Constructing network type converter recovery method, medium and equipment

Abstract

The invention provides a method, medium and equipment for recovering a follow-up network type converter, and relates to the technical field of fault recovery of the follow-up network type converter of a micro-grid, and the method comprises the steps: locally updating the state initial value, gain function and allowable energy range of input and output variables of each subsystem; generating a feasible basic recovery strategy set S; carrying out load flow calculation on the system after the recovery strategy Sk is executed to obtain a steady-state equilibrium point; judging whether the system meets a network interconnection relation condition and a small gain condition or not; if the system does not meet the network interconnection relation condition or the small gain condition, refusing to execute the recovery strategy Sk; judging whether the system meets the local input-output stability condition of the interconnection system or not, and if the system cannot meet the local input-output stability condition of the interconnection system by adjusting the state initial value of the subsystem, refusing to execute the recovery strategy Sk; and executing the recovery strategy Sk. According to the invention, the capability of quickly recovering power supply after the independent micro-grid is disturbed can be improved.

Description

Technical Field

[0001] This invention relates to the field of fault recovery technology for microgrid grid-connected converters, and particularly to a method, medium, and equipment for recovering grid-connected converters. Background Technology

[0002] As a flexible and efficient distributed energy system, independent microgrids exhibit highly dynamic and complex network topology and operating conditions. Furthermore, the nonlinear limiting components in grid-connected converters within microgrids contribute to their complex operating characteristics, making it difficult for mathematical models to accurately characterize these features. This poses a significant challenge to traditional fault recovery methods in solving the LISS / LIOS gain function. The flexible and variable network topology and operating conditions of independent microgrids lead to frequent equipment switching and topology reconfiguration during the recovery phase. Traditional model-based stability analysis methods require rebuilding the state-space model after each topology change, which is insufficient to meet the real-time requirements for rapid power restoration in independent microgrids. More seriously, transient shocks caused by DG switching and line grid connection during system recovery control may trigger protection devices, leading to system recovery failure.

[0003] In independent microgrid operation scenarios, the large disturbance problems caused by frequent device switching and topology reconfiguration are more prominent. Classical methods for large disturbance stability analysis mainly include time-domain simulation and the direct method (Lyapunov method). Time-domain simulation: Modeling each component and the network topology to form a detailed model of the entire system, numerical integration is used to solve the dynamic trajectory of the system after the disturbance, and stability is judged based on the changes in system state. This method has the advantages of high model refinement and intuitive and accurate results, and is widely used in engineering practice. Direct method: Rapidly judging system stability by constructing a transient energy function, avoiding the complex process of calculating the trajectory of the entire system's state variables. The advantage of this method is its fast computation speed, which can quickly provide stability judgment results, but its core challenge is how to construct a suitable energy function and calculate the critical energy.

[0004] For time-domain simulation methods, firstly, model calculations are time-consuming, generally only applicable to offline analysis and difficult to meet the needs of online analysis. Secondly, the simulation process requires accurate acquisition of parameters for each component, but the time-varying characteristics of DG parameters in independent microgrids make modeling errors unavoidable. Furthermore, when the system operating conditions change abruptly, the original simulation model lacks real-time adjustment capabilities. For direct methods, although many studies have proposed various Lyapunov-type transient energy function construction methods for classical power systems, the high proportion of IDG and nonlinear elements such as amplitude limiting in independent microgrids greatly increase the difficulty of constructing Lyapunov functions. Classical large-disturbance stability analysis methods share common limitations: the effectiveness of stability analysis methods is highly dependent on an accurate system model. When the measurements and parameters of some subsystems are unknown, the applicability of classical methods decreases significantly, making it difficult to meet the plug-and-play requirements of independent microgrids. Summary of the Invention

[0005] The purpose of this invention is to provide a method, medium, and device for restoring power to a grid-connected converter, aiming to improve the ability of independent microgrids to quickly restore power supply after disturbance. The specific technical solution is as follows:

[0006] A method for restoring a grid-type converter includes the following steps:

[0007] S100, Each subsystem locally updates the initial state value, gain function, and allowable energy range of input and output variables of the subsystem;

[0008] S200. Generate a feasible basic recovery strategy set S that satisfies the static capacity constraint. The basic recovery strategy set S includes multiple recovery strategies Si. k Let k = 1, 2, ..., n, and let k = 1 to select the initial recovery strategy S. k ;

[0009] S300, Execute recovery strategy S k The system then performs power flow calculations to obtain the steady-state equilibrium point. Update recovery strategy S k Determine the input matrix Z and output matrix G of the interconnected system; determine whether the system satisfies the network interconnection condition and the low gain condition.

[0010] S400. If the system does not meet the network interconnection condition or the low gain condition, the recovery strategy S will not be executed. k Let k = k + 1 and return to step S300; if the system satisfies the network interconnection condition and the small gain condition, proceed to step S500.

[0011] S500. Determine whether the system meets the local input-output stability condition of the interconnected system. If not, if it can be stabilized by adjusting the initial state value of the subsystem. To ensure the system meets the local input-output stability condition of the interconnected system, proceed to step S600. If it cannot be achieved by adjusting the initial state value of the subsystem... To ensure the system meets the local input-output stability condition of the interconnected system, the recovery strategy S is rejected. k If k = k + 1, return to step S300; if yes, proceed to step S600.

[0012] S600, Execute recovery strategy S k .

[0013] Furthermore, the interconnected system formed by N subsystems is described using state variable x, input variable u, output variable y, and external disturbance w as follows:

[0014] .

[0015] Furthermore, the subsystem satisfies the following subsystem condition: If any subsystem i = 1, 2, ..., N in an interconnected system containing N subsystems satisfies the definitions of local input-state stability and local input-output stability, that is, for any... , , as well as , , , To represent the constraints on the degree to which each vector of the i-th subsystem deviates from the equilibrium point, subsystem i satisfies the local input integral-state integral estimate and the local input integral-output integral estimate considering external disturbances:

[0016] .

[0017] Furthermore, the conditions for network interconnection are as follows: Existence The solution u=k(y) of the system's input-output relationship satisfies:

[0018]

[0019] Where k represents the functional relationship between the system input u and the output y, and the connection matrix is... Here is the system impedance matrix. This represents the mutual impedance between node i and node j. This represents the self-impedance of node i.

[0020] Furthermore, the small gain condition is as follows:

[0021]

[0022] in, Let G represent the spectral radius of matrix G.

[0023] Furthermore, the local input-output stability conditions of the interconnected system are as follows:

[0024] in, Represents the identity matrix; ,diag is the symbol for a diagonal matrix, meaning that the diagonal is A matrix where all other elements are 0, elements For the i-th subsystem , For each subsystem, there is an input-output gain vector; The operator ∘ represents a composite function; Let be the input-output gain vector of the disturbance signal. It is the gain function of the perturbation; This is the local input range vector of the system; This is the local output range vector of the system.

[0025] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the above-described grid-type converter recovery method.

[0026] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the steps of the grid-type converter recovery method as described above.

[0027] The present invention provides a method, medium, and device for restoring a grid-type converter, which has the following beneficial effects:

[0028] Based on existing recovery control methods, namely large disturbance stability and static capacity limitation, this invention fully considers the equipment limiting links and system protection device triggering conditions in actual systems. It evaluates the system through stability criteria and provides corresponding strategy adjustment methods for different situations, thereby improving the reliability and feasibility of the recovery control strategy and reducing the risk of instability during the recovery process in independent microgrids. Attached Figure Description

[0029] Figure 1 It is a diagram showing the relationship between local input and stable states in different regions;

[0030] Figure 2 This is a flowchart illustrating a recovery method for a grid-type converter provided by the present invention.

[0031] Figure 3 This is a general flowchart of an embodiment of the present invention;

[0032] Figure 4This is a structural block diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The advantages and features of the present invention will become clearer from the following description. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clearly illustrate the purpose of the embodiments of the present invention.

[0034] Explanation of terms and abbreviations:

[0035] Distributed generation (DG);

[0036] Input-to-State Stability (ISS);

[0037] : The negative real number field;

[0038] : n-dimensional real number field;

[0039] Comparison function (K function): , a function that maps from the non-negative real number field to the non-negative real number field, and has the properties of continuity, strict monotonically increasing and taking the value of zero at the zero point;

[0040] Function: A K-function whose output value also tends to infinity as the input value tends to infinity;

[0041] KL function: for : If a function is decreasing with respect to t and approaches 0 as t approaches infinity, and is a K function with respect to s, then it is called a KL function.

[0042] Introduction to Local Input-State Stability (LISS): Classical Lyapunov stability focuses on the stability of a system with no input (u(t) = 0) near its equilibrium point (usually the origin). If the initial state x(0) is close to the equilibrium point, the state trajectory x(t) will remain near the equilibrium point (stability) and eventually converge to the equilibrium point (asymptotic stability). It does not directly consider the influence of external input u(t). Input-State Stability (ISS) is a robust extension of Lyapunov stability theory to handle bounded input u(t). Its core idea can be summarized as the system's state x(t) ultimately being "dominated" by the magnitude of the input u(t): the state x(t) is bounded, and this bound depends on both the magnitude of the initial state x(0) and the "magnitude" of the input u(t) (usually expressed as its...). norm (Measured by...) When the input u(t) eventually decays to zero, the state x(t) will also eventually converge to the equilibrium point (usually the origin).

[0043] LISS is a crucial stability concept in nonlinear control system theory, representing a localized version of input-state stability. It describes the behavior of a system when its initial state is close to its equilibrium point and the input signal amplitude is small. Many real-world systems may not satisfy ISS across the entire state space and with arbitrarily large inputs, but they may exhibit robust stability behavior similar to ISS within a local region near the equilibrium point (origin) and with sufficiently small input amplitudes.

[0044] like Figure 1 As shown, when the initial value of the system state variable and the input signal are within the specified initial value range and input range, the trajectory of its state variable will eventually converge to the interior of the region. The size of this convergence region is related to the positive infinity norm of the input u.

[0045] Introduction to Recovery Control: Microgrids are characterized by frequent extreme events such as typhoons, earthquakes, and cyberattacks, which can easily paralyze the main power grid. They must have off-grid operation capabilities, allowing them to operate independently of the main grid. They must possess rapid response capabilities, with critical loads like hospitals and data centers requiring power restoration within minutes. High penetration of renewable energy sources, such as intermittent power sources like photovoltaics and wind turbines, increases the uncertainty of the recovery process. Recovery control is a key technology for ensuring the rapid, safe, and orderly restoration of power to microgrids after major power outages or faults. It involves intelligent control throughout the entire process, from "black start" to gradual load restoration, and is crucial for improving the resilience and reliability of the power system. The core objectives of recovery control are: minimizing outage time, maximizing the restoration of critical loads, and maintaining voltage / frequency stability. Generally, recovery control consists of three stages. The first is black start: using onboard energy storage or diesel generators to start the first power source from a completely dark state. The second is network reconfiguration, which involves segmented switching based on real-time topology detection to rebuild the microgrid topology, avoiding inrush current impacts and ensuring the safety of the reconfigured topology. The final stage is gradual load restoration, which can restore loads according to their importance or in stages.

[0046] Stability analysis principle based on LISS theory:

[0047] Consider an affine nonlinear system consisting of a state variable x, an input variable u, and an output variable y (general variable symbols are in italics, such as a; symbols for multidimensional quantities such as matrices, vectors, and tensors are in bold italics, such as b and C):

[0048]

[0049] in , , , : This represents the derivative of x with respect to time. and It is a continuous function of x — f is a mapping of n-dimensional state variables x. Mapping an n-dimensional vector to an n-dimensional vector. It is a mapping of n-dimensional state variables x, mapping the n-dimensional vector x to an n×m-dimensional matrix, and and It satisfies the local Lipschitz condition, where h is a function of x and u, and its dimension depends on the requirements; D represents the domain of the state variable x. U represents the operating range (i.e., domain) of the input variable u that the system can tolerate. , They represent All elements belong to n-dimensional real space, and all elements belong to m-dimensional real space.

[0050] For an affine nonlinear system (1), the conditions for local input-state stability (LISS) and local input-output stability (LIOS) are defined. If any (x is a denoted state variable, and its domain is represented by D) Let x represent an initial state of the state variable, and its domain be denoted by . (U is the same as above), there exists a function ( express It is a KL function (hereinafter the same), an input-state asymptotic gain function. , , ( , , All If a function is found that makes equation (2) true, then the system satisfies the LISS condition.

[0051]

[0052] : Represents the infinity norm of a vector, which is the maximum value among all elements of the vector. This represents the maximum value among all elements of u. , Parentheses () indicate that the element before the parentheses is a function of the element inside the parentheses. , Each represents x as (time t, initial state) The input u) is a function, and x is a function of the variable s (s used in the following formulas does not have a special meaning; s is the integration variable, also called a "dummy variable," used to represent the continuously changing parameter during the integration process, here representing time t), and y() and u() are similarly expressed. Relevant literature has proven that the system LISS condition is equivalent to the local input integral-state integral estimation condition, and the integral-integral equivalent form of equation (2) is:

[0053]

[0054] If any There exists a function Input-state asymptotic gain function , , If equation (4) holds, then the system satisfies the LIOS condition:

[0055]

[0056] The integral-integral equivalent form of equation (4) is:

[0057]

[0058] Determining the stability of a system under large disturbances requires quantifying the degree to which the system deviates from its equilibrium point under the disturbance, thereby assessing its impact on system stability. This method selects the system's steady-state equilibrium point as the benchmark for calculating integral energy. The integral energy expression for the subsystem's inputs, state variables, and outputs, based on the equilibrium point, is defined as follows:

[0059]

[0060] in, , and These represent the equilibrium values ​​of the input, state, and output variables of the steady-state subsystem at time t = 0; the superscript ~ indicates the difference between the corresponding variable and the equilibrium point. This represents the values ​​of the state variable x and the equilibrium point. The difference.

[0061] Stability criteria for interconnected systems include: subsystem conditions, network interconnection conditions, low-gain conditions, and local LIOS conditions. Consider an interconnected system consisting of N interconnected subsystems, described by state variables x, input variables u, output variables y, and external disturbance w:

[0062]

[0063] (1) Subsystem condition: If an interconnected system containing N subsystems has any subsystem i = 1, 2, ..., N (the subscript i represents the i-th subsystem, such as...), then... Represents the i-th subsystem Here, the definitions of LISS and LIOS are satisfied, that is, for any ( Representing vectors The modulus of the vector is the square root of the sum of the squares of all its elements. Given a number for the i-th subsystem, Geometrically, the difference vector between the initial state and the equilibrium point vector of the i-th subsystem cannot be too large (i.e., exceeding...). )), , as well as , ( , , Let scalar represent the constraint on the degree (magnitude) of each vector of the i-th subsystem deviating from the equilibrium point, hereinafter referred to as the local x-range. Subsystem i satisfies the local input integral-state integral estimate and the local input integral-output integral estimate considering external disturbances:

[0064]

[0065] (2) Conditions for network interconnection: Existence The solution to the system's input-output relationship, u=k(y) (where u=k(y) represents the functional relationship between the system's input u and output y, denoted by the symbol k), satisfies:

[0066]

[0067] (3) Low gain condition:

[0068]

[0069] (4) Local LIOS conditions in interconnected systems:

[0070]

[0071] in Represents the spectral radius of matrix G; connects the matrices. Here is the system impedance matrix. This represents the mutual impedance between node i and node j. Indicates the self-impedance of node i; Represents the identity matrix; (diag is the symbol for a diagonal matrix, meaning the diagonal is...) A matrix where all other elements are 0, elements For the i-th subsystem (See equation (2)), Let i be the input-output gain vector of each subsystem, where the subscript i represents the input-output gain of the i-th subsystem. The operator ∘ represents a composite function, i.e. ; Let be the input-output gain vector of the disturbance signal. It is the perturbation gain function, where the subscript i indicates the perturbation-output gain of the i-th subsystem; This is the local input range vector of the system, where the subscript i represents the local input range of the i-th subsystem; Let i be the local output range vector of the system, where the subscript i represents the local output range of the i-th subsystem.

[0072] Subsystem LISS / LIOS property simulation estimation method:

[0073] In practical independent microgrid systems, the IDG (Independent Microgrid) has numerous operational limitations and safety constraints, exhibiting strong nonlinear characteristics. This poses a significant challenge to solving the LISS / LIOS properties using purely analytical methods. This method employs batch simulation-based approaches to approximate the LISS / LIOS gains of the subsystem. This method not only adapts to the nonlinear characteristics of the system but also provides a practical solution for the stability analysis of complex systems.

[0074] The GFM-IDG (grid-connected converter), serving as the system's supporting power source, can be equivalent to a voltage source in terms of external characteristics. External disturbances can be simulated through current disturbances, thus current can be chosen as the input variable. Since the impact of current on this type of subsystem is reflected in voltage, voltage is chosen as the subsystem's output variable. For the GFL-IDG (grid-connected converter), voltage disturbances are chosen as the input variable to simulate the impact of grid voltage fluctuations, and current is chosen as the output variable, as shown in Table 1 below.

[0075] Table 1 Subsystem Input / Output Variables

[0076]

[0077] Microgrid subsystems in actual operation are subjected to multiple dynamic disturbances, including renewable energy output fluctuations caused by meteorological factors, grid-side equipment switching operations, and short-circuit faults. This method uses a rectangular wave as the reference disturbance signal, which can effectively cover the waveform characteristics under typical disturbance scenarios and provide comprehensive and reliable results for system dynamic response analysis.

[0078] According to standard GB / T 34120-2023, energy storage converters must meet the technical requirement of continuous operation for 2 seconds under a rated voltage of not less than 0.9 pu. When the grid voltage drops, the energy storage system can be considered to have reached another stable state during this fault period. Therefore, an upper limit of the disturbance duration is set at t = 2 seconds. Based on this, the norm form constraint and integral form constraint of the voltage are defined as follows:

[0079]

[0080] Note that U here represents a scalar—voltage. Denotes the infinite norm of the scalar U. This represents the difference between the voltage and the equilibrium point value; s is also a dummy variable in the integral expression. According to standard GB / T15945-2008, the allowable deviation of the steady-state frequency for an independent microgrid is set to ±0.5Hz, or ±0.01pu. To avoid the system frequency hovering outside the steady-state operating range for extended periods, the maximum hovering time when the system frequency is below the steady-state operating range is set to 10s. Therefore, based on the aforementioned protection device trigger threshold, the norm-form and integral-form constraints on the frequency are defined as follows:

[0081]

[0082] Here, f represents scalar frequency. The infinite norm representing the frequency f, This represents the difference between the frequency and the equilibrium point value, and s is also a dummy variable in the integral expression. This constraint provides a basis for subsequent judgment on whether the subsystem exceeds the stability constraint. Taking LIOS as an example, the simulation estimation steps of the subsystem properties are introduced. Assume that in the integral definition... Class function , and It is a uniformly linear function:

[0083]

[0084] It is a linear function, i.e. In the formula It is a constant. These are the coefficients of the linear function. , Similarly, the LIOS criterion (5) can be rewritten as:

[0085]

[0086] For the i-th subsystem, simulation methods are used to estimate the LIOS properties. and The specific steps are as follows:

[0087] (1) Subsystem modeling and stability region delineation:

[0088] First, establish the mapping relationship between the subsystem's state space and its stable domain, and select the subsystem's state variables. and observable input variables and output variables And set the range for stable operation of the subsystem according to the system protection configuration.

[0089] (2) Input-output gain function estimation:

[0090] Next, the subsystem is set to operate at its rated operating condition equilibrium point, i.e. Multiple sets of rectangular wave input disturbances with different amplitudes and durations are applied. The integral energy of the output response is obtained through data simulation. Calculate the gain function based on the input-output energy relationship.

[0091]

[0092] according to The property of the function is that it takes the maximum gain under all steady-state conditions. As the subsystem input-output gain estimate ,Right now (The subscript max represents the maximum value among all the calculated values.) , This means that the simulation estimates calculated by different groups are... , (maximum value).

[0093] (3) Function estimation:

[0094] Finally, different initial system values ​​are set. ,Will Substitute into equation (12) and change Get different We can obtain:

[0095]

[0096] Pick As the current initial value Function estimation. In summary, based on the above steps, the gain function of the i-th subsystem can be obtained. and Estimation. The estimation of LISS attributes is similar to that of LIOS attributes. The main difference is that it is necessary to estimate the integral energy of the state variables and use the relationship between the state variables and external inputs for estimation, which will not be elaborated here.

[0097] Example 1: This example provides a recovery method for a grid-type converter. (See attached document.) Figure 2 , 3 As shown, the method includes the following steps:

[0098] S100, each subsystem locally updates the initial state value, gain function, and allowable energy range of input and output variables of the subsystem.

[0099] Specifically, firstly, system information is updated: each subsystem locally updates its initial state value, gain function, and allowable energy range of input and output variables, and interacts with each other through the communication network, while simultaneously updating the microgrid system topology.

[0100] S200. Generate a feasible basic recovery strategy set S that satisfies the static capacity constraint. The basic recovery strategy set S includes multiple recovery strategies Si. k Let k = 1, 2, ..., n, and let k = 1 to select the initial recovery strategy S. k .

[0101] Once the system completes the load shedding process and the communication network is reconnected, the system needs to reconnect the distributed generation (DG) and loads. At this time, information from all subsystems in the network can be exchanged through the communication network, and there are no communication islands. The recovery control strategy set is a collection of recovery strategies for the power grid subsystems such as loads, lines, and DG to be restored. A recovery control strategy is a strategy to restore in a certain quantity and order. For example, if there are two lines "Line1" and "Line2" in the power grid, and one of them needs to be restored, the recovery control strategy set is S = {connect Line1, connect Line2}, which can be either {connect Line1} or {connect Line2}.

[0102] S300, Execute recovery strategy S k The system then performs power flow calculations to obtain the steady-state equilibrium point. Update recovery strategy S k Given the input matrix Z and output matrix G of the interconnected system, determine whether the system satisfies the network interconnection condition and the small gain condition.

[0103] S400. If the system does not meet the network interconnection condition or the low gain condition, the recovery strategy S will not be executed. k Let k = k + 1 and return to step S300; if the system satisfies the network interconnection condition and the small gain condition, proceed to step S500.

[0104] In this embodiment, the interconnection relationship condition and the small gain condition are judged sequentially, referred to as condition (2) and condition (3) respectively. If condition (2) or condition (3) is not satisfied, it indicates that the strategy fails to meet the stability criterion. Since the parameters of each DG in the system and the restored topology have been determined, i.e., the input-output gain function γ and the interconnection system input-output matrix Z have been determined, if the system adopts this strategy for restoration control in this scenario, it may trigger transient constraints, leading to transient instability. Therefore, the current strategy S will be rejected. k Continuing to analyze the next basic recovery strategy S k+1 If conditions (2) and (3) are satisfied, then the local LIOS conditions of the interconnected system are further judged.

[0105] S500. Determine whether the system meets the local input-output stability condition of the interconnected system. If not, if it can be stabilized by adjusting the initial state value of the subsystem. To ensure the system meets the local input-output stability condition of the interconnected system, proceed to step S600. If it cannot be achieved by adjusting the initial state value of the subsystem... To ensure the system meets the local input-output stability condition of the interconnected system, the recovery strategy S is rejected. k If k = k + 1, return to step S300; if yes, proceed to step S600.

[0106] In this embodiment, the LIOS condition of the interconnected system is finally determined, and will be referred to as condition (4) below. If condition (4) is not met, it indicates that the initial value of the system state under the current strategy is... Deviating from the steady-state equilibrium point The degree of instability is relatively large. Although there is still a risk of transient instability at this point, it is advisable to adjust the output or load of the distributed generation (DG) and adjust the initial state values ​​of the subsystem. This enables the system to satisfy the local LIOS conditions of the interconnected system. This can be achieved by adjusting the initial values ​​of the subsystem states. If condition (4) is met, it means that the adjusted system can meet the stability criterion. In this case, after the system executes the current strategy, the system's protection device will not be triggered, and the corresponding recovery operation can be performed. Otherwise, if the condition is still not met, the current strategy is rejected, and the next basic recovery strategy Sk+1 is selected.

[0107] S600, Execute recovery strategy S k .

[0108] The recovery method for grid-type converters provided by this invention, based on existing recovery control methods, namely large disturbance stability and static capacity limitation, fully considers the equipment limiting links and system protection device triggering conditions existing in the actual system. It evaluates through stability criteria and provides corresponding strategy adjustment methods for different situations, thereby improving the reliability and feasibility of the recovery control strategy and reducing the risk of instability during the recovery process in independent microgrids.

[0109] Example 2: This example provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described grid-type converter recovery method.

[0110] The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0111] Example 3: This example provides a computer device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the above-described grid-type converter recovery method.

[0112] like Figure 4 As shown, the computer device 70 may include: at least one processor 71, such as a CPU (Central Processing Unit), at least one communication interface 73, a memory 74, and at least one communication bus 72. The communication bus 72 is used to enable communication between these components. The communication interface 73 may include a display screen and a keyboard; optionally, the communication interface 73 may also include a standard wired interface or a wireless interface. The memory 74 may be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 74 may also be at least one storage device located remotely from the aforementioned processor 71. The memory 74 stores application programs, and the processor 71 calls the program code stored in the memory 74 to execute any of the above-described method steps.

[0113] The communication bus 72 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 72 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0114] The memory 74 may include volatile memory, such as random-access memory (RAM); the memory may also include non-volatile memory, such as flash memory, hard disk drive (HDD) or solid-state drive (SSD); the memory 74 may also include a combination of the above types of memory.

[0115] The processor 71 can be a central processing unit (CPU), a network processor (NP), or a combination of CPU and NP.

[0116] The processor 71 may further include a hardware chip. This hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0117] Optionally, the memory 74 is also used to store program instructions. The processor 71 can invoke the program instructions to implement the grid-type converter recovery method of the present invention.

[0118] Those skilled in the art should understand that the present invention can be implemented in many other specific forms without departing from the spirit and scope of the invention. Any changes or modifications made by those skilled in the art based on the embodiments of the present invention and the above disclosure shall fall within the protection scope of the claims.

Claims

1. A method for restoring a grid-type converter, characterized in that, Includes the following steps: S100, Each subsystem locally updates the initial state value, gain function, and allowable energy range of input and output variables of the subsystem; S200. Generate a feasible basic recovery strategy set S that satisfies the static capacity constraint. The basic recovery strategy set S includes multiple recovery strategies Si. k Let k = 1, 2, ..., n, and let k = 1 to select the initial recovery strategy S. k ; S300, Execute recovery strategy S k The system then performs power flow calculations to obtain the steady-state equilibrium point. ; Update recovery strategy S k Determine the input matrix Z and output matrix G of the interconnected system; determine whether the system satisfies the network interconnection condition and the low gain condition. S400. If the system does not meet the network interconnection condition or the low gain condition, the recovery strategy S will not be executed. k Let k = k + 1 and return to step S300; if the system satisfies the network interconnection condition and the small gain condition, proceed to step S500. S500. Determine whether the system meets the local input-output stability condition of the interconnected system. If not, if it can be stabilized by adjusting the initial state value of the subsystem. To ensure the system meets the local input-output stability condition of the interconnected system, proceed to step S600. If it cannot be achieved by adjusting the initial state value of the subsystem... To ensure the system meets the local input-output stability condition of the interconnected system, the recovery strategy S is rejected. k If k = k + 1, return to step S300; if yes, proceed to step S600. S600, Execute recovery strategy S k .

2. The recovery method for a grid-type converter according to claim 1, characterized in that, An interconnected system consisting of N subsystems can be described using state variables x, input variables u, output variables y, and external disturbance w as follows: 。 3. The recovery method for a grid-type converter according to claim 2, characterized in that, The subsystem satisfies the following subsystem condition: If any subsystem i = 1, 2, ..., N in an interconnected system containing N subsystems satisfies the definitions of local input-state stability and local input-output stability, that is, for any... , , as well as , , , To represent the constraints on the degree to which each vector of the i-th subsystem deviates from the equilibrium point, subsystem i satisfies the local input integral-state integral estimate and the local input integral-output integral estimate considering external disturbances: 。 4. The recovery method for a grid-type converter according to claim 3, characterized in that, The conditions for network interconnection are as follows: Existence The solution u=k(y) of the system's input-output relationship satisfies: ； Where k represents the functional relationship between the system input u and the output y, and the connection matrix is... Here is the system impedance matrix. This represents the mutual impedance between node i and node j. This represents the self-impedance of node i.

5. The recovery method for a grid-type converter according to claim 4, characterized in that, The conditions for low gain are as follows: ； in, Let G represent the spectral radius of matrix G.

6. The recovery method for a grid-type converter according to claim 5, characterized in that, The local input-output stability conditions of an interconnected system are as follows: in, Represents the identity matrix; ,diag is the symbol for a diagonal matrix, meaning that the diagonal is A matrix where all other elements are 0, elements For the i-th subsystem , For each subsystem, there is an input-output gain vector; The operator ∘ represents a composite function; Let be the input-output gain vector of the disturbance signal. It is the gain function of the perturbation; This is the local input range vector of the system; This is the local output range vector of the system.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the grid-type converter recovery method as described in any one of claims 1-6.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the grid-type converter recovery method as described in any one of claims 1-6.

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