Signal processing method and device based on micro-grid, and electronic equipment

Abstract

The invention discloses a signal processing method and device based on a micro-grid and electronic equipment. The method relates to the field of power system stability analysis, and comprises the following steps: constructing a target micro-grid model based on a plurality of components included in a micro-grid; based on the target micro-grid model, performing bifurcation operation on a plurality of balance points in the micro-grid to obtain a bifurcation result; based on the bifurcation result, obtaining a state space result of the target microgrid model; and determining a signal stability result of the micro-grid based on the bifurcation result and the state space result. According to the method, the technical problems that the method for analyzing the signal stability of the micro-grid in the related technology is a linearized method, is lack of relation with the dynamic process of the system, can only be used for evaluating the stability of the small signal, and cannot provide a system oscillation and instability mechanism which is more concerned by operation and maintenance personnel are solved.

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] In order to improve the operating efficiency of microgrids, power systems usually operate near their stability limits. Small signal stability ensures the local stability of the system at the limit operating point. The evaluation of small signal stability of station microgrids provides a basis for the division of stable operation areas of microgrids. On the premise of ensuring stable operation of the system, it facilitates the adjustment of microgrid operating parameters and the improvement of economic benefits.

[0003] There are several existing methods for evaluating the stability of small signals in the system, such as eigenvalue analysis, impedance analysis, frequency scanning, and complex torque coefficient. However, these methods are all linear methods, lacking the connection with the dynamic process of the system, and can only be used to evaluate the stability of small signals, but cannot provide the system oscillation and instability mechanism that the operation and maintenance personnel are more concerned about. This results in low accuracy in the analysis of microgrid signal stability.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. 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 problem that the methods for signal stability analysis of a microgrid in related technologies are all linear methods, lack connection with the dynamic process of the system, can only be used for the evaluation of small signal stability, but cannot provide the technical problems of 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 influence 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 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 a chaotic state when the preset key parameter changes, and the balance point is used to indicate a working 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 influence of the bifurcation operation on the stability of the balance point of the microgrid; based on the bifurcation result and the state space result, determining the signal stability result of the microgrid.

[0007] According to another aspect of an embodiment of the present invention, a signal processing device based on a microgrid is further 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 influence 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 a 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 a chaotic state when the preset key parameter changes, and the balance point is used to indicate a working point in 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 influence of the bifurcation operation on the stability of the balance point of the microgrid; a signal stability determination module, for determining the 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, there is also provided an electronic device, 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 influence 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 a 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, and the balance point is used to indicate a working point in 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 balance point stability of the microgrid; based on the bifurcation result and the state space result, the signal stability result of the microgrid is determined, so as to achieve the purpose of analyzing the signal stability of the microgrid from two dimensions of key parameters and state space through static bifurcation and dynamic bifurcation analysis, as well as state space analysis dimension, thereby achieving the technical effect of improving the accuracy of microgrid signal analysis, and further solving the problem that the methods for signal stability analysis of microgrids in related technologies are all linear methods, lacking 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. 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 is a structural schematic 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 signal processing method based on a microgrid 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 is a schematic diagram of a signal processing device based on a microgrid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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 described embodiments 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 creative work should fall within the scope of protection of the present invention.

[0017] It should be noted that the terms "first", "second", etc. in the specification 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 data 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 that are 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 terms or nouns involved in the present invention are explained below:

[0019] The Hartman-Grobman theorem is a theorem about nonlinear dynamic systems, which describes 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 the equilibrium point, the local dynamic behavior of a nonlinear system can be described by the eigenvalues ​​of a linear system, thereby simplifying the analysis and understanding of the stability and evolution characteristics of a nonlinear system.

[0020] According to an embodiment of the present invention, a method embodiment of 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 comprises the following steps:

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

[0023] Optionally, a microgrid refers to a small power system that is composed of multiple distributed energy resources (such as solar energy, wind energy, fuel cells, etc.), capable of independent operation, autonomous dispatch and interconnection. A microgrid can be interconnected with the main power grid, or it can operate independently during a power outage to provide local users with a reliable, efficient and clean power supply. The multiple components included in a microgrid may include, but are not limited to: multiple distributed energy devices (such as solar energy, wind energy, fuel cells, etc.), energy storage devices, loads, etc.

[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 group 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 point as the parameters change. Under the preset key parameter changes, 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, and obtaining 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 the initial microgrid model.

[0028] Optionally, a nonlinear differential-algebraic equation group model is used to characterize the nonlinear operating characteristics of the corresponding components. It can be, 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, rectifiers) in the microgrid to obtain nonlinear differential-algebraic equation group models corresponding to multiple components. The model of each component will contain differential and algebraic equations describing 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 group models corresponding to multiple components. The nonlinear differential-algebraic equation group 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 group models corresponding to multiple components and the power flow calculation results corresponding to multiple components, the integrated model obtained is used as the initial microgrid model. The initial microgrid model not only contains differential equations describing the dynamic behavior of components in the microgrid (such as differential equations describing the charging and discharging process of energy storage equipment), but also contains algebraic equations describing the steady-state characteristics of the microgrid (i.e., the results of power flow calculation). These equations together constitute a complete mathematical model that can describe the dynamic and steady-state behavior of the microgrid. In summary, power flow calculation is a key step in establishing the initial microgrid model, which provides algebraic equations describing the steady-state characteristics of the microgrid; and the initial microgrid model combines the differential equations of each component in the microgrid with the algebraic equations of power flow calculation to form a mathematical model that can fully describe the dynamic and steady-state behavior of the microgrid.

[0030] Optionally, a nonlinear differential-algebraic equation group model of each component in the microgrid is established, and power flow calculation is performed based on the nonlinear differential-algebraic equation group model of each component to obtain an integrated nonlinear differential-algebraic equation group model of the microgrid, that is, 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, and f and g are n-dimensional and m-dimensional column vectors of the microgrid state equation and algebraic equation, respectively. The nonlinear differential-algebraic equation system model of the microgrid constitutes the (n+k)-dimensional constraint manifold of the microgrid in the (n+m+k)-dimensional space of (x, y, p).

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

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

[0035] The preset key parameters describe the physical characteristics, control strategies, and operating environment of the microgrid. In the mathematical model of the nonlinear differential-algebraic equation system, these preset key parameters may include, but are not limited to: time constants related to the decay of each state quantity; coefficients of load dynamic characteristics; parameters of electrical network components in the microgrid (such as resistance, inductance, capacitance, etc.); parameters involved in the load change law when various bifurcations occur in the microgrid, etc. These preset key parameters have an important impact on the dynamic behavior and stability of the microgrid. By adjusting these preset key parameters, the operating characteristics and stability of the microgrid can be changed.

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

[0037]

[0038] Solve the equilibrium point of the microgrid under fixed parameter conditions. Based on the boundary method and the minimum expansion system, the numerical extension of the microgrid equilibrium solution manifold under parameter changes is realized, and then the k-dimensional equilibrium solution manifold E of the microgrid is obtained, which is expressed by the following formula.

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

[0040] In an optional embodiment, based on the equilibrium solution manifold, the initial microgrid model is linearized to obtain a target microgrid model, including: determining a target balance point from multiple balance points based on the equilibrium solution manifold; calculating a target Jacobian matrix corresponding to the target balance point; and according to the target Jacobian matrix, linearizing the initial microgrid model 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, the nonlinear model may be more complicated to analyze, so the analysis can be simplified by linearization. Based on the Hartman-Grobman theorem, the nonlinear model of the microgrid can be linearized using the Jacobian matrix (a local linear approximation of the description function near a certain point). After linearization, it is easier to analyze the small signal stability of each equilibrium point in the microgrid, where the Jacobian matrix can be used to describe the properties at the corresponding equilibrium point, and the Jacobian matrix contains partial derivative information of 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 the parameter space as the 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 applying the theorem, as well as the positions of these equilibrium points in the 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 * ) makes the flow of the microgrid nonlinear model Satisfy the following form:

[0045]

[0046] in, For Flow About (x * ,y * ,p * ). This also shows that the nonlinear model of the microgrid and its linearized model (target microgrid model) are at any hyperbolic equilibrium point (x * ,y * ,p * ) are topologically equivalent in 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, and 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 of g is similar to 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 f 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. Therefore, the instability mechanism of microgrid can be further analyzed by nonlinear bifurcation theory.

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

[0055] It can be understood that when any of the preset key parameters corresponding to the microgrid (called bifurcation parameters) changes, the balance point of the microgrid may change, resulting in a change in the topological structure of the system state space. In microgrids, due to the presence of a large number of distributed power sources and complex electrical wiring networks, the nonlinear characteristics of the system are obvious, so bifurcation is 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 existence of a variety of distributed energy resources and a variety of 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 changes suddenly with the change of preset key parameters. 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 a microgrid to obtain a bifurcation result, including: determining a parameter variation range of a preset key parameter based on a balanced 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 variation 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 variation information; and determining a bifurcation result based on the bifurcation point and the bifurcation point type.

[0059] Optionally, the changes of preset key parameters near the equilibrium point are analyzed to analyze the impact of small changes of 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.) changes suddenly when the system parameters change continuously, which can be a Hopf bifurcation of 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, the bifurcation points and the types of bifurcation points in the microgrid can be identified based on the characteristics of the eigenvalues ​​of the Jacobian matrix corresponding to each equilibrium point changing with the preset key parameters (i.e., eigenvalue change information), 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 eigenvalues ​​of the first Jacobian matrix cross the imaginary axis from the real axis of the complex plane, the bifurcation point is determined to be 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.

[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 of the eigenvalue, the topological structure of the system also changes, which is specifically manifested as a sudden change in the number and stability of equilibrium points. This change is the core feature of 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, the static bifurcation may be a bifurcation caused by a topological change in the number and stability of the microgrid balance points, and the eigenvalues ​​of the Jacobian matrix A cross the imaginary axis from the real axis of the complex plane. According to the way the eigenvalues ​​cross, the static bifurcation points can be divided into saddle-node bifurcations SNB, limit-induced bifurcations LIB, and singular-induced bifurcations SIB, and the specific formula is 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, It means that the bifurcation parameter approaches the critical parameter p from the right side of the number axis. c , It means 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 for a topological change in the microgrid state space, and the eigenvalues ​​of the state Jacobian matrix A cross the imaginary axis in the complex plane in the form of 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 They 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] Among them, R represents a real number set, t is the running time of the microgrid state trajectory, and t' is a certain moment in the state trajectory operation.

[0069] Stable limit cycle Γ s This will cause the microgrid to produce continuous equal-amplitude oscillations. 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, obtaining a state space result of the target microgrid model, wherein the state space result is used to characterize the influence of the bifurcation operation on the stability of the balance point of the microgrid;

[0071] Optionally, the balance point stability of the microgrid is the stability of the microgrid state space. The state space stability is used to indicate whether the state variables (such as voltage, current frequency, etc.) can maintain or recover to the balance point when the microgrid is disturbed (such as load changes, energy equipment failures, etc.) during operation. The bifurcation results include static bifurcation analysis results and dynamic bifurcation analysis results. The local stability of the balance point is analyzed by static bifurcation and dynamic bifurcation, so as to more accurately analyze the impact of the bifurcation point on the non-local stability of the microgrid state space topology structure.

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

[0073] Optionally, bifurcation is a phenomenon in which the stability of the system structure changes suddenly. When a certain parameter of the system changes, the equilibrium point of the system may change, resulting in a change in the topological structure of the system state space. The second equilibrium point may be an isolated equilibrium point, which is used to indicate an independent and stable equilibrium point in the microgrid. The isolated equilibrium point does not coexist or depend on other equilibrium points. The target disturbance may be a small signal disturbance, that is, a disturbance with a disturbance amplitude less than a predetermined amplitude. In the island operation mode of the microgrid, when the large power grid fails, the microgrid needs to be able to maintain its own stable operation. The isolated equilibrium point is a unique and stable state point in the state space of the microgrid, which is of great significance in the microgrid. By analyzing the stability and manifold of the isolated equilibrium point, the dynamic behavior of the microgrid near the point can be understood, and then the stability and dynamic characteristics of the entire system can be inferred. By analyzing the local topological structure of the microgrid state space, it is possible to determine under what conditions the microgrid can maintain stable operation, and formulate corresponding control strategies accordingly.

[0074] It should be noted that the local stable manifold refers to a set of trajectories near an isolated equilibrium point, which allows the operating state of the microgrid to return to the equilibrium point after being disturbed by a small signal. It reflects the stability range and stability properties of the microgrid near this point. The local unstable manifold refers to a set of trajectories near an isolated equilibrium point, which allows the operating state of the microgrid to move away from the equilibrium point after being disturbed by a small signal. It reflects the instability range and instability properties of the microgrid near this point. By analyzing the local stable manifolds and unstable manifolds corresponding to the isolated equilibrium point, the local topological structure of the microgrid near this 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 to understanding and controlling the dynamic behavior of the microgrid.

[0075] Specifically, the occurrence of bifurcation will affect the topological structure of the station microgrid state space. The local stable manifold 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 * The globally stable manifold and globally unstable manifolds

[0078] Step S108: determining the 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 analyzing the stability of the microgrid signal from two dimensions, key parameters and state space, is achieved through static bifurcation and dynamic bifurcation analysis, as well as state space analysis. The above method not only considers the stability of the microgrid system under different states, but also considers the impact of parameter changes on the stability of the microgrid system, which can significantly improve the accuracy of the microgrid signal analysis.

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

[0081] Optionally, the hyperbolic equilibrium points in the equilibrium manifold solution (i.e., the equilibrium points where the real part of the eigenvalue is not zero) are the key to analyzing the stability of small signals. Near these points, by analyzing the eigenvalues ​​of the corresponding Jacobian matrix of the linearized microgrid model (i.e., the target microgrid model), it is possible to determine whether the microgrid is stable, as well as the degree and direction of instability. Bifurcation is an important feature of the dynamic behavior of nonlinear systems, which describes the changes in the number and stability of equilibrium points or periodic orbits when the operating parameters of the microgrid change. Static bifurcation focuses on the changes in the number and stability of the equilibrium points of the microgrid operation with the parameters, such as saddle-node bifurcation and limit-induced bifurcation. Dynamic bifurcation (such as Hopf bifurcation) focuses on the transition of the microgrid from a stable equilibrium point to an unstable periodic orbit. The analysis of bifurcation points helps to predict the operating characteristics of the microgrid under different parameters and discover possible instability risks in advance. In addition to analyzing the stability of the equilibrium point itself, it is also necessary to consider the stability of the state space near the equilibrium point. By analyzing the stability of the state space near the equilibrium point, the sensitivity and recovery ability of the microgrid to disturbances 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 factors considered are more comprehensive, and the microgrid signal stability results obtained on this basis are more accurate and reliable.

[0083] Optionally, the small signal stability of the microgrid can be comprehensively evaluated from the two dimensions of state space and parameter space based on, but not limited to, the hyperbolic equilibrium point results, static bifurcation results, dynamic bifurcation results, and state space results to obtain the signal stability results corresponding to the microgrid. The state space describes all possible operating states of the microgrid, while the parameter space describes all possible values ​​of the preset key parameters of the microgrid. In the parameter space, by defining the small signal stability region, it can be determined under which parameter combinations the microgrid system is stable. This helps guide the design and operation of the microgrid. In the state space, the stability of the state space near the equilibrium point can be evaluated by stable manifold analysis, and the attraction domain of the small signal stable equilibrium point can be determined. This helps to 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: state space and 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. Multi-parameter small signal stability region Ω in 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 through the stable manifold. The formula of 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 the stable manifold is equivalent to describing the set of points in the state space of the microgrid that tend to a certain fixed point or periodic orbit when time tends to positive infinity. In short, it represents how the operating state of the microgrid converges to a certain stable equilibrium point or periodic trajectory over time. In the small signal stability analysis of the microgrid, the stable manifold can be used to describe how the system state gradually recovers to a stable equilibrium point along a certain trajectory when the system is subjected to a small disturbance. The attraction domain is a set closely related to a specific attraction set (usually a stable equilibrium point in a microgrid). The trajectory starting from any point in the set will eventually tend to the specific attraction set when it is not disturbed by external disturbances or after the disturbance disappears. In the small signal stability analysis of the microgrid, the attraction domain evaluation is an important step in determining the robustness and stability of the system. By determining the attraction domain, we can understand the area range in which the operating state of the microgrid can return to the stable equilibrium point after a small disturbance, thereby evaluating the stability and reliability of the microgrid operation.

[0089] Specifically, the stable equilibrium point of the microgrid can be determined first. Then, the stable manifolds near the stable equilibrium point are analyzed to understand how the operating state of the microgrid gradually converges to the stable equilibrium point along these manifolds. Then, based on the information of the stable manifolds, the attraction domain of the stable equilibrium point is determined. The dynamic behavior of the microgrid under different initial conditions can be simulated to observe which initial conditions can cause the operating state of the microgrid to converge to the stable equilibrium point. Finally, the analysis results of the stable manifold and the attraction domain are combined to comprehensively evaluate the attraction domain of the small signal stable equilibrium point of the microgrid. The evaluation results can provide information on the stability and robustness of the microgrid under different initial conditions, providing an important reference for the stable operation and optimal control of the microgrid.

[0090] Through the above steps S102 to S108, the purpose of analyzing the stability of microgrid signals from two dimensions, namely, key parameters and state space, can be achieved through static bifurcation 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 signal stability analysis of microgrids in related technologies are all linear methods, lacking connection with the dynamic process of the system, and can only be used for the evaluation of small signal stability, but cannot provide technical problems of system oscillation and instability mechanisms 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 mode, the method is applied to a permanent magnet direct-drive wind power grid-connected system, Figure 2 : is a structural schematic 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 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 is the proportional coefficient of the inner loop PI regulator of the d-axis current on the stator side of the generator, k 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 is the proportional coefficient of the inner loop PI regulator of the q-axis current on the stator side of the generator, k iq is the integral coefficient of the inner loop PI regulator of the q-axis current on the stator side of the generator, k pt is the proportional coefficient of the torque outer loop PI regulator, k 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 is the integral coefficient of the grid-side current inner loop PI regulator, k 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, such as Figure 3 As shown, the method mainly includes the following steps:

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

[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. Linearization of microgrid model: Based on the Hartman-Grobman theorem, the nonlinear model of the microgrid 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 of bifurcation point losing small signal stability through static bifurcation; for static bifurcation with topological changes in the number and stability of the station microgrid balance state, the eigenvalue of its state Jacobian matrix crosses the imaginary axis from the real axis of the complex plane. According to the way of eigenvalue crossing, the static bifurcation point can be divided into saddle node bifurcation SNB, limit induced bifurcation LIB and singular induced bifurcation SIB.

[0105] S5. Dynamic bifurcation analysis: Analyze the mechanism by which the bifurcation point loses small signal stability through dynamic Hopf bifurcation; for the dynamic Hopf bifurcation 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 in the form of conjugate complex roots.

[0106] S6. State space analysis: Based on the bifurcation results obtained by static bifurcation analysis and dynamic bifurcation analysis, the influence mechanism of bifurcation operation on the state space stability of 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, where 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 small signal stability of the microgrid are shown in Figure 2. 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 by the microgrid generator to the grid. The three-dimensional coordinates are all in per unit (pu). Ho P represents the microgrid parameter P when the same-site bifurcation occurs w The value of P F P represents the microgrid parameter P at the time when the saddle-node bifurcation occurs. w The value of P H represents the microgrid parameter P at the time when Hopf bifurcation occurs w The value of .

[0108] The following evaluation results are all calculated with the parameter Pw The increasing process of P shows the change process of the small signal stability of the microgrid. w =P Ho = 0.366pu, the stable manifold and 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 in w As the unstable limit cycle increases, it 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 When =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 realized by a nonlinear bifurcation analysis method, and the construction of the attraction domain of small signal stable equilibrium points in the state space is realized 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 modes, 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 of predetermined functions. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0110] According to an embodiment of the present invention, a device embodiment for implementing the above-mentioned microgrid-based signal processing method is also provided. Figure 5 is a schematic diagram of the structure of a signal processing device based on a microgrid according to an embodiment of the present invention. Figure 5As shown, the above-mentioned signal processing device based on microgrid 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 bifurcation operations on multiple balance points in the microgrid based on the target microgrid model to obtain bifurcation results, wherein the bifurcation results are 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. The static bifurcation is used to indicate that the balance point in the microgrid undergoes a sudden change as the preset key parameters change. The dynamic bifurcation is used to indicate that when the preset key parameters change, the balance point of the microgrid changes from a stable state to a periodic oscillation or chaotic state. The balance point is used to indicate the working point where the state variables in the microgrid model remain unchanged;

[0113] A state space result acquisition module 504, connected to the bifurcation result acquisition module 502, is used 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 influence 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 building module 500 is set to build a target microgrid model based on multiple components included in the microgrid; a bifurcation result acquisition module 502 is connected to the model building 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 influence 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 with the change of preset key parameters, 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 a working 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, used to obtain the state space results of the target microgrid model based on the bifurcation results, wherein the state space results are used to characterize the impact of the bifurcation operation on the stability of the balance point 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 results of the microgrid based on the bifurcation results and the state space results, achieving the purpose of analyzing the signal stability of the microgrid 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 signal stability analysis of microgrids in related technologies are all linear methods, lack of 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 problems 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 above-mentioned model building 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, and the examples and application scenarios implemented by the above-mentioned modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment. It should be noted that the above-mentioned modules can be run in a computer terminal as part of the device.

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

[0119] The above-mentioned microgrid-based signal processing device may also 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 kernel, which retrieves the corresponding program module from the memory. The kernel may be one or more. The 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. The 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 running, the device where the non-volatile storage medium is located is controlled to execute any of the 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: based on multiple components included in the microgrid, a target microgrid model is constructed; 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. 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 a chaotic state when a preset key parameter changes. The balance point is used to indicate a working point where the state variables in the microgrid model remain unchanged; 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; based on the bifurcation result and the state space result, the signal stability result of the microgrid is determined.

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

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

[0126] Optionally, the above-mentioned computer program product, when executed on a data processing device, is suitable for executing a program that initializes the following method steps: constructing a target microgrid model based on multiple components included in the microgrid; based on the target microgrid model, performing 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 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 a chaotic state when a preset key parameter changes. The balance point is used to indicate a working 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; based on the bifurcation result and the state space result, determining the signal stability result of the microgrid.

[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: based on multiple components included in the microgrid, a target microgrid model is constructed; 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 influence 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 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 a chaotic state when a preset key parameter changes. The balance point is used to indicate a working point in which a state variable in the microgrid model remains unchanged; 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 influence of the bifurcation operation on the stability of the balance point of the microgrid; based on the bifurcation result and the state space result, a signal stability result of the microgrid is determined.

[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 emphasis. 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 schematic. For example, the division of the above modules can be a logical function division. There may be other division methods in actual implementation, 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 separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on 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, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0133] If the above-mentioned integrated module is implemented in the form of a software function 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 is essentially 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, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform 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 the microgrid, a target microgrid model is constructed; 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 influence 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 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 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 which a state variable in the microgrid model remains unchanged; 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 influence of the bifurcation operation on the stability of the balance point of the microgrid; Based on the bifurcation result and the state space result, a signal stability result of the microgrid is determined.

2. The method according to claim 1, characterized in that The target microgrid model is constructed based on multiple components included in the microgrid, including: Based on the multiple components, construct 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 trajectories of the multiple balance points included in the microgrid as parameters change respectively; Based on the balanced solution manifold, the initial microgrid model is linearized to obtain the target microgrid model.

3. The method according to claim 2, characterized in that The initial microgrid model is constructed based on the multiple components, including: Establishing nonlinear differential-algebraic equation group models corresponding to the plurality of components respectively; Performing power flow calculation based on 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 voltage distribution and power distribution of the corresponding components; The initial microgrid model is obtained by 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.

4. The method according to claim 2, characterized in that: The method of linearizing the initial microgrid model based on the balanced 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; According to the target Jacobian matrix, the initial microgrid model is linearized to obtain the target microgrid model.

5. The method according to claim 2, 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: Based on the equilibrium solution manifold, determining a parameter variation range of the preset key parameter; In the process of the preset key parameter changing within the parameter variation range, determining a first balancing point where a sudden change occurs from a plurality of balancing points of the microgrid; Calculating a first Jacobian matrix corresponding to the first equilibrium point; In the process that the preset key parameter changes within the parameter variation range, determining eigenvalue change information of the first Jacobian matrix; Based on the characteristic value change information, determining a bifurcation point in the microgrid and a corresponding bifurcation point type; The bifurcation result is determined based on the bifurcation point and the bifurcation point type.

6. The method according to claim 5, characterized in that The determining, based on the characteristic value change information, a bifurcation point in the microgrid and a corresponding bifurcation point type 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.

7. The method according to claim 1, characterized in that The step of obtaining a state space result of the target microgrid model based on the bifurcation result includes: Based on the bifurcation result, determining a second balance point in the microgrid; Determine 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 parameter trajectory change set that enables the microgrid to 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 parameter trajectory change set that moves the microgrid away from the second balance point after being subjected to the target disturbance within the predetermined neighborhood; Based on the local stable manifold and the local unstable manifold, a state space topological structure corresponding to the target microgrid model is constructed, wherein the state space topological structure is used to indicate the connection mode and stability relationship between different states in the state space of the microgrid; The state space topological structure is analyzed to obtain the state space result.

8. The method according to any one of claims 1 to 7, characterized in that The determining, based on the bifurcation result and the state space result, a signal stability result of the microgrid comprises: Analyze the 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 the influence of the hyperbolic equilibrium point on the signal stability in the microgrid, and the hyperbolic equilibrium point is a equilibrium point in the target microgrid model corresponding to the eigenvalue of the Jacobian matrix having a non-zero real part; Based on the hyperbolic equilibrium point result, the bifurcation result and the state space result, a signal stability result of the microgrid is determined.

9. A signal processing device based on a microgrid, characterized in that: include: A model building module, used for building a target microgrid model based on multiple components included in the microgrid; 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 influence of the bifurcation operation on the signal stability in the microgrid, and the bifurcation operation includes a static bifurcation and a dynamic bifurcation, wherein 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, and the dynamic bifurcation is used to indicate a bifurcation in which a 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 in which a state variable in the microgrid model remains unchanged; A state space result acquisition module, used 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 influence 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.

10. An electronic device, characterized in that: It includes 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 as described in any one of claims 1 to 8.

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