Self-tuning method and system for grid-connected adaptability of grid-forming type energy storage converter

Abstract

The embodiment of the invention provides a grid-connection adaptive self-tuning method and system for a grid-construction type energy storage converter, and the method comprises the steps: firstly, carrying out the online identification of key power grid resistance and inductance parameters, constructing a mathematical model capable of reflecting the characteristics of a current system, and then, carrying out the calculation of the grid-connection adaptive self-tuning of the grid-construction type energy storage converter through setting an ideal system dynamic response target. And reversely solving the optimal virtual inertia and damping coefficient value capable of realizing the target under the specific power grid condition. In this way, real-time and accurate online adaptive adjustment can be carried out on the control parameters of the network-forming type energy storage converter along with the change of the working condition of the power grid, so that the operation robustness of the converter in complex scenes such as strong and weak network switching is remarkably improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of parameter adaptive tuning technology, specifically to a grid-connected adaptive self-tuning method and system for grid-connected energy storage converters. Background Technology

[0002] As the proportion of new energy sources in the power system continues to rise, the dynamic characteristics of the power grid are becoming increasingly complex. Grid-connected energy storage converters play a crucial role in improving grid stability because they can actively provide voltage and frequency support to the grid and simulate the external characteristics of synchronous generators. However, the stable grid-connected operation of grid-connected converters is closely related to the setting of their control parameters, especially the virtual inertia and damping coefficient. The equivalent impedance of the power grid is not constant and changes dynamically with line switching, load fluctuations, and changes in new energy output, which poses a severe challenge to the grid-connected adaptability of the converter. If the control parameters are set to fixed values, optimal performance can only be achieved under specific grid conditions. Once the grid operating conditions change, the fixed parameters will be difficult to adapt, which may lead to a decline in system dynamic performance or even power oscillations, threatening the safety and stability of the system. Therefore, developing a grid-connected adaptive self-tuning scheme that enables adaptive adjustment of control parameters has become the key to the large-scale application of grid-connected energy storage technology.

[0003] Currently, conventional grid-connected converter control strategies typically employ fixed parameters tuned offline. The inherent flaw of this approach lies in the fundamental mismatch between the static nature of its control parameters and the dynamic nature of the external power grid environment. When significant changes occur in grid characteristics, such as a shift from a strong grid to a weak grid at the grid connection point, the grid impedance increases significantly. The fixed virtual inertia and damping coefficients optimized for strong grid conditions will no longer provide sufficient damping for the system, leading to a deterioration in the system's dynamic characteristics. This parameter mismatch directly weakens the system's ability to suppress disturbances, potentially triggering persistent low-frequency oscillations even under small power fluctuations, and in severe cases, causing system instability. Therefore, the inability of existing technologies to adaptively tune control parameters online in response to changes in grid impedance is a major technical bottleneck limiting the reliable operation of grid-connected energy storage converters in variable grid environments.

[0004] To overcome the above problems, an optimized self-tuning method for grid-connected adaptive grid-connected energy storage converters is needed. Summary of the Invention

[0005] The present invention aims to at least solve one of the technical problems existing in the prior art, and provides a grid-connected adaptive self-tuning method and system for grid-connected energy storage converters.

[0006] In a first aspect, embodiments of the present invention provide a grid-connected adaptive self-tuning method for a grid-connected energy storage converter, comprising: Obtain the grid resistance and grid inductance; A small-signal state-space model of the system is constructed based on the grid resistance and grid inductance to obtain the system state matrix and system input matrix; The target pole locations are determined based on the target damping ratio and the target natural oscillation angular frequency to obtain the target system poles; Inverse analysis of the system state matrix is ​​performed based on the target system poles to obtain the optimal virtual inertia and optimal damping coefficient. The optimal virtual inertia and optimal damping coefficient, as well as the currently effective virtual inertia and damping coefficient, are updated smoothly to obtain the updated virtual inertia and updated damping coefficient.

[0007] Secondly, embodiments of the present invention provide a grid-connected adaptive self-tuning system for a grid-connected energy storage converter, comprising: The data acquisition module is used to acquire the grid resistance and grid inductance; The system small-signal state-space model construction module is used to construct a system small-signal state-space model based on grid resistance and grid inductance to obtain the system state matrix and system input matrix. The target pole location determination module is used to determine the target pole location based on the target damping ratio and the target natural oscillation angular frequency to obtain the target system poles; The reverse analysis module is used to perform reverse analysis of the system state matrix based on the target system poles to obtain the optimal virtual inertia and optimal damping coefficient. The parameter smoothing update module is used to perform parameter smoothing updates on the optimal virtual inertia and optimal damping coefficient, as well as the currently effective virtual inertia and currently effective damping coefficient, to obtain updated virtual inertia and updated damping coefficient.

[0008] Compared with existing technologies, this invention provides a grid-connected adaptive self-tuning method and system for grid-connected energy storage converters. First, it identifies key grid resistance and inductance parameters online and constructs a mathematical model reflecting the current system characteristics based on these parameters. Then, by setting an ideal system dynamic response target, it solves inversely for the optimal virtual inertia and damping coefficient values ​​that achieve this target under specific grid conditions. In this way, the control parameters of the grid-connected energy storage converter can be adaptively adjusted online in real time and with precision according to changes in grid operating conditions, thereby significantly improving the converter's operational robustness in complex scenarios such as strong and weak grid switching. Attached Figure Description

[0009] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0010] Figure 1 A flowchart of a grid-connected adaptive self-tuning method for a grid-connected energy storage converter according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the data flow of the grid-connected adaptive self-tuning method for grid-connected energy storage converters according to an embodiment of the present invention; Figure 3 This is a block diagram of a grid-connected adaptive self-tuning system for a grid-connected energy storage converter according to an embodiment of the present invention. Detailed Implementation

[0011] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0012] Unless otherwise specifically stated, the technical or scientific terms used in the embodiments of this invention should be understood in their ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains. The terms "comprising" or "including," as used in the embodiments of this invention, do not limit the shapes, numbers, steps, actions, operations, components, elements, and / or groups thereof mentioned, nor do they exclude the appearance or addition of one or more other different shapes, numbers, steps, actions, operations, components, elements, and / or groups thereof, or the inclusion of these.

[0013] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale, and techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail; however, where appropriate, the illustrated techniques, methods, and apparatus should be considered part of the specification. In all the examples shown and discussed herein, any other specific example may have different values. It should be noted that similar symbols and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0014] In the description of the embodiments of the present invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In the embodiments of the present invention, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in the embodiments of the present invention, as well as the features of different embodiments or examples.

[0015] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0016] In the technical solution of this invention, a grid-connected adaptive self-tuning method for grid-connected energy storage converters is proposed. Figure 1 This is a flowchart of a grid-connected adaptive self-tuning method for a grid-connected energy storage converter according to an embodiment of the present invention. Figure 2 This is a system architecture diagram of a grid-connected adaptive self-tuning method for grid-connected energy storage converters according to an embodiment of the present invention. Figure 1 and Figure 2 As shown, the grid-connected adaptive self-tuning method for a grid-connected energy storage converter according to an embodiment of the present invention includes the following steps: S1, obtaining the grid resistance and grid inductance; S2, constructing a small-signal state-space model of the system based on the grid resistance and grid inductance to obtain the system state matrix and system input matrix; S3, determining the target pole positions based on the target damping ratio and the target natural oscillation angular frequency to obtain the target system poles; S4, performing reverse analysis on the system state matrix based on the target system poles to obtain the optimal virtual inertia and optimal damping coefficient; S5, performing parameter smoothing updates on the optimal virtual inertia and optimal damping coefficient, as well as the currently effective virtual inertia and currently effective damping coefficient, to obtain updated virtual inertia and updated damping coefficient.

[0017] Specifically, S1 involves acquiring the grid resistance and grid inductance. It should be understood that the grid-connected stability and dynamic performance of a grid-connected energy storage converter are largely influenced by its interaction with the grid, and the physical basis of this interaction is determined by the grid's equivalent impedance. Grid resistance and grid inductance are two core physical quantities constituting the grid's equivalent impedance, jointly determining the system's power transmission characteristics, damping characteristics, and response mode to disturbances. In modern power systems, due to factors such as load changes, line maintenance, or the integration of new energy sources, the grid topology and operating mode frequently change, causing the equivalent impedance of the converter's grid connection point to change dynamically. If the converter's control parameters (such as virtual inertia and damping coefficient) remain fixed, they will be unable to match the changing grid impedance, potentially leading to insufficient system damping, power oscillations, and even system instability in severe cases. Therefore, accurately and in real-time acquiring the grid resistance and grid inductance is a prerequisite for achieving adaptive tuning of control parameters. These two parameters are the bridge connecting the physical power grid and the control algorithm. Their identification accuracy directly determines the accuracy of the subsequent small-signal model construction and the effectiveness of the optimal control parameter calculation. They are the cornerstone for ensuring that the entire self-tuning scheme can achieve the expected technical effect.

[0018] Among them, grid resistance and grid inductance refer to the resistance and inductance components of the Thevenin equivalent circuit viewed from the connection point between the energy storage converter and the grid, i.e., the common coupling point, towards the grid side.

[0019] In practice, firstly, two low-amplitude sinusoidal disturbance signals of different frequencies are superimposed on the d-axis current reference value to obtain the d-axis current reference after the disturbance is injected. In this process, two signals of different frequencies are chosen to provide a sufficient number of independent equations, thus enabling a unique solution to the two unknowns: grid resistance and grid inductance. It is worth noting that the amplitude of the disturbance signals must be designed to be sufficiently small to ensure that it does not significantly interfere with the normal operation of the power grid during the identification process.

[0020] Next, the voltage response at the corresponding frequency generated at the PCC point after the superimposed perturbation is acquired to obtain the first frequency d-axis voltage and current response phasor, the first frequency q-axis voltage and current response phasor, the second frequency d-axis voltage and current response phasor, and the second frequency q-axis voltage and current response phasor. Specifically, by using digital signal processing techniques (such as Fast Fourier Transform FFT or Gosser algorithm) to accurately extract the amplitude and phase information of the voltage and current components corresponding to the two injected perturbation frequencies from the voltage and current signals measured at the PCC point, the first frequency d-axis voltage and current response phasor, the first frequency q-axis voltage and current response phasor, the second frequency d-axis voltage and current response phasor, and the second frequency q-axis voltage and current response phasor are obtained.

[0021] Furthermore, based on the d-axis voltage and current response phasors of the first frequency, the q-axis voltage and current response phasors of the first frequency, the d-axis voltage and current response phasors of the second frequency, and the q-axis voltage and current response phasors of the second frequency, an overdetermined linear equation system is constructed to obtain the coefficient matrix and the observation vector. Specifically, according to the electrical relationship of the power grid, in the dq rotating coordinate system, the voltage response phasor at point PCC satisfies the following relationship with the injected current disturbance phasor:

[0022]

[0023] in To account for the grid impedance affected by rotation in the synchronous coordinate system, the complex equation is expanded into real and imaginary parts. For each disturbance frequency, two linear equations are obtained. Therefore, using disturbances at two different frequencies, four sets of equations can be obtained. Based on the phasors of the d-axis voltage and current response at the first frequency, the phasors of the q-axis voltage and current response at the first frequency, the phasors of the d-axis voltage and current response at the second frequency, and the phasors of the q-axis voltage and current response at the second frequency, a system of equations relating grid resistance and grid inductance is constructed, in the form of: .

[0024] Subsequently, the coefficient matrix and observation vector are solved using least squares, and the parameters are output to obtain the grid resistance and grid inductance. That is, the coefficient matrix A and observation vector b constructed in the previous step are solved using least squares, and the parameters are output to obtain the grid resistance and grid inductance. The least squares method can find an optimal solution. This minimizes the sum of squared errors between the model's predicted values ​​and the actual observed values, thereby improving the robustness and accuracy of the identification results. Its standard solution can be obtained through the following matrix operations:

[0025] The optimal estimated values ​​for grid resistance and grid inductance can be obtained through the above calculations.

[0026] Specifically, in step S2, a small-signal state-space model of the system is constructed based on the grid resistance and grid inductance to obtain the system state matrix and system input matrix. It should be understood that power electronic systems are inherently highly nonlinear, making direct analysis and controller design extremely complex. By constructing a small-signal model, the dynamic behavior of the system near a specific steady-state operating point can be approximated as a linear system. This allows the application of mature linear system theories, such as eigenvalue analysis (pole placement), root locus, and frequency domain analysis, to accurately quantify and design the system's stability and dynamic performance. More importantly, this model directly uses the grid resistance and grid inductance identified online in the previous step as its built-in parameters. This means that the constructed model is no longer a static model based on preset or typical operating conditions, but an adaptive dynamic model that can reflect changes in grid impedance in real time. This ensures that subsequent control parameter tuning can achieve the expected optimal dynamic performance under any grid conditions.

[0027] The small-signal state-space model is a standard mathematical representation used to describe the dynamic characteristics of linear time-invariant (LTI) or nonlinear systems under small perturbations near their equilibrium point. It characterizes the internal state evolution of the system through a set of first-order differential equations, in its standard form:

[0028]

[0029] Among them, the state vector x is the minimum set of variables required to describe the dynamics of the system, such as the virtual power angle difference and virtual frequency difference in a grid-type converter. The state matrix A, also known as the system matrix, determines the mutual influence between the internal state variables of the system and the dynamic characteristics of the system itself. Its eigenvalues ​​(i.e., system poles) directly determine the stability and response speed of the system. The input matrix B describes the influence of external input signals u (such as changes in control commands) on the system state.

[0030] In practical implementation, the first step is to establish the nonlinear dynamic equations of the system. Grid-type storage converters typically employ a virtual synchronous generator (VSG) control strategy, whose core dynamic behavior is described by the swing equations simulating the rotor motion of a synchronous generator. Simultaneously, the electromagnetic transient relationship between the converter and the power grid must also be considered. The main equations include: Virtual rotor motion equations:

[0031] Virtual work angle equation:

[0032] AC power transfer equation:

[0033] in, It is the amplitude of the internal potential of the converter. It is the voltage amplitude of the remote power grid. yes and The angle between them, It is the modulus of the power grid's equivalent impedance. It is the power grid impedance angle. This refers to the active power output by the converter.

[0034] Secondly, the nonlinear equations are linearized. The current steady-state operating point of the system is selected. As the operating point for linearization, small perturbations are introduced into all variables, for example... , Expanding the above equations using Taylor series and neglecting higher-order terms yields the linearized small-signal dynamic equations: Linearized virtual rotor motion equations:

[0035] Linearized virtual work angle equation:

[0036] Linearized power transfer equations:

[0037] in, It is the synchronous power coefficient; Then, a state-space model is constructed. The system's state variables are selected as... The above linearized differential equations are then rearranged into standard state-space form. ,Will Substituting the expression into the linearized rotor motion equation, we get:

[0038] Thus, the small-signal state-space model of the system is obtained. The system state matrix A and the system input matrix B are respectively:

[0039]

[0040] Wherein, the input vector u is the change in the power reference value. .

[0041] Specifically, S3 determines the target pole locations based on the target damping ratio and the target natural oscillation angular frequency to obtain the target system poles. It should be understood that in modern control theory, the dynamic performance of a linear time-invariant system, including its stability, response speed, and oscillation characteristics, is entirely determined by the positions of the eigenvalues ​​of its system state matrix—i.e., the system poles—on the complex plane. The real part of the poles determines the decay rate of the system's transient response, while the imaginary part determines its oscillation frequency. Therefore, by actively setting an ideal pole location for the system, a clear and quantifiable optimization objective can be provided for the entire self-tuning process, transforming a complex, multivariable control parameter tuning problem into a clearly defined pole placement problem. Thus, in the technical solution of this invention, the actual system poles after parameter updates can be accurately configured to or as close as possible to this pre-set target pole location, thereby ensuring that the system maintains consistent and design-desired excellent dynamic performance regardless of changes in external power grid conditions.

[0042] Among them, the target damping ratio It is a dimensionless parameter used to describe the degree of oscillation decay in the step response of a second-order system. It is a key indicator for measuring the system's stability margin. Typically, The value range is between 0 and 1. In one example, when... When the coefficient of performance is 0.707, the system achieves a good balance between response speed and overshoot; when... At time 1, the system is in a critically damped state, with the fastest response and no overshoot; when At time 1, the system is underdamped and exhibits overshoot and oscillation; the target natural oscillation frequency is... It is the natural angular frequency of the system under undamped conditions, measured in radians per second (rad / s). It is directly related to the speed of the system's response. The larger the value, the faster the system response speed is usually; the target system poles are the ideal pole positions that the system is expected to reach, calculated based on the target damping ratio and the target natural oscillation angular frequency, providing specific mathematical coordinates for subsequent back-analysis of control parameters.

[0043] In practical implementation, the standard model of a second-order system in control theory can be used to combine the two performance indicators ( and The mapping is represented by a pair of conjugate poles on the complex plane, which can be expressed by the formula:

[0044] in, The target is the natural oscillation angular frequency. The target damping ratio. It is a pair of conjugate complex numbers, in the form of Among them, the real part The decay envelope of the system response determines the system's response; the larger its absolute value, the faster the system response decays, and the more stable the system. The imaginary part... This represents the actual damped oscillation angular frequency of the system, which determines the oscillation period of the system response. Through this calculation, the abstract performance requirements of the system are concretized into two precise points on the complex plane, providing clear targets for subsequent parameter tuning.

[0045] Specifically, in step S4, the system state matrix is ​​analyzed in reverse based on the target system poles to obtain the optimal virtual inertia and optimal damping coefficient. The preceding steps have constructed a system state matrix A containing real-time grid information (through synchronous power coefficients) and defined the target poles of the ideal dynamic response. However, the virtual inertia J and damping coefficient D in state matrix A are still undetermined variables. That is, in the technical solution of this invention, given the system structure and desired output characteristics, the input parameters required to achieve these characteristics are solved in reverse. This reverse analysis ensures that the calculated J and D are optimal solutions for the current specific grid operating conditions, allowing the adjusted system closed-loop poles to be precisely located at the target position. This fundamentally avoids the performance degradation problem of traditional fixed-parameter methods when the grid changes, giving the grid-connected converter grid-adaptive capability.

[0046] Among them, the optimal virtual inertia and optimal damping coefficient refer to a specific set of virtual inertia and damping coefficient values ​​that enable the actual closed-loop poles of the grid-type converter system to completely coincide with the poles of the target system under the current grid conditions (characterized by Ks), representing the optimal parameter configuration for achieving the preset dynamic performance.

[0047] In practical implementation, firstly, the coefficients of the system characteristic equation are symbolically derived for the grid resistance, grid inductance, grid nominal angular frequency, converter output voltage amplitude, remote grid voltage amplitude, and power angle at the current operating point to obtain the synchronization power coefficient and electrical damping coefficient. The characteristic equation of the actual system is derived from the state matrix A. For the state matrix A=[[0,1],[- / ,-D / Its characteristic equation is:

[0048] Meanwhile, the characteristic equation of the desired system is determined by the target pole in the previous step. The standard characteristic equation of a second-order system with such a pair of conjugate complex roots is defined as follows:

[0049] Next, based on the synchronous power coefficient and electrical damping coefficient, and combined with the target damping ratio, target natural oscillation angular frequency, and target system poles, virtual inertia equations and damping coefficient equations are constructed. To ensure that the actual system poles coincide with the target poles, its actual characteristic equation must be identical to the desired characteristic equation. By comparing the coefficients of terms of the same power in the two characteristic equations, a system of equations concerning the unknowns J and D can be established. (Comparison of s...) 1 The coefficients of the terms are obtained as follows:

[0050] Comparison s 0 The coefficients of the constant term are obtained as follows:

[0051] The above equations are the virtual inertia equation and the damping coefficient equation; Furthermore, based on the synchronization power coefficient and the electrical damping coefficient, the virtual inertia equation and the damping coefficient equation are solved parametrically to obtain the optimal virtual inertia and the optimal damping coefficient. Specifically, the virtual inertia equation is solved parametrically using the following formula to obtain the optimal virtual inertia:

[0052] in, It is the optimal virtual inertia; Then, the damping coefficient equation is parametrically solved using the following formula to obtain the optimal damping coefficient; the formula is:

[0053] in, It is the optimal damping coefficient.

[0054] Specifically, in step S5, the optimal virtual inertia and optimal damping coefficient, as well as the currently effective virtual inertia and currently effective damping coefficient, are updated using parameter smoothing to obtain updated virtual inertia and updated damping coefficients. It should be understood that if the newly calculated optimal parameters ( , Abruptly and suddenly replacing the currently effective parameters can cause significant shocks to the converter's control system. Especially when the two parameter values ​​differ greatly, the sudden change can lead to drastic adjustments in the system's internal state, potentially causing instantaneous large fluctuations in output power and current, or even triggering new oscillations, jeopardizing the system's stable operation. Therefore, in the technical solution of this invention, the optimal virtual inertia and optimal damping coefficient, as well as the currently effective virtual inertia and damping coefficient, are updated smoothly, allowing the control parameters to transition smoothly and gradually from their current values ​​to their target values. Through smooth parameter updates, transient shocks during parameter switching can be effectively suppressed, ensuring that the disturbance to the power grid during self-tuning is minimized. It is worth noting that smooth parameter updates are a control strategy designed to avoid abrupt changes in controller parameters. It uses a preset rate or algorithm to gradually change the parameter values ​​from their initial values ​​to their target values ​​over one or more control cycles, forming a ramp-like or curved transition rather than a step-like jump.

[0055] In practice, firstly, based on preset upper and lower limits for virtual inertia, the optimal virtual inertia is limited to obtain the limited optimal virtual inertia. Similarly, based on preset upper and lower limits for damping coefficient, the optimal damping coefficient is limited to obtain the limited optimal damping coefficient. Specifically, for the optimal virtual inertia, the calculation logic for the limiting process is as follows: firstly, the calculated optimal virtual inertia is compared with the preset upper limit value for virtual inertia, and the smaller value is taken; then, the result obtained in the previous step is compared with the preset lower limit value for virtual inertia, and the larger value is taken. The final result obtained in this way is the limited optimal virtual inertia. It is worth mentioning that the optimal damping coefficient also adopts the exact same calculation logic, that is, by comparing it twice with the preset upper and lower limits for damping coefficient, it is ensured that its result is limited to a safe range.

[0056] In an embodiment of the present invention, based on preset upper and lower limits of virtual inertia, virtual inertia limiting is performed on the optimal virtual inertia to obtain the optimal virtual inertia after limiting, including: limiting the optimal virtual inertia using the following formula, wherein the formula is:

[0057] in, and This indicates the preset upper and lower limits of virtual inertia. The optimal virtual inertia; Furthermore, the optimal damping coefficient is limited using the following formula:

[0058] in, and These are the preset upper and lower limits of the damping coefficient. The optimal damping coefficient; Furthermore, the optimal virtual inertia after the limiting process and the currently effective virtual inertia are smoothed to obtain an updated virtual inertia; similarly, the optimal damping coefficient after the limiting process and the currently effective damping coefficient are smoothed to obtain an updated damping coefficient. Here, taking the smoothing process of virtual inertia as an example, its specific calculation logic includes the following steps: First, calculate the maximum change in a single step, the value of which is equal to the preset maximum allowable rate of change of virtual inertia multiplied by the system's execution cycle; next, calculate the target deviation, the value of which is equal to the optimal virtual inertia after the limiting process minus the currently effective virtual inertia; then, calculate the virtual inertia limit change in this cycle, the calculation logic of which is: first, compare the target deviation obtained in the previous step with the maximum change in a single step calculated in the first step, and take the smaller value of the two; then, compare this result with the negative value of the maximum change in a single step, and take the larger value of the two. The final result is the actual allowable change in virtual inertia within this cycle, effectively limited to the maximum change in a single positive or negative step. Subsequently, the updated virtual inertia is calculated, its value equal to the currently effective virtual inertia plus the virtual inertia limit calculated in the third step. It is worth mentioning that the smoothing of the damping coefficient also uses the exact same logic and calculation steps.

[0059] In an embodiment of the present invention, the optimal virtual inertia after clipping and the currently effective virtual inertia are smoothed using the following formula:

[0060]

[0061]

[0062]

[0063] in, The maximum allowable rate of change of virtual inertia. For the execution cycle, This represents the maximum change in a single step. The optimal virtual inertia after amplitude limiting is... For the currently active virtual inertia, For target deviation, To limit the change in virtual inertia, For the updated virtual inertia.

[0064] In summary, the grid-connected adaptive self-tuning method for grid-connected energy storage converters according to embodiments of the present invention is explained. First, key grid resistance and inductance parameters are identified online, and a mathematical model reflecting the current system characteristics is constructed based on these parameters. Then, by setting an ideal system dynamic response target, the optimal virtual inertia and damping coefficient values ​​that can achieve this target under specific grid conditions are solved in reverse. In this way, the control parameters of the grid-connected energy storage converter can be adaptively adjusted online in real time and with precision according to changes in grid operating conditions, thereby significantly improving the converter's operational robustness in complex scenarios such as strong and weak grid switching.

[0065] Furthermore, a grid-connected adaptive self-tuning system for grid-connected energy storage converters is also provided.

[0066] Figure 3 This is a block diagram of a grid-connected adaptive self-tuning system for a grid-connected energy storage converter according to an embodiment of the present invention. Figure 3 As shown, the grid-connected adaptive self-tuning system 300 for a grid-connected energy storage converter according to an embodiment of the present invention includes: a data acquisition module 310 for acquiring grid resistance and grid inductance; a system small-signal state-space model construction module 320 for constructing a system small-signal state-space model based on grid resistance and grid inductance to obtain a system state matrix and a system input matrix; a target pole location determination module 330 for determining the target pole location based on a target damping ratio and a target natural oscillation angular frequency to obtain a target system pole; a reverse analysis module 340 for performing reverse analysis on the system state matrix based on the target system pole to obtain an optimal virtual inertia and an optimal damping coefficient; and a parameter smoothing update module 350 for performing parameter smoothing updates on the optimal virtual inertia and optimal damping coefficient, as well as the currently effective virtual inertia and currently effective damping coefficient, to obtain an updated virtual inertia and an updated damping coefficient.

[0067] Furthermore, the data acquisition module 310 is specifically used for: superimposing two low-amplitude sinusoidal disturbance signals of different frequencies on the d-axis current reference value to obtain the d-axis current reference after the disturbance injection; acquiring the voltage response at the corresponding frequency generated at the PCC point after the disturbance is superimposed to obtain the first frequency d-axis voltage and current response phasor, the first frequency q-axis voltage and current response phasor, the second frequency d-axis voltage and current response phasor, and the second frequency q-axis voltage and current response phasor; constructing an overdetermined linear equation system based on the first frequency d-axis voltage and current response phasor, the first frequency q-axis voltage and current response phasor, the second frequency d-axis voltage and current response phasor, and the second frequency q-axis voltage and current response phasor to obtain the coefficient matrix and the observation vector; and performing least squares solution and parameter output on the coefficient matrix and the observation vector to obtain the grid resistance and grid inductance.

[0068] As described above, the grid-connected adaptive self-tuning system 300 for a grid-connected energy storage converter according to embodiments of the present invention can be implemented in various wireless terminals, such as servers with a grid-connected adaptive self-tuning algorithm for a grid-connected energy storage converter. In one possible implementation, the grid-connected adaptive self-tuning system 300 for a grid-connected energy storage converter according to embodiments of the present invention can be integrated into a wireless terminal as a software module and / or a hardware module. For example, the grid-connected adaptive self-tuning system 300 can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal; of course, the grid-connected adaptive self-tuning system 300 can also be one of many hardware modules of the wireless terminal.

[0069] Alternatively, in another example, the grid-connected adaptive self-tuning system 300 for the grid-connected energy storage converter and the wireless terminal can also be separate devices, and the grid-connected adaptive self-tuning system 300 for the grid-connected energy storage converter can be connected to the wireless terminal via wired and / or wireless networks, and transmit interactive information in accordance with an agreed data format.

[0070] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A grid-connected adaptive self-tuning method for a grid-connected energy storage converter, characterized in that, include: Obtain the grid resistance and grid inductance; A small-signal state-space model of the system is constructed based on the grid resistance and grid inductance to obtain the system state matrix and system input matrix; The target pole locations are determined based on the target damping ratio and the target natural oscillation angular frequency to obtain the target system poles; Inverse analysis of the system state matrix is ​​performed based on the target system poles to obtain the optimal virtual inertia and optimal damping coefficient. The optimal virtual inertia and optimal damping coefficient, as well as the currently effective virtual inertia and damping coefficient, are updated smoothly to obtain the updated virtual inertia and updated damping coefficient.

2. The grid-connected adaptive self-tuning method for grid-connected energy storage converters according to claim 1, characterized in that, Obtain the grid resistance and grid inductance, including: Two low-amplitude sinusoidal perturbation signals of different frequencies are superimposed on the d-axis current reference value to obtain the d-axis current reference after the perturbation. The voltage response at the corresponding frequency generated at the PCC point after the superimposed disturbance is collected to obtain the first frequency d-axis voltage and current response phasor, the first frequency q-axis voltage and current response phasor, the second frequency d-axis voltage and current response phasor, and the second frequency q-axis voltage and current response phasor. Based on the first frequency d-axis voltage and current response phasor, the first frequency q-axis voltage and current response phasor, the second frequency d-axis voltage and current response phasor, and the second frequency q-axis voltage and current response phasor, an overdetermined linear equation system is constructed to obtain the coefficient matrix and the observation vector. The coefficient matrix and observation vector are solved by least squares and the parameters are output to obtain the grid resistance and grid inductance.

3. The grid-connected adaptive self-tuning method for grid-connected energy storage converters according to claim 1, characterized in that, Determining the target pole locations based on the target damping ratio and the target natural angular frequency to obtain the target system poles includes: determining the target pole locations based on the target damping ratio and the target natural angular frequency using the following formula, where the formula is: in, The target is the natural oscillation angular frequency. The target damping ratio.

4. The grid-connected adaptive self-tuning method for grid-connected energy storage converters according to claim 1, characterized in that, Inverse analysis of the system state matrix based on the target system poles yields the optimal virtual inertia and optimal damping coefficient, including: Symbolic derivation of the coefficients of the system characteristic equation is performed on the grid resistance, grid inductance, grid nominal angular frequency, converter output voltage amplitude, remote grid voltage amplitude, and power angle at the current operating point to obtain the synchronous power coefficient and electrical damping coefficient. Based on the synchronous power coefficient and electrical damping coefficient, and combined with the target damping ratio, target natural oscillation angular frequency and target system poles, a virtual inertia equation and damping coefficient equation are constructed. Based on the synchronous power coefficient and the electrical damping coefficient, the virtual inertia equation and the damping coefficient equation are solved by parameterization to obtain the optimal virtual inertia and the optimal damping coefficient.

5. The grid-connected adaptive self-tuning method for grid-connected energy storage converters according to claim 1, characterized in that, The optimal virtual inertia and optimal damping coefficient, as well as the currently effective virtual inertia and damping coefficient, are updated using parameter smoothing to obtain the updated virtual inertia and updated damping coefficient, including: Based on preset upper and lower limits of virtual inertia, the optimal virtual inertia is subjected to virtual inertia limiting to obtain the optimal virtual inertia after limiting. Based on the preset upper and lower limits of the damping coefficient, the optimal damping coefficient is limited to obtain the optimal damping coefficient after the limiting process. The optimal virtual inertia after the clipping process and the currently effective virtual inertia are smoothed to obtain the updated virtual inertia. The optimal damping coefficient after the limiting process and the currently effective damping coefficient are smoothed to obtain the updated damping coefficient.

6. The grid-connected adaptive self-tuning method for grid-connected energy storage converters according to claim 5, characterized in that, Based on preset upper and lower limits for virtual inertia, virtual inertia limiting is applied to the optimal virtual inertia to obtain the optimal virtual inertia after limiting, including: applying virtual inertia limiting to the optimal virtual inertia using the following formula, wherein the formula is: in, and This indicates the preset upper and lower limits of virtual inertia. This is the optimal virtual inertia.

7. The grid-connected adaptive self-tuning method for grid-connected energy storage converters according to claim 5, characterized in that, Based on preset upper and lower limits of the damping coefficient, the optimal damping coefficient is limited to obtain the optimal damping coefficient after limiting, including: limiting the optimal damping coefficient using the following formula, wherein the formula is: in, and These are the preset upper and lower limits of the damping coefficient. This is the optimal damping coefficient.

8. The grid-connected adaptive self-tuning method for grid-connected energy storage converters according to claim 5, characterized in that, To obtain an updated virtual inertia, the optimal virtual inertia after clipping and the currently effective virtual inertia are smoothed using the following formula: in, The maximum allowable rate of change of virtual inertia. For the execution cycle, This represents the maximum change in a single step. The optimal virtual inertia after amplitude limiting is... For the currently active virtual inertia, For target deviation, To limit the change in virtual inertia, For the updated virtual inertia.

9. A grid-connected adaptive self-tuning system for a grid-connected energy storage converter, characterized in that, include: The data acquisition module is used to acquire the grid resistance and grid inductance; The system small-signal state-space model construction module is used to construct a system small-signal state-space model based on grid resistance and grid inductance to obtain the system state matrix and system input matrix. The target pole location determination module is used to determine the target pole location based on the target damping ratio and the target natural oscillation angular frequency to obtain the target system poles; The reverse analysis module is used to perform reverse analysis of the system state matrix based on the target system poles to obtain the optimal virtual inertia and optimal damping coefficient. The parameter smoothing update module is used to perform parameter smoothing updates on the optimal virtual inertia and optimal damping coefficient, as well as the currently effective virtual inertia and currently effective damping coefficient, to obtain updated virtual inertia and updated damping coefficient.

10. The grid-connected adaptive self-tuning system for grid-connected energy storage converters according to claim 9, characterized in that, The data acquisition module is further used for: Two low-amplitude sinusoidal perturbation signals of different frequencies are superimposed on the d-axis current reference value to obtain the d-axis current reference after the perturbation. The voltage response at the corresponding frequency generated at the PCC point after the superimposed disturbance is collected to obtain the first frequency d-axis voltage and current response phasor, the first frequency q-axis voltage and current response phasor, the second frequency d-axis voltage and current response phasor, and the second frequency q-axis voltage and current response phasor. Based on the first frequency d-axis voltage and current response phasor, the first frequency q-axis voltage and current response phasor, the second frequency d-axis voltage and current response phasor, and the second frequency q-axis voltage and current response phasor, an overdetermined linear equation system is constructed to obtain the coefficient matrix and the observation vector. The coefficient matrix and observation vector are solved by least squares and the parameters are output to obtain the grid resistance and grid inductance.

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