-time convergence adaptive rate and the mathematical model of the LCL grid-connected converter.

7. A parameter estimation system for an LCL-type grid-connected converter, characterized in that, The system includes: A model construction module for constructing a mathematical model of the LCL grid-connected converter based on Kirchhoff's theorem and the Pade approximation transformation method, and obtaining the voltage and current models of the LCL grid-connected converter; A solution module for constructing an auxiliary matrix based on the voltage and current signals, and obtaining parameter estimation error information based on the auxiliary matrix; A prediction module for constructing a parameter estimation error leakage term with a sliding mode form based on the parameter estimation error information, and determining a finite-time convergence adaptive rate according to the parameter estimation error leakage term to obtain the parameter prediction result of the LCL grid-connected converter; The model construction module is further configured to establish a current-voltage equation of an LCL-type grid-connected converter including digital control delay based on Kirchhoff's theorem and the Pade approximation transformation method; By separately analyzing the three phases of the LCL-type grid-connected converter and defining state variables, the current-voltage equation is transformed to obtain a state-space expression of the LCL-type grid-connected converter; The state-space expression is simplified into a form of a regression matrix multiplied by an unknown parameter matrix to obtain the target mathematical model of the LCL-type grid-connected converter.

8. An electronic device, characterized in that, Comprising: A processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the parameter estimation method of the LCL-type grid-connected converter according to any one of claims 1 to 6 are executed.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is run by the processor, the steps of the parameter estimation method of the LCL-type grid-connected converter according to any one of claims 1 to 6 are executed.

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