Microgrid-based signal processing method, device, and electronic equipment

Abstract

The present invention discloses a signal processing method, device and electronic device based on a microgrid. Relating to the field of power system stability analysis, the method comprises: constructing a target microgrid model based on multiple components included in the microgrid; performing bifurcation operations on multiple balance points in the microgrid based on the target microgrid model to obtain bifurcation results; obtaining state space results of the target microgrid model based on the bifurcation results; and determining the signal stability results of the microgrid based on the bifurcation results and the state space results. The present invention solves the technical problem that the methods for signal stability analysis of microgrids in related technologies are all linear methods, lack connection with the system dynamic process, can only be used for the evaluation of small signal stability, but cannot provide the system oscillation and instability mechanisms that operation and maintenance personnel are more concerned about.

Description

Technical Field

[0001] The present invention relates to the field of power system stability analysis, and in particular to a signal processing method, device and electronic equipment based on a microgrid. Background Art

[0002] To improve microgrid efficiency, power systems typically operate near their stability limits. Small-signal stability ensures local system stability at these critical operating points. Assessing the small-signal stability of station-based microgrids provides a foundation for delineating stable operating zones. This facilitates adjustment of microgrid operating parameters and improves economic efficiency while ensuring system stability.

[0003] Existing methods for evaluating system small-signal stability include eigenvalue analysis, impedance analysis, frequency sweeps, and the complex torque coefficient method. However, these methods are all linear and lack a connection to system dynamics. They can only assess small-signal stability but fail to address the mechanisms of system oscillation and instability, which are of greater concern to operations and maintenance personnel. This results in low accuracy in microgrid signal stability analysis.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The embodiments of the present invention provide a signal processing method, device, and electronic device based on a microgrid, so as to at least solve the technical problem that the methods for signal stability analysis of microgrids in related technologies are all linear methods, lack connection with the dynamic process of the system, can only be used for evaluating small signal stability, but cannot provide the system oscillation and instability mechanism that operation and maintenance personnel are more concerned about.

[0006] According to one aspect of an embodiment of the present invention, a signal processing method based on a microgrid is provided, comprising: constructing a target microgrid model based on multiple components included in the microgrid; performing a bifurcation operation on multiple balance points in the microgrid based on the target microgrid model to obtain a bifurcation result, wherein the bifurcation result is used to characterize the impact of the bifurcation operation on the signal stability in the microgrid, the bifurcation operation includes a static bifurcation and a dynamic bifurcation, the static bifurcation is used to indicate a bifurcation in which the balance point in the microgrid undergoes a sudden change as a preset key parameter changes, the dynamic bifurcation is used to indicate a bifurcation in which the balance point of the microgrid changes from a stable state to a periodic oscillation or chaotic state when the preset key parameter changes, and the balance point is used to indicate an operating point at which the state variables in the microgrid model remain unchanged; obtaining a state space result of the target microgrid model based on the bifurcation result, wherein the state space result is used to characterize the impact of the bifurcation operation on the stability of the balance point of the microgrid; and determining a signal stability result of the microgrid based on the bifurcation result and the state space result.

[0007] According to another aspect of an embodiment of the present invention, a signal processing device based on a microgrid is provided, comprising: a model construction module for constructing a target microgrid model based on multiple components included in the microgrid; a bifurcation result acquisition module for performing a bifurcation operation on multiple balance points in the microgrid based on the target microgrid model to obtain a bifurcation result, wherein the bifurcation result is used to characterize the impact of the bifurcation operation on the signal stability in the microgrid, and the bifurcation operation includes a static bifurcation and a dynamic bifurcation. The static bifurcation is used to indicate a bifurcation in which the balance point in the microgrid undergoes a sudden change as a preset key parameter changes. The dynamic bifurcation is used to indicate a bifurcation in which the balance point of the microgrid changes from a stable state to a periodic oscillation or chaotic state when the preset key parameter changes. The balance point indicates an operating point at which the state variables in the microgrid model remain unchanged; a state space result acquisition module for acquiring a state space result of the target microgrid model based on the bifurcation result, wherein the state space result is used to characterize the impact of the bifurcation operation on the stability of the balance point of the microgrid; and a signal stability determination module for determining a signal stability result of the microgrid based on the bifurcation result and the state space result.

[0008] According to another aspect of an embodiment of the present invention, an electronic device is also provided, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the microgrid-based signal processing methods.

[0009] In an embodiment of the present invention, a target microgrid model is constructed based on multiple components included in a microgrid; based on the target microgrid model, a bifurcation operation is performed on multiple balance points in the microgrid to obtain a bifurcation result, wherein the bifurcation result is used to characterize the impact of the bifurcation operation on the signal stability in the microgrid, and the bifurcation operation includes a static bifurcation and a dynamic bifurcation. The static bifurcation is used to indicate a bifurcation in which the balance point in the microgrid undergoes a sudden change as a preset key parameter changes, and the dynamic bifurcation is used to indicate a bifurcation in which the balance point of the microgrid changes from a stable state to a periodic oscillation or chaotic state when the preset key parameter changes. The balance point is used to indicate an operating point at which the state variables in the microgrid model remain unchanged; based on the bifurcation result, an The state space result of the target microgrid model is obtained, wherein the state space result is used to characterize the influence of the bifurcation operation on the equilibrium point stability of the microgrid; based on the bifurcation result and the state space result, the signal stability result of the microgrid is determined, thereby achieving the purpose of performing microgrid signal stability analysis from two dimensions, namely key parameters and state space, through static bifurcation and dynamic bifurcation analysis, as well as state space analysis dimensions, thereby achieving the technical effect of improving the accuracy of microgrid signal analysis, and further solving the problem that the methods for performing microgrid signal stability analysis in related technologies are all linear methods, lack connection with the system dynamic process, can only be used for the evaluation of small signal stability, but cannot provide the system oscillation and instability mechanism that operation and maintenance personnel are more concerned about. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0011] Figure 1 is a flow chart of a signal processing method based on a microgrid according to an embodiment of the present invention;

[0012] Figure 2 1 is a schematic structural diagram of an optional permanent magnet direct drive wind power grid-connected system according to an embodiment of the present invention;

[0013] Figure 3 is a flow chart of an optional microgrid-based signal processing method according to an embodiment of the present invention;

[0014] Figure 4 is a schematic diagram of stability results of an optional microgrid according to an embodiment of the present invention;

[0015] Figure 5 2 is a schematic diagram of a microgrid-based signal processing device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0017] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] First, to facilitate understanding of the embodiments of the present invention, some of the terms or nouns involved in the present invention are explained below:

[0019] The Hartman-Grobman theorem is a theorem about nonlinear dynamical systems. It states that the local dynamic behavior of a nonlinear system near an equilibrium point can be described by a linear approximation. The theorem states that near an equilibrium point, the local dynamic behavior of a nonlinear system can be described by the eigenvalues ​​of a linear system, thus simplifying the analysis and understanding of the stability and evolution characteristics of nonlinear systems.

[0020] According to an embodiment of the present invention, an embodiment of a method for signal processing based on a microgrid is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0021] Figure 1 : is a flow chart of a signal processing method based on a microgrid according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0022] Step S102: constructing a target microgrid model based on multiple components included in the microgrid;

[0023] Alternatively, a microgrid refers to a small power system composed of multiple distributed energy resources (such as solar, wind, and fuel cells), capable of independent operation, self-dispatching, and interconnection. A microgrid can be interconnected with the main power grid or operate independently during power outages, providing local users with a reliable, efficient, and clean power supply. The multiple components of a microgrid may include, but are not limited to: multiple distributed energy devices (such as solar, wind, and fuel cells), energy storage devices, and loads.

[0024] Optionally, the target microgrid model can be obtained by integrating nonlinear differential-algebraic equation models corresponding to multiple components, and is used to characterize the nonlinear operating characteristics of each component in the microgrid, as well as the interactions and mutual influences between them.

[0025] In an optional embodiment, a target microgrid model is constructed based on multiple components included in the microgrid, including: constructing an initial microgrid model based on the multiple components, wherein the initial microgrid model is a nonlinear model; determining a balanced solution manifold of the microgrid based on the initial microgrid model; and linearizing the initial microgrid model based on the balanced solution manifold to obtain a target microgrid model.

[0026] Optionally, the initial microgrid model can be obtained based on a nonlinear differential-algebraic equation model corresponding to multiple components, which is used to characterize the nonlinear operating characteristics of each component in the microgrid, as well as the interactions and mutual influences between them; the equilibrium solution manifold is used to describe the trajectories of the multiple equilibrium points included in the microgrid as the parameters change, that is, the equilibrium solution manifold describes the trajectory of the equilibrium points as the parameters change. Under the change of preset key parameters, the equilibrium point is extended to obtain the equilibrium solution manifold. The equilibrium point in the microgrid is used to indicate the operating point where the state variables in the microgrid model remain unchanged, that is, the operating point where the state variables of the microgrid reach a static or steady state. It should be noted that the microgrid system includes multiple nonlinear and mutually coupled components. Linearization near the equilibrium point can convert these nonlinear equations into linear equations, thereby simplifying the complexity of the microgrid model and the difficulty of solving it.

[0027] In an optional embodiment, an initial microgrid model is constructed based on multiple components, including: establishing nonlinear differential-algebraic equation group models corresponding to the multiple components; performing power flow calculations based on the nonlinear differential-algebraic equation group models corresponding to the multiple components to obtain power flow calculation results corresponding to the multiple components, wherein the power flow calculation results are at least used to indicate the voltage distribution and power distribution of the corresponding components; and performing model integration based on the nonlinear differential-algebraic equation group models corresponding to the multiple components and the power flow calculation results corresponding to the multiple components to obtain the initial microgrid model.

[0028] Optionally, a nonlinear differential-algebraic equation model is used to characterize the nonlinear operating characteristics of the corresponding components. It can be used, but is not limited to, nonlinear modeling of components such as power sources (such as distributed generators, renewable energy generation systems), energy storage devices (such as batteries, supercapacitors), loads, and power electronic interfaces (such as inverters and rectifiers) in the microgrid to obtain nonlinear differential-algebraic equation models corresponding to multiple components. The model of each component will contain differential and algebraic equations that describe its dynamic behavior and steady-state characteristics. The power flow calculation is mainly used to determine the steady-state voltage, current, and power distribution of the microgrid under given operating conditions. The power flow calculation results are obtained by solving the nonlinear differential-algebraic equation models corresponding to multiple components. The nonlinear differential-algebraic equation models corresponding to the multiple components describe the relationship between the voltage and current of each node in the microgrid, as well as the power balance between the power source and the load.

[0029] By integrating the nonlinear differential-algebraic equation models corresponding to multiple components and the power flow calculation results corresponding to multiple components, the resulting integrated model serves as the initial microgrid model. This initial microgrid model not only includes differential equations describing the dynamic behavior of components in the microgrid (such as the differential equations describing the charging and discharging processes of energy storage devices), but also algebraic equations describing the steady-state characteristics of the microgrid (i.e., the results of the power flow calculation). Together, these equations form a complete mathematical model capable of describing both the dynamic and steady-state behavior of the microgrid. In summary, power flow calculation is a key step in establishing the initial microgrid model, providing the algebraic equations describing the steady-state characteristics of the microgrid. The initial microgrid model combines the differential equations of each component in the microgrid with the algebraic equations of the power flow calculation to form a mathematical model that comprehensively describes both the dynamic and steady-state behavior of the microgrid.

[0030] Optionally, a nonlinear differential-algebraic equation model of each component in the microgrid is established, and power flow calculation is performed based on the nonlinear differential-algebraic equation model of each component to obtain an integrated nonlinear differential-algebraic equation model of the microgrid, i.e., an initial microgrid model, which is expressed by the following formula:

[0031]

[0032] Where x=[x1,x2,…,x n ] T is the n-dimensional column vector of the corresponding state variable in the microgrid, Represents the derivative vector of the state variable, which is used to reflect the dynamic change process of the state variable; y=[y1,y2,…,y m ] T is the m-dimensional column vector of the corresponding algebraic variables in the microgrid, p=[p1,p2,…,p k ,] T is a k-dimensional column vector of preset key parameters, where n, m, and k represent the number of corresponding state variables, algebraic variables, and preset key parameters in the microgrid, respectively. f and g are n-dimensional and m-dimensional column vectors of the microgrid's state equation and algebraic equation, respectively. The nonlinear differential-algebraic equation model of the microgrid forms an (n+k)-dimensional constraint manifold in the (n+m+k)-dimensional space of (x, y, p).

[0033] State variables can be used to describe the dynamic characteristics of the generator, excitation system, and load in a microgrid system. In the mathematical model of a nonlinear differential-algebraic equation system, state variables can include, but are not limited to, generator rotor angle, exciter output, and load node voltage and angle. These variables describe the dynamic behavior of each component in the microgrid over time.

[0034] Algebraic variables represent the algebraic variables required to interconnect components containing state quantities in a system through an electrical network. In the mathematical model of a nonlinear differential-algebraic equation system, algebraic variables can include, but are not limited to, the phase angle and amplitude of node voltages and the stator current of a generator. These variables are generated when components in a microgrid are interconnected through the electrical network and are used to describe the static structure or constraints of the microgrid system.

[0035] Preset key parameters describe the microgrid's physical characteristics, control strategy, and operating environment. Within the mathematical model of a nonlinear differential-algebraic equation system, these parameters may include, but are not limited to: time constants related to the decay of various state variables; coefficients for the dynamic characteristics of the load; parameters of the microgrid's electrical network components (such as resistance, inductance, and capacitance); and parameters that consider load variations when the microgrid undergoes various bifurcations. These preset key parameters significantly influence the dynamic behavior and stability of the microgrid, and their adjustment can alter the microgrid's operational characteristics and stability.

[0036] By setting the differential equations in the microgrid nonlinear differential-algebraic equations model to 0, the formula is as follows:

[0037]

[0038] Solve for the equilibrium point of the microgrid under fixed parameter conditions. Based on the boundary method and the minimum extended system, the numerical extension of the microgrid equilibrium solution manifold under parameter changes is achieved, and the k-dimensional equilibrium solution manifold E of the microgrid is obtained, which is expressed as follows.

[0039] E={(x,y,p):f(x,y,p)=0,g(x,y,p)=0}

[0040] In an optional embodiment, the initial microgrid model is linearized based on the equilibrium solution manifold to obtain a target microgrid model, including: determining a target equilibrium point from multiple equilibrium points based on the equilibrium solution manifold; calculating a target Jacobian matrix corresponding to the target equilibrium point; and linearizing the initial microgrid model according to the target Jacobian matrix to obtain a target microgrid model.

[0041] Optionally, in the established initial microgrid model, it is first necessary to determine the balance point in the microgrid system. The balance point can be obtained by, but is not limited to, setting the derivatives of each equation in the initial microgrid model to 0 and solving them, thereby obtaining multiple balance points included in the microgrid.

[0042] Optionally, since the initial microgrid model is a nonlinear model, which can be complex to analyze, linearization can be used to simplify the analysis. Based on the Hartman-Grobman theorem, the nonlinear microgrid model can be linearized using the Jacobian matrix (which describes the local linear approximation of a function near a certain point). After linearization, the small-signal stability of each equilibrium point in the microgrid can be more easily analyzed. The Jacobian matrix can be used to describe the properties at the corresponding equilibrium point. The Jacobian matrix contains partial derivative information for each component and network structure in the microgrid. It should be noted that the equilibrium manifold solution directly shows how the equilibrium point moves or evolves in parameter space as key parameters change. Since the application of the Hartman-Grobman theorem is local, that is, it is valid near the equilibrium point, by solving the equilibrium manifold, it is possible to determine which equilibrium points are suitable for application of the theorem and the location of these equilibrium points in parameter space, so as to determine the target equilibrium point from multiple equilibrium points and construct the target microgrid model.

[0043] Specifically, in the process of linearizing the initial microgrid model, the linearization of the initial microgrid line model can be performed based on the Jacobian matrix (such as the target Jacobian matrix) corresponding to a specific equilibrium point (such as the target equilibrium point). The target equilibrium point can be, but is not limited to, a hyperbolic equilibrium point (x * ,y * ,p * ), where x* 、y * 、p * They respectively represent the values ​​of the corresponding state variables, algebraic variables and preset key parameters at the hyperbolic equilibrium point.

[0044] Optionally, for any hyperbolic equilibrium point (x * ,y * ,p * ), based on the Hartman-Grobman theorem, we can get that at the hyperbolic equilibrium point (x * ,y * ,p * ) in a certain area makes the flow of the microgrid nonlinear model Satisfy the following form:

[0045]

[0046] in, For flow About (x * ,y * ,p * ) linearized flow. This also shows that the microgrid nonlinear model and its linearized model (target microgrid model) at any hyperbolic equilibrium point (x * ,y * ,p * ) are topologically equivalent within their neighborhood.

[0047] Therefore, for the study of the local state space characteristics at the equilibrium point, the nonlinear model of the microgrid can be linearized by the Jacobian matrix as follows:

[0048]

[0049] Among them, Δx represents the linearized variable of the system state variable at the equilibrium point, is the derivative of the linearized variable Δx, J represents the complete Jacobian matrix of the microgrid, and the submatrix D x f、D x g represents the Jacobian matrix of function f with respect to x and y, respectively, D y f and D y g represents the Jacobian matrix of function g with respect to x and y respectively, where D x The expression of the matrix operation f is D x g、D x g、D y The operation method of g is the same as that of D x f is the same and will not be repeated here.

[0050] When D y When g is non-singular, the linearized equations of the microgrid can eliminate algebraic variables to obtain the following ordinary differential equations as the target microgrid model:

[0051]

[0052] The Jacobian matrix A=D corresponding to the target microgrid model x fD y f(D y g) -1 D x g.

[0053] For any non-hyperbolic equilibrium point (x h ,y h ,p h ), which may lead to the failure of local linearization theory, so the instability mechanism of microgrid can be further analyzed through nonlinear bifurcation theory.

[0054] Step S104: performing a bifurcation operation on multiple balance points in the microgrid based on the target microgrid model to obtain a bifurcation result;

[0055] It is understandable that when any of the preset key parameters corresponding to the microgrid (called bifurcation parameters) changes, the microgrid's equilibrium point may shift, causing a change in the topology of the system's state space. In microgrids, due to the presence of a large number of distributed power sources and complex electrical wiring networks, the system's nonlinear characteristics are significant, making bifurcation more likely to occur.

[0056] Optionally, the bifurcation result is used to characterize the impact of the bifurcation operation on the signal stability in the microgrid, where the signal stability is the small signal stability corresponding to the microgrid. Small signal stability refers to the stability problem of the microgrid under small disturbances. In the microgrid, due to the presence of various distributed energy resources and various control devices, the system may be affected by external disturbances or internal changes and become unstable. The response of the microgrid under small disturbances can be studied through small signal stability analysis. The bifurcation result is used to characterize the impact of the bifurcation operation on the signal stability in the microgrid. The bifurcation operation includes static bifurcation and dynamic bifurcation. Static bifurcation is used to indicate the bifurcation in which the balance point in the microgrid undergoes a sudden change as the preset key parameters change. Dynamic bifurcation is used to indicate the bifurcation in which the balance point of the microgrid changes from a stable state to a periodic oscillation or chaotic state when the preset key parameters change. The balance point is used to indicate the working point where the state variables in the microgrid model remain unchanged.

[0057] Optionally, bifurcation operations are performed on multiple equilibrium points in the microgrid, and the local stability of the equilibrium points is analyzed through static bifurcation and dynamic bifurcation, so as to more accurately analyze the impact of bifurcation points on the non-local stability of the microgrid state space topology structure.

[0058] In an optional embodiment, based on a target microgrid model, a bifurcation operation is performed on multiple balance points in the microgrid to obtain a bifurcation result, including: determining a parameter variation range of a preset key parameter based on a balance solution manifold; determining a first balance point where a sudden change occurs from multiple balance points of the microgrid in a process in which the preset key parameter changes within the parameter variation range; calculating a first Jacobian matrix corresponding to the first balance point; determining eigenvalue change information of the first Jacobian matrix in a process in which the preset key parameter changes within the parameter variation range; determining a bifurcation point in the microgrid and a corresponding bifurcation point type based on the eigenvalue change information; and determining a bifurcation result based on the bifurcation point and the bifurcation point type.

[0059] Optionally, the changes in preset key parameters near the equilibrium point are analyzed to analyze the impact of small changes in preset key parameters (such as load, renewable energy generation, and control strategy) near the equilibrium point on the operating state of the microgrid system. Static bifurcation refers to the phenomenon that the stability of the equilibrium point of the microgrid system changes near the bifurcation value, which can be a bifurcation caused by topological changes in the number and stability of the microgrid equilibrium points; dynamic bifurcation refers to the phenomenon that the dynamic behavior of the system (such as equilibrium points, periodic orbits, etc.) suddenly changes when the system parameters change continuously, which can be a Hopf bifurcation caused by topological changes in the microgrid state space. When the preset key parameters near the equilibrium point change, the eigenvalue change characteristics of the Jacobian matrix corresponding to different types of bifurcation points are different. Based on this, based on the characteristics of the eigenvalue changes of the Jacobian matrix corresponding to each equilibrium point with the preset key parameters (i.e., eigenvalue change information), the bifurcation points and types of bifurcation points in the microgrid can be identified, and the bifurcation results can be accurately identified and analyzed. The static bifurcation points may include, but are not limited to, saddle-node bifurcations (SNB), limit-induced bifurcations (LIB), and singular-induced bifurcations (SIB), and the dynamic bifurcation points may include, but are not limited to, Hopf bifurcations.

[0060] In an optional embodiment, based on the eigenvalue change information, the bifurcation point in the microgrid and the corresponding bifurcation point type are determined, including: when the eigenvalue change information indicates that the eigenvalue of the first Jacobian matrix crosses the imaginary axis from the real axis of the complex plane, determining that the bifurcation point is a static bifurcation point, and the corresponding bifurcation point type is a static bifurcation point type; or when the eigenvalue change information indicates that the eigenvalue of the first Jacobian matrix crosses the imaginary axis in the complex plane in the form of conjugate complex roots, determining that the bifurcation point is a dynamic bifurcation point, and the corresponding bifurcation point type is a dynamic bifurcation point type.

[0061] Optionally, when the eigenvalues ​​of the Jacobian matrix cross the imaginary axis from the real axis of the complex plane, it corresponds to a real bifurcation in a static bifurcation. In this process, the eigenvalue changes from negative to positive (or vice versa), which causes the stability of the system to change when the parameters change, that is, a bifurcation phenomenon occurs. Due to the change in the eigenvalue, the topological structure of the system also changes, which is manifested as a sudden change in the number of equilibrium points and stability. This change is the core feature of a static bifurcation. When the eigenvalues ​​of the Jacobian matrix cross the imaginary axis from below or above the complex plane in the form of conjugate complex roots, it corresponds to a dynamic bifurcation. Unlike static bifurcation, dynamic bifurcation involves changes in the number and stability of closed trajectories (such as limit cycles). The crossing of the eigenvalue across the imaginary axis in the form of conjugate complex roots usually means the existence of closed trajectories (such as limit cycles) in the system and changes in their stability. This change is an important feature of dynamic bifurcation.

[0062] Optionally, a static bifurcation can be a bifurcation caused by a topological change in the number and stability of the microgrid's balancing points, where the eigenvalues ​​of the Jacobian matrix A cross from the real axis to the imaginary axis of the complex plane. Based on the way the eigenvalues ​​cross, static bifurcations can be divided into saddle-node bifurcations (SNBs), limit-induced bifurcations (LIBs), and singular-induced bifurcations (SIBs). The specific formulas are as follows:

[0063]

[0064]

[0065] Where λ represents an eigenvalue of the Jacobian matrix A that is about to cross the imaginary axis, Re(λ) represents the real part of the eigenvalue, p is the bifurcation parameter of the system, and p c represents the critical parameter value where bifurcation occurs, Indicates that the bifurcation parameter approaches the critical parameter p from the right side of the number axis c , Indicates that the bifurcation parameter approaches the critical parameter p from the left side of the number axis c , a represents a real number less than 0, and b represents a real number greater than 0. In addition, the above bifurcations all consider the case where the eigenvalue crosses the imaginary axis from the negative half plane of the complex plane.

[0066] Optionally, the dynamic bifurcation can be a Hopf bifurcation that causes a topological change in the microgrid state space, where the eigenvalues ​​of the state Jacobian matrix A cross the imaginary axis in the complex plane as conjugate complex roots. The limit cycle generated by the Hopf bifurcation has global properties and nonlinear characteristics. The stable limit cycle Γ generated by supercritical and subcritical Hopf bifurcations s and the unstable limit cycle Γ u are the ω limit set and α limit set of the trajectory in a neighborhood on both sides of the limit cycle, respectively. The corresponding formulas are expressed as follows:

[0067]

[0068] Where R represents a set of real numbers, t is the running time of the microgrid state trajectory, and t' is a certain moment in the running of the state trajectory.

[0069] Stable limit cycle Γ s This will cause the microgrid to produce continuous constant amplitude oscillation. Unstable limit cycle Γ u The microgrid will generate a stable equilibrium point as the center of attraction, Γ u is the attraction domain of the attraction boundary, and Γ u The trajectories in the outer neighborhood will oscillate and diverge.

[0070] Step S106: Based on the bifurcation result, a state space result of the target microgrid model is obtained, wherein the state space result is used to characterize the impact of the bifurcation operation on the stability of the balance point of the microgrid;

[0071] Optionally, the microgrid's equilibrium stability refers to the stability of the microgrid's state space. State space stability indicates the ability of state variables (such as voltage, current, and frequency) to maintain or recover to equilibrium when subjected to disturbances (such as load changes or energy device failures) during microgrid operation. Bifurcation results include static and dynamic bifurcation analysis results. By analyzing the local stability of the equilibrium point through static and dynamic bifurcations, we can more accurately analyze the impact of bifurcation points on the nonlocal stability of the microgrid's state space topology.

[0072] In an optional embodiment, based on the bifurcation result, a state space result of the target microgrid model is obtained, including: determining a second balance point in the microgrid based on the bifurcation result; determining a local stable manifold and a local unstable manifold corresponding to the second balance point, wherein the local stable manifold is used to indicate a set of parameter trajectory changes that the microgrid can return to the second balance point after being subjected to a target disturbance within a predetermined neighborhood of the second balance point; the local stable manifold is used to indicate a set of parameter trajectory changes that the microgrid is away from the second balance point after being subjected to a target disturbance within a predetermined neighborhood; based on the local stable manifold and the local unstable manifold, a state space topology structure corresponding to the target microgrid model is constructed, wherein the state space topology structure is used to indicate a connection mode and stability relationship between different states in the state space of the microgrid; and the state space topology structure is analyzed to obtain a state space result.

[0073] Optionally, a bifurcation is a phenomenon in which the stability of a system structure changes suddenly. When a certain system parameter changes, the system's equilibrium point may shift, resulting in a change in the topology of the system's state space. This second equilibrium point can be an isolated equilibrium point. An isolated equilibrium point indicates an independent and stable equilibrium point in the microgrid that does not coexist or depend on other equilibrium points. The target disturbance can be a small signal disturbance, i.e., a disturbance with an amplitude less than a predetermined amplitude. In the microgrid's islanded operation mode, when a fault occurs in the main grid, the microgrid must be able to maintain its own stable operation. An isolated equilibrium point is a unique and stable state point in the microgrid's state space and is of great significance in the microgrid. By analyzing the stability and manifold of an isolated equilibrium point, the dynamic behavior of the microgrid near that point can be understood, and the stability and dynamic characteristics of the entire system can be inferred. By analyzing the local topology of the microgrid's state space, the conditions under which the microgrid can maintain stable operation can be determined, and corresponding control strategies can be formulated accordingly.

[0074] It should be noted that a locally stable manifold refers to a set of trajectories near an isolated equilibrium point that allows the microgrid's operating state to return to that equilibrium point after being perturbed by a small signal. This reflects the microgrid's stable range and stable properties near that point. A locally unstable manifold refers to a set of trajectories near an isolated equilibrium point that causes the microgrid's operating state to move away from that equilibrium point after being perturbed by a small signal. This reflects the microgrid's unstable range and unstable properties near that point. By analyzing the locally stable and unstable manifolds corresponding to an isolated equilibrium point, the local topology of the microgrid near that point can be clearly depicted, including which states are stable and which are unstable, as well as the conversion relationship between them. This is crucial for understanding and controlling the dynamic behavior of the microgrid.

[0075] Specifically, the occurrence of bifurcation will affect the topology of the station microgrid state space. Local stable manifolds can be used and unstable manifolds Characterize the local topology of the state space:

[0076]

[0077] Among them, x * It is used to simply represent an isolated equilibrium point in the state space of the microgrid, and U is the equilibrium point x * A neighborhood of . From this we can get the equilibrium point x * Globally stable manifold and globally unstable manifolds

[0078] Step S108: determining a signal stability result of the microgrid based on the bifurcation result and the state space result.

[0079] Optionally, based on the bifurcation results and the state space results, the signal stability results of the microgrid are comprehensively determined. It should be noted that the purpose of performing microgrid signal stability analysis from two dimensions, key parameters and state space, through static bifurcation analysis and dynamic bifurcation analysis, as well as state space analysis, is to not only consider the stability of the microgrid system under different states, but also the impact of parameter changes on the stability of the microgrid system, which can significantly improve the accuracy of microgrid signal analysis.

[0080] In an optional embodiment, based on the bifurcation result and the state space result, the signal stability result of the microgrid is determined, including: analyzing the hyperbolic equilibrium point in the target microgrid model to obtain the hyperbolic equilibrium point result, wherein the hyperbolic equilibrium point analysis is used to indicate the impact of the hyperbolic equilibrium point on the signal stability in the microgrid, and the hyperbolic equilibrium point is the equilibrium point in the target microgrid model, where the eigenvalue of the corresponding Jacobian matrix has a non-zero real part; based on the hyperbolic equilibrium point result, the bifurcation result and the state space result, the signal stability result of the microgrid is determined.

[0081] Alternatively, hyperbolic equilibrium points (i.e., equilibrium points with nonzero real parts of eigenvalues) in the equilibrium manifold solution are key to analyzing small-signal stability. Near these points, eigenvalue analysis of the corresponding Jacobian matrix of the linearized microgrid model (i.e., the target microgrid model) can be used to determine the stability of the microgrid's operation, as well as the degree and direction of instability. Bifurcation is a key characteristic of the dynamic behavior of nonlinear systems. It describes the changes in the number and stability of equilibrium points or periodic orbits as the microgrid's operating parameters change. Static bifurcations, such as saddle-node bifurcations and limit-induced bifurcations, focus on the number and stability of microgrid equilibrium points as parameters change. Dynamic bifurcations (such as Hopf bifurcations) focus on the transition from stable equilibrium points to unstable periodic orbits. Analysis of bifurcation points helps predict the operating characteristics of the microgrid under different parameters and proactively identify potential instability risks. In addition to analyzing the stability of the equilibrium points themselves, the stability of the state space near the equilibrium points also needs to be considered. By analyzing the stability of the state space near the equilibrium points, the microgrid's sensitivity to disturbances and its resilience can be understood.

[0082] Through the above method, in the process of microgrid small signal stability analysis, the hyperbolic equilibrium point results, static bifurcation results, dynamic bifurcation results and state space results are comprehensively considered, the consideration factors are more comprehensive, and the microgrid signal stability results obtained on this basis are more accurate and reliable.

[0083] Optionally, a comprehensive assessment of the small-signal stability of the microgrid can be conducted from both the state space and parameter space dimensions, based on, but not limited to, hyperbolic equilibrium point results, static bifurcation results, dynamic bifurcation results, and state space results, to obtain the corresponding signal stability results for the microgrid. The state space describes all possible operating states of the microgrid, while the parameter space describes all possible values ​​of the microgrid's corresponding preset key parameters. In the parameter space, by defining the small-signal stability region, it is possible to determine for which parameter combinations the microgrid system is stable. This helps guide the design and operation of the microgrid. In the state space, stable manifold analysis can be used to evaluate the stability of the state space near the equilibrium point and determine the attraction domain of the small-signal stable equilibrium point. This helps understand the dynamic behavior of the microgrid system after being disturbed.

[0084] Specifically, a comprehensive evaluation of the small signal stability of the microgrid can be given from two dimensions: the state space and the parameter space. In the parameter space, the small signal stability region of the microgrid is defined to evaluate the small signal stability of the microgrid. The multi-parameter small signal stability region Ω in the parameter space SSSR The formula is as follows:

[0085] Ω SSSR ={p∈R k :max{Re(λ)}<0}

[0086] In the state space, the attraction domain of the small-signal stable equilibrium point of the microgrid is evaluated using the stable manifold. The formula for the attraction domain of the small-signal stable equilibrium point in the state space is expressed as follows.

[0087]

[0088] It should be noted that a stable manifold is equivalent to describing the set of points in a microgrid's state space that converge toward a specific fixed point or periodic orbit as time approaches positive infinity. Simply put, it represents how the microgrid's operating state converges to a stable equilibrium point or periodic trajectory over time. In microgrid small-signal stability analysis, a stable manifold can be used to describe how the system state gradually recovers to a stable equilibrium point along a trajectory when subjected to small perturbations. The domain of attraction is a set closely associated with a specific attracting set (usually a stable equilibrium point in microgrids). A trajectory starting from any point in this set, if not subject to external perturbations or after the perturbation disappears, will eventually converge to this specific attracting set. In microgrid small-signal stability analysis, assessing the domain of attraction is a crucial step in determining system robustness and stability. By determining the domain of attraction, we can understand the region within which the microgrid's operating state can return to a stable equilibrium point after a small perturbation, thereby assessing the stability and reliability of the microgrid's operation.

[0089] Specifically, the microgrid's stable equilibrium point can be first determined. Next, the stable manifolds near the stable equilibrium point are analyzed to understand how the microgrid's operating state gradually converges to the stable equilibrium point along these manifolds. Then, based on the information from the stable manifolds, the attraction domain of the stable equilibrium point can be determined. The dynamic behavior of the microgrid under different initial conditions can be simulated to observe which initial conditions lead the microgrid's operating state to converge to the stable equilibrium point. Finally, by combining the analysis results of the stable manifolds and the attraction domain, a comprehensive evaluation of the attraction domain of the microgrid's small-signal stable equilibrium point can be performed. This evaluation provides information on the stability and robustness of the microgrid under different initial conditions, providing an important reference for stable operation and optimal control of the microgrid.

[0090] Through the above steps S102 to S108, the purpose of performing microgrid signal stability analysis from two dimensions, namely key parameters and state space, can be achieved through static bifurcation analysis and dynamic bifurcation analysis, as well as state space analysis, thereby achieving the technical effect of improving the accuracy of microgrid signal analysis, and further solving the problem that the methods for performing microgrid signal stability analysis in related technologies are all linear methods, lack connection with the dynamic process of the system, and can only be used for the evaluation of small signal stability, but cannot provide the technical problem of system oscillation and instability mechanism that operation and maintenance personnel are more concerned about.

[0091] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method, which is applied to a permanent magnet direct-drive wind power grid-connected system. Figure 2 : is a structural diagram of an optional permanent magnet direct drive wind power grid-connected system according to an embodiment of the present invention. Specifically, the energy conversion system of the microgrid is modeled as follows:

[0092]

[0093] Among them, i sd is the d-axis current on the stator side of the generator, i sq is the q-axis current on the stator side of the generator, u sd is the d-axis voltage on the stator side of the generator, u sq is the q-axis voltage on the stator side of the generator, L sd is the d-axis self-inductance of the generator stator side, L sq is the q-axis self-inductance of the generator stator side, ω s is the rotor electrical angular velocity, R s is the stator resistance, Ψ f is the rotor flux, P w is the wind power, T e is the electromagnetic torque, n p is the pole pair number, and D is the viscous friction coefficient.

[0094] The machine-side control module of the microgrid is modeled as:

[0095]

[0096] Among them, k pd k is the proportional coefficient of the inner loop PI regulator of the d-axis current on the stator side of the generator, id is the integral coefficient of the inner loop PI regulator of the d-axis current on the stator side of the generator, k pq k is the proportional coefficient of the inner loop PI regulator of the q-axis current on the stator side of the generator, iq k is the integral coefficient of the inner loop PI regulator of the q-axis current on the stator side of the generator, pt k is the proportional coefficient of the torque outer loop PI regulator, it is the integral coefficient of the torque outer loop PI regulator, i sd.ref is the given value of the d-axis current, i sq.ref is the given value of q-axis current, T e.ref is the given value of the generator electromagnetic torque.

[0097] The grid-side control module of the microgrid is modeled as:

[0098]

[0099] Among them, k pi is the proportional coefficient of the grid-side current inner loop PI regulator, k ii k is the integral coefficient of the grid-side current inner loop PI regulator, pu k is the proportional coefficient of the grid-side DC voltage outer loop PI regulator, iu is the integral coefficient of the grid-side DC voltage outer loop PI regulator, i gd.ref is the given value of the grid-side d-axis current, i gq.ref is the given value of the grid-side q-axis current, u dc.ref is the given value of the grid-side DC capacitor voltage.

[0100] The small signal stability of the microgrid is evaluated from two dimensions: state space and parameter space. Figure 3 is a flow chart of an optional signal processing method based on a microgrid according to an embodiment of the present invention. Figure 3 As shown, the method mainly includes the following steps:

[0101] S1. Station microgrid modeling: Establish a nonlinear differential-algebraic equation model of the station microgrid components and network structure.

[0102] S2. Solving the equilibrium solution manifold: Extend the equilibrium point under the change of key parameters to obtain the equilibrium solution manifold of the station microgrid, where the equilibrium solution manifold is used to describe the trajectory of the equilibrium point in the microgrid as the parameters change.

[0103] S3. Microgrid model linearization: Based on the Hartman-Grobman theorem, the microgrid nonlinear model is linearized through the Jacobian matrix, the small signal stability of the hyperbolic equilibrium point is analyzed, and the hyperbolic equilibrium point analysis results are obtained.

[0104] S4. Static Bifurcation Analysis: Analyze the mechanism by which bifurcations lose small-signal stability through static bifurcations. For static bifurcations in which the number and stability of the station microgrid's equilibrium states undergo topological changes, the eigenvalues ​​of the state Jacobian matrix cross from the real axis to the imaginary axis in the complex plane. Based on the eigenvalue crossing pattern, static bifurcations can be classified as saddle-node bifurcations (SNBs), limit-induced bifurcations (LIBs), and singular-induced bifurcations (SIBs).

[0105] S5. Dynamic Bifurcation Analysis: Analyze the mechanism by which the bifurcation point loses small-signal stability through dynamic Hopf bifurcation. For dynamic Hopf bifurcations in which the state space of the station microgrid undergoes topological changes, the eigenvalues ​​of its state Jacobian matrix cross the imaginary axis in the complex plane as conjugate complex roots.

[0106] S6. State space analysis: Based on the bifurcation results obtained from static bifurcation analysis and dynamic bifurcation analysis, the impact mechanism of bifurcation operation on the state space stability of the microgrid is analyzed;

[0107] S7. Small signal stability assessment: Based on the analysis results of steps S3 to S6, a comprehensive assessment of the small signal stability of the microgrid is given from the two dimensions of state space and parameter space. Specifically, based on the small signal stability analysis of the hyperbolic equilibrium point of the station microgrid, the static and dynamic bifurcation analysis of the bifurcation point, and the stability analysis of the state space near the equilibrium point, a comprehensive assessment of the small signal stability of the microgrid can be given from the two dimensions of state space and parameter space to obtain the stability result of the microgrid. The visual representation of the stability result of the microgrid is as follows: Figure 4 As shown, Figure 4 The comprehensive evaluation results of the microgrid small signal stability are shown in the figure. L represents the voltage amplitude on the load side of the microgrid, δ L Represents the voltage phase angle on the load side of the microgrid, P w Represents the active power provided to the grid by the microgrid generator. The three-dimensional coordinates are all in per unit (pu). Ho represents the microgrid parameter P at the time when the same-clinic bifurcation occurs w The value of P F P represents the microgrid parameter P at the time when saddle-node bifurcation occurs w The value of P H represents the microgrid parameter P at the time when Hopf bifurcation occurs w value.

[0108] The following evaluation results are all calculated with the parameter Pw The increasing process of P w =P Ho = 0.366pu, the stable manifold and the unstable manifold of the saddle point E2 form a Figure 4 The red homoclinic ring is shown in . At this time, the saddle point E2 is a homoclinic saddle point. The homoclinic ring is both a stable manifold of E2 and an unstable manifold of E2. The formation of the homoclinic ring will greatly limit the attraction domain of the stable focus E1, so that the attraction domain of E1 is reduced from almost the entire state space (the state space except the saddle point E2) to the homoclinic ring. After the homoclinic bifurcation occurs, the homoclinic ring disappears, but a series of unstable limit cycles are left, such as Figure 4 As shown in the blue circle sequence. w As the value of P increases, the unstable limit cycle gradually shrinks until P w =P H =1.261pu, the limit cycle shrinks to the equilibrium point E1, making E1 change from a stable focus to an unstable focus, that is, a Hopf bifurcation occurs. At this time, the microgrid loses the local stability of the equilibrium point E1, and there is no stable equilibrium point, which will completely lose the small signal stability. w =P SNB =1.483pu, a saddle-node bifurcation occurs in the system, and the focus E1 collides with the saddle point E2 and disappears. After that, there is no equilibrium point in the microgrid, and naturally it is impossible to maintain small-signal stability.

[0109] In an embodiment of the present invention, the evaluation of small-signal stability under simultaneous changes in multiple parameters is achieved through a nonlinear bifurcation analysis method, and the construction of an attraction domain of a small-signal stable equilibrium point in the state space is achieved by retaining the nonlinear characteristics of the microgrid system. In the actual operation of the station microgrid, the comprehensive evaluation of the small-signal stability of the microgrid by the embodiment of the present invention improves the safety and reliability of the stable operation of the microgrid and reduces the risk of instability when the microgrid operates near the stability boundary. In this embodiment, a signal processing device based on a microgrid is also provided, which is used to implement the above-mentioned embodiments and preferred implementation methods, and those that have been explained will not be repeated. As used below, the terms "module" and "device" can implement a combination of software and / or hardware that implements predetermined functions. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceived.

[0110] According to an embodiment of the present invention, there is also provided an embodiment of a device for implementing the above-mentioned microgrid-based signal processing method. Figure 5 is a structural diagram of a signal processing device based on a microgrid according to an embodiment of the present invention. Figure 5As shown, the above-mentioned microgrid-based signal processing device includes: a model building module 500, a bifurcation result acquisition module 502, a state space result acquisition module 504, and a signal stability determination module 506, wherein:

[0111] A model building module 500 is used to build a target microgrid model based on multiple components included in the microgrid;

[0112] A bifurcation result acquisition module 502 is connected to the model construction module 500 and is used to perform a bifurcation operation on multiple balance points in the microgrid based on the target microgrid model to obtain a bifurcation result, wherein the bifurcation result is used to characterize the impact of the bifurcation operation on the signal stability in the microgrid. The bifurcation operation includes a static bifurcation and a dynamic bifurcation. The static bifurcation is used to indicate a bifurcation in which the balance point in the microgrid undergoes a sudden change as a preset key parameter changes. The dynamic bifurcation is used to indicate a bifurcation in which the balance point of the microgrid changes from a stable state to a periodic oscillation or chaotic state when the preset key parameter changes. The balance point is used to indicate an operating point in the microgrid model where the state variables remain unchanged;

[0113] A state space result acquisition module 504 is connected to the bifurcation result acquisition module 502 and is used to obtain a state space result of the target microgrid model based on the bifurcation result, wherein the state space result is used to characterize the impact of the bifurcation operation on the balance point stability of the microgrid;

[0114] The signal stability determination module 506 is connected to the state space result acquisition module 504 and is used to determine the signal stability result of the microgrid based on the bifurcation result and the state space result.

[0115] In an embodiment of the present invention, a model construction module 500 is provided for constructing a target microgrid model based on multiple components included in the microgrid; a bifurcation result acquisition module 502 is connected to the model construction module 500 and is used to perform a bifurcation operation on multiple balance points in the microgrid based on the target microgrid model to obtain a bifurcation result, wherein the bifurcation result is used to characterize the impact of the bifurcation operation on the signal stability in the microgrid, and the bifurcation operation includes a static bifurcation and a dynamic bifurcation. The static bifurcation is used to indicate a bifurcation in which the balance point in the microgrid undergoes a sudden change as the preset key parameters change, and the dynamic bifurcation is used to indicate a bifurcation in which the balance point of the microgrid changes from a stable state to a periodic oscillation or chaotic state when the preset key parameters change. The balance point is used to indicate an operating point in which the state variables in the microgrid model remain unchanged; a state space result acquisition module 504 is connected to the bifurcation result acquisition module 5 02, is used to obtain the state space result of the target microgrid model based on the bifurcation result, wherein the state space result is used to characterize the influence of the bifurcation operation on the balance point stability of the microgrid; the signal stability determination module 506, connected to the state space result acquisition module 504, is used to determine the signal stability result of the microgrid based on the bifurcation result and the state space result, thereby achieving the purpose of performing microgrid signal stability analysis from two dimensions of key parameters and state space through static bifurcation and dynamic bifurcation analysis, as well as state space analysis dimensions, thereby achieving the technical effect of improving the accuracy of microgrid signal analysis, and further solving the problem that the methods for performing microgrid signal stability analysis in related technologies are all linear methods, lack of connection with the system dynamic process, and can only be used for the evaluation of small signal stability, but cannot provide the technical problem of system oscillation and instability mechanism that operation and maintenance personnel are more concerned about.

[0116] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0117] It should be noted that the model construction module 500, bifurcation result acquisition module 502, state space result acquisition module 504, and signal stability determination module 506 correspond to steps S102 to S108 in the embodiment. The examples and application scenarios implemented by these modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that these modules, as part of the device, can be run on a computer terminal.

[0118] It should be noted that the optional or preferred implementation of this embodiment can be found in the relevant description in the embodiment, which will not be repeated here.

[0119] The above-mentioned microgrid-based signal processing device may further include a processor and a memory. The above-mentioned model construction module 500, bifurcation result acquisition module 502, state space result acquisition module 504, signal stability determination module 506, etc. are all stored in the memory as program modules, and the processor executes the above-mentioned program modules stored in the memory to realize corresponding functions.

[0120] The processor includes a core, which retrieves corresponding program modules from memory. There can be one or more cores. Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0121] According to an embodiment of the present application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein when the program is executed, the device containing the non-volatile storage medium is controlled to execute any of the above-mentioned microgrid-based signal processing methods.

[0122] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group, and the non-volatile storage medium includes a stored program.

[0123] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: construct a target microgrid model based on multiple components included in the microgrid; based on the target microgrid model, perform a bifurcation operation on multiple balance points in the microgrid to obtain a bifurcation result, wherein the bifurcation result is used to characterize the impact of the bifurcation operation on the signal stability in the microgrid, and the bifurcation operation includes static bifurcation and dynamic bifurcation. The static bifurcation is used to indicate a bifurcation in which the balance point in the microgrid undergoes a sudden change as the preset key parameters change. The dynamic bifurcation is used to indicate a bifurcation in which the balance point of the microgrid changes from a stable state to a periodic oscillation or chaotic state when the preset key parameters change. The balance point is used to indicate an operating point where the state variables in the microgrid model remain unchanged; based on the bifurcation result, obtain a state space result of the target microgrid model, wherein the state space result is used to characterize the impact of the bifurcation operation on the stability of the balance point of the microgrid; based on the bifurcation result and the state space result, determine the signal stability result of the microgrid.

[0124] According to an embodiment of the present application, an embodiment of a processor is further provided. Optionally, in this embodiment, the processor is used to run a program, wherein when the program is run, any of the above-mentioned microgrid-based signal processing methods is executed.

[0125] According to an embodiment of the present application, an embodiment of a computer program product is also provided, which, when executed on a data processing device, is suitable for executing a program that initializes any one of the steps of the microgrid-based signal processing method described above.

[0126] Optionally, the above-mentioned computer program product, when executed on a data processing device, is suitable for executing a program initialized with the following method steps: constructing a target microgrid model based on multiple components included in the microgrid; performing a bifurcation operation on multiple balance points in the microgrid based on the target microgrid model to obtain a bifurcation result, wherein the bifurcation result is used to characterize the impact of the bifurcation operation on the signal stability in the microgrid, and the bifurcation operation includes a static bifurcation and a dynamic bifurcation. The static bifurcation is used to indicate a bifurcation in which the balance point in the microgrid undergoes a sudden change as a preset key parameter changes. The dynamic bifurcation is used to indicate a bifurcation in which the balance point of the microgrid changes from a stable state to a periodic oscillation or chaotic state when the preset key parameter changes. The balance point is used to indicate an operating point where the state variables in the microgrid model remain unchanged; based on the bifurcation result, obtaining a state space result of the target microgrid model, wherein the state space result is used to characterize the impact of the bifurcation operation on the stability of the balance point of the microgrid; and determining the signal stability result of the microgrid based on the bifurcation result and the state space result.

[0127] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: constructing a target microgrid model based on multiple components included in a microgrid; performing a bifurcation operation on multiple balance points in the microgrid based on the target microgrid model to obtain a bifurcation result, wherein the bifurcation result is used to characterize the impact of the bifurcation operation on signal stability in the microgrid. The bifurcation operation includes a static bifurcation and a dynamic bifurcation. The static bifurcation is used to indicate a bifurcation in which a balance point in the microgrid undergoes a sudden change as a preset key parameter changes. The dynamic bifurcation is used to indicate a bifurcation in which the balance point of the microgrid changes from a stable state to a periodic oscillation or chaotic state when a preset key parameter changes. The balance point is used to indicate an operating point in the microgrid model where state variables remain unchanged; obtaining a state space result of the target microgrid model based on the bifurcation result, wherein the state space result is used to characterize the impact of the bifurcation operation on the stability of the balance point of the microgrid; and determining a signal stability result of the microgrid based on the bifurcation result and the state space result.

[0128] The above sequence of the embodiments of the present invention is for description only and does not represent the superiority or inferiority of the embodiments.

[0129] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the above modules can be a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, modules or indirect coupling or communication connection of modules, which can be electrical or other forms.

[0131] The modules described above as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0132] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0133] If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a non-volatile storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program codes.

[0134] The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A signal processing method based on a microgrid, characterized in that: include: Based on multiple components included in a microgrid, a target microgrid model is constructed, including: establishing nonlinear differential-algebraic equation group models corresponding to the multiple components; performing power flow calculation based on the nonlinear differential-algebraic equation group models corresponding to the multiple components to obtain power flow calculation results corresponding to the multiple components, wherein the power flow calculation results are at least used to indicate the voltage distribution and power distribution of the corresponding components; performing model integration based on the nonlinear differential-algebraic equation group models corresponding to the multiple components and the power flow calculation results corresponding to the multiple components to obtain an initial microgrid model, wherein the initial microgrid model is a nonlinear model; based on the initial microgrid model, determining a balance solution manifold of the microgrid, wherein the balance solution manifold is used to describe the trajectories of multiple balance points included in the microgrid as parameters change; based on the balance solution manifold, linearizing the initial microgrid model to obtain the target microgrid model; Based on the target microgrid model, a bifurcation operation is performed on multiple balance points in the microgrid to obtain a bifurcation result, wherein the bifurcation result is used to characterize the impact of the bifurcation operation on signal stability in the microgrid, and the bifurcation operation includes a static bifurcation and a dynamic bifurcation. The static bifurcation is used to indicate a bifurcation in which a balance point in the microgrid undergoes a sudden change as a preset key parameter changes. The dynamic bifurcation is used to indicate a bifurcation in which a balance point in the microgrid changes from a stable state to a periodic oscillation or chaotic state when the preset key parameter changes. The balance point is used to indicate an operating point in which a state variable in the microgrid model remains unchanged; Based on the bifurcation result, obtaining a state space result of the target microgrid model, wherein the state space result is used to characterize the impact of the bifurcation operation on the stability of the balance point of the microgrid; A signal stability result of the microgrid is determined based on the bifurcation result and the state space result.

2. The method according to claim 1, characterized in that The linearizing the initial microgrid model based on the equilibrium solution manifold to obtain a target microgrid model includes: determining a target equilibrium point from the plurality of equilibrium points based on the equilibrium solution manifold; Calculating a target Jacobian matrix corresponding to the target equilibrium point; The initial microgrid model is linearized according to the target Jacobian matrix to obtain the target microgrid model.

3. The method according to claim 1, characterized in that The step of performing a bifurcation operation on a plurality of balance points in the microgrid based on the target microgrid model to obtain a bifurcation result includes: Determining a parameter variation range of the preset key parameter based on the equilibrium solution manifold; In the process of the preset key parameter changing within the parameter variation range, determining a first balance point where a sudden change occurs from a plurality of balance points of the microgrid; Calculating a first Jacobian matrix corresponding to the first equilibrium point; Determining eigenvalue change information of the first Jacobian matrix during a process in which the preset key parameter changes within the parameter variation range; Determining a bifurcation point in the microgrid and a corresponding bifurcation point type based on the characteristic value change information; The bifurcation result is determined based on the bifurcation point and the bifurcation point type.

4. The method according to claim 3, characterized in that The determining of a bifurcation point in the microgrid and a corresponding bifurcation point type based on the characteristic value change information includes: When the eigenvalue change information indicates that the eigenvalue of the first Jacobian matrix crosses the imaginary axis from the real axis of the complex plane, determining that the bifurcation point is a static bifurcation point and the corresponding bifurcation point type is a static bifurcation point type; or When the eigenvalue change information indicates that the eigenvalues ​​of the first Jacobian matrix cross the imaginary axis in the complex plane in the form of conjugate complex roots, the bifurcation point is determined to be a dynamic bifurcation point, and the corresponding bifurcation point type is a dynamic bifurcation point type.

5. The method according to claim 1, wherein The obtaining of a state space result of the target microgrid model based on the bifurcation result includes: determining a second balance point in the microgrid based on the bifurcation result; Determining a local stable manifold and a local unstable manifold corresponding to the second equilibrium point, wherein the local stable manifold is used to indicate a set of parameter trajectory changes that enable the microgrid to return to the second equilibrium point after being subjected to a target disturbance within a predetermined neighborhood of the second equilibrium point; and the local stable manifold is used to indicate a set of parameter trajectory changes that enable the microgrid to move away from the second equilibrium point after being subjected to the target disturbance within the predetermined neighborhood; Based on the local stable manifold and the local unstable manifold, constructing a state space topology structure corresponding to the target microgrid model, wherein the state space topology structure is used to indicate the connection mode and stability relationship between different states in the state space of the microgrid; The state space topology structure is analyzed to obtain the state space result.

6. The method according to any one of claims 1 to 5, characterized in that Determining a signal stability result of the microgrid based on the bifurcation result and the state space result includes: Analyzing a hyperbolic equilibrium point in the target microgrid model to obtain a hyperbolic equilibrium point result, wherein the hyperbolic equilibrium point analysis is used to indicate an impact of the hyperbolic equilibrium point on signal stability in the microgrid, and the hyperbolic equilibrium point is a equilibrium point in the target microgrid model for which an eigenvalue of a corresponding Jacobian matrix has a non-zero real part; A signal stability result of the microgrid is determined based on the hyperbolic equilibrium point result, the bifurcation result, and the state space result.

7. A signal processing device based on a microgrid, characterized in that: include: A model construction module is used to construct a target microgrid model based on multiple components included in the microgrid, including: establishing nonlinear differential-algebraic equation group models corresponding to the multiple components; performing power flow calculation based on the nonlinear differential-algebraic equation group models corresponding to the multiple components to obtain power flow calculation results corresponding to the multiple components, wherein the power flow calculation results are at least used to indicate the voltage distribution and power distribution of the corresponding components; performing model integration based on the nonlinear differential-algebraic equation group models corresponding to the multiple components and the power flow calculation results corresponding to the multiple components to obtain an initial microgrid model, wherein the initial microgrid model is a nonlinear model; based on the initial microgrid model, determining the equilibrium solution manifold of the microgrid, wherein the equilibrium solution manifold is used to describe the trajectories of multiple equilibrium points included in the microgrid as parameters change; based on the equilibrium solution manifold, linearizing the initial microgrid model to obtain the target microgrid model; a bifurcation result acquisition module, configured to perform a bifurcation operation on a plurality of balance points in the microgrid based on the target microgrid model to obtain a bifurcation result, wherein the bifurcation result is used to characterize the impact of the bifurcation operation on signal stability in the microgrid, and the bifurcation operation includes a static bifurcation and a dynamic bifurcation. The static bifurcation is used to indicate a bifurcation in which a balance point in the microgrid undergoes a sudden change as a preset key parameter changes. The dynamic bifurcation is used to indicate a bifurcation in which a balance point in the microgrid changes from a stable state to a periodic oscillation or chaotic state when the preset key parameter changes. The balance point is used to indicate an operating point in which a state variable in the microgrid model remains unchanged; a state space result acquisition module, configured to acquire a state space result of the target microgrid model based on the bifurcation result, wherein the state space result is used to characterize the impact of the bifurcation operation on the stability of the balance point of the microgrid; A signal stability determination module is used to determine a signal stability result of the microgrid based on the bifurcation result and the state space result.

8. An electronic device, characterized in that: The method comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the microgrid-based signal processing method according to any one of claims 1 to 6.

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