Practical Stability Region Partitioning Method and Device for Wind Power Grid-Connected System Considering Probabilistic Characteristics

Abstract

The present invention discloses a method and device for dividing a practical stability domain of a wind power grid-connected system considering probability characteristics, which relates to the field of data processing technology. By performing probability modeling analysis on the uncertainties of the active power and load power of a wind farm, a first probability parameter of the active power change and a second probability parameter of the load power change are obtained. The probability parameter item is updated based on the first probability parameter and the second probability parameter. When it is determined that the value of the probability parameter item reaches a specific critical value, it is judged whether the Hessian matrix of a differential equation group at an equilibrium point in an equilibrium point set is a singular matrix. If it is a singular matrix, a bifurcation analysis is performed on the wind power active power and the load reactive power of a target node to obtain a bifurcation curve and each bifurcation point. The practical stability domain is divided based on each bifurcation point and the bifurcation curve, so that the influence of the uncertainty of the wind power injected active power and the load reactive power on the practical stability domain is taken into account when dividing the practical stability domain.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method and device for dividing a practical stability region of a wind power grid-connected system considering probability characteristics. Background Art

[0002] With the increasing scale and penetration of wind power generation, the impact of large-scale wind power integration on power system stability, particularly its practical stability, has attracted widespread attention. The randomness, intermittency, and volatility of wind power introduce numerous uncertainties into the actual operation of power systems, leading to frequent grid instability incidents. To ensure the safe and stable operation of power systems, ensure equipment safety, and improve power quality, research on the practical stability domain of wind power grid-connected systems is urgently needed.

[0003] Currently, mainstream research assumes that the operating conditions of a doubly-fed induction generator (DFIG) wind turbine output are precisely known. Deterministic analysis is used to study the interaction between the DFIG's dynamic characteristics and those of other power system components. However, this approach fails to account for the uncertainty and randomness of wind power. Furthermore, a grid-connected wind turbine system is inherently a high-order, multivariable, nonlinear system, whose stability is significantly affected by key parameters such as load and generator output. These aforementioned approaches primarily rely on linearization analysis, studying practical stability at specific operating points in state space. This is a "point-by-point" approach, meaning it can only analyze practical stability under a specific operating state. Any changes in the system's operating state (such as changes in wind farm output) require reanalysis, making it difficult to comprehensively describe the system's practical stability and potentially leading to misjudgments of system stability. As the system's operating point changes, its nonlinear characteristics also change, making linear methods unable to accurately describe the system's stability boundaries and stability regions.

[0004] Therefore, it is an urgent problem to provide a practical stability domain division method for wind power grid-connected systems considering probabilistic characteristics in order to accurately divide the stability area of the power system under the combined influence of wind power injection and load uncertainty. Summary of the Invention

[0005] The present disclosure provides a method and device for dividing the practical stability domain of a wind power grid-connected system considering probabilistic characteristics. The main purpose is to accurately divide the stable area of the power system under the combined influence of wind power injection and load uncertainty, thereby realizing the analysis of the practical stability of the power system.

[0006] According to a first aspect of the present disclosure, a practical stability region partitioning method for a wind power grid-connected system considering probabilistic characteristics is provided, which includes:

[0007] Probabilistic modeling analysis is performed on the uncertainties of the active power and load power of the wind farm to obtain a first probability parameter of the active power change and a second probability parameter of the load power change;

[0008] Constructing probability parameter terms that describe the random fluctuations of the active power and the load power of the wind farm, and modeling the wind power grid-connected system as a set of nonlinear differential-algebraic equation models based on the probability parameter terms, wherein the set of nonlinear differential-algebraic equation models is used to characterize the nonlinear dynamic characteristics of the wind power grid-connected system;

[0009] updating the value of the probability parameter item to the first probability parameter and the second probability parameter, and when it is determined that the value of the probability parameter item reaches a specific critical value, determining whether a Hessian matrix of the differential equation system at an equilibrium point in the equilibrium point set is a singular matrix, wherein the set of nonlinear differential-algebraic equation system models includes the differential equation system and the algebraic equation system;

[0010] If the Hessian matrix of the differential equation system at the equilibrium point is the singular matrix, a bifurcation analysis is performed on the wind power active power injected into the nonlinear differential-algebraic equation system model and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point;

[0011] Based on the bifurcation points and the bifurcation curves, a practical stability domain is divided to obtain the influence trend of the changes in the wind power active power and the load reactive power on the nonlinear dynamic characteristics of the wind power grid-connected system.

[0012] Optionally, when it is determined that the value of the probability parameter item reaches a specific critical value, determining whether the Hessian matrix of the differential equation system at an equilibrium point in the equilibrium point set is a singular matrix includes: defining a potential function of the set of nonlinear differential-algebraic equation system models;

[0013] Acquire all equilibrium points in the potential function to obtain the equilibrium point set of the wind power grid-connected system;

[0014] When it is determined that the value of the probability parameter item reaches the specific critical value, it is determined whether the Hessian matrix of the differential equation group at the equilibrium point in the equilibrium point set is a singular matrix.

[0015] Optionally, performing bifurcation analysis on the wind power active power injected into the set of nonlinear differential-algebraic equations model and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point includes:

[0016] Determining whether, at the equilibrium point in the equilibrium point set, the characteristic roots of the state matrix of the wind power grid-connected system satisfy a target judgment condition and whether the conjugate complex eigenvalue trajectory of the wind power grid-connected system satisfies a transversal condition, the target judgment condition being that the state matrix has a pair of conjugate pure imaginary characteristic roots intersecting with an imaginary axis, and no other characteristic roots with zero real parts exist on the imaginary axis, the transversal condition being that the conjugate complex eigenvalue trajectory transversely intersects the imaginary axis, the state matrix including the wind power active power and the load reactive power, the state matrix being obtained by linearizing the set of nonlinear differential-algebraic equation models;

[0017] When it is determined that the state matrix satisfies the judgment condition and the conjugate complex eigenvalue trajectory does not satisfy the transversal condition, it is determined that a degenerate Hopf bifurcation occurs in the wind power grid-connected system at the equilibrium point, and the equilibrium point is marked as the bifurcation point until all the bifurcation points are marked from the equilibrium point set;

[0018] The bifurcation curve is drawn based on the bifurcation points.

[0019] Optionally, dividing the practical stability domain based on the bifurcation points and the bifurcation curves includes:

[0020] Setting a preset number of probability values for the specific critical value;

[0021] Respectively obtaining the wind power active power and load reactive power under the condition of the preset number of probability values;

[0022] Obtaining the bifurcation points and the bifurcation curves corresponding to the wind power active power and the load reactive power respectively under the condition of the preset number of probability values;

[0023] Based on the bifurcation points and bifurcation curves corresponding to the wind power active power and the load reactive power, practical stable domains corresponding to the preset number of probability values are divided.

[0024] Optionally, modeling the wind power grid-connected system as a set of nonlinear differential-algebraic equations includes:

[0025] Constructing the differential equation group based on the system state vector, system algebraic vector, control parameter vector and probability parameter vector of the wind power grid-connected system;

[0026] Constructing a group of algebraic equations based on the system state vector, system algebraic vector, control parameter vector and probability parameter vector of the wind power grid-connected system;

[0027] The set of nonlinear differential-algebraic equation models is constructed based on the differential equations and the algebraic equations.

[0028] Optionally, performing probability modeling analysis on the uncertainties of the active power and load power of the wind farm respectively to obtain the first probability parameter of the active power change and the second probability parameter of the load power change includes:

[0029] Constructing a first probability density function for the wind speed of the wind farm based on a two-parameter Weibull distribution;

[0030] Calculating the first probability parameter based on the first probability density function and a preset functional relationship, where the preset functional relationship is a functional relationship between the active power and the wind speed;

[0031] Constructing a second probability density function corresponding to the load power based on a normal distribution probability model;

[0032] The second probability parameter is calculated based on the second probability density function.

[0033] According to a second aspect of the present disclosure, a practical stability region division device for a wind power grid-connected system considering probabilistic characteristics is provided, comprising:

[0034] An analysis unit, configured to perform probability modeling analysis on the uncertainties of the active power and the load power of the wind farm, respectively, to obtain a first probability parameter of the active power change and a second probability parameter of the load power change;

[0035] a construction unit, configured to construct a probability parameter term describing the random fluctuation of the active power and the load power of the wind farm, and model the wind power grid-connected system as a set of nonlinear differential-algebraic equations based on the probability parameter term, wherein the set of nonlinear differential-algebraic equations is used to characterize the nonlinear dynamic characteristics of the wind power grid-connected system;

[0036] an updating unit, configured to update the value of the probability parameter item to the first probability parameter and the second probability parameter, and, when it is determined that the value of the probability parameter item reaches a specific critical value, determine whether a Hessian matrix of the differential equation system at an equilibrium point in the equilibrium point set is a singular matrix, wherein the set of nonlinear differential-algebraic equation system models includes the differential equation system and the algebraic equation system;

[0037] an acquisition unit, configured to, when the Hessian matrix of the differential equation group at the equilibrium point is the singular matrix, perform a bifurcation analysis on the wind power active power injected into the set of nonlinear differential-algebraic equation group models and the load reactive power of the target node, so as to obtain a bifurcation curve and each bifurcation point;

[0038] A division unit is used to divide the practical stability domain based on the bifurcation points and the bifurcation curve to obtain the influence trend of the changes in the wind power active power and the load reactive power on the nonlinear dynamic characteristics of the wind power grid-connected system.

[0039] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0040] at least one processor; and

[0041] a memory communicatively connected to the at least one processor; wherein,

[0042] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0043] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0044] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.

[0045] The present disclosure provides a practical method and device for dividing the stability domain of a wind power grid-connected system considering probability characteristics, which performs probability modeling analysis on the uncertainties of the active power and load power of the wind farm respectively, and obtains a first probability parameter of the active power change and a second probability parameter of the load power change; constructs a probability parameter term that describes the random volatility of the active power and the load power of the wind farm, and models the wind power grid-connected system as a set of nonlinear differential-algebraic equations based on the probability parameter term, and the set of nonlinear differential-algebraic equations is used to characterize the nonlinear dynamic characteristics of the wind power grid-connected system; updates the value of the probability parameter term to the first probability parameter and the second probability parameter , when it is determined that the value of the probability parameter item reaches a specific critical value, it is judged whether the Hessian matrix at an equilibrium point in the equilibrium point set of the differential equation group is a singular matrix, and the set of nonlinear differential-algebraic equation group models includes the differential equation group and the algebraic equation group; if the Hessian matrix is the singular matrix, a bifurcation analysis is performed on the wind power active power injected into the set of nonlinear differential-algebraic equation group models and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point; based on the each bifurcation point and the bifurcation curve, a practical stability domain is divided to obtain the influence trend of the changes in the wind power active power and the load reactive power on the nonlinear dynamic characteristics of the wind power grid-connected system. Compared with related technologies, by performing probability modeling analysis on the uncertainties of the active power and load power of the wind farm respectively, a first probability parameter of the active power change and a second probability parameter of the load power change are obtained, the probability parameter item is updated based on the first probability parameter and the second probability parameter, and when it is determined that the value of the probability parameter item reaches a specific critical value, it is judged whether the Hessian matrix at an equilibrium point in the equilibrium point set of the differential equation system is a singular matrix. If the Hessian matrix is the singular matrix, the corresponding equilibrium point is degenerate, and the change of the probability parameter item will cause a change in the bifurcation characteristics of the wind power grid-connected system. Therefore, a bifurcation analysis is performed on the wind power active power and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point; based on the each bifurcation point and the bifurcation curve, a practical stability domain is divided, thereby considering the impact of the uncertainty of the wind power injected active power and the load reactive power on the practical stability domain of the wind power grid-connected system, and the practical stability domain of the wind power grid-connected system can be more accurately divided. Based on the divided practical stability domain, a theoretical basis and practical guidance are provided for improving the robustness and reliability of the wind power grid-connected system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0047] Figure 1 A flow chart of a practical method for dividing the stability region of a wind power grid-connected system considering probabilistic characteristics provided by an embodiment of the present disclosure;

[0048] Figure 2 A schematic structural diagram of a wind power grid-connected system provided by an embodiment of the present disclosure;

[0049] Figure 3 A schematic diagram of a stable domain partition result under different probability conditions provided by an embodiment of the present disclosure;

[0050] Figure 4 A schematic structural diagram of a practical stability region division device for a wind power grid-connected system considering probabilistic characteristics provided by an embodiment of the present disclosure;

[0051] Figure 5 A schematic block diagram of an exemplary electronic device 300 provided in accordance with an embodiment of the present disclosure. DETAILED DESCRIPTION

[0052] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0053] The following detailed description is an exemplary description and is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention.

[0054] In addition, the terms "first," "second," and the like in the specification and claims of the present disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein.

[0055] The following describes a practical method and apparatus for dividing a wind power grid-connected system's stability domain considering probabilistic characteristics according to an embodiment of the present disclosure with reference to the accompanying drawings.

[0056] To provide a practical stability region division method and apparatus for a wind power grid-connected system that considers probabilistic characteristics, at least from a nonlinear dynamics perspective, to accurately divide the stability region of the power system under the combined influence of wind power injection and load uncertainty, thereby enabling analysis of the practical stability of the power system, this embodiment provides a practical stability region division method for a wind power grid-connected system that considers probabilistic characteristics.

[0057] Figure 1This is a flow chart of a practical method for dividing the stability region of a wind power grid-connected system considering probability characteristics provided by an embodiment of the present disclosure. Figure 1 As shown, the method includes the following steps:

[0058] Step 101: performing probability modeling analysis on the uncertainties of the active power and load power of the wind farm, respectively, to obtain a first probability parameter of the active power change and a second probability parameter of the load power change;

[0059] Step 102: constructing probability parameter terms that describe the random fluctuations of the active power and the load power of the wind farm, and modeling the wind power grid-connected system as a set of nonlinear differential-algebraic equation models based on the probability parameter terms. The set of nonlinear differential-algebraic equation models is used to characterize the nonlinear dynamic characteristics of the wind power grid-connected system.

[0060] Step 103: updating the value of the probability parameter item to the first probability parameter and the second probability parameter; and when it is determined that the value of the probability parameter item reaches a specific critical value, determining whether a Hessian matrix of the differential equation system at an equilibrium point in the equilibrium point set is a singular matrix, wherein the set of nonlinear differential-algebraic equation system models includes the differential equation system and the algebraic equation system;

[0061] Step 104: If the Hessian matrix of the differential equation system at the equilibrium point is the singular matrix, a bifurcation analysis is performed on the wind power active power injected into the nonlinear differential-algebraic equation system model and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point;

[0062] Step 105 : dividing a practical stability domain based on the bifurcation points and the bifurcation curves, and obtaining the influence trend of the changes in the wind power active power and the load reactive power on the nonlinear dynamic characteristics of the wind power grid-connected system.

[0063] The present disclosure provides a practical stability domain partitioning method for a wind power grid-connected system considering probability characteristics, which performs probability modeling analysis on the uncertainties of the active power and load power of the wind farm respectively, and obtains a first probability parameter of the active power change and a second probability parameter of the load power change; constructs a probability parameter term that describes the random volatility of the active power and the load power of the wind farm, and models the wind power grid-connected system as a set of nonlinear differential-algebraic equations based on the probability parameter term, and the set of nonlinear differential-algebraic equations is used to characterize the nonlinear dynamic characteristics of the wind power grid-connected system; updates the value of the probability parameter term to the first probability parameter and the second probability parameter, and in determining the probability parameter When the value of the term reaches a specific critical value, it is determined whether the Hessian matrix of the differential equation group at an equilibrium point in the equilibrium point set is a singular matrix, and the set of nonlinear differential-algebraic equation group models includes the differential equation group and the algebraic equation group; if the Hessian matrix of the differential equation group at the equilibrium point is the singular matrix, a bifurcation analysis is performed on the wind power active power injected into the set of nonlinear differential-algebraic equation group models and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point; based on the each bifurcation point and the bifurcation curve, a practical stability domain is divided to obtain the influence trend of the changes in the wind power active power and the load reactive power on the nonlinear dynamic characteristics of the wind power grid-connected system. Compared to related technologies, this method performs probabilistic modeling analysis on the uncertainties of wind farm active power and load power, obtaining a first probability parameter for the active power change and a second probability parameter for the load power change. The probability parameter term is updated based on the first and second probability parameters. When the value of the probability parameter term reaches a specific critical value, it is determined whether the Hessian matrix of the differential equation system at an equilibrium point in the equilibrium point set is singular. If the Hessian matrix is singular, the corresponding equilibrium point is degenerate, and changes in the probability parameter term will cause changes in the bifurcation characteristics of the wind power grid-connected system. A bifurcation analysis is then performed on the wind power active power and the load reactive power of the target node to obtain a bifurcation curve and bifurcation points. A practical stability region is divided based on the bifurcation points and the bifurcation curve, thereby taking into account the impact of the uncertainties of wind power injected active power and load reactive power on the practical stability region of the wind power grid-connected system. This allows for a more accurate division of the practical stability region of the wind power grid-connected system, and provides a theoretical basis and practical guidance for improving the robustness and reliability of the wind power grid-connected system based on the divided practical stability region.

[0064] As a refinement of the embodiment of the present disclosure, when determining that the value of the probability parameter item reaches a specific critical value in step 103, then determining whether the Hessian matrix of the differential equation group at an equilibrium point in the equilibrium point set is a singular matrix, the following implementation method may also be adopted but is not limited to, for example: defining a potential function of the set of nonlinear differential-algebraic equation models; obtaining all equilibrium points in the potential function to obtain the equilibrium point set of the wind power grid-connected system; when determining that the value of the probability parameter item reaches the specific critical value, then determining whether the Hessian matrix of the differential equation group at an equilibrium point in the equilibrium point set is a singular matrix. It should be understood that if the Hessian matrix is determined to be a singular matrix, it can be determined that the corresponding equilibrium point is degenerate, and further it can be determined that the change in the probability parameter item will cause a change in the bifurcation characteristics of the wind power grid-connected system.

[0065] In order to facilitate the understanding of the above embodiment, this embodiment expands the above embodiment with the help of formulas, including: assuming that the dynamic characteristics of the wind power grid-connected system can be represented by a smooth potential function V Export,

[0066] Based on formula (1), the differential equations can be expressed as:

[0067] f=-gradV(x,y,v,q) (1)

[0068] Denotes that the system of equations f is the negative gradient of the potential function V. In addition, the mutation manifold S can be defined as is the set of all critical points of the potential function V, that is, the balance points of the wind power grid-connected system. The critical points are the description of the potential function, and the balance points are the description of the wind power grid-connected system. However, the critical points and the balance points are essentially the same, that is, the balance point set is obtained by obtaining all critical points through the potential function. Where x = [x1, x2, ..., x n ], is the system state vector; y=[y1,y2,...,y m ], is the system algebraic vector; v=[v1,v2,...,v p ], is the control parameter vector; q=[q1,q2,...,q i ], is the probability parameter vector; f is a set of differential equations that characterizes the dynamic characteristics of each component of the power system. When the probability parameter vector q, i.e., the value of the probability parameter term, reaches a certain critical value, degenerate bifurcation may occur, thereby causing a change in the bifurcation characteristics of the wind power grid-connected system. The properties of the critical point, i.e., the equilibrium point, are determined by the singularity of the Hessen matrix. For a critical point That is, an equilibrium point. If the differential equation system f is The second derivative at is a non-degenerate quadratic form, and the Hessen matrix is non-singular, that is, the determinant of the differential equation system is:

[0069]

[0070] Among them, x i 、x j are all system state vectors. If equation (2) holds, then the equilibrium point If the wind power grid-connected system is non-degenerate, the bifurcation characteristics of the wind power grid-connected system will not change with the change of the probability parameter q; on the contrary, if is a degenerate quadratic form, that is The corresponding equilibrium point is degenerate. At the degenerate equilibrium point, the state variable x changes with the probability parameter q, which complicates the dynamic behavior of the wind power grid-connected system. That is, when bifurcations degenerate, bifurcations in the wind power grid-connected system exhibit non-persistent behavior.

[0071] As a refinement of the above embodiment, when performing bifurcation analysis on the wind power active power injected into the set of nonlinear differential-algebraic equations model and the load reactive power of the target node in step 104 to obtain the bifurcation curve and each bifurcation point, the following implementation method may also be adopted but is not limited to, for example: judging whether the characteristic roots of the state matrix of the wind power grid-connected system at the balance point in the balance point set meet the target judgment condition and whether the conjugate complex eigenvalue trajectory of the wind power grid-connected system meets the transversal condition, the target judgment condition being that the state matrix has a pair of conjugate pure imaginary characteristic roots intersecting the imaginary axis, and no other characteristic roots with zero real part exist on the imaginary axis. The transversal condition is that the conjugate complex eigenvalue trajectory intersects the imaginary axis cross-section, the state matrix includes the wind power active power and the load reactive power, and the state matrix is obtained by linearizing the set of nonlinear differential-algebraic equation models; when it is determined that the state matrix meets the judgment condition and the conjugate complex eigenvalue trajectory does not meet the transversal condition, it is determined that the wind power grid-connected system has a degenerate Hopf bifurcation at the equilibrium point, and the equilibrium point is marked as the bifurcation point until the bifurcation points are marked from the equilibrium point set; the bifurcation curve is drawn based on the bifurcation points, and the conjugate complex eigenvalue trajectory is the trajectory of the characteristic root.

[0072] In order to facilitate the understanding of the above embodiment, this embodiment expands the above embodiment with the help of formulas, including: performing bifurcation analysis on the wind power active power and load reactive power injected into the wind power grid-connected system, and using bifurcation theory to obtain the equilibrium manifold and detect the bifurcation point. When the probability parameter k causes the bifurcation characteristics of the wind power grid-connected system to undergo topological changes, that is, when the bifurcation degenerates, it may cause changes in the topological properties of the practical stability domain of the wind power grid-connected system. It should be understood that the wind power grid-connected system is modeled as a set of nonlinear differential-algebraic equations. Therefore, injecting the wind power active power and the load reactive power into the wind power grid-connected system is to perform bifurcation analysis using the wind power active power and the load reactive power as probability parameters of the set of nonlinear differential-algebraic equations. For the wind power grid-connected system, the following conditions are simultaneously satisfied at the equilibrium point e:

[0073] (a): State matrix A of wind power grid-connected system sys Has a pair of conjugate pure imaginary characteristic roots that intersect the imaginary axis, λ e =±jβ, and there are no other characteristic roots with zero real part on the imaginary axis;

[0074] (b): dRe(λ e ) / dk≠0.

[0075] At this point, the wind power grid-connected system experiences a Hopf bifurcation at the equilibrium point e. Condition (a) is the judgment condition, and condition (b) is the transversal condition. Condition (b) indicates that the conjugate complex eigenvalue trajectory of the wind power grid-connected system intersects the imaginary axis transversely. If the wind power grid-connected system satisfies condition (a) but not condition (b) at the equilibrium point, that is, dRe(λ) / dk = 0, a degenerate Hopf bifurcation occurs at this point, causing changes in the dynamic characteristics of the wind power grid-connected system. The sign of condition (b) indicates the appearance or disappearance of a limit cycle. At a Hopf bifurcation, a limit cycle appears near the equilibrium point e. A Hopf bifurcation with a stable limit cycle is called a supercritical Hopf bifurcation, while one with an unstable limit cycle is called a subcritical Hopf bifurcation. With small perturbations in the probability parameter q, topological changes in the phase trajectory may occur, leading to instability of the wind power grid-connected system. That is, when condition (b) is no longer met, the system undergoes a degenerate Hopf bifurcation. The emergence of degenerate Hopf bifurcation will change the distribution of the overall Hopf bifurcation points of the system, directly leading to changes in the stability boundary of the system equilibrium manifold. As the cumulative probability p of the active power output of the wind farm and the reactive power of the load changes, the degenerate Hopf bifurcation point splits into different super / subcritical Hopf points. Thereafter, as q changes, the super / subcritical Hopf points that were originally relatively close gradually move away, and the originally complete and continuous stability domain breaks at certain key parameter points, and the stability domain of the wind power grid-connected system is divided into multiple discontinuous isolated sub-intervals. Between these sub-intervals, the wind power grid-connected system exhibits an obvious unstable state. The occurrence of degenerate Hopf bifurcation results in an unstable area within the continuous and stable practical stability domain, and the stability of the wind power grid-connected system under small disturbances is significantly weakened. The cumulative probability is the probability corresponding to the first probability parameter or the second probability parameter.

[0076] As a refinement of the embodiment of the present disclosure, when executing step 105 to divide the practical stability domain based on the bifurcation points and the bifurcation curve, the following implementation methods may also be used, but are not limited to, for example: setting a preset number of probability values for the specific critical value; respectively obtaining the wind power active power and load reactive power under the preset number of probability value conditions; obtaining the bifurcation points and bifurcation curves corresponding to the wind power active power and load reactive power under the preset number of probability value conditions; and dividing the practical stability domains corresponding to the preset number of probability value conditions based on the bifurcation points and bifurcation curves corresponding to the wind power active power and load reactive power. That is, wind power active power and load reactive power under different probability conditions are selected, and by setting the preset number of probability values as the values of the specific critical value, a plurality of different cumulative probability values are limited, thereby obtaining the different probability conditions, using bifurcation theory to detect the location of the Hopf bifurcation point of the wind power grid-connected system, and constructing a system bifurcation diagram based on this. By comparing and analyzing the changes in the system bifurcation diagram and the bifurcation point position with the cumulative probability, the dynamic behavior of the system is analyzed, and the practical stable domain of the system under different probability conditions is divided.

[0077] In order to facilitate understanding of the contents involved in the above embodiment, this embodiment is combined with Figure 2 and Figure 3 Provide exemplary instructions, Figure 3 The wind power active power P injected into the wind power grid-connected system when the cumulative probability p is 40%, 50% and 60% m and the load reactive power Q at target node 9 L The changes in the dynamic characteristics and stability of the wind power grid-connected system caused by the changes in , and the bifurcation diagram of the wind power grid-connected system under different probability conditions (such as Figure 3 shown). Figure 3 The solid lines of different colors represent the practical stable domain when the cumulative probability p takes different values, and the dashed lines of different colors represent the unstable parameter domain. Figure 2 This example description is for Figure 2 The practical stability domain of the multi-machine wind power grid-connected system containing double-fed induction wind turbines is divided. The wind power active power P injected into the wind power grid-connected system is used as the m and the load reactive power Q at node 9 L Perform bifurcation analysis and divide the practical stability domain under different probability conditions. The wind farm grid connection point voltage U at node 12 is s Reflects system stability. Figure 2 1-12 are different input and output nodes in the wind power grid-connected system, where node 12 is the output port of the dual induction generator (DFIG), s is the voltage phase angle of the wind farm grid connection point voltage, P mThe active power injected into the grid by the wind turbine is the wind power active power. The dynamic load is connected to bus 9, P L and Q L are the active power and reactive power of the dynamic load respectively, and the load reactive power is the reactive power Q of the dynamic load. L , the apparent power can be expressed as P L +jQ L , j represents an imaginary unit; the wind farm active power is the wind power active power input by node 12, the load power includes the active power and reactive power of the dynamic load at node 9, and G1-G4 represent the four synchronous generators in the system respectively, Figure 3 The saddle-node bifurcation point generation process shown in Figure 1 is that if the state matrix has a zero eigenvalue and all other eigenvalues have negative real parts, a saddle-node bifurcation (SNB) occurs. A saddle-node bifurcation reflects the change in the number of equilibrium points in a wind power grid-connected system with parameters and falls under the category of static bifurcation.

[0078] The study found that the bifurcation characteristics of the wind power grid-connected system change significantly with changes in the cumulative probability p, manifesting as a segmented stability region. The primary cause of this segmented stability region is attributed to the degenerate Hopf bifurcation (DHB) in the wind power grid-connected system and the resulting changes in the distribution of Hopf points. Different values of the cumulative probability p can expand or contract the practical stability region of the wind power grid-connected system.

[0079] As a refinement of the above embodiment, when executing step 102 to model the wind power grid-connected system as a set of nonlinear differential-algebraic equation models, the following implementation methods can also be adopted but are not limited to, for example: constructing the differential equation group based on the system state vector, system algebraic vector, control parameter vector and probability parameter vector of the wind power grid-connected system; constructing the algebraic equation group based on the system state vector, system algebraic vector, control parameter vector and probability parameter vector of the wind power grid-connected system; constructing the set of nonlinear differential-algebraic equation models based on the differential equation group and the algebraic equation group.

[0080] To facilitate understanding of the above embodiment, this embodiment further illustrates the above embodiment with the aid of formulas, including: for a wind power grid-connected system containing probabilistic parameters, its dynamic characteristics can be described by a nonlinear differential-algebraic equation of the following form:

[0081]

[0082] Where x=[x1,x2,...,x n ], is the system state vector; y=[y1,y2,...,y m ], is the system algebraic vector; v=[v1,v2,...,v p], is the control parameter vector; q=[q1,q2,...,q i ], q is the probability parameter vector; f is a set of differential equations, which characterizes the dynamic characteristics of each component of the wind power grid-connected system; g is a set of algebraic equations, such as the power flow equation on the system transmission line.

[0083] The point where f(x,y,v,q)=0 and g(x,y,v,q)=0 is the equilibrium point of the wind power grid-connected system. At the equilibrium point ξ, if It is non-singular, and the reduced-order linearized differential equation can be obtained:

[0084]

[0085] Among them, Δx is the system state variable offset, Its derivative, A sys is the state matrix of the wind power grid-connected system.

[0086] As a refinement of the above embodiment, when executing step 101 to perform probability modeling analysis on the uncertainties of the active power and load power of the wind farm respectively, and obtaining the first probability parameter of the active power change and the second probability parameter of the load power change, the following implementation methods can also be adopted but not limited to, for example: constructing a first probability density function for the wind speed of the wind farm based on a two-parameter Weibull distribution; calculating the first probability parameter based on the first probability density function and a preset functional relationship, wherein the preset functional relationship is the functional relationship between the active power and the wind speed; constructing a second probability density function corresponding to the load power based on a normal distribution probability model; and calculating the second probability parameter based on the second probability density function.

[0087] To facilitate understanding of the above embodiment, this embodiment expands upon the above embodiment with reference to formulas, including: the output power of a wind farm is closely related to changes in wind speed, and the statistical characteristics of wind speed can be approximately described by a two-parameter Weibull distribution, the first probability density function of which is as follows:

[0088]

[0089] Among them, c is the scale parameter reflecting the average wind speed of the wind farm; k is the shape parameter describing the wind speed distribution characteristics; and v is the wind speed.

[0090] According to the probability distribution information of wind speed, the sampling data of wind speed can be obtained. Combined with the nonlinear relationship between wind farm output power and wind speed, the random distribution of output power can be obtained. w The preset functional relationship with wind speed is as follows:

[0091]

[0092] Among them, P W Represents the actual active power output of the wind turbine; the different wind speed characteristics of the wind turbine are determined by the rated wind speed v r , starting wind speed v ci and shutdown wind speed v co To define; under rated conditions, P r It is the rated active power that the wind turbine can achieve.

[0093] Load power exhibits characteristics such as time-varying and randomness due to the influence of multiple factors. A probability model based on normal distribution is usually used to describe its uncertainty. Its second probability density function is:

[0094]

[0095] Where Q is the load reactive power; μ Q represents the average load, σ Q is the standard deviation.

[0096] In summary, the embodiments of the present disclosure can achieve the following effects:

[0097] 1. From the perspective of nonlinear dynamics, analyze the influence of uncertainty and volatility on the stability and dynamic response of the system under different probability conditions, so as to define the practical stability domain.

[0098] 2. Taking into account the impact of wind power injection power and load uncertainty on the system's practical stability domain, the stability of the wind power grid-connected system can be evaluated more accurately and comprehensively. The divided practical stability domain provides a theoretical basis and practical guidance for improving the robustness and reliability of the system.

[0099] Corresponding to the aforementioned method for dividing a practical stability region for a wind power grid-connected system taking into account probabilistic characteristics, the present invention also provides a device for dividing a practical stability region for a wind power grid-connected system taking into account probabilistic characteristics. Since the device embodiments of the present invention correspond to the aforementioned method embodiments, details not disclosed in the device embodiments can be referred to the aforementioned method embodiments and will not be further described in this invention.

[0100] Figure 4 This is a schematic diagram of the structure of a practical stability region division device for a wind power grid-connected system considering probabilistic characteristics provided by an embodiment of the present disclosure, such as Figure 4 Shown, including:

[0101] An analysis unit 21 is configured to perform probability modeling analysis on the uncertainties of the active power and the load power of the wind farm, respectively, to obtain a first probability parameter of the active power change and a second probability parameter of the load power change;

[0102] A construction unit 22 is configured to construct a probability parameter term describing the random fluctuation of the active power and the load power of the wind farm, and model the wind power grid-connected system as a set of nonlinear differential-algebraic equations based on the probability parameter term, wherein the set of nonlinear differential-algebraic equations is used to characterize the nonlinear dynamic characteristics of the wind power grid-connected system;

[0103] an updating unit 23, configured to update the value of the probability parameter item to the first probability parameter and the second probability parameter, and when it is determined that the value of the probability parameter item reaches a specific critical value, determine whether the Hessian matrix of the differential equation system at an equilibrium point in the equilibrium point set is a singular matrix, wherein the set of nonlinear differential-algebraic equation system models includes the differential equation system and the algebraic equation system;

[0104] an acquisition unit 24 configured to, when the Hessian matrix of the differential equation system at the equilibrium point is the singular matrix, the corresponding equilibrium point is degenerate, and a change in the probability parameter term will cause a change in the bifurcation characteristics of the system, perform a bifurcation analysis on the wind power active power injected into the set of nonlinear differential-algebraic equations model and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point;

[0105] The division unit 25 is configured to divide a practical stability domain based on the bifurcation points and the bifurcation curves, and obtain an influence trend of the changes in the wind power active power and the load reactive power on the nonlinear dynamic characteristics of the wind power grid-connected system.

[0106] The present disclosure provides a practical stability domain division device for a wind power grid-connected system considering probability characteristics, which performs probability modeling analysis on the uncertainties of the active power and load power of the wind farm respectively, and obtains a first probability parameter of the active power change and a second probability parameter of the load power change; constructs a probability parameter term that describes the random volatility of the active power and the load power of the wind farm, and models the wind power grid-connected system as a set of nonlinear differential-algebraic equations based on the probability parameter term, and the set of nonlinear differential-algebraic equations is used to characterize the nonlinear dynamic characteristics of the wind power grid-connected system; updates the value of the probability parameter term to the first probability parameter and the second probability parameter, and When the value of the probability parameter item reaches a specific critical value, it is determined whether the Hessian matrix at an equilibrium point in the equilibrium point set of the differential equation group is a singular matrix, and the set of nonlinear differential-algebraic equation group models includes the differential equation group and the algebraic equation group; if the Hessian matrix is the singular matrix, a bifurcation analysis is performed on the wind power active power injected into the set of nonlinear differential-algebraic equation group models and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point; based on the each bifurcation point and the bifurcation curve, a practical stability domain is divided to obtain the influence trend of the changes in the wind power active power and the load reactive power on the nonlinear dynamic characteristics of the wind power grid-connected system. Compared with related technologies, by performing probability modeling analysis on the uncertainties of the active power and load power of the wind farm respectively, a first probability parameter of the active power change and a second probability parameter of the load power change are obtained, the probability parameter item is updated based on the first probability parameter and the second probability parameter, and when it is determined that the value of the probability parameter item reaches a specific critical value, it is judged whether the Hessian matrix at an equilibrium point in the equilibrium point set of the differential equation system is a singular matrix. If the Hessian matrix is the singular matrix, the corresponding equilibrium point is degenerate, and the change of the probability parameter item will cause a change in the bifurcation characteristics of the wind power grid-connected system. Therefore, a bifurcation analysis is performed on the wind power active power and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point; based on the each bifurcation point and the bifurcation curve, a practical stability domain is divided, thereby considering the impact of the uncertainty of the wind power injected active power and the load reactive power on the practical stability domain of the wind power grid-connected system, and the practical stability domain of the wind power grid-connected system can be more accurately divided. Based on the divided practical stability domain, a theoretical basis and practical guidance are provided for improving the robustness and reliability of the wind power grid-connected system.

[0107] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principles are the same, which is not limited in this embodiment.

[0108] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0109] Figure 5 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0110] like Figure 5 As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 302 or a computer program loaded from a storage unit 308 into a RAM (Random Access Memory) 303. Various programs and data required for the operation of the device 300 can also be stored in the RAM 303. The computing unit 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0111] Various components in device 300 are connected to I / O interface 305, including: an input unit 306, such as a keyboard, mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, optical disk, etc.; and a communication unit 309, such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows device 300 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0112] The computing unit 301 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), various specialized AI (Artificial Intelligence) computing chips, various computing units that run machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the method for partitioning a practical stability region of a wind power grid-connected system considering probabilistic characteristics. For example, in some embodiments, the method for partitioning a practical stability region of a wind power grid-connected system considering probabilistic characteristics can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the calculation unit 301 may be configured in any other appropriate manner (for example, by means of firmware) to execute the aforementioned practical stability region partitioning method for a wind power grid-connected system considering probabilistic characteristics.

[0113] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System on Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0114] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0115] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0116] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0117] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0118] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. This client-server relationship is established by computer programs running on the respective computers, establishing a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and VPS services ("Virtual Private Servers" or simply "VPS"). The server may also be a server in a distributed system or a server integrated with blockchain.

[0119] It's important to note that artificial intelligence (AI) is the study of how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). This encompasses both hardware and software technologies. AI hardware technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily encompass computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graphs.

Claims

1. A practical stability region partitioning method for wind power grid-connected systems considering probabilistic characteristics is characterized by: include: Probabilistic modeling analysis is performed on the uncertainties of the active power and load power of the wind farm to obtain a first probability parameter of the active power change and a second probability parameter of the load power change; Constructing probability parameter terms that describe the random fluctuations of the active power and the load power of the wind farm, and modeling the wind power grid-connected system as a set of nonlinear differential-algebraic equation models based on the probability parameter terms, wherein the set of nonlinear differential-algebraic equation models is used to characterize the nonlinear dynamic characteristics of the wind power grid-connected system; updating the value of the probability parameter item to the first probability parameter and the second probability parameter, and when it is determined that the value of the probability parameter item reaches a specific critical value, determining whether a Hessian matrix of the differential equation system at an equilibrium point in the equilibrium point set is a singular matrix, wherein the set of nonlinear differential-algebraic equation system models includes the differential equation system and the algebraic equation system; If the Hessian matrix of the differential equation system at the equilibrium point is the singular matrix, a bifurcation analysis is performed on the wind power active power injected into the set of nonlinear differential-algebraic equation system models and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point, including: Determining whether, at the equilibrium point in the equilibrium point set, the characteristic roots of the state matrix of the wind power grid-connected system satisfy a target judgment condition and whether the conjugate complex eigenvalue trajectory of the wind power grid-connected system satisfies a transversal condition, the target judgment condition being that the state matrix has a pair of conjugate pure imaginary characteristic roots intersecting the imaginary axis, and no other characteristic roots with zero real parts exist on the imaginary axis, the transversal condition being that the conjugate complex eigenvalue trajectory transversely intersects the imaginary axis, the state matrix including the wind power active power and the load reactive power, and the state matrix being obtained by linearizing the set of nonlinear differential-algebraic equation models at the equilibrium point; When it is determined that the state matrix satisfies the judgment condition and the conjugate complex eigenvalue trajectory does not satisfy the transversal condition, it is determined that a degenerate Hopf bifurcation occurs in the wind power grid-connected system at the equilibrium point, and the equilibrium point is marked as the bifurcation point until all the bifurcation points are marked from the equilibrium point set; Drawing the bifurcation curve based on the bifurcation points; Based on the bifurcation points and the bifurcation curves, a practical stability domain is divided to obtain the influence trend of the changes in the wind power active power and the load reactive power on the nonlinear dynamic characteristics of the wind power grid-connected system.

2. The method according to claim 1, characterized in that When it is determined that the value of the probability parameter item reaches a specific critical value, determining whether the Hessian matrix at an equilibrium point in the equilibrium point set of the differential equation system is a singular matrix includes: defining a potential function of the set of nonlinear differential-algebraic equations model; Acquire all equilibrium points in the potential function to obtain the equilibrium point set of the wind power grid-connected system; When it is determined that the value of the probability parameter item reaches the specific critical value, it is determined whether the Hessian matrix of the differential equation group at the equilibrium point in the equilibrium point set is a singular matrix.

3. The method according to claim 2, characterized in that The dividing of the practical stability domain based on the bifurcation points and the bifurcation curves includes: Setting a preset number of probability values for the specific critical value; Respectively obtaining the wind power active power and load reactive power under the condition of the preset number of probability values; Obtaining the bifurcation points and the bifurcation curves corresponding to the wind power active power and the load reactive power respectively under the condition of the preset number of probability values; Based on the bifurcation points and bifurcation curves corresponding to the wind power active power and the load reactive power, practical stable domains corresponding to the preset number of probability values are divided.

4. The method according to claim 3, characterized in that The wind power grid-connected system is modeled as a set of nonlinear differential-algebraic equations, including: Constructing the differential equation group based on the system state vector, system algebraic vector, control parameter vector and probability parameter vector of the wind power grid-connected system; Constructing the algebraic equation group based on the system state vector, system algebraic vector, control parameter vector and probability parameter vector of the wind power grid-connected system; The set of nonlinear differential-algebraic equation models is constructed based on the differential equations and the algebraic equations.

5. The method according to any one of claims 1 to 4, characterized in that The probabilistic modeling analysis is performed on the uncertainties of the active power and the load power of the wind farm to obtain the first probability parameter of the active power change and the second probability parameter of the load power change, respectively. The first probability parameter of the active power change and the second probability parameter of the load power change are obtained. Constructing a first probability density function for the wind speed of the wind farm based on a two-parameter Weibull distribution; Calculating the first probability parameter based on the first probability density function and a preset functional relationship, where the preset functional relationship is a functional relationship between the active power and the wind speed; Constructing a second probability density function corresponding to the load power based on a normal distribution probability model; The second probability parameter is calculated based on the second probability density function.

6. A practical stability region division device for wind power grid-connected systems considering probabilistic characteristics, characterized in that: include: An analysis unit, configured to perform probability modeling analysis on the uncertainties of the active power and the load power of the wind farm, respectively, to obtain a first probability parameter of the active power change and a second probability parameter of the load power change; a construction unit, configured to construct a probability parameter term describing the random fluctuation of the active power and the load power of the wind farm, and model the wind power grid-connected system as a set of nonlinear differential-algebraic equations based on the probability parameter term, wherein the set of nonlinear differential-algebraic equations is used to characterize the nonlinear dynamic characteristics of the wind power grid-connected system; an updating unit, configured to update the value of the probability parameter item to the first probability parameter and the second probability parameter, and, when it is determined that the value of the probability parameter item reaches a specific critical value, determine whether a Hessian matrix of the differential equation system at an equilibrium point in the equilibrium point set is a singular matrix, wherein the set of nonlinear differential-algebraic equation system models includes the differential equation system and the algebraic equation system; An acquisition unit is configured to perform a bifurcation analysis on the wind power active power injected into the set of nonlinear differential-algebraic equations model and the load reactive power of the target node when the Hessian matrix of the differential equations at the equilibrium point is the singular matrix, so as to obtain a bifurcation curve and each bifurcation point; comprising: Determining whether, at the equilibrium point in the equilibrium point set, the characteristic roots of the state matrix of the wind power grid-connected system satisfy a target judgment condition and whether the conjugate complex eigenvalue trajectory of the wind power grid-connected system satisfies a transversal condition, the target judgment condition being that the state matrix has a pair of conjugate pure imaginary characteristic roots intersecting the imaginary axis, and no other characteristic roots with zero real parts exist on the imaginary axis, the transversal condition being that the conjugate complex eigenvalue trajectory transversely intersects the imaginary axis, the state matrix including the wind power active power and the load reactive power, and the state matrix being obtained by linearizing the set of nonlinear differential-algebraic equation models at the equilibrium point; When it is determined that the state matrix satisfies the judgment condition and the conjugate complex eigenvalue trajectory does not satisfy the transversal condition, it is determined that a degenerate Hopf bifurcation occurs in the wind power grid-connected system at the equilibrium point, and the equilibrium point is marked as the bifurcation point until all the bifurcation points are marked from the equilibrium point set; Drawing the bifurcation curve based on the bifurcation points; A division unit is used to divide the practical stability domain based on the bifurcation points and the bifurcation curve to obtain the influence trend of the changes in the wind power active power and the load reactive power on the nonlinear dynamic characteristics of the wind power grid-connected system.

7. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 5.

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