Wind power integration system practical stability domain division method and device considering probability characteristics

Abstract

The invention discloses a practical stability domain division method and a practical stability domain division device for a wind power integration system considering probability characteristics, and relates to the technical field of data processing. Obtaining a first probability parameter of active power change and a second probability parameter of load power change, and updating a probability parameter item based on the first probability parameter and the second probability parameter; judging whether a Hessian matrix of the differential equation set at one balance point in the balance point set is a singular matrix, and if the Hessian matrix is the singular matrix, performing bifurcation analysis on the wind power active power and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point; and dividing the practical stability domain based on each bifurcation point and the bifurcation curve, and considering the influence of the uncertainty of the wind power injection active power and the load reactive power on the practical stability domain when the practical stability domain is divided.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a practical stability domain division method and device for 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 access on the stability of the power system, especially on the practical stability of the power system, has attracted widespread attention. The randomness, intermittency and volatility of wind power have brought many uncertainties to the actual operation of the power system, resulting in frequent grid instability accidents. In order to ensure the safe and stable operation of the power system and the safety of equipment, and to improve the quality of power, it is urgent to carry out research on issues related to the practical stability domain of wind power grid-connected systems.

[0003] At present, the mainstream research direction assumes that the operating conditions of the wind power output of the doubly-fed induction generator (DFIG) are exactly known, and studies the interaction between the dynamic characteristics of the DFIG and the dynamic characteristics of other components of the power system based on deterministic analysis, but this method does not take into account the uncertainty and randomness of wind power. In addition, the wind power grid-connected system is essentially a high-order multivariable nonlinear system, and its stability is significantly affected by key parameters such as load and generator output. The above methods are mainly based on linear analysis, and the practical stability of specific working points in the state space is studied. It belongs to the "point-by-point" method, that is, it can only analyze the practical stability under a specific operating state. Once the system operating state changes (such as changes in wind farm output, etc.), it is necessary to re-analyze, and it is difficult to describe the practical stability of the system as a whole, so it may cause misjudgment of the system stability. If the system operating point changes, its nonlinear characteristics will change accordingly, and the linear method cannot accurately describe the stability boundary and stability domain of the system.

[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 a practical stability domain of a wind power grid-connected system considering probabilistic characteristics, the main purpose of which is to accurately divide the stability 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 division 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 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 volatility 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 equations based on the probability parameter terms, wherein the set of nonlinear differential-algebraic equations 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 the Hessian matrix of the differential equation group at an equilibrium point in the equilibrium point set is a singular matrix, wherein the set of nonlinear differential-algebraic equation group models includes the differential equation group and the algebraic equation group;

[0010] 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 nonlinear differential-algebraic equation group 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 model of the set of nonlinear differential-algebraic equation systems;

[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, the 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 the bifurcation curve and each bifurcation point includes:

[0016] Determine whether the characteristic root of the state matrix of the wind power grid-connected system satisfies the target judgment condition and whether the conjugate complex eigenvalue trajectory of the wind power grid-connected system satisfies the transversal condition at the balance point in the balance point set, the target judgment condition being that the state matrix has a pair of conjugate pure imaginary characteristic roots intersecting with the imaginary axis, and there are no other characteristic roots with zero real parts on the imaginary axis, the transversal condition being that the conjugate complex eigenvalue trajectory cross-intersects with the imaginary axis, 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 equations model;

[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 each bifurcation point is 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] Under the condition of obtaining the preset number of probability values, the bifurcation points and the bifurcation curves corresponding to the wind power active power and the load reactive power respectively;

[0023] Based on the bifurcation points and bifurcation curves corresponding to the wind power active power and the load reactive power, the practical stability 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 models 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 a system state vector, a system algebraic vector, a control parameter vector and a probability parameter vector of the wind power grid-connected system;

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

[0028] Optionally, the performing probability modeling analysis on the uncertainties of the active power and the 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, wherein 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 probability characteristics is provided, comprising:

[0034] An analysis unit, configured to perform probability modeling analysis on the uncertainties of 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;

[0035] A construction unit is used to construct a probability parameter term describing the random volatility of the active power and the load power of the wind farm, and based on the probability parameter term, the wind power grid-connected system is modeled as a set of nonlinear differential-algebraic equation group models, wherein the set of nonlinear differential-algebraic equation group models 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 the Hessian matrix of the differential equation group at an equilibrium point in the equilibrium point set is a singular matrix, wherein the set of nonlinear differential-algebraic equation group models includes the differential equation group and the algebraic equation group;

[0037] an acquisition unit, 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 acquire a bifurcation curve and each bifurcation point;

[0038] The 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 of 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, and 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 respectively performs probability modeling analysis on the uncertainties of the active power and load power of a wind farm, 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 equation group models based on the probability parameter term, and the set of nonlinear differential-algebraic equation group models 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 set of equilibrium points 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 the related art, 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 of the differential equation group at an equilibrium point in the equilibrium point set is a singular matrix, if the Hessian matrix is ​​the singular matrix, then the corresponding equilibrium point is degenerate, and the change of the probability parameter item will cause the change of the bifurcation characteristics of the wind power grid-connected system, then a bifurcation analysis is performed on the wind power active power and the load reactive power of the target node to obtain the bifurcation curve and each bifurcation point; based on the each bifurcation point and the bifurcation curve, the practical stability domain is divided, so as to realize the consideration of the influence of the uncertainty of the active power injected by wind 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, and the theoretical basis and practical guidance are provided for improving the robustness and reliability of the wind power grid-connected system based on the divided practical stability domain. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. 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 accompanying drawings:

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

[0048] Figure 2 A schematic diagram of the structure 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 diagram of the structure of a practical stability region division device for a wind power grid-connected system considering probability characteristics provided by an embodiment of the present disclosure;

[0051] Figure 5 A schematic block diagram of an exemplary electronic device 300 provided for 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 the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0053] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, 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", etc. in the specification and claims of the present disclosure 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 terms used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in sequences other than those illustrated or described herein.

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

[0056] In order to provide a practical stability domain division method and device for a wind power grid-connected system considering probability characteristics at least from the perspective of nonlinear dynamics, so as to accurately divide the stable area of ​​the power system under the joint influence of wind power injection and load uncertainty, thereby realizing the analysis of the practical stability of the power system. This embodiment provides a practical stability domain division method for a wind power grid-connected system considering probability characteristics.

[0057] Figure 1The present invention provides a flow chart of a practical method for dividing the stability region of a wind power grid-connected system considering probability characteristics. Figure 1 As shown, the method comprises the following steps:

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

[0059] Step 102, constructing a probability parameter term describing the random volatility 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 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;

[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 the Hessian matrix of the differential equation group at an equilibrium point in the equilibrium point set is a singular matrix, wherein the set of nonlinear differential-algebraic equation group models includes the differential equation group and the algebraic equation group;

[0061] Step 104: 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 nonlinear differential-algebraic equation group 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 curve, and obtaining the influence trend of the change of 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 invention provides a practical stable domain partition method for a wind power grid-connected system considering probability characteristics, which performs probability modeling analysis on the uncertainties of active power and load power of a wind farm, respectively, to obtain 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 equation group models based on the probability parameter term, wherein the set of nonlinear differential-algebraic equation group models 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 after 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 with the related art, the first probability parameter of the active power change and the second probability parameter of the load power change are obtained by performing probability modeling analysis on the uncertainty of the active power and load power of the wind farm respectively, and 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 the differential equation group at an equilibrium point in the equilibrium point set 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 the change of the bifurcation characteristics of the wind power grid-connected system. Then, a bifurcation analysis is performed on the wind power active power and the load reactive power of the target node to obtain the bifurcation curve and each bifurcation point; the practical stability domain is divided based on the each bifurcation point and the bifurcation curve, so as to realize the consideration of the influence of the uncertainty of the active power injected by wind 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 divided more accurately, and the theoretical basis and practical guidance are provided for improving the robustness and reliability of the wind power grid-connected system based on the divided practical stability domain.

[0064] As a refinement of the embodiment of the present disclosure, when executing step 103 and determining that the value of the probability parameter item reaches a 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, the following implementation method may also be adopted but is not limited to, for example: defining the potential function of the set of nonlinear differential-algebraic equation group 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 by determining that the Hessian matrix is ​​a singular matrix, it can be determined that the corresponding equilibrium point is degenerate, and then it is 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 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] It means 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 point of the wind power grid-connected system. The critical point is the description of the potential function, and the balance point is the description of the wind power grid-connected system. However, the critical point and the balance point 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, which 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, thus causing the change of 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 degraded. At the degraded equilibrium point, the state variable x changes with the probability parameter term q, which will complicate the dynamic behavior of the wind power grid-connected system. That is, when the bifurcation is degraded, the bifurcation in the wind power grid-connected system exhibits non-persistence.

[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 root of the state matrix of the wind power grid-connected system at the balance point in the balance point set meets 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 there are no other characteristic roots with zero real part 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 equations model; 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 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-mentioned embodiment, this embodiment expands the above-mentioned 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 time, the wind power grid-connected system has 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 in a transversal manner. If the wind power grid-connected system satisfies condition (a) but does not satisfy condition (b) at the equilibrium point, that is, dRe(λ) / dk=0, a degenerate Hopf bifurcation occurs at this point, causing the dynamic characteristics of the wind power grid-connected system to change. The sign of condition (b) indicates the generation or disappearance of the limit cycle. At the Hopf bifurcation, the limit cycle appears near the equilibrium point e. A Hopf bifurcation with a stable limit cycle is called a supercritical Hopf bifurcation, and one with an unstable limit cycle is called a subcritical Hopf bifurcation. With the change of small perturbations in the probability parameter term q, topological changes in the phase trajectory may occur, resulting in instability of the wind power grid-connected system. That is, when condition (b) is no longer satisfied, a degenerate Hopf bifurcation occurs in the system. 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. With the change of the cumulative probability p of the active power output of the wind farm and the reactive power of the load, the degenerate Hopf bifurcation point splits into different super / subcritical Hopf points. Thereafter, with the change of q, the relatively close super / subcritical Hopf points 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 in 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 adopted 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; 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, the wind power active power and load reactive power under different probability conditions are selected, and the preset number of probability values ​​are set as the values ​​of the specific critical value to limit the values ​​of multiple different cumulative probabilities, thereby obtaining the different probability conditions, using the bifurcation theory to detect the position of the Hopf bifurcation point of the wind power grid-connected system, and constructing the system bifurcation diagram based on this. By comparing and analyzing the changes of 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 the understanding of the contents involved in the above embodiments, this embodiment is combined with Figure 2 and Figure 3 Provide an exemplary description, 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 in the figure 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 exemplary 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 m and the load reactive power Q at node 9 L Perform bifurcation analysis to divide the practical stability domain under different probability conditions. The wind farm grid connection point voltage U at node 12 s Reflects system stability. Figure 2 1-12 shown in the figure 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 respectively the active power and reactive power of the dynamic load, 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 generation process of the saddle-node bifurcation point shown in is that if the state matrix has a zero characteristic root and the remaining characteristic roots have negative real parts, a saddle-node bifurcation (SNB) occurs. Saddle-node bifurcation reflects the change of the number of equilibrium points of the wind power grid-connected system with parameters and belongs to the category of static bifurcation.

[0078] The study found that with the change of cumulative probability p, the bifurcation characteristics of the wind power grid-connected system have changed significantly, which is specifically manifested as the segmentation phenomenon of the stability domain. The main reason for the segmentation phenomenon of the stability domain can be attributed to the degenerate Hopf bifurcation (DHB) in the wind power grid-connected system and the distribution change of the Hopf points caused by it. Different cumulative probability p values ​​can expand or reduce the practical stability domain 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 equations, the following implementation methods may also be adopted but are not limited to, for example: constructing the differential equations 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 equations 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 equations based on the differential equations and the algebraic equations.

[0080] In order to facilitate the understanding of the above embodiment, this embodiment expands the above embodiment in combination with formulas, including: for a wind power grid-connected system containing probability parameters, its dynamic characteristics can be described by a nonlinear differential-algebraic equation in 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 that satisfies both 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 Non-singular, we can get the reduced linear differential equation:

[0084]

[0085] Among them, Δx is the system state variable offset, Its derivative, A sys It is the state matrix of 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 to obtain the first probability parameter of the active power change and the second probability parameter of the load power change, the following implementation methods may also be adopted but are 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] In order to facilitate the understanding of the above embodiment, this embodiment expands the above embodiment in combination with a formula, including: the output power of the wind farm is closely related to the change of wind speed, and the statistical characteristics of the wind speed can be approximately described by a two-parameter Weibull distribution, and its first probability density function 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. Combining the nonlinear relationship between wind farm output power and wind speed, the random distribution of output power can be obtained. Wind power output active power P 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 , Start 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 is affected by many factors and exhibits characteristics such as time-varying and randomness. 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. Analyze the influence of uncertainty and volatility on the stability and dynamic response of the system under different probability conditions from the perspective of nonlinear dynamics, 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 practical stability domain of the system, 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 above-mentioned method for dividing a practical stability domain of a wind power grid-connected system considering probability characteristics, the present invention also proposes a device for dividing a practical stability domain of a wind power grid-connected system considering probability characteristics. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, the details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be described in detail in the present invention.

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

[0101] The analysis unit 21 is used 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 used to construct a probability parameter term describing the random volatility 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, used for 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 the Hessian matrix of the differential equation group at an equilibrium point in the equilibrium point set is a singular matrix, wherein the set of nonlinear differential-algebraic equation group models includes the differential equation group and the algebraic equation group;

[0104] The acquisition unit 24 is used for 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 when the Hessian matrix of the differential equations at the equilibrium point is the singular matrix, and the corresponding equilibrium point is degenerate, and the change of the probability parameter term will cause the change of the bifurcation characteristics of the system, so as to obtain the bifurcation curve and each bifurcation point;

[0105] The division unit 25 is used to divide the practical stability domain based on the bifurcation points and the bifurcation curves to obtain the influence trend of the changes of 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 invention 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 active power and load power of a wind farm, respectively, to obtain a first probability parameter of the active power change and a second probability parameter of the load power change; constructs probability parameter items that describe 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 equation group models based on the probability parameter items, wherein the set of nonlinear differential-algebraic equation group models is used to characterize the nonlinear dynamic characteristics of the wind power grid-connected system; updates the value of the probability parameter item to the first probability parameter and the second probability parameter, and in the case of determining 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 the related art, 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 of the differential equation group at an equilibrium point in the equilibrium point set is a singular matrix, if the Hessian matrix is ​​the singular matrix, then the corresponding equilibrium point is degenerate, and the change of the probability parameter item will cause the change of the bifurcation characteristics of the wind power grid-connected system, then a bifurcation analysis is performed on the wind power active power and the load reactive power of the target node to obtain the bifurcation curve and each bifurcation point; based on the each bifurcation point and the bifurcation curve, the practical stability domain is divided, so as to realize the consideration of the influence of the uncertainty of the active power injected by wind 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, and the theoretical basis and practical guidance are provided for improving the robustness and reliability of the wind power grid-connected system based on the divided practical stability domain.

[0107] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is 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 an embodiment 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 processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required 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 to a RAM (Random Access Memory) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the 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] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The computing unit 301 may be a variety of general and / or special processing components 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 Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as a practical stability domain partitioning method for a wind power grid-connected system considering probabilistic characteristics. For example, in some embodiments, the practical stability domain partitioning method for a wind power grid-connected system considering probabilistic characteristics may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may 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 may be performed. Alternatively, in other embodiments, the computing 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 wind power grid-connected systems considering probabilistic characteristics.

[0113] Various embodiments of the systems and techniques described above herein may 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 may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general-purpose programmable processor that may 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 may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, 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 equipment. 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 device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, 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 may 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 a 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 may 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 the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0119] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

Claims

1. A practical stability region division method for wind power grid-connected system considering probabilistic characteristics, characterized in that: include: Probabilistic modeling analysis is performed on the uncertainties of 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 volatility 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 equations based on the probability parameter terms, wherein the set of nonlinear differential-algebraic equations 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 the Hessian matrix of the differential equation group at an equilibrium point in the equilibrium point set is a singular matrix, wherein 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 nonlinear differential-algebraic equation group model and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point; 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 of the differential equation system in the equilibrium point set 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 step of 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: Determine whether the characteristic root of the state matrix of the wind power grid-connected system satisfies the target judgment condition and whether the conjugate complex eigenvalue trajectory of the wind power grid-connected system satisfies the transversal condition at the balance point in the balance point set, the target judgment condition being that the state matrix has a pair of conjugate pure imaginary characteristic roots intersecting with the imaginary axis, and there are no other characteristic roots with zero real parts on the imaginary axis, the transversal condition being that the conjugate complex eigenvalue trajectory cross-intersects with the imaginary axis, 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 equations model; 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 each bifurcation point is marked from the equilibrium point set; The bifurcation curve is drawn based on the bifurcation points.

4. The method according to claim 3, characterized in that The dividing of the practical stability domain based on the bifurcation points and the bifurcation curves comprises: 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; Under the condition of obtaining the preset number of probability values, the bifurcation points and the bifurcation curves corresponding to the wind power active power and the load reactive power respectively; Based on the bifurcation points and bifurcation curves corresponding to the wind power active power and the load reactive power, the practical stability domains corresponding to the preset number of probability values ​​are divided.

5. The method according to claim 4, 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, the system algebraic vector, the control parameter vector and the probability parameter vector of the wind power grid-connected system; The set of nonlinear differential-algebraic equation models is constructed based on the set of differential equations and the set of algebraic equations.

6. The method according to any one of claims 1 to 5, characterized in that: The probability modeling analysis is performed on the uncertainties of the active power and the 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, including: 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 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.

7. A practical stability region division device for wind power grid-connected system considering probability characteristics, characterized in that: include: An analysis unit, configured to perform probability modeling analysis on the uncertainties of 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; A construction unit is used to construct a probability parameter term describing the random volatility of the active power and the load power of the wind farm, and based on the probability parameter term, the wind power grid-connected system is modeled as a set of nonlinear differential-algebraic equation group models, wherein the set of nonlinear differential-algebraic equation group models 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 the Hessian matrix of the differential equation group at an equilibrium point in the equilibrium point set is a singular matrix, wherein the set of nonlinear differential-algebraic equation group models includes the differential equation group and the algebraic equation group; an acquisition unit, 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 acquire a bifurcation curve and each bifurcation point; The 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 of the wind power active power and the load reactive power on the nonlinear dynamic characteristics of the wind power grid-connected system.

8. 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 6.

9. 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 1-6.

10. 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 6.

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