Method and device for determining small disturbance probability stability of power system

Abstract

An embodiment of the present invention provides a method and device for determining the probabilistic stability of a power system under small disturbances. The method includes: obtaining data from a synchronized phasor measurement unit (PMU) of the power system; establishing a first system model based on the PMU data; the first system model is used to represent the relationship between node voltage and node injection current, and the first system model includes n nodes; the nodes are used to represent generator sets of the power system; n is an integer greater than 1; based on the eigenvalues of the node admittance matrix of the first system model, determining a normal eigenvalue among the eigenvalues; and determining whether the power system is probabilistically asymptotically stable using a Lyapunov function based on the normal eigenvalue among the eigenvalues and the next wind speed state. The method of the embodiment of the present invention improves the efficiency of small disturbance probabilistic stability analysis of the power system.

Description

Technical Field

[0001] The present invention relates to the electrical field, and in particular to a method and device for determining the stability of a small interference probability of an electric power system. Background Art

[0002] The probabilistic stability of a power system under small disturbances is a crucial prerequisite for its safe and stable operation. This refers to the system's ability to automatically recover to its initial stable operating state after experiencing a small disturbance without experiencing spontaneous oscillation or non-periodic loss of step. With the large-scale integration of wind power into power systems, system stability issues are becoming increasingly prominent, particularly low-frequency oscillations caused by weak or negative damping, which can even cause generator disconnection in severe cases. Furthermore, the uncertainty and uncontrollability of wind power generation, as well as the interaction between wind turbines and the original power system, pose even greater challenges to the stable operation of power systems.

[0003] In the existing technology, the small-disturbance probabilistic stability analysis of the power system is analyzed and calculated based on the mathematical modeling of the power system. However, when the number of wide-area time series data of the synchronized phasor measurement unit (PMU) is large, various mathematical approximations and complex calculations will be involved, making it difficult to construct its probability distribution function and accurately describe its probability statistical characteristics. As a result, its calculation complexity is too high and the efficiency of the small-disturbance probabilistic stability analysis is low. Summary of the Invention

[0004] In response to the problems in the prior art, embodiments of the present invention provide a method and apparatus for determining the stability of a small disturbance probability of an electric power system.

[0005] Specifically, the embodiments of the present invention provide the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for determining a stable small disturbance probability of a power system, comprising:

[0007] Acquire synchronized phasor measurement unit (PMU) data of the power system;

[0008] A first system model is established based on the PMU data; the first system model is used to represent the relationship between the node voltage and the node injection current, and the first system model includes n nodes; the node is used to represent the generator set of the power system; n is an integer greater than 1;

[0009] determining a normal eigenvalue among the eigenvalues based on the eigenvalues of the node admittance matrix of the first system model;

[0010] According to the normal eigenvalues in the eigenvalues and the next wind speed state, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable.

[0011] Furthermore, based on the eigenvalues of the node admittance matrix of the first system model, determining normal eigenvalues among the eigenvalues includes:

[0012] Determine the importance score of the eigenvalue based on the Gini index of the eigenvalue;

[0013] According to the importance scores of the eigenvalues, normal eigenvalues among the eigenvalues are determined.

[0014] Furthermore, according to the importance score of the eigenvalue, the normal eigenvalue among the eigenvalues is determined, including:

[0015] The eigenvalues whose distance from the origin of the importance evaluation coordinate is less than the first preset value are regarded as normal eigenvalues among the eigenvalues.

[0016] Furthermore, according to the normal eigenvalues in the eigenvalues and the next wind speed state, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable, including:

[0017] If the next wind speed state does not change, determine the posterior probability that the normal eigenvalue in the eigenvalue falls in the left half plane;

[0018] According to the posterior probability that the normal eigenvalues in the eigenvalues fall in the left half plane, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable.

[0019] Furthermore, according to the normal eigenvalues in the eigenvalues and the next wind speed state, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable, including:

[0020] If the next wind speed state changes, then based on the normal eigenvalue in the eigenvalue, predict the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls on the left half plane;

[0021] Based on the posterior probability that the normal eigenvalue falls in the left half plane, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable.

[0022] Furthermore, based on the normal eigenvalues in the eigenvalues, the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls in the left half plane is predicted, including:

[0023] According to the prior probability density of the normal eigenvalues in the eigenvalues, variational Bayes is used to predict the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls in the left half plane.

[0024] Furthermore, determining the posterior probability that a normal eigenvalue in the eigenvalue falls in the left half plane includes:

[0025] Determine the prior probability that a normal eigenvalue in the eigenvalues falls in the left half plane;

[0026] Based on the prior probability that the normal eigenvalues in the eigenvalues fall in the left half plane, the posterior probability that the normal eigenvalues in the eigenvalues fall in the left half plane is determined using variational Bayes.

[0027] Furthermore, based on the posterior probability that the normal eigenvalues in the eigenvalues fall in the left half plane, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable, including:

[0028] According to the posterior probability that the normal eigenvalues in the eigenvalues fall in the left half plane, a symmetric matrix with m nodes is obtained using the Lyapunov function; m is an integer greater than 0;

[0029] If the symmetric matrix is positive definite, then the power system is determined to be probabilistically asymptotically stable.

[0030] Furthermore, if the modulus of the normal eigenvalue is less than a second preset value, it is determined that the node corresponding to the normal eigenvalue is probabilistically asymptotically stable.

[0031] In a second aspect, an embodiment of the present invention further provides a device for determining the stability of a small disturbance probability of a power system, comprising:

[0032] An acquisition module, used to acquire PMU data of a synchronized phasor measurement device of a power system;

[0033] The classification module is configured to establish a first system model based on the PMU data; the first system model is configured to represent a relationship between a node voltage and a node injection current, the first system model including n nodes; the node is configured to represent a generator set of the power system; n is an integer greater than 1; and based on eigenvalues of a node admittance matrix of the first system model, determine a normal eigenvalue among the eigenvalues;

[0034] The determination module is used for determining whether the power system is probabilistically asymptotically stable by using a Lyapunov function according to a normal eigenvalue in the eigenvalue and a next wind speed state.

[0035] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for determining the stability of the small interference probability of the power system as described in the first aspect are implemented.

[0036] In a fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for determining the stability of a small disturbance probability of an electric power system as described in the first aspect.

[0037] In a fifth aspect, an embodiment of the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method for determining the stability of a small disturbance probability of a power system as described in the first aspect.

[0038] The method and device for determining the small disturbance probabilistic stability of an electric power system provided by an embodiment of the present invention can determine the eigenvalues closely related to the small disturbance stability of the electric power system by acquiring the synchronous phasor measurement device PMU data of the electric power system and establishing a system model; further, by determining the normal eigenvalues among the eigenvalues, the normal eigenvalues are processed as associated data, and the abnormal eigenvalues are discarded as unassociated data, thereby achieving the effect of reducing the amount of calculation and the complexity of calculation; finally, based on the normal eigenvalues and the next wind speed state, the Lyapunov function is used to determine whether the electric power system is probabilistically asymptotically stable, that is, the complex problem of determining whether the electric power system is probabilistically asymptotically stable is converted into a mathematical operation problem of determining whether the electric power system satisfies the Lyapunov function, thereby simplifying the process of analyzing the small disturbance probabilistic stability of the electric power system and improving the efficiency of the small disturbance probabilistic stability analysis of the electric power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 1 is a flow chart of a method for determining the stability of a small disturbance probability of a power system provided by an embodiment of the present invention;

[0041] Figure 2 1 is another flow chart of a method for determining the stability of a small disturbance probability of a power system provided by an embodiment of the present invention;

[0042] Figure 3 This is a wind power system diagram provided by an embodiment of the present invention for simulation verification;

[0043] Figure 4 is the probability density distribution of the real part of the characteristic value of the power system provided by the embodiment of the present invention;

[0044] Figure 5 The accuracy of the system eigenvalue falling in the left half plane and the abnormal eigenvalue loss curve provided by the embodiment of the present invention are shown in FIG.

[0045] Figure 6 is the probability that the eigenvalue is a negative real part predicted by the Markov chain-based Monte Carlo method provided in an embodiment of the present invention;

[0046] Figure 7 2 is a schematic structural diagram of a device for determining the stability of a small disturbance probability of a power system provided by an embodiment of the present invention;

[0047] Figure 8 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0049] The method of the embodiment of the present invention can be applied to the scenario of small disturbance probability stability analysis of the power system to improve the efficiency of the small disturbance probability stability analysis of the power system.

[0050] The methods of the embodiments of the present invention can be applied to power systems. Driven by the explosive growth of my country's total electricity demand and the rapid development of environmental protection technology, energy transition has become a hot research topic. New energy sources such as wind, solar, and photovoltaic power generation are key indicators of my country's energy transition, with wind power accounting for a significant proportion of renewable energy generation. Due to the uncertainty of wind speed variations, the output power of wind turbines is characterized by volatility, randomness, and uncontrollability, making accurate prediction of turbine output difficult. The integration of large-scale wind turbines into the grid increases the difficulty of optimizing power system scheduling, and also affects operational stability and safety. Therefore, the impact of wind power integration on the small-disturbance stability of power systems has gradually become a focus of scholarly attention. Small-disturbance stability is one of the key prerequisites for the safe and stable operation of power systems. Low-frequency oscillations, such as those caused by weak or negative damping, can even cause generator disconnection in severe cases. Combined with the uncertainty and uncontrollability of wind power generation, as well as the interaction between wind turbines and the original power system, these factors pose even greater challenges to the stable operation of power systems. Therefore, it is crucial to study the adverse effects of wind power integration on the small-disturbance stability and damping characteristics of power systems.

[0051] In related technologies, the small-disturbance probabilistic stability analysis of the power system is analyzed and calculated based on the mathematical modeling of the power system. However, when the number of wide-area time series data of the synchronous phasor measurement device is large, various mathematical approximations and complex calculations will be involved, making it difficult to construct its probability distribution function and accurately describe the probability statistical characteristics, resulting in excessive computational complexity and low efficiency of the small-disturbance probabilistic stability analysis.

[0052] The method for determining the small disturbance probabilistic stability of an electric power system according to an embodiment of the present invention can determine the eigenvalues closely related to the small disturbance stability of the electric power system by acquiring the synchronous phasor measurement device PMU data of the electric power system and establishing a system model; further, by determining the normal eigenvalues among the eigenvalues, the normal eigenvalues are processed as associated data, and the abnormal eigenvalues are discarded as unassociated data, thereby achieving the effect of reducing the amount of calculation and the complexity of calculation; finally, based on the normal eigenvalues and the next wind speed state, the Lyapunov function is used to determine whether the electric power system is probabilistically asymptotically stable, that is, the complex problem of determining whether the electric power system is probabilistically asymptotically stable is converted into a mathematical operation problem of determining whether the electric power system satisfies the Lyapunov function, thereby simplifying the process of analyzing the small disturbance probabilistic stability of the electric power system and improving the efficiency of the small disturbance probabilistic stability analysis of the electric power system.

[0053] The following combination Figures 1-8 The technical solution of the present invention is described in detail with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0054] Figure 1 FIG. 1 is a flow chart of an embodiment of a method for determining the stability of a small disturbance probability of a power system provided by an embodiment of the present invention. Figure 1 As shown, the method provided in this embodiment includes:

[0055] Step 101: Acquire data of a synchronized phasor measurement unit (PMU) of a power system;

[0056] Specifically, in order to monitor the dynamic safety and stability performance of the power system, by installing a synchronized phasor measurement device (PMU) in the power system, a real-time dynamic monitoring system for the power system can be constructed to monitor and analyze the dynamic process of the power system.

[0057] PMU data has been widely used in power system dynamic monitoring, state estimation, system protection, regional stability control, system analysis, and forecasting, and is crucial for ensuring the safe operation of power grids. Therefore, acquiring PMU data from power systems allows for further monitoring and analysis of the power system.

[0058] Step 102: Establish a first system model based on the PMU data; the first system model is used to represent the relationship between the node voltage and the node injection current, and the first system model includes n nodes; the node is used to represent the generator set of the power system; n is an integer greater than 1;

[0059] Optionally, a normal eigenvalue among the eigenvalues may be determined based on the eigenvalues of the node admittance matrix of the first system model;

[0060] Specifically, assume that the number of nodes in a certain area of a power system containing a doubly-fed wind turbine generator is n, and the nodes are used to represent the generator sets of the power system; after obtaining the PMU data of the power system, the first system model is established according to the node voltage equation as follows:

[0061]

[0062] Wherein, the first system model is used to represent the relationship between node voltage and node injection current; i, j = 1, 2, ..., n is the node number; Fnn is the node admittance matrix; Fij and Fji are the mutual admittances between nodes ij; Fii and Fjj are the self-admittances of nodes i and j respectively; and are the node voltage and node injection current respectively; and are the voltage and injected current at nodes i and j, respectively.

[0063] Based on the eigenvalues of the node admittance matrix of the first system model, a normal eigenvalue among the eigenvalues is determined; wherein the eigenvalues of the node admittance matrix of the first system model can be calculated according to an eigenvalue calculation formula, and the eigenvalue formula is:

[0064] |λ i IF nn |=0 (2)

[0065] Eigenvalue λ i =xi+jyi, where xi and yi are the coordinates of the eigenvalues on the imaginary axis; the real part of the eigenvalue represents the damping of the oscillation mode, and the imaginary part of the eigenvalue represents the frequency of the oscillation mode; the quantitative information of the oscillation mode can be effectively calculated through the eigenvalues of the node admittance matrix of the first system model.

[0066] When there are a large number of PMU fundamental current or participating node voltage data in the power system, the power system often corresponds to hundreds or thousands of eigenvalues, resulting in low efficiency of small disturbance probability stability analysis of the power system. Therefore, it is necessary to reduce the eigenvalues when establishing the model; by classifying all eigenvalues, normal eigenvalues are treated as correlated data, and abnormal eigenvalues are discarded as uncorrelated data. This reduces the amount of calculation and complexity for further data analysis and processing, and improves the efficiency of small disturbance probability stability analysis of the power system.

[0067] Step 103: Based on the normal eigenvalues in the eigenvalues and the next wind speed state, use the Lyapunov function to determine whether the power system is probabilistically asymptotically stable.

[0068] Specifically, for wind power systems, due to the influence of wind speed, their equilibrium state will also fluctuate within a large range; further, based on the normal eigenvalue and the next wind speed state, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable, that is, the complex problem of determining whether the power system is probabilistically asymptotically stable is converted into a mathematical operation problem of determining whether the power system satisfies the Lyapunov function, which simplifies the process of small disturbance probabilistic stability analysis of the power system and improves the efficiency of small disturbance probabilistic stability analysis of the power system.

[0069] The method of the above embodiment can determine the eigenvalues closely related to the small disturbance stability of the power system by obtaining the synchronous phasor measurement device PMU data of the power system and establishing a system model; further, by determining the normal eigenvalues among the eigenvalues, the normal eigenvalues are processed as associated data, and the abnormal eigenvalues are discarded as unassociated data, thereby achieving the effect of reducing the amount of calculation and the complexity of calculation; finally, based on the normal eigenvalues and the next wind speed state, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable, that is, the complex problem of determining whether the power system is probabilistically asymptotically stable is converted into a mathematical operation problem of determining whether the power system satisfies the Lyapunov function, which simplifies the process of small disturbance probabilistic stability analysis of the power system and improves the efficiency of small disturbance probabilistic stability analysis of the power system.

[0070] In one embodiment, determining a normal eigenvalue among the eigenvalues based on the eigenvalues of the node admittance matrix of the first system model includes:

[0071] Determine the importance score of the eigenvalue based on the Gini index of the eigenvalue;

[0072] According to the importance scores of the eigenvalues, normal eigenvalues among the eigenvalues are determined.

[0073] Specifically, based on the Gini index of the eigenvalue, the importance score of the eigenvalue is determined; according to the importance score of the eigenvalue, the normal eigenvalue in the eigenvalue is determined; that is, when the number of PMU fundamental current or participating node voltage data in the power system is large, the power system often corresponds to hundreds or thousands of eigenvalues, resulting in low efficiency of small disturbance probability stability analysis of the power system, so it is necessary to reduce the eigenvalues when establishing the model; specifically, by comparing the contribution of each eigenvalue, the power system eigenvalues are classified and the normal eigenvalue in the power system eigenvalue is determined; that is, the Gini index is used as the contribution evaluation index, the Gini index is represented by G, the importance score of each eigenvalue of the power system is represented by W(Gini), and the normal eigenvalue in the eigenvalue is determined according to the importance score W of the eigenvalue; wherein, the Gini index is used as the contribution evaluation index, and its calculation formula is:

[0074]

[0075] k means there are k categories, pmk means the proportion of category k in node m;

[0076] Eigenvalue λ i The importance of node m, that is, the change in the Gini index before and after the node m branches is

[0077] Wim (Gini) =Gm-Gl-Gr (4)

[0078] Among them, Gl and Gr represent the Gini index of the two new nodes after branching.

[0079] If the feature λ i The nodes that appear in decision tree j are set M, then λ i The importance of the jth tree is

[0080] Wim (Gini) =∑ m∈M Wim (Gini) (5)

[0081] Assume that there are q trees in RF, then

[0082]

[0083] Finally, all the importance scores obtained are normalized:

[0084]

[0085] That is, the importance score W of each eigenvalue of the power system is calculated according to formula (4), and all eigenvalues of the power system are classified using formula (7). Then, the normal eigenvalues are treated as related data, and the abnormal eigenvalues are discarded as unrelated data. This also achieves the effect of determining the normal eigenvalues among the eigenvalues through the importance score of the eigenvalue corresponding to the power system; all eigenvalues of the power system are classified through the importance score W of each eigenvalue, which is suitable for processing large-scale data and has high accuracy in the state of data missing. It can process both continuous data and discrete data; normal eigenvalues are treated as related data, and abnormal eigenvalues are discarded as unrelated data. For further data analysis and processing, the amount of calculation and complexity of calculation are reduced, and the efficiency of small disturbance probability stability analysis of the power system is improved.

[0086] The method of the above embodiment determines the importance score of each eigenvalue based on the Gini index of the eigenvalue of the power system, and determines the normal eigenvalue among the eigenvalues according to the importance score of each eigenvalue; that is, by comparing the contribution of each eigenvalue in the power system, the eigenvalue of the power system is classified and the normal eigenvalue is determined; after determining the normal eigenvalue of the power system, the normal eigenvalue can be processed as related data, and the abnormal eigenvalue can be discarded as unrelated data, which reduces the amount of calculation and complexity for further data analysis and processing, and improves the efficiency of small disturbance probability stability analysis of the power system.

[0087] In one embodiment, determining normal feature values among the feature values according to the importance scores of the feature values includes:

[0088] The eigenvalues whose distance from the origin of the importance evaluation coordinate is less than the first preset value are regarded as normal eigenvalues among the eigenvalues.

[0089] Specifically, the eigenvalues whose importance scores are less than the first preset value from the origin of the coordinates are regarded as normal eigenvalues among the eigenvalues; that is, according to the importance scores W of the various eigenvalues of the power system, all the eigenvalues of the power system are classified, and the eigenvalues whose distance from the origin of the coordinates is less than the first preset eigenvalue τ are regarded as normal eigenvalues, and the remaining eigenvalues are regarded as abnormal eigenvalues; the normal eigenvalues are treated as associated data, and the abnormal eigenvalues are discarded as irrelevant data; wherein τ can be the average value of the importance W of all eigenvalues, or a preset threshold; that is, the normal eigenvalues and abnormal eigenvalues among the eigenvalues can be determined through the importance scores of the eigenvalues of the power system and the eigenvalues of the first preset value; after determining the normal eigenvalues of the power system, the normal eigenvalues can be treated as associated data, and the abnormal eigenvalues can be discarded as irrelevant data, which reduces the amount of calculation and complexity for further data analysis and processing, and improves the efficiency of the small disturbance probability stability analysis of the power system.

[0090] The method of the above embodiment takes the eigenvalue whose distance from the origin of the importance evaluation coordinate is less than the first preset value as the normal eigenvalue among the eigenvalues; that is, the first preset value is used as the judgment standard for determining whether the eigenvalue of the power system is a normal eigenvalue, that is, the eigenvalue whose distance from the origin of the importance evaluation coordinate is less than the first preset value is taken as the normal eigenvalue, and the eigenvalue whose distance from the origin of the importance evaluation coordinate is greater than or equal to the first preset value is taken as the abnormal eigenvalue, thereby efficiently realizing the determination of the normal eigenvalue; after determining the normal eigenvalue of the power system, the normal eigenvalue can be processed as associated data, and the abnormal eigenvalue can be discarded as unassociated data, thereby reducing the amount of calculation and complexity of the power system stability analysis, and improving the efficiency of the power system small disturbance probability stability analysis.

[0091] In one embodiment, determining whether the power system is probabilistically asymptotically stable using a Lyapunov function based on a normal eigenvalue among the eigenvalues and a next wind speed state includes:

[0092] If the next wind speed state does not change, determine the posterior probability that the normal eigenvalue in the eigenvalue falls in the left half plane;

[0093] Based on the posterior probability that the normal eigenvalues in the eigenvalues fall in the left half plane, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable.

[0094] Among them, determining the posterior probability that the normal eigenvalue in the eigenvalue falls in the left half plane includes:

[0095] Determine the prior probability that a normal eigenvalue in the eigenvalues falls in the left half plane;

[0096] Based on the prior probability that the normal eigenvalues in the eigenvalues fall in the left half plane, the posterior probability that the normal eigenvalues in the eigenvalues fall in the left half plane is determined using variational Bayes.

[0097] Specifically, the small disturbance stability of the power system is closely related to the eigenvalue. The real part of the eigenvalue represents the damping of the oscillation mode, the imaginary part of the eigenvalue represents the frequency of the oscillation mode, the probability that the eigenvalue falls on the left half plane and the real part of the eigenvalue are less than zero, and according to the real and imaginary parts of the eigenvalue, the damping ratio and other characteristics of the oscillation of the power system can be obtained, and the quantitative information of the oscillation mode of the power system can be effectively calculated; however, when the number of PMU fundamental current or participating node voltage data is large, as the state update process continues, the Gaussian component of the posterior probability density function will increase geometrically, resulting in excessive computational complexity. There is a defect of low computational efficiency; therefore, to determine the posterior probability that the normal eigenvalue in the eigenvalue falls on the left half plane, it is necessary to introduce the posterior probability density function into the Bayesian framework to obtain the state approximate posterior probability density function, reduce the computational complexity, and improve the computational efficiency; that is, to determine the prior probability that the normal eigenvalue in the eigenvalue falls on the left half plane, and then use variational Bayes to determine the posterior probability that the normal eigenvalue in the eigenvalue falls on the left half plane; specifically, assuming that the normal eigenvalues of the system of n nodes are used as the sample space, the probability p of the occurrence of m+1 events can be predicted according to the probability formula (8), as shown below:

[0098] P(Y m+1 =y m+1 |D,ε)=∫P(ω|D,ε)P(Y m+1 =y m+1 |ω,ε)dω

[0099] =∫ωP(ω|D,ε)dω

[0100] =E[P(ω|D,ε)] (8)

[0101] Y is the event variable; y is the state variable value; event Y m+1 =y m+1 The Bayesian probability of occurrence is the expected value of the prior probability ω relative to the posterior probability. The normal eigenvalue data is used as the sample space D. According to the Bayesian formula, the formula for calculating the posterior probability density p(ω|D, ε) from the prior probability p(ω|ε) is:

[0102]

[0103] ω is the prior probability of an event; P is the probability density function; ε is the observer's prior knowledge.

[0104] Let y = y 1 ,y 2 ,...,y r There are r possible states, A i is the number of times the eigenvalue in the sample space falls on the left half plane, recorded as event y = y i , then the Dirichlet distribution of the prior probability is:

[0105]

[0106] Among them k =P(y=y k |ω,ε),k=1,2,…,r; And k > 0, k = 1, 2, ..., r; the posterior probability distribution of the normal eigenvalue of the power system with n nodes falling on the left half plane is:

[0107] P(ω|n k+1 , ε)=Dir(ω|ζ1+A1,ζ2+A2,...,ζ r +A r ) (11)

[0108] Substituting Equation (11) into Equation (8), we can calculate the probability that the normal characteristic value of the power system with n nodes falls on the left half plane:

[0109]

[0110]

[0111] Furthermore, based on the posterior probability that the normal eigenvalue falls on the left half plane, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable. That is, the complex problem of determining whether the power system is probabilistically asymptotically stable is converted into a mathematical operation problem of determining whether the power system satisfies the Lyapunov function. This avoids various mathematical approximations and complex calculations, solves the problem of excessive computational complexity in the probabilistic stability analysis of small disturbances in the power system, and improves the efficiency of the probabilistic stability analysis of small disturbances in the power system.

[0112] The method of the above embodiment, when there is a large amount of PMU data, can efficiently determine the posterior probability that the normal eigenvalue in the eigenvalue falls on the left half plane by introducing variational Bayesian to obtain the state approximate posterior probability density function, thereby reducing the computational complexity and improving the computational efficiency, solving the problem of excessive computational complexity in the power system small disturbance probabilistic stability analysis, and improving the efficiency of the power system small disturbance probabilistic stability analysis.

[0113] In one embodiment, determining whether the power system is probabilistically asymptotically stable using a Lyapunov function based on a normal eigenvalue among the eigenvalues and a next wind speed state includes:

[0114] If the next wind speed state changes, then based on the normal eigenvalue in the eigenvalue, predict the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls on the left half plane;

[0115] Based on the posterior probability that the normal eigenvalue falls in the left half plane, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable.

[0116] Among them, according to the normal eigenvalue in the eigenvalue, the posterior probability of predicting the normal eigenvalue corresponding to the next wind speed state falling on the left half plane includes:

[0117] According to the prior probability density of the normal eigenvalues in the eigenvalues, variational Bayes is used to predict the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls in the left half plane.

[0118] Specifically, for a power system with n nodes and doubly-fed wind turbines, when the wind speed changes, the balance state of the power system will also fluctuate within a large range. The second system model is:

[0119]

[0120]

[0121] Among them, F′ if , F′ jf , F′ fi , F′ fj and F′ff is the newly introduced admittance element after adding the node; F′ ii , F′ jj , F′ ji and F′ ij is the admittance element that changes after adding the node; U f is the node voltage in the previous equilibrium state. When the wind speed state changes, calculate the probability that the normal eigenvalue in the eigenvalue falls on the left half plane. For F=(F1,F2,...,F n ) of the Bayesian network structure, including a one-to-one mapping and the eigenvalue λ in the variable F i The directed acyclic graph O and the probability distribution set P corresponding to each variable, each feature variable is independent, that is:

[0122]

[0123] Where P(λ i |O, D) is the prior probability density of the i-th characteristic variable, and then the normal characteristic values of the power system of n nodes satisfying Equation (11) fall on the left half plane posterior probability distribution, then the posterior probability of the normal characteristic value in the characteristic value of D falling on the left half plane is predicted:

[0124]

[0125] in, ζ ijk =P(λ=λ ijk |ω,ε),k=1,2,…,r.

[0126] That is, when the next wind speed state changes, the posterior probability that the normal eigenvalue in the predicted eigenvalue falls on the left half plane is determined, and the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable; that is, the complex problem of determining whether the power system is probabilistically asymptotically stable when the next wind speed state changes is converted into a mathematical operation problem of determining whether the power system satisfies the Lyapunov function, avoiding various mathematical approximations and complex calculations, solving the problem of excessive computational complexity in the small disturbance probabilistic stability analysis of the power system, and improving the efficiency of the small disturbance probabilistic stability analysis of the power system.

[0127] According to the method of the above embodiment, when the next wind speed state changes, by predicting the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls on the left half plane, the Lyapunov function can be used to determine whether the power system is probabilistically asymptotically stable, thereby avoiding various mathematical approximations and complex calculations, solving the problem of excessive computational complexity in the probabilistic stability analysis of small disturbances in the power system, and improving the efficiency of the probabilistic stability analysis of small disturbances in the power system.

[0128] In one embodiment, determining whether the power system is probabilistically asymptotically stable using a Lyapunov function based on the posterior probability that a normal eigenvalue among the eigenvalues falls in the left half plane includes:

[0129] According to the posterior probability that the normal eigenvalues in the eigenvalues fall in the left half plane, a symmetric matrix with m nodes is obtained using the Lyapunov function; m is an integer greater than 0;

[0130] If the symmetric matrix is positive definite, then the power system is determined to be probabilistically asymptotically stable.

[0131] Specifically, considering that the spatiotemporal correlation between connected power sources will affect the probabilistic stability factor of the wind power system, the spatiotemporal correlation of each power source is closely related to their geographical location distributed according to a certain time difference. Assume that the correlation parameter between two power sources separated by more than 100 km at different times is 0, and the correlation parameter between two power sources separated by less than 100 km is 1. Then, the spatiotemporal correlation coefficient can reflect the geographical distance between each power source at different times. The method of constructing m grid-connected wind power sources and then using the Lyapunov function to determine whether the system is asymptotically stable with small interference probabilities is as follows:

[0132] According to the first system model and the second system model, a system model is established:

[0133]

[0134] Where V is an n×n non-singular matrix.

[0135] Convert the matrix V into the following non-singular diagonal matrix form V Λ :

[0136]

[0137] Then, determine whether the system is stable with small disturbance probability, let

[0138]

[0139] Among them, G(n′+1,n′) is the transfer matrix, It represents the probability that the n′th normal eigenvalue among the eigenvalues falls in the left half plane.

[0140] Select Lyapunov function

[0141]

[0142] Take the Lyapunov first-order difference:

[0143]

[0144] For any positive symmetric matrix Q(n′), according to Lyapunov discrete system determination method:

[0145] G T (n′+1,n′)Ω m G(n′+1,n′)-Ω m = -Q(n′) (19)

[0146] We can find:

[0147]

[0148] When Ωm is positive definite, that is, the symmetric matrix containing m grid-connected wind power sources is a positive definite matrix, V k (Φ k (λ n′ ),n′) is positive definite; according to Lyapunov's stability theorem of linear time-varying discrete systems, the system is probabilistically asymptotically stable.

[0149] When the wind speed state changes, the posterior probability that the normal eigenvalue in the predicted eigenvalue falls on the left half plane can be determined using the same method as above using the Lyapunov function to determine whether the power system is probabilistically asymptotically stable.

[0150] The method of the above embodiment uses the Lyapunov function to obtain a symmetric matrix with m nodes based on the posterior probability that the normal eigenvalues in the eigenvalues fall on the left half plane. If the symmetric matrix is a positive definite matrix, it is determined that the power system is probabilistically asymptotically stable. That is, using the Lyapunov function, by determining whether the symmetric matrix of the grid-connected wind power source is a positive definite matrix, it is possible to determine whether the power system is probabilistically asymptotically stable, avoiding various mathematical approximations and complex calculations, solving the problem of excessive computational complexity in the small disturbance probabilistic stability analysis of the power system, simplifying the process of the small disturbance probabilistic stability analysis of the power system, clarifying the specific method and judgment criteria for the small disturbance probabilistic stability analysis of the power system based on the Lyapunov function, and improving the efficiency of the small disturbance probabilistic stability analysis of the power system.

[0151] In one embodiment, the method for determining the small disturbance probabilistic stability of the power system further includes: if the modulus of the normal eigenvalue is less than a second preset value, determining that the node corresponding to the normal eigenvalue is probabilistically asymptotically stable.

[0152] Specifically, according to Lyapunov's stability theorem, the necessary and sufficient condition for the system to be stable at the equilibrium point is: for any given positive symmetric matrix Q, there exists a positive symmetric matrix P that satisfies:

[0153] V T PV-P=-Q (21)

[0154] and

[0155]

[0156] is the Lyapunov function of the system.

[0157] Select Q = I, I is the unit matrix, and substitute into formula (21):

[0158]

[0159] Right now:

[0160]

[0161] From the above formula, we can conclude that:

[0162]

[0163] To make P positive definite is a symmetric matrix, then it is required

[0164]

[0165] That is, when the normal eigenvalue of the system is within the unit circle, the system equilibrium point is asymptotically stable; that is, when the modulus of the normal eigenvalue is less than the second preset value, it is determined that the node corresponding to the normal eigenvalue is probabilistically asymptotically stable.

[0166] The method of the above embodiment, based on the Lyapunov function, can determine whether the module value of the normal eigenvalue is less than the second preset value, thereby determining whether the node corresponding to the normal eigenvalue is probabilistically asymptotically stable; solves the problem of excessive computational complexity of the probabilistic stability analysis of small disturbances in the power system, and improves the efficiency of the probabilistic stability analysis of small disturbances in the power system.

[0167] For example, another flowchart of the method for determining the stability of a small disturbance probability of a power system provided by an embodiment of the present invention is as follows: Figure 2 As shown:

[0168] Step 1: Establish a first system model based on PMU data;

[0169] Specifically, in order to monitor the dynamic safety and stability performance of the power system, a real-time dynamic monitoring system for the power system can be constructed by installing a synchronous phasor measurement device PMU in the power system to realize the monitoring and analysis of the dynamic process of the power system; assuming that the number of nodes in a certain area of the power system containing a doubly fed wind turbine generator is n, the node is used to represent the generator set of the power system; after obtaining the PMU data of the power system, a first system model is established according to the node voltage equation.

[0170] Step 2: Determine the system characteristic value;

[0171] Based on the eigenvalues of the nodal admittance matrix of the first system model, the normal eigenvalues among the eigenvalues are determined; the real part of the eigenvalue represents the damping of the oscillation mode, and the imaginary part of the eigenvalue represents the frequency of the oscillation mode; the eigenvalues of the nodal admittance matrix of the first system model can be used to effectively calculate the quantitative information of the oscillation mode; then, the statistics of the linear eigenvalues are calculated based on the trace of the matrix, and the linear eigenvalue statistics (LES) index is used to extract features from the PMU data matrix.

[0172] Step 3: Determine whether the eigenvalue is normal based on the Gini index and importance score W corresponding to each eigenvalue;

[0173] Specifically, when there are a large number of PMU fundamental current or participating node voltage data in the power system, the power system often corresponds to hundreds or thousands of eigenvalues, resulting in low efficiency of small disturbance probability stability analysis of the power system. Therefore, it is necessary to reduce the eigenvalues when establishing the model; by comparing the contribution of each eigenvalue, the power system eigenvalues are classified, and the normal eigenvalues among the power system eigenvalues are determined; that is, the Gini index is used as a contribution evaluation index, and the importance score of each eigenvalue of the power system is expressed by W (Gini). According to the eigenvalue importance score W, the normal eigenvalue among the eigenvalues is determined; by classifying all eigenvalues, the normal eigenvalues are treated as correlated data, and the abnormal eigenvalues are discarded as uncorrelated data, which reduces the amount of calculation and complexity for further data analysis and processing, and improves the efficiency of small disturbance probability stability analysis of the power system.

[0174] Step 4: Generate sample space D to determine the posterior probability distribution;

[0175] Specifically, the normal eigenvalue data is used as the sample space D, and the posterior probability density is calculated using the prior probability. When the number of PMU fundamental current or participating node voltage data is large, as the state update process continues, the Gaussian component of the posterior probability density function will increase geometrically, resulting in excessive computational complexity and low computational efficiency. Therefore, it is necessary to introduce the posterior probability density function into the Bayesian framework to obtain the state approximate posterior probability density function, reduce computational complexity, and improve computational efficiency, that is, determine the posterior probability distribution based on the prior probability.

[0176] Step 5: Check whether the wind speed changes;

[0177] If the current wind speed has not changed, execute step 9; if the current wind speed has changed, execute step 6.

[0178] Step 6: Determine the Gini index of the eigenvalue corresponding to the next wind speed state;

[0179] For a power system with n nodes and doubly-fed wind turbines, when the wind speed state changes, the equilibrium state of the power system will also fluctuate within a large range. A second system model is established to determine the Gini index of the eigenvalue corresponding to the next wind speed state.

[0180] Step 7: Determine whether the eigenvalue is normal based on the system eigenvalue importance score W;

[0181] Specifically, when the wind speed state changes, the normal eigenvalue among the eigenvalues corresponding to the next wind speed state is determined according to the system eigenvalue importance score W.

[0182] Step 8: predict the posterior probability that the normal eigenvalue in the eigenvalue falls in the left half plane;

[0183] Specifically, when the wind speed state changes, the posterior probability that the normal eigenvalue in the eigenvalues in the sample space D falls on the left half plane is predicted by variational Bayes, which reduces the computational complexity and improves the computational efficiency.

[0184] Step 9: Determine the probability that the normal eigenvalue falls in the left half plane according to variational Bayes;

[0185] When the wind speed state has not changed, to determine the posterior probability that the normal eigenvalue in the eigenvalue falls in the left half plane, it is necessary to introduce the posterior probability density function into the Bayesian framework to obtain the state approximate posterior probability density function, reduce the computational complexity and improve the computational efficiency; that is, to determine the prior probability that the normal eigenvalue in the eigenvalue falls in the left half plane, and then use variational Bayes to determine the posterior probability that the normal eigenvalue in the eigenvalue falls in the left half plane.

[0186] Step 10: Use the Lyapunov function to determine whether the system is probabilistically asymptotically stable.

[0187] Specifically, based on the normal eigenvalue and the next wind speed state, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable. That is, the complex problem of determining whether the power system is probabilistically asymptotically stable is converted into a mathematical operation problem of determining whether the power system satisfies the Lyapunov function. This avoids various mathematical approximations and complex calculations, solves the problem of excessive computational complexity in the power system small disturbance probabilistic stability analysis, and improves the efficiency of the power system small disturbance probabilistic stability analysis.

[0188] The method for determining the small disturbance probabilistic stability of an electric power system in an embodiment of the present invention can determine the eigenvalues closely related to the small disturbance stability of the electric power system by obtaining the synchronous phasor measurement device PMU data of the electric power system and establishing a system model; further, by determining the normal eigenvalues in the eigenvalues, the normal eigenvalues are processed as associated data, and the abnormal eigenvalues are discarded as unassociated data, thereby achieving the effect of reducing the amount of calculation and the complexity of calculation; finally, based on the normal eigenvalues and the next wind speed state, the Lyapunov function is used to determine whether the electric power system is probabilistically asymptotically stable, that is, the complex problem of determining whether the electric power system is probabilistically asymptotically stable is converted into a mathematical operation problem of determining whether the electric power system satisfies the Lyapunov function, which simplifies the process of analyzing the small disturbance probabilistic stability of the electric power system, clarifies the specific method and judgment criteria for analyzing the small disturbance probabilistic stability of the electric power system based on the Lyapunov function, and improves the efficiency of the small disturbance probabilistic stability analysis of the electric power system.

[0189] For example, the simulation verification of the method for determining the stability of small disturbance probability of power system provided by the embodiment of the present invention is as follows: Figure 3-6 As shown:

[0190] Specifically, such as Figure 3 As shown in the figure, a wind farm is connected to a 39-node system, a PMU is installed at each node, the system voltage level is 345kV, the frequency is 60Hz, the sampling frequency is 3kHz, the load of each bus in the system is set to a constant power model, random fluctuations in wind speed are considered, and then a small interference probability analysis is performed on the system; PMUs are added at nodes 28 and 29, and a branch is separated, and multiple wind turbines are equivalent to a wind farm; the wind farm capacity is 60MW, consisting of 45 doubly fed induction wind turbines with a rated power of 1.5W.

[0191] By actively changing the wind turbine output to simulate changes in external wind speed, the PMU data measured by the system is analyzed and PMU feature data is extracted as the sample space. Positive abnormal eigenvalues are determined based on the importance score of each eigenvalue. Then, based on the determined normal data, the statistical characteristics of the probability distribution of the system eigenvalues are obtained through simulation. The statistical characteristics include the expectation and standard deviation, as shown in Table 1:

[0192] Table 1

[0193]

[0194]

[0195] For the above power system, a small disturbance probability stability analysis is performed. Taking the real part of the eigenvalue as an example, the Monte Carlo simulation of 11700 random samplings is used as the accurate value. The simulation verification of the embodiment of the present invention is compared with the method of calculating the posterior probability density from the prior probability according to the Bayesian theory. The last characteristic data processing is used as the standard. The two methods fit the probability density curve of the real part of the eigenvalue as shown in the figure. Figure 4 As shown; the probability density function of the real part of the eigenvalue and the probability of the negative real part falling in the stable range are calculated respectively when the wind turbine output is 45MW. Figure 4 and Figure 5 shown by Figure 4 The probability density curve of the real part of the eigenvalue of the medium-sized wind turbine output 45MW shows that in this state, the eigenvalue may have a positive real part, and the power angle stability may become unstable; Figure 5 According to the accuracy curve of the eigenvalue with negative real part and the abnormal eigenvalue loss curve under 60 runs at different wind speeds, it can be seen that the probability of negative real part eigenvalue gradually increases, that is, the probability of the final system stability is 81.2%; the abnormal eigenvalue loss gradually decreases and finally stabilizes at a probability of 0.5.

[0196] Through simulation verification, the Monte Carlo simulation probability prediction method based on Markov chain is used as a comparative simulation method, such as Figure 6 As shown in , the probability that its eigenvalue falls within the negative real part fluctuates around 45%, and the system stability probability is much lower than the power system small disturbance probability stability determination method provided by the embodiment of the present invention. This shows that when the number of PMU wide-area time series data is large, the power system small disturbance probability stability determination method provided by the embodiment of the present invention is more consistent with the description of small disturbance probability stability.

[0197] The method and device for determining the small disturbance probabilistic stability of an electric power system provided by the embodiment of the present invention can determine the eigenvalues closely related to the small disturbance stability of the electric power system by acquiring the synchronous phasor measurement device PMU data of the electric power system and establishing a system model; further, by determining the normal eigenvalues among the eigenvalues, the normal eigenvalues are processed as associated data, and the abnormal eigenvalues are discarded as unassociated data, thereby achieving the effect of reducing the amount of calculation and the complexity of calculation; finally, based on the normal eigenvalues and the next wind speed state, the Lyapunov function is used to determine whether the electric power system is probabilistically asymptotically stable, that is, the complex problem of determining whether the electric power system is probabilistically asymptotically stable is converted into a mathematical operation problem of determining whether the electric power system satisfies the Lyapunov function, avoiding various mathematical approximations and complex calculations, solving the problem of excessive computational complexity in the small disturbance probabilistic stability analysis of the electric power system, and improving the efficiency of the small disturbance probabilistic stability analysis of the electric power system.

[0198] The following describes a device for determining a stable small disturbance probability of an electric power system provided by the present invention. The device for determining a stable small disturbance probability of an electric power system described below and the method for determining a stable small disturbance probability of an electric power system described above can refer to each other.

[0199] Figure 7 Schematic diagram of the structure of the device for determining the stability of a small disturbance probability of a power system provided by the present invention. The device for determining the stability of a small disturbance probability of a power system provided by this embodiment includes:

[0200] An acquisition module 710 is used to acquire synchronized phasor measurement unit (PMU) data of the power system;

[0201] The classification module 720 is configured to establish a first system model based on the PMU data; the first system model is configured to represent a relationship between a node voltage and a node injection current, and the first system model includes n nodes; a node is configured to represent a generator set in the power system; n is an integer greater than 1; and based on eigenvalues of a node admittance matrix of the first system model, determine a normal eigenvalue among the eigenvalues.

[0202] The determination module 730 is configured to determine whether the power system is probabilistically asymptotically stable using a Lyapunov function according to the normal eigenvalues in the eigenvalues and the next wind speed state.

[0203] Optionally, the classification module 720 is specifically configured to determine normal eigenvalues among the eigenvalues based on the eigenvalues of the node admittance matrix of the first system model, including:

[0204] Determine the importance score of the eigenvalue based on the Gini index of the eigenvalue;

[0205] According to the importance scores of the eigenvalues, normal eigenvalues among the eigenvalues are determined.

[0206] Optionally, the classification module 720 is specifically configured to determine normal eigenvalues among the eigenvalues according to the importance scores of the eigenvalues, including:

[0207] The eigenvalues whose distance from the origin of the importance evaluation coordinate is less than the first preset value are regarded as normal eigenvalues among the eigenvalues.

[0208] Optionally, the determining module 730 is specifically configured to determine whether the power system is probabilistically asymptotically stable using a Lyapunov function according to the normal eigenvalues in the eigenvalues and the next wind speed state, including:

[0209] If the next wind speed state does not change, determine the posterior probability that the normal eigenvalue in the eigenvalue falls in the left half plane;

[0210] According to the posterior probability that the normal eigenvalues in the eigenvalues fall in the left half plane, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable.

[0211] Optionally, the determining module 730 is specifically configured to determine whether the power system is probabilistically asymptotically stable using a Lyapunov function according to the normal eigenvalues in the eigenvalues and the next wind speed state, including:

[0212] If the next wind speed state changes, then based on the normal eigenvalue in the eigenvalue, predict the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls on the left half plane;

[0213] Based on the posterior probability that the normal eigenvalue falls in the left half plane, the Lyapunov function is used to determine whether the power system is probabilistically asymptotically stable.

[0214] Optionally, the determination module 730 is specifically configured to predict the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls on the left half plane using variational Bayes based on the prior probability density of the normal eigenvalue in the eigenvalue.

[0215] Optionally, the determining module 730 is specifically configured to determine whether the power system is probabilistically asymptotically stable using a Lyapunov function based on a posterior probability that a normal eigenvalue among the eigenvalues falls in the left half plane, including:

[0216] According to the posterior probability that the normal eigenvalues in the eigenvalues fall in the left half plane, a symmetric matrix with m nodes is obtained using the Lyapunov function; m is an integer greater than 0;

[0217] If the symmetric matrix is positive definite, then the power system is determined to be probabilistically asymptotically stable.

[0218] Optionally, the determining module 730 is specifically configured to: if the modulus of the normal eigenvalue is less than a second preset value, determine that the node corresponding to the normal eigenvalue is probabilistically asymptotically stable.

[0219] The device of the embodiment of the present invention is used to execute the method in any of the aforementioned method embodiments. Its implementation principle and technical effects are similar and will not be repeated here.

[0220] Figure 8An example physical structure diagram of an electronic device is provided. The electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communications bus 840. The processor 810, the communications interface 820, and the memory 830 communicate with each other via the communications bus 840. The processor 810 may call logic instructions in the memory 830 to execute a method for determining the probabilistic stability of a power system under small disturbances. The method includes: obtaining data from a synchronized phasor measurement unit (PMU) of the power system; establishing a first system model based on the PMU data; the first system model is used to represent the relationship between node voltage and node injection current, and the first system model includes n nodes; the nodes are used to represent generator sets of the power system; n is an integer greater than 1; determining normal eigenvalues among the eigenvalues based on the eigenvalues of the node admittance matrix of the first system model; and determining whether the power system is probabilistically asymptotically stable using a Lyapunov function based on the normal eigenvalues among the eigenvalues and a next wind speed state.

[0221] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0222] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the small interference probability stability determination method of the power system provided by the above methods, the method including: obtaining synchronous phasor measurement device PMU data of the power system; establishing a first system model based on the PMU data; the first system model is used to represent the relationship between node voltage and node injection current, and the first system model includes n nodes; the node is used to represent the generator set of the power system; n is an integer greater than 1; based on the eigenvalues of the node admittance matrix of the first system model, determining the normal eigenvalues in the eigenvalues; based on the normal eigenvalues in the eigenvalues and the next wind speed state, using the Lyapunov function to determine whether the power system is probabilistically asymptotically stable.

[0223] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0224] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0225] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for determining the stability of a power system with a small disturbance probability, characterized in that: include: Acquiring synchronized phasor measurement unit (PMU) data of the power system; Establishing a first system model based on the PMU data; The first system model is used to represent the relationship between the node voltage and the node injection current, and the first system model includes n nodes; the nodes are used to represent the generator sets of the power system; n is an integer greater than 1; determining normal eigenvalues among the eigenvalues based on the eigenvalues of the node admittance matrix of the first system model; determining whether the power system is probabilistically asymptotically stable using a Lyapunov function according to a normal eigenvalue among the eigenvalues and a next wind speed state; The determining whether the power system is probabilistically asymptotically stable using a Lyapunov function according to a normal eigenvalue among the eigenvalues and a next wind speed state includes: If the next wind speed state does not change, determining a posterior probability that a normal eigenvalue among the eigenvalues falls within a left half plane; and determining whether the power system is probabilistically asymptotically stable using a Lyapunov function based on the posterior probability that the normal eigenvalue among the eigenvalues falls within the left half plane; If the next wind speed state changes, predicting, based on the normal eigenvalues in the eigenvalues, a posterior probability that the normal eigenvalue corresponding to the next wind speed state falls on the left half plane; and determining, based on the posterior probability that the normal eigenvalue falls on the left half plane, using a Lyapunov function to determine whether the power system is probabilistically asymptotically stable; The method further comprises: predicting the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls on the left half plane based on the normal eigenvalue in the eigenvalue, comprising: predicting the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls on the left half plane based on the prior probability density of the normal eigenvalue in the eigenvalue using variational Bayesian. Determining the posterior probability that the normal eigenvalue in the eigenvalue falls in the left half plane includes: determining the prior probability that the normal eigenvalue in the eigenvalue falls in the left half plane; and determining the posterior probability that the normal eigenvalue in the eigenvalue falls in the left half plane using variational Bayes based on the prior probability that the normal eigenvalue in the eigenvalue falls in the left half plane; Determining whether the power system is probabilistically asymptotically stable using a Lyapunov function based on a posterior probability that a normal eigenvalue among the eigenvalues falls on a left half plane includes: obtaining a symmetric matrix having m nodes using a Lyapunov function based on a posterior probability that a normal eigenvalue among the eigenvalues falls on a left half plane; m is an integer greater than 0; and determining that the power system is probabilistically asymptotically stable if the symmetric matrix is a positive definite matrix; If the modulus of the normal eigenvalue is less than a second preset value, it is determined that the node corresponding to the normal eigenvalue is probabilistically asymptotically stable.

2. The method for determining the stability of a power system with a small disturbance probability according to claim 1, wherein: The determining, based on the eigenvalues of the node admittance matrix of the first system model, a normal eigenvalue among the eigenvalues, includes: Determining an importance score of the eigenvalue based on a Gini index of the eigenvalue; Determine normal eigenvalues among the eigenvalues according to the importance scores of the eigenvalues.

3. The method for determining the stability of a power system with a small disturbance probability according to claim 2, wherein: The determining, based on the importance scores of the eigenvalues, normal eigenvalues among the eigenvalues includes: The eigenvalues whose distance from the origin of the importance evaluation coordinate is less than the first preset value are regarded as normal eigenvalues among the eigenvalues.

4. A device for determining the stability of a small disturbance probability of an electric power system, using the method for determining the stability of a small disturbance probability of an electric power system according to any one of claims 1 to 3, characterized in that: include: An acquisition module, configured to acquire PMU data of a synchronized phasor measurement device of the power system; A classification module, configured to establish a first system model based on the PMU data; The first system model is used to represent the relationship between the node voltage and the node injection current, and the first system model includes n nodes; the nodes are used to represent the generator sets of the power system; n is an integer greater than 1; based on the eigenvalues of the node admittance matrix of the first system model, determining a normal eigenvalue among the eigenvalues; a determination module, configured to determine whether the power system is probabilistically asymptotically stable using a Lyapunov function according to a normal eigenvalue among the eigenvalues and a next wind speed state; The determining whether the power system is probabilistically asymptotically stable using a Lyapunov function according to a normal eigenvalue among the eigenvalues and a next wind speed state includes: If the next wind speed state does not change, determining a posterior probability that a normal eigenvalue among the eigenvalues falls within a left half plane; and determining whether the power system is probabilistically asymptotically stable using a Lyapunov function based on the posterior probability that the normal eigenvalue among the eigenvalues falls within the left half plane; If the next wind speed state changes, predicting, based on the normal eigenvalues in the eigenvalues, a posterior probability that the normal eigenvalue corresponding to the next wind speed state falls on the left half plane; and determining, based on the posterior probability that the normal eigenvalue falls on the left half plane, using a Lyapunov function to determine whether the power system is probabilistically asymptotically stable; The method further comprises: predicting the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls on the left half plane based on the normal eigenvalue in the eigenvalue, comprising: predicting the posterior probability that the normal eigenvalue corresponding to the next wind speed state falls on the left half plane based on the prior probability density of the normal eigenvalue in the eigenvalue using variational Bayesian. Determining the posterior probability that the normal eigenvalue in the eigenvalue falls in the left half plane includes: determining the prior probability that the normal eigenvalue in the eigenvalue falls in the left half plane; and determining the posterior probability that the normal eigenvalue in the eigenvalue falls in the left half plane using variational Bayes based on the prior probability that the normal eigenvalue in the eigenvalue falls in the left half plane; Determining whether the power system is probabilistically asymptotically stable using a Lyapunov function based on a posterior probability that a normal eigenvalue among the eigenvalues falls on a left half plane includes: obtaining a symmetric matrix having m nodes using a Lyapunov function based on a posterior probability that a normal eigenvalue among the eigenvalues falls on a left half plane; m is an integer greater than 0; and determining that the power system is probabilistically asymptotically stable if the symmetric matrix is a positive definite matrix; If the modulus of the normal eigenvalue is less than a second preset value, it is determined that the node corresponding to the normal eigenvalue is probabilistically asymptotically stable.

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