Network construction converter control parameter optimization method for improving small interference stability

Abstract

The invention relates to the technical field of power electronic equipment and converter control, in particular to a network construction converter control parameter optimization method for improving small interference stability, which comprises the following steps: firstly, establishing a power system linearization model considering virtual inertia, and associating a state space matrix with control parameters; and deriving a quantitative relationship between the parameter sensitivity and the characteristic value based on a matrix perturbation theory, constructing a linear programming model taking the minimum spectral intercept as a target, and determining an optimal perturbation direction by constraining the curvature of the characteristic value and the spectral intercept. And solving the optimal perturbation step length in the neighborhood by adopting a backtracking iterative algorithm, and correcting the control parameters by combining the direction and the step length. According to the method, through characteristic value sensitivity analysis and spectral intercept constraint, rapid optimal configuration of parameters of the network-forming converter is realized, the dynamic stability of the system is improved, and the method is suitable for parameter setting of a new energy grid-connected scene.

Description

Technical Field

[0001] The present invention relates to the technical field of power electronic equipment and converter control, and in particular to a method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability. Background Art

[0002] Grid-connected converters can actively provide inertia support and flexibly distribute damping, which can improve system stability and has been widely used. However, with the increasing incorporation of grid-connected units, the risk of medium- and low-frequency oscillations caused by the mismatch between their controller parameters and the system has become one of the main issues hindering the efficient use of renewable energy. Research has shown that the system's small-disturbance stability after grid-connected converters is closely related to the grid's virtual inertia and damping configuration, and improper parameter setting can increase the risk of medium- and low-frequency oscillations. Research on the optimal configuration of its virtual inertia and damping is of great significance for enhancing the safety and stability of renewable energy power systems, improving anti-disturbance capabilities, and ensuring the safe and stable operation of the power grid.

[0003] The existing technology proposes a power system stabilizer (PSS) based on damping injection at the renewable energy power station. By monitoring the port frequency of the renewable energy power station, it further constructs a compensation control for modulating the output power or voltage of the power station, thereby achieving equivalent damping to indirectly suppress the low-frequency oscillation of the system. In addition, relevant scholars have also attempted to quantify the impact of the parameters of the mesh-type damping controller on the small disturbance stability of the system through singular value decomposition. However, the oscillation improvement effect of the above-mentioned damping controller structure design methods mostly depends on specific examples and specific application scenarios, and cannot guarantee strong universal applicability. In addition, it involves improvements and changes at the specific controller structure level, which may be difficult to implement in engineering.

[0004] The core idea of ​​data-driven approaches is to use machine learning (ML) or reinforcement learning (RL) techniques to learn from a large amount of measured or simulated power system operating data. Through data fitting or cross-training, these approaches dynamically or adaptively generate optimized control parameter configurations for grid-connected converters that enhance small-disturbance stability under predicted operating conditions. In recent years, researchers have attempted to apply various methods, such as neural networks (NNs) and improved Bang-Bang control, to adaptive adjustment of the virtual inertia of grid-connected converters using machine learning or reinforcement learning techniques. These methods use data-driven tools to search for feasible solutions for grid-connected parameter configurations that enhance small-disturbance stability within a preset frequency safety constraint. However, while these data-driven approaches have achieved some progress in improving small-disturbance stability through adaptive adjustment mechanisms, they rely heavily on the quantity, representativeness, and accuracy of sample data. Furthermore, these approaches lack the ability to provide reliable insights into the physical evolution of VSG virtual inertia and damping during dynamic response.

[0005] The above-mentioned defects indicate that there is still a lack of efficient and mature collaborative optimization methods for multi-mechanism grid-type inertia / damping systems. It is worth noting that the perturbation method has attracted widespread attention in the field of system parameter design and dynamic characteristics analysis due to its advantages of fast speed and strong universality in sensitivity solution and eigenvalue estimation. However, existing research on power system stability analysis based on the perturbation method only stays at the approximate solution of system eigenvalues ​​within the local perturbation range and the relationship between grid strength and system small disturbance stability. It has not yet solved the problem of insufficient system stability margin caused by improper setting of grid-type parameters in high-proportion renewable energy power systems. In view of the limitations of the above-mentioned research status, there is an urgent need to develop a grid-type converter parameter configuration system optimization method with both high accuracy and wide applicability to meet the severe challenge of the surge in medium and low-frequency oscillation risks in current high-proportion renewable energy power systems under variable scenarios. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method for optimizing the control parameters of a grid-connected converter to improve the stability of small disturbances in response to the above-mentioned deficiencies in the prior art, so as to solve the technical problem that it is difficult to establish a quantitative analytical relationship between the grid-connected control parameters and the system's small disturbance characteristic values.

[0007] The purpose of the present invention is achieved by the following technical solutions: In a first aspect, the present invention provides a method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability, comprising: Constructing a linearized model of a power system based on a grid-type converter to obtain a state-space matrix; associating the state-space matrix with control parameters of the grid-type converter; the control parameters of the grid-type converter including an inertia time constant and a damping coefficient of an active / reactive outer loop control; Based on the matrix perturbation theory, the sensitivity quantitative relationship between the control parameters and eigenvalues ​​of the grid-type converter is established; Using a linear constraint method to constrain the eigenvalue curvature and the system spectral intercept, a linear programming model is constructed with the optimization objective of minimizing the power system spectral intercept after the subsequence perturbation, and the optimal perturbation direction is obtained by solving the linear programming model; The optimal perturbation step size in the neighborhood of the optimization sequence is solved according to the backtracking iterative algorithm; The optimal perturbation direction is combined with the optimal perturbation step size to modify the network control parameters in the linearized model, thereby obtaining the optimal network control parameter configuration within the neighborhood of the current optimization sequence.

[0008] As a further improvement of the present invention, a quantitative relationship between the control parameter sensitivity and the eigenvalue of the grid-type converter is established according to the matrix perturbation theory, specifically including: Analyzing the eigenvalues ​​and spectral intercepts of the power system according to Lyapunov's first method; Obtaining a linearized model of a power system based on a grid-connected converter, and obtaining a state space matrix corresponding to a small signal of the power system according to the linearized model; According to the matrix perturbation theory, the state space matrix is ​​Taylor expanded according to the control parameters to obtain the first-order sensitivity and second-order sensitivity of the real part of the eigenvalue with respect to the control parameters. According to the first-order sensitivity and the second-order sensitivity, an analytical expression for estimating the second-order expansion of the real component of the eigenvalue with respect to the perturbation quantity is obtained.

[0009] As a further improvement of the present invention, the linear programming model is:

[0010] Where, is the perturbation spectral intercept during the k-th optimization; is the i-th eigenvalue, is the spectral intercept after perturbation during the k-1th optimization, is the upper bound of the safety constraint on the real part of the eigenvalue.

[0011] As a further improvement of the present invention, a linear constraint method is used to constrain the eigenvalue curvature and the system spectral intercept, specifically including: The curvature change constraint of the real part of the eigenvalue is:

[0012] The first-order variation constraint of the real part of the eigenvalue is:

[0013] Where, is the projection in the perturbation direction, is a column vector, is the first-order variation partial derivative tensor, is the perturbation direction, is the spectral intercept, is the independent variable.

[0014] As a further improvement of the present invention, the optimal perturbation step size in the neighborhood of the optimization sequence is solved according to the backtracking iterative algorithm, specifically including: Solve the linear programming problem to find the best perturbation direction to determine the local optimal optimization direction of the control parameters; According to the optimization direction and initial step size of the control parameters, the change of the objective function after perturbation is obtained; When the change in the objective function after perturbation satisfies the first condition, the backtracking search algorithm is used to accept the step size and directly enter the next optimization iteration; When the first condition cannot be met, the backtracking search mechanism is triggered to adjust the perturbation step size until the optimization iteration stop condition is reached and the optimal perturbation step size is obtained.

[0015] As a further improvement of the present invention, the change in the objective function after perturbation specifically includes:

[0016] Where, is the rate of change of the objective function after perturbation, is the perturbation quantity, is the state space matrix, is the initial value of the control parameter, is the optimal perturbation direction.

[0017] As a further improvement of the present invention, the calculation expression for perturbation step length adjustment is:

[0018] Where, is the adjusted perturbation step size, is the attenuation factor, is the initial perturbation step size.

[0019] In a second aspect, the present invention provides a system for optimizing control parameters of a grid-connected converter for improving small-disturbance stability, which is used to implement the above-mentioned method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability, including: A power system linear conversion module constructs a linearized model of the power system based on the grid-type converter to obtain a state-space matrix; the state-space matrix is ​​associated with the control parameters of the grid-type converter; the control parameters of the grid-type converter include the inertia time constant and damping coefficient of the active / reactive outer loop control; Data quantization module, which establishes the sensitivity quantization relationship between the control parameters of the grid-type converter and the real part of the eigenvalue based on the matrix perturbation theory; The perturbation direction solving module uses a linear constraint method to constrain the eigenvalue curvature and the system spectral intercept, constructs a linear programming model with the optimization goal of minimizing the power system spectral intercept after the subsequence perturbation, and solves the linear programming model to obtain the optimal perturbation direction; The intake step size solution module solves the optimal perturbation step size in the neighborhood of the optimization sequence based on the backtracking iterative algorithm; The control parameter optimization module combines the optimal perturbation direction with the optimal perturbation step size, modifies the network control parameters in the linearized model, and obtains the optimal network control parameter configuration in the neighborhood of the current optimization sequence.

[0020] In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute the above-mentioned method for optimizing the control parameters of a grid-connected converter to improve small-disturbance stability.

[0021] In a fourth aspect, the present invention provides a computing device, comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for executing the above-mentioned method for optimizing the control parameters of the grid-connected converter to improve small-disturbance stability.

[0022] The beneficial effect of the present invention is that the present invention provides a method for optimizing the control parameters of a grid-connected converter to improve small-disturbance stability. First, the dynamic characteristics of a complex power system are simplified through linearization processing. Then, the matrix perturbation theory is used to clarify the impact of changes in control parameters on the system eigenvalues. Then, a linear optimization model is used to find the optimal perturbation direction and step size that can minimize the system spectral intercept, thereby ensuring that the power system can remain stable after experiencing local parameter changes. Finally, the actual grid-connected control parameters are adjusted in combination with the optimal perturbation direction and step size to obtain the optimal grid-connected control parameter configuration, ensuring the efficient and stable operation of the power system. The present invention can not only provide a new quantitative research idea for the analysis of the multi-machine interaction stability mechanism of a power system with a high proportion of new energy, but also form a systematic guidance method for improving the small-disturbance stability margin of a power system with a high proportion of new energy through the optimization configuration of grid-connected converter parameters, providing important theoretical support for clarifying the stable grid connection requirements of heterogeneous and heterogeneous power electronic equipment and the future transformation planning of new energy sites. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 This is a control structure diagram of the VSG type grid-type converter provided by the present invention.

[0025] Figure 2 This is a control structure diagram of the PQ control type grid-following converter provided by the present invention.

[0026] Figure 3 This is a flow chart of the control parameter optimization method for a grid-type converter provided by the present invention.

[0027] Figure 4 This is the IEEE 14 high-proportion new energy topology diagram provided by the present invention.

[0028] Figure 5 It is a structural schematic diagram of the electronic equipment provided by the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose and technical solution of the present invention clearer and easier to understand, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0030] The technical solutions of the present invention will be described clearly and completely below with reference to the accompanying drawings and specific embodiments. The described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0031] Example 1 like Figures 1 to 3 As shown, this embodiment provides a method for optimizing control parameters of a grid-connected converter to improve small-disturbance stability. Specific implementation methods are as follows.

[0032] First, a linearized model of the power system based on a grid-type converter is constructed to obtain a state-space matrix; this matrix is ​​then associated with the control parameters of the grid-type converter. The control parameters of the grid-type converter include the inertia time constant and damping coefficient of the active / reactive outer loop control. By introducing matrix perturbation theory, a Taylor expansion of the sensitivity of the state-space matrix with respect to the control parameters and the system's small-disturbance eigenvalues ​​is established, theoretically obtaining a quantitative analytical relationship between the real component of the eigenvalue and the grid-type virtual inertia / damping. By retaining the first and second order terms of the quantitative analytical expression, a second-order prediction of the real component of the system eigenvalue with respect to the parameter perturbation under the grid-type virtual inertia / damping perturbation can be achieved.

[0033] Based on the matrix perturbation theory, the sensitivity quantitative relationship between the control parameters and the real part of the eigenvalue of the grid-type converter is established.

[0034] The linear constraint method is used to constrain the eigenvalue curvature and the system spectral intercept, and a linear programming model is constructed with the optimization goal of minimizing the power system spectral intercept after the subsequence perturbation. The optimal perturbation direction is obtained by solving the linear programming model.

[0035] The optimal perturbation step size in the neighborhood of the optimization sequence is solved based on the backtracking iterative algorithm.

[0036] The optimal perturbation direction and the optimal perturbation step size are combined to modify the network control parameters in the linearized model and obtain the optimal network control parameter configuration within the neighborhood of the current optimization sequence.

[0037] In this embodiment, the focus is on the low and medium frequency bands of new energy systems under the electromechanical transient time scale ( Hz) small disturbance stability problem, according to the time scale separation characteristics of each control link of the system, for the VSG type network, the voltage and current inner loop control link can be ignored, and only the virtual synchronous dynamic process (such as Figure 1 Similarly, for the grid-following converter, its current inner loop is approximated as ideal control, and the main focus is on the dynamic characteristics of its phase-locked loop and power outer loop (such as Figure 2As shown in Figure 2 ). Furthermore, given that the network time constant is relatively short relative to the time scale of the control parameters of each synchronous generator, a quasi-steady-state node admittance matrix of a short-distance transmission line is used to characterize the network dynamics. All loads in the test case system adopt a constant impedance model.

[0038] All constant impedance loads are incorporated into the network admittance matrix in the form of equivalent admittance, and the differential algebraic equations of the system linearization model are written in matrix form. An analytical relationship is found that can be used to quantify and estimate the network virtual inertia, damping parameters and system spectral intercept. Finally, the present invention builds the above-mentioned transient model in the MATLAB Simulink platform and performs automatic linearization to generate the state space matrix corresponding to the small signal model of the corresponding system. It also combines pattern analysis with the estimated analytical-sequential optimization algorithm developed by the present invention to achieve optimal configuration of the multi-mechanism network virtual inertia / damping.

[0039] According to Lyapunov's first method, the basic information of the stability criterion of the entire system is obtained by obtaining any characteristic value of the power system in the following way.

[0040] For any eigenvalue of the whole system have: (1) (2) in is the real part of the rightmost eigenvalue of the system (spectral intercept), which reflects the stability margin of the system oscillation mode. According to the Lyapunov first method stability criterion, we know that: When , the system is asymptotically stable at the equilibrium point, and The larger the absolute value, the larger the system's small disturbance stability margin. According to equations (1) and (2), we can add linear constraints to the real part of the perturbed eigenvalue to ensure that the system's small disturbance stability has a certain safety stability margin during the optimization process. In addition, considering that the real part of each eigenvalue of the system may have different response trends to perturbations, in order to ensure that the system as a whole presents an optimization effect after perturbation, all eigenvalues ​​of the system must be better than the system spectrum intercept before perturbation. Therefore, the present invention minimizes the system spectrum intercept after the subsequence perturbation. As the optimization target, the system spectrum intercept before each iteration optimization is Safe upper bound for the real part of all eigenvalues .

[0041] The optimization objective function (i.e., linear programming model) in this embodiment is:

[0042]

[0043] (3) Where, is the perturbation spectral intercept during the k-th optimization; is the i-th eigenvalue, is the spectral intercept after perturbation during the k-1th optimization, is the upper bound of the safety constraint on the real part of the eigenvalue.

[0044] Among them, the eigenvalues ​​and spectral intercepts of the power system are analyzed according to Lyapunov's first method; a linearized model of the power system based on the grid-type converter is obtained, and the state space matrix corresponding to the small signal model of the power system is obtained according to the linearized model; based on the matrix perturbation theory, the state space matrix is ​​Taylor expanded according to the control parameters, and the first-order sensitivity and second-order sensitivity with respect to the control parameters are further derived, and the perturbation amount is obtained; based on the first-order sensitivity and second-order sensitivity, the second-order expansion estimation expression of the eigenvalue with respect to the perturbation amount is obtained.

[0045] Specifically, the matrix equation of the differential-algebraic equation group of the power system linearization model in this embodiment is: (4) in is a column vector consisting of all state variables, is a column vector of all algebraic variables, , , , is the block matrix of the Jacobian matrix. Generally, the system state space matrix can be simplified by the following formula : (5) According to the control parameters, the state space matrix is ​​Taylor expanded to obtain the state space matrix with respect to the control parameters. Expanded. Assume that the initial state space matrix before perturbation is , initial value of control parameters , impose a first-order perturbation on the control parameters back, , the state space matrix can be expanded as: (6) (7) in, is the parameter perturbation column vector The k-th dimension element of .

[0046] When considering the meshed virtual inertia / damping as a control parameter, the system state space matrix linearly depends on the inverse of the meshed virtual inertia coefficient , Or virtual damping coefficient , This means that when the inverse of the virtual inertia coefficient or the virtual damping coefficient of the network is perturbed, the state space matrix is ​​related to the control parameter The partial derivative tensor of is determined, and there is only a first-order partial derivative tensor, that is: (8) Using formula (8) combined with matrix perturbation theory, the state space matrix is ​​derived about the structure The first-order partial derivative tensor obtains the eigenvalue with respect to the linear perturbation direction of the control parameter First-order sensitivity and second-order sensitivity . Then, the simplification can be obtained to obtain the eigenvalues ​​with respect to the actual perturbation The estimated analytical expression of the second-order expansion of : (9) The estimated eigenvalue analytical formula (9) takes the real part and substitutes it into the system spectrum intercept optimization model (3), and the spectrum intercept of the whole system with respect to the inverse of the virtual inertia of the network or the virtual damping perturbation direction can be established. and perturbation step size Optimization model:

[0047]

[0048]

[0049] (10) in Indicates the The optimized system eigenvalues Furthermore, in the analysis of the eigenvalue perturbation direction, in order to facilitate analytical derivation, it is assumed that the control parameter perturbation step size is , then the second-order estimated analytical expression of the eigenvalue is: (11) Among them, about The first-order term Determine the dominant change trend of the eigenvalue change, the second-order term This describes the curvature effect caused by parameter perturbations, which can subsequently be used to quantify the nonlinear characteristics of eigenvalue changes. However, the optimization problem constructed based on this second-order predictor falls into the realm of second-order cone programming (SOCP), and the associated computational complexity grows superlinearly with the parameter dimension. To overcome the computational bottleneck in high-dimensional parameter spaces, we add constraints on the nonlinear variation of the predicted eigenvalues, reducing the analytical expression for the second-order perturbation predictor of the eigenvalues ​​to a linear form, thereby modeling the step-by-step optimization problem as a linear program (LP).

[0050] Consider the real part of the estimated eigenvalue: (12) Second-order terms is a quadratic structure of optimization parameters, and All elements are real numbers, which can be corrected by introducing , ensuring that the estimated eigenvalue quadratic term correlation matrix can be similarly diagonalized. In this case, the quadratic term can be rewritten as: (13) Further, due to is a symmetric matrix, which can be transformed into a similarity transformation form about a diagonal matrix: (14) in is an orthogonal matrix, for The diagonal matrix of all eigenvalues ​​of . At this point, the parameter perturbation direction can be Projection to In the feature space of , then the quadratic term of the real part of the estimated eigenvalue can be further expressed as: (15) In order to suppress the nonlinear change trend of the real part of the eigenvalue and ensure the accuracy of the linear quantitative estimation, it is necessary to limit the projection The projection amplitude in the feature space is .

[0051] Under these conditions, the change in the quadratic eigenvalue term caused by parameter perturbation is close to zero, and the change in the eigenvalue is completely dominated by the change in the first-order term (linear). Based on this, a linear programming model for the real part of the system eigenvalue with respect to the control parameter perturbation can be constructed.

[0052] The model is based on the perturbation direction is the decision variable, and the goal is to minimize the real part of the rightmost eigenvalue (spectral intercept) , and satisfy the following constraints: The curvature change constraint of the real part of the eigenvalue is: (16) The first-order variation constraint of the real part of the eigenvalue is: (17) Where, is the projection in the perturbation direction, is a column vector, is the first-order variation partial derivative tensor, is the perturbation direction, is the spectral intercept, is the independent variable. Each component element of is a very small constant. The curvature change constraint in this embodiment is to constrain the projection of the control parameter perturbation in the direction of each eigenvalue of the constructed Hessian matrix (ie, the second-order response coefficient matrix).

[0053] Finally, by integrating the above constraints into a linear programming model, the optimal perturbation direction of the sub-optimization problem can be obtained with the help of the solver, thereby providing optimization guidance for the configuration of network parameter tuning.

[0054] After obtaining the optimal perturbation direction, the choice of the actual step size of the parameter perturbation will directly determine the convergence speed and numerical stability of the sequence optimization algorithm, and indirectly determine the optimization effect of the algorithm. If a fixed step size strategy is adopted, the real part of the eigenvalue may oscillate due to nonlinear effects, or even iterative divergence may occur. To this end, the present invention adopts a backtracking search strategy to improve the stability and convergence efficiency of the network parameter sequence optimization algorithm by adjusting the dynamic perturbation step size. Solve the optimal perturbation step size in the neighborhood of the optimization sequence according to the backtracking iterative algorithm, specifically including: when the change in the objective function after the perturbation meets the first condition, the backtracking search algorithm is used to accept the step size and directly enter the next optimization iteration; when the first condition cannot be met, the backtracking search mechanism is triggered to adjust the perturbation step size until the optimization iteration stop condition is reached to obtain the optimal perturbation step size. The first condition is .

[0055] Specifically, if the optimal perturbation direction determined by the LP solver is , initial step length , then the change of the objective function after parameter perturbation is: (18) like , the backtracking search algorithm accepts the step size and directly enters the next optimization iteration; otherwise, the backtracking search mechanism is triggered and the perturbation step size is adjusted according to the exponential decay rule: (19) Where, is the adjusted perturbation step size, is the attenuation factor, is the initial perturbation step size. The attenuation factor defaults to 0.5 and can be dynamically adjusted based on the rate of improvement of the objective function. If the maximum number of backtracking searches exceeds the upper limit and still fails to achieve the objective function optimization result, the backtracking search is automatically exited to avoid invalid searches and improve the efficiency of the optimization algorithm. Furthermore, during the backtracking search process, a convergence condition for the imaginary part of the eigenvalue is added to ensure that after the perturbation optimization of the system spectral intercept, the oscillation frequency of the mode corresponding to the dangerous eigenvalue does not exceed the safety threshold. At this point, the sequential optimization algorithm can determine the optimal perturbation of the network-type control parameters.

[0056] When the improvement of the power system spectrum intercept is less than the convergence criterion value , or the upper limit of optimization iterations is reached , combining the optimal perturbation direction obtained by LP linear programming and the optimal perturbation step size of backtracking search, and making targeted corrections to the network parameters, which is the optimal configuration scheme of the network parameters in the neighborhood of the current optimization subsequence.

[0057] This embodiment quantitatively estimates the real part of the eigenvalue through matrix perturbation theory, and forms a sequence optimization model to minimize the system spectral intercept to improve the stability margin of the power system, thereby ensuring that the system can maintain a high anti-disturbance capability under different control parameter configurations. This method effectively solves the problems of low inertia and weak damping that are common in high-proportion new energy systems, especially in the case of frequent fluctuations in new energy power systems, and significantly enhances the dynamic stability and anti-interference capability of the system. In traditional control methods, the calculation of eigenvalue sensitivity is often complex and difficult to solve, while this embodiment uses a second-order estimated analytical expression to simplify this process, breaking through the calculation bottleneck in traditional methods, and providing an efficient and accurate parameter optimization method, which significantly reduces calculation time and resource consumption.

[0058] Example 2 This embodiment further explains the virtual inertia optimization method for the grid-type converter provided in Example 1 in conjunction with an application example.

[0059] In order to verify the effectiveness of the algorithm proposed in the embodiment, the present invention provides the following Figure 4 The topology of the IEEE 14-node high-proportion renewable energy system after renewable energy transformation is shown in the figure. Node 1 is connected to a second-order synchronous generator, nodes 2 and 6 are connected to a VSG-type grid-forming converter, and nodes 3 and 8 are connected to a PQ-controlled grid-following converter. All loads in the system adopt a constant impedance model.

[0060] The optimization algorithm is used to optimize the virtual inertia / damping of the grid converter unit under the same initial configuration, and the parameter configurations before and after the optimization of 5 groups of experiments are displayed, including the inverse of the virtual inertia of the grid. and network virtual damping At the same time, the corresponding system spectrum intercepts after 5 groups of experimental optimization were counted. Improved performance and the highest oscillation frequency among all oscillation modes The relevant data are shown in Tables 1 to 3: Table 1: Network parameter configuration scheme before optimization

[0061] Table 2: Recommended network configuration after algorithm optimization

[0062] Table 3: Statistical results of key stability indicators after optimization

[0063] Table 2 shows the power flow distribution, system topology and operation mode data from the MATPOWER format IEEE 14 standard example, the system benchmark capacity =100MVA, reference voltage =220kV, base frequency =50Hz.

[0064] The key information from the optimized system eigenvalues ​​in Table 3 indicates that the algorithm significantly improves the overall small-disturbance stability of the system. In particular, the sequential optimization algorithm developed in this paper effectively adjusts parameter perturbations to improve the system's spectral intercept. This optimization process not only increases the system's stability margin but also ensures that the oscillation frequencies of each mode after spectral intercept optimization remain within a reasonable range, avoiding excessive oscillation frequencies. This further enhances the practicality and feasibility of the algorithm-recommended parameter configuration in engineering applications. However, under each of the optimized configurations shown in Table 2 (experimental groups 1-5), the system spectral intercept improved to -0.5025. Further analysis through participating factor analysis revealed that this oscillation mode is closely related to the parameter configuration of the grid-following converter. The analysis results indicate that the oscillation risk of the optimized example system is primarily driven by improper tuning of the grid-following phase-locked loop parameters, rather than improper configuration of the grid-connecting control parameters.

[0065] In summary, the optimization algorithm of this invention has the potential to fully exploit the optimal configuration of grid-connected converter parameters, providing more reliable stability assurance for power systems. Especially in the complex environment of renewable energy grid integration, this algorithm can effectively guide how to optimize grid-connected parameter configuration to meet the requirements of stable grid integration, thereby further improving the system's stability, anti-disturbance capabilities, and overall control capabilities.

[0066] Example 3 This embodiment provides a system for optimizing control parameters of a grid-connected converter for improving small-disturbance stability, specifically including the following modules: A power system linear conversion module constructs a linearized model of the power system based on the grid-type converter to obtain a state-space matrix; the state-space matrix is ​​associated with the control parameters of the grid-type converter; the control parameters of the grid-type converter include the inertia time constant and damping coefficient of the active / reactive outer loop control; Data quantization module, which establishes the quantitative relationship between the control parameter sensitivity and eigenvalue of the grid-type converter based on matrix perturbation theory; The perturbation direction solving module uses a linear constraint method to constrain the eigenvalue curvature and the system spectral intercept, constructs a linear programming model with the optimization goal of minimizing the power system spectral intercept after the subsequence perturbation, and solves the linear programming model to obtain the optimal perturbation direction; Substitute it into the step size solution module and solve the optimal perturbation step size in the neighborhood of the optimization sequence according to the backtracking iterative algorithm; The control parameter optimization module combines the optimal perturbation direction with the optimal perturbation step size, modifies the network control parameters in the linearized model, and obtains the optimal network control parameter configuration in the neighborhood of the current optimization sequence.

[0067] The implementation steps of each module have been described in detail in Example 1 and will not be repeated here.

[0068] Example 4 In another embodiment of the present invention, a computer-readable storage medium is provided as a storage component within a terminal device, whose primary function is to store programs and data. It should be noted that the computer-readable storage medium herein encompasses not only the terminal device's built-in storage component but also any supported expansion storage components. Essentially, it is a tangible medium capable of containing or storing programs that can be accessed by, or run in conjunction with, an instruction execution system, device, or component.

[0069] The storage medium provides a storage area for the terminal's operating system and stores one or more instructions suitable for the processor to load and execute. These instructions can constitute one or more computer programs containing program codes.

[0070] In particular, examples (a non-exclusive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable magnetic disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any reasonable combination of the foregoing.

[0071] The storage medium may also include a data signal transmitted as part of a baseband portion or carrier wave, which carries readable program code. Such a transmitted data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any reasonable combination of the two. In addition, computer-readable storage media may also refer to other readable media other than traditional readable storage media, which are capable of sending, transmitting, or transmitting programs for use by or in conjunction with an instruction execution system, device, or component. The program code on the storage medium may be transmitted via any suitable medium, including but not limited to wireless, wired, optical cable, or any reasonable combination thereof.

[0072] The program code used to implement the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as "C." The program code can be executed entirely on the user's computing device, partially on the user's device as a standalone software package, partially distributed across the user's device and a remote computing device, or entirely on a remote computing or device server. When a remote computing device is involved, the device can be connected to the user's computing device via any type of network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computing device via the Internet through an Internet service provider (ISP).

[0073] The processor can load and execute one or more instructions stored in a computer-readable storage medium to implement corresponding steps of the method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability described in Example 1.

[0074] Example 5 Reference Figure 5 Another embodiment of the present invention provides a terminal device, which is specifically a computer device 60. This computer device 60 is mainly composed of three parts, namely a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and capable of running on the processor 61. Among them, the responsibility of the processor 61 is to execute the computer program to implement the method for optimizing the control parameters of the network converter for improving the stability of small interference as described in Example 1. The memory 62 is used to store computer programs and other programs and data required for the operation of the device. When the computer program 63 runs on the processor 61, it can implement the method for optimizing the control parameters of the network converter for improving the stability of small interference. In order to avoid repetition of content, the relevant details will not be described in detail here.

[0075] The computer device 60 has many different forms. It can be a desktop computer, a notebook computer, a handheld computer, or a computing device such as a cloud server.

[0076] The processor 60 may be a central processing unit, or other types of general-purpose processors, central processing units, graphics processing units, digital signal processors, application-specific integrated circuits, field programmable gate arrays, or other programmable logic devices, discrete gate or transistor logic devices, quantum computing-based data processing logic, discrete hardware components, etc. The general-purpose processor referred to herein refers to a microprocessor or any conventional processor.

[0077] Memory 62 can be an internal storage unit of computer device 60, such as its hard drive or memory, or an external storage device, such as a plug-in hard drive, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard. Memory 62 not only stores computer programs but also other programs and data required for device operation, and temporarily stores data that has been or is about to be output.

[0078] In the various embodiments provided herein, references to memory, databases, or other media will encompass at least one of non-volatile memory and volatile memory. There are many types of non-volatile memory, including read-only memory, magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory, magnetic random access memory, ferroelectric memory, phase change memory, graphene memory, and the like. Volatile memory may include random access memory (RAM) or external cache memory. It should be noted that RAM has various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

Claims

1. A method for optimizing control parameters of a grid-connected converter to improve small-disturbance stability, characterized in that: include: Constructing a linearized model of a power system based on a grid-type converter to obtain a state space matrix; associating the state space matrix with control parameters of the grid-type converter; The control parameters of the grid-type converter include the active / reactive outer loop control inertia time constant and damping coefficient; The quantitative relationship between the control parameters and eigenvalue sensitivity of the grid-type converter is established based on the matrix perturbation theory. Using a linear constraint method to constrain the eigenvalue curvature and the system spectral intercept, a linear programming model is constructed with the optimization objective of minimizing the power system spectral intercept after the subsequence perturbation, and the optimal perturbation direction is obtained by solving the linear programming model; The optimal perturbation step size in the neighborhood of the optimization sequence is solved according to the backtracking iterative algorithm; The optimal perturbation direction is combined with the optimal perturbation step size to modify the network control parameters in the linearized model, thereby obtaining the optimal network control parameter configuration within the neighborhood of the current optimization sequence.

2. The method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability according to claim 1, characterized in that: Based on the matrix perturbation theory, the quantitative relationship between the control parameter sensitivity and eigenvalue of the grid-type converter is established, including: Analyzing the eigenvalues ​​and spectral intercepts of the power system according to Lyapunov's first method; Obtaining a linearized model of a power system based on a grid-connected converter, and obtaining a state space matrix corresponding to a small signal of the power system according to the linearized model; According to the matrix perturbation theory, the state space matrix is ​​Taylor expanded according to the control parameters to obtain the first-order sensitivity and second-order sensitivity of the real part of the eigenvalue with respect to the control parameters. According to the first-order sensitivity and the second-order sensitivity, an analytical expression for estimating the second-order expansion of the real component of the eigenvalue with respect to the perturbation quantity is obtained.

3. The method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability according to claim 1, characterized in that: The linear programming model is: Where, is the perturbation spectral intercept during the k-th optimization; is the i-th eigenvalue, is the spectral intercept after perturbation during the k-1th optimization, is the upper bound of the safety constraint on the real part of the eigenvalue.

4. The method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability according to claim 3, characterized in that: The linear constraint method is used to constrain the eigenvalue curvature and the system spectral intercept, including: The curvature change constraint of the real part of the eigenvalue is: The first-order variation constraint of the real part of the eigenvalue is: Where, is the projection in the perturbation direction, is a column vector, is the first-order variation partial derivative tensor, is the perturbation direction, is the spectral intercept, is the independent variable.

5. The method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability according to claim 1, characterized in that: The optimal perturbation step size in the neighborhood of the optimization sequence is solved according to the backtracking iterative algorithm, specifically including: Solve the linear programming problem to obtain the local optimal perturbation direction of the control parameters; According to the optimization direction and initial step size of the control parameters, the change of the objective function after perturbation is obtained; When the change in the objective function after perturbation satisfies the first condition, the backtracking search algorithm is used to accept the step size and directly enter the next optimization iteration; When the first condition cannot be met, the backtracking search mechanism is triggered to adjust the perturbation step size until the optimization iteration stop condition is reached and the optimal perturbation step size is obtained.

6. The method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability according to claim 5, characterized in that: The change in the objective function after perturbation, specifically including: Where, is the change of the objective function after perturbation, is the perturbation quantity, is the state space matrix, is the initial value of the control parameter, is the optimal perturbation direction.

7. The method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability according to claim 5, characterized in that: The calculation expression of perturbation step length adjustment is: Where, is the adjusted perturbation step size, is the attenuation factor, is the initial perturbation step size.

8. A system for optimizing control parameters of a grid-connected converter for improving small-disturbance stability, for implementing the method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability according to any one of claims 1 to 7, characterized in that: include: A power system linear conversion module constructs a linearized model of the power system based on the grid-type converter to obtain a state-space matrix; the state-space matrix is ​​associated with the control parameters of the grid-type converter; the control parameters of the grid-type converter include the inertia time constant and damping coefficient of the active / reactive outer loop control; Data quantization module, which establishes the quantitative relationship between the control parameter sensitivity and eigenvalue of the grid-type converter based on matrix perturbation theory; The perturbation direction solving module uses a linear constraint method to constrain the eigenvalue curvature and the system spectral intercept, constructs a linear programming model with the optimization goal of minimizing the power system spectral intercept after the subsequence perturbation, and solves the linear programming model to obtain the optimal perturbation direction; Substitute it into the step size solution module and solve the optimal perturbation step size in the neighborhood of the optimization sequence according to the backtracking iterative algorithm; The control parameter optimization module combines the optimal perturbation direction with the optimal perturbation step size, modifies the network control parameters in the linearized model, and obtains the optimal network control parameter configuration in the neighborhood of the current optimization sequence.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute the method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability according to any one of claims 1 to 7.

10. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for executing the method for optimizing control parameters of a grid-connected converter for improving small-disturbance stability as described in any one of claims 1 to 7.

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