Scene reduction-based power angle stability evaluation method and system for power system

Abstract

The invention discloses an electric power system power angle stability evaluation method and system based on scene reduction. The method comprises the steps of obtaining load scene data of a large-scale electric power system and constructing a multi-element load scene data matrix; performing regularization processing, sub-matrix division processing and mirroring processing on the basis of the multi-element load scene data matrix to generate a power angle scene data matrix; performing clustering and reduction processing based on the power angle scene data matrix to obtain a clustering result; fitting a voltage phasor trajectory by using a cubic spline interpolation method based on a clustering result; and evaluating the power angle transient stability of the power system according to the voltage phasor trajectory. The system comprises a first scene matrix construction module, a second scene matrix construction module, a clustering module, a track fitting module and an evaluation module. By using the method, the calculation efficiency and the evaluation efficiency of power angle stability analysis are improved. The method can be widely applied to the field of power system analysis.

Description

Technical Field

[0001] The present invention relates to the field of power system analysis, and in particular to a method and system for evaluating power system power angle stability based on scenario reduction. Background Art

[0002] A large number of renewable energy sources, especially wind power, are being connected to the grid on a large scale. However, the randomness, volatility and low anti-interference performance of wind power compared to traditional synchronous generators have brought severe challenges to the power angle stability of the power system. Since wind turbines do not have the rotational inertia of traditional synchronous generators, they cannot effectively smooth out the power angle oscillation through inertial response. The volatility and intermittency of wind power make its impact on the stability of the power grid more complicated. Under the large-scale grid connection conditions of wind farms, the equivalent impedance of the power system increases, the power flow distribution changes, and the electrical distance between the sending and receiving grids increases, which seriously affects the power angle stability. Once the external transmission channel fails, the unbalanced power in the grid will cause the power angle oscillation of the synchronous generator, which may cause the power angle to be unstable in severe cases and further cause the system to be disconnected. At the same time, when a large disturbance occurs in the power grid, the malfunction or refusal of emergency control measures such as relay protection devices, automatic machine cutting, and load shedding may cause a larger-scale power angle instability, leading to a chain reaction, such as large-scale grid disconnection of wind turbines. With the increase in wind power penetration, the risk of power angle instability and wind power grid disconnection has increased significantly. How to accurately predict system instability has become the focus and difficulty of current power grid stability research. Summary of the invention

[0003] In view of this, in order to solve the technical problem that the accuracy of power angle stability assessment is low due to the complexity and volatility of scenarios caused by large-scale wind power access in the existing power angle stability assessment method, in the first aspect, the present invention proposes a power angle stability assessment method for a power system based on scenario reduction, the method comprising the following steps:

[0004] Obtain load scenario data of large-scale power systems under large-scale wind power access conditions and construct a multivariate load scenario data matrix;

[0005] Based on the multi-load scenario data matrix, regularization processing, sub-matrix division processing and mirror processing are performed to generate the angle scenario data matrix;

[0006] Based on the power angle scene data matrix, clustering and reduction processing are performed to obtain clustering results;

[0007] Based on the clustering results, the voltage phasor trajectory is fitted using the cubic spline interpolation method;

[0008] Evaluate the transient stability of power system power angle based on voltage phasor trajectory.

[0009] In some embodiments, the step of obtaining load scenario data of a large-scale power system under large-scale wind power access conditions and constructing a multivariate load scenario data matrix specifically includes:

[0010] Obtain multi-dimensional load scenario data;

[0011] Generate the original data matrix with all row vectors in the specified time period of the multivariate load scenario data;

[0012] The original data matrix is ​​processed to eliminate abnormal data and obtain the load scenario data matrix.

[0013] Through this preferred step, the data matrix is ​​subjected to exception processing according to the determination rules so as to obtain an optimized scene data matrix.

[0014] In some embodiments, the step of performing regularization processing, submatrix division processing and mirror processing based on the multivariate load scenario data matrix to generate the angle scenario data matrix specifically includes:

[0015] Regularize the multivariate load scenario data matrix, introduce the time weight function, and obtain the regularized matrix;

[0016] The scene fluctuation parameter is introduced to divide the regularized matrix into matrix blocks of preset size to obtain the original sub-matrix;

[0017] Construct a mirror matrix based on the original submatrix to obtain a mirror submatrix;

[0018] The dynamic time distance between the submatrix and the mirror submatrix is ​​calculated, and the alignment is optimized to reconstruct the power angle scene data matrix.

[0019] In some embodiments, the step of performing clustering and reduction processing based on the power angle scene data matrix to obtain a clustering result specifically includes:

[0020] Initialize the number of clusters and fuzzification parameters, introduce dynamic adjustment parameters of fuzziness, and construct fuzzy membership based on the power angle scene data matrix. The fuzzy membership U ij Represents the probability that data point i belongs to cluster j;

[0021] Update the cluster center of each cluster based on the fuzzy membership;

[0022] The fuzzy membership and cluster center are iteratively updated until the maximum number of iterations is met. After each iteration, the number of clusters and fuzzification parameters are adaptively adjusted according to the clustering quality to find the best clustering result and obtain the clustering result.

[0023] In some embodiments, the step of fitting the voltage phasor trajectory using a cubic spline interpolation method based on the clustering result specifically includes:

[0024] Based on the clustering results, the time points of the scenario voltage phasor trajectory data are selected and divided into intervals;

[0025] Construct a cubic polynomial on each interval and calculate the cubic spline coefficients by establishing a system of equations through interpolation and continuity;

[0026] Boundary conditions are introduced, and the cubic spline function on each interval is constructed based on the cubic spline coefficients. The voltage phasor trajectory is obtained by splicing them into a whole.

[0027] In a second aspect, the present invention further proposes a power system power angle stability assessment system based on scenario reduction, the system comprising:

[0028] The first scenario matrix construction module is used to obtain load scenario data of a large-scale power system under the condition of large-scale wind power access and construct a multivariate load scenario data matrix;

[0029] The second scenario matrix construction module performs regularization processing, sub-matrix division processing and mirror processing based on the multi-load scenario data matrix to generate the angle scenario data matrix;

[0030] The clustering module performs clustering and reduction processing based on the power angle scene data matrix to obtain the clustering results;

[0031] Trajectory fitting module, based on clustering results, uses cubic spline interpolation method to fit the voltage phasor trajectory;

[0032] An evaluation module is used to evaluate the transient stability of the power system power angle based on the voltage phasor trajectory.

[0033] Based on the above scheme, the present invention provides a method and system for evaluating the power angle stability of an electric power system based on scenario reduction. Compared with the prior art, the present invention has the following advantages and strengths: 1. No new hardware equipment is required, and it is suitable for direct deployment of existing systems. 2. Through the construction of the scenario variable matrix, the state of the system under different working conditions and operating conditions is accurately reflected. 3. The improved dynamic time warping method is used to align and fuzzy clustering to dual optimize the clustering results to improve the evaluation accuracy. 4. The improved fuzzy C-means clustering algorithm is used to further improve the clustering quality and system analysis accuracy. 5. The trajectory fitting method based on cubic spline interpolation realizes the dual functions of reactive compensation for voltage and power angle stability management, thereby improving the transient stability of the power grid. The present invention effectively copes with the scene complexity and volatility caused by large-scale wind power access, and solves the problem of difficulty in accurately evaluating power angle stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flowchart of the steps of a method for evaluating power angle stability of a power system based on scenario reduction of the present invention;

[0035] Figure 2 It is a structural principle diagram of the box diagram method of a specific embodiment of the present invention;

[0036] Figure 3 It is a schematic diagram of complex scene generation according to a specific embodiment of the present invention;

[0037] Figure 4 is a schematic diagram of scene clustering and reduction according to a specific embodiment of the present invention;

[0038] Figure 5 It is a voltage phasor trajectory diagram when the voltage is stable according to a specific embodiment of the present invention;

[0039] Figure 6 It is a voltage phasor trajectory diagram when voltage is unstable according to a specific embodiment of the present invention;

[0040] Figure 7 It is a structural block diagram of a power system power angle stability assessment system based on scenario reduction of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] It should be noted that, for the convenience of description, only the parts related to the relevant invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0043] It should be understood that the "system", "device", "unit" and / or "module" used in this application is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the word can be replaced by other expressions.

[0044] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "a kind" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements. The elements defined by the sentence "includes a..." do not exclude the existence of other identical elements in the process, method, commodity or device that includes the elements.

[0045] In the description of the embodiments of the present application, "plurality" means two or more than two. The following terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0046] In addition, flow charts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, the various steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0047] Reference Figure 1 , which is a flow chart of an optional example of a method for evaluating the power angle stability of a power system based on scenario reduction proposed by the present invention. The method can be applied to a computer device. The evaluation method proposed in this embodiment may include but is not limited to the following steps:

[0048] Step S1, obtaining load scenario data and constructing a load scenario data matrix;

[0049] Step S2, performing regularization processing and mirror processing based on the load scenario data matrix to generate a work angle scenario data matrix;

[0050] Step S3, performing clustering and reduction processing based on the power angle scene data matrix to obtain a clustering result;

[0051] Step S4: based on the clustering result, divide the interval and fit the voltage phasor trajectory;

[0052] Step S5: evaluating the power angle transient stability of the power system according to the voltage phasor trajectory.

[0053] In some feasible embodiments, the step S1 specifically includes:

[0054] S1.1. Obtain multi-scale load scenario data of large-scale power systems under the condition of large-scale wind power access;

[0055] S1.2, generating a raw data matrix based on the vector form of load scenario data;

[0056] The multivariate load scenario data matrix refers to the row vectors composed of wind power output and load data under different wind power penetration rates, and all the row vectors within a specified time period constitute the original data matrix.

[0057] S1.3. The original data matrix is ​​subjected to abnormal judgment and correction processing by the box plot method to obtain the load scenario data matrix.

[0058] Among them, the box diagram structure principle diagram refers to Figure 2 ;

[0059] First, the data set and its occurrence probability are recorded as the following set:

[0060]

[0061] Among them, ξ 1,1 represents the first eigenvalue in the first scene; ξ nt,n0 represents the n0th eigenvalue of the ntth scene; P s represents the weight distribution vector of the scene after reduction; p 1 Indicates the weight of the first scene.

[0062] Using the ζ of each row of matrix o i Calculate the 25% quartile (Q1) and 75% quartile (Q3), and calculate the interquartile range (IQR):

[0063] IQR=Q3-Q1

[0064] For each row of data, use Q1 and Q3 to calculate the upper and lower limits of the outlier:

[0065] Lower Bound=Q1-1.5×IQR

[0066] Upper Bound=Q3+1.5×IQR

[0067] For each row of the matrix O, determine whether each data point is less than the lower limit of the outlier or greater than the upper limit of the outlier. If the above conditions are met, the point is judged as an outlier and replaced with the mean or median of the non-outlier data. After completing the abnormal data processing, generate a new data matrix:

[0068]

[0069] In some feasible embodiments, the step S2 specifically includes:

[0070] S2.1. Data matrix O s Perform regularization and calculate O s_max The maximum and minimum values ​​of O s_min In addition, the time weight function w(t) is introduced to generate the regularization matrix O′:

[0071]

[0072] Where w(t) = e -λt ,λ is the attenuation coefficient of time weight, which is used to adjust the attenuation rate of time weight.

[0073] S2.2. Divide the matrix O′ into small matrix blocks of 1×dim, and divide each row into 24 sub-matrices. Define the scene fluctuation parameter δ i,j , used to characterize the impact of wind power fluctuations on each sub-matrix, is constructed as follows:

[0074] K i,j =(ξ i,1 ξ i,2 …ξ i,dim )+δ i,j

[0075]

[0076] where δ i,j According to the historical data of wind power fluctuation, it is estimated to reflect the amplitude of wind power fluctuation at each moment; each submatrix K i,j The dimension is 1×dim, and regularization is used to ensure that the data is compared within a uniform scale.

[0077] S2.3. Construct the mirror matrix O′ of matrix O′ m , the matrix is ​​partitioned into sub-matrices in a similar way to the original matrix O', and the mirror matrix is ​​expressed as:

[0078]

[0079] S2.4. Calculate the dynamic time distance between the sub-matrix and the mirror sub-matrix, and optimize the alignment to generate the corner scene data matrix.

[0080] By calculating each submatrix K i,j And its corresponding mirror submatrix K′ i,j The dynamic time warping distance between them is calculated by adding the time weight parameter w(t) to enhance the processing capability of dynamic time changes and find the minimum path alignment to optimize the alignment. For any i and j, the optimization objective function is:

[0081]

[0082] where a i and b j Denotes the matrix block K i,j and K′ i,j The elements in w(t k ) represents the weight of each time point, L represents the length of the path after the best alignment, and the goal is to find the path that makes K i,j and K′ i,j The alignment with the smallest path distance.

[0083] The optimized matrix is ​​reconstructed as:

[0084]

[0085] Reference Figure 3 , which is a diagram showing the grid data (voltage and power angle) scenario after variable matrix optimization. In this example, grid data of 21 wind power scenarios are sampled by controlling different wind power penetration rates, and then visualized after the above matrix optimization.

[0086] In some feasible embodiments, the step S3 specifically includes:

[0087] S3.1, introducing a dynamic adjustment parameter of fuzziness, and constructing fuzzy membership based on the power angle scene data matrix;

[0088] Initialize the number of clusters c and the fuzzification parameter m, randomly assign initial values, and the fuzzy membership U ij Represents the probability that data point i belongs to cluster j and satisfies the normalization condition:

[0089]

[0090] In the clustering process, the fuzzy dynamic adjustment parameter μ is introduced, and the fuzzy membership U i,j The update formula is:

[0091]

[0092] Among them, α′ i,j is the submatrix of the optimized matrix of the i-th data point, v j is the cluster center of the jth cluster, μ is the dynamic fuzzy parameter adjusted according to the fluctuation characteristics of the wind power scene, and its value depends on the scene fluctuation parameter δ i,j When δ i,j When δ is larger, the fuzziness increases and the cluster boundaries become more blurred; i,j When it is smaller, the fuzziness is reduced and the clustering is more accurate.

[0093] S3.2, according to the current fuzzy membership U ij , update the cluster center v of each cluster j j , the formula is:

[0094]

[0095] In each iteration, the cluster center is dynamically updated according to the fuzzy membership and the distribution of data points. j , recalculate the fuzzy membership matrix U i,j Through repeated iterations, U i,j and v j , until the convergence condition is met or the maximum number of iterations is reached.

[0096] S3.3. Calculate the clustering effectiveness index XBI index. After each iteration, adaptively adjust the number of clusters c and the fuzzification parameter m according to the clustering quality until the optimal clustering structure is found.

[0097] Calculate each cluster C i The sum of the squared distances of all points to their cluster centers:

[0098]

[0099] Calculate the minimum square distance between cluster centers:

[0100]

[0101] Calculate Xie-Beni index:

[0102]

[0103] The smaller the index is, the tighter the clusters are and the more separated the clusters are, indicating a better clustering effect.

[0104] Reference Figure 4 , is the scene clustering result diagram. In this example, the clustering parameter c=2 is initialized. Through adaptive fuzzy optimization, the scene generated by S2 is clustered and reduced into two typical scenes, which effectively condenses the original scene characteristics.

[0105] In some feasible embodiments, the step S4 specifically includes:

[0106] S4.1. Determine nodes and intervals: For the clustering results of S3 above, select the time points of the scene voltage phasor trajectory data and divide them into intervals. Let the partition interval be [a, b], and select n+1 nodes x in this interval. 0 ,x 1 ,…,x n , satisfying a=x 0 <x 1 <… <x n = b, the voltage phasor value corresponding to each node is f(x i )=f i .

[0107] S4.2. Construct the cubic spline function value: In each interval [x i ,x i+1 ], construct a cubic polynomial:

[0108] S i (x) = a i (xx i ) 3 +b i (xxi ) 2 +c i (xx i )+d i ,i=0,1,2,…,n-1)

[0109] Among them, a i ,b i ,c i ,d i are the unknown coefficients. Since there are n intervals and 4 coefficients need to be determined for each interval, a total of 4n unknowns need to be determined. Since the cubic spline interpolation function has a second-order continuous derivative on the interval, the following conditions must be met: at each node, the continuity of the function value, derivative, and second-order derivative must be guaranteed. By interpolation and continuity, a system of equations is established to calculate the cubic spline coefficients, a total of (4n-2) equations:

[0110]

[0111] S4.3, introduce boundary conditions and use natural boundary conditions for calculation, that is, given the second-order derivative values ​​of the two end points, S" (x 0 )=f”(x 0 ) and S”(x n )=f”(x n ), in the special case f”(x 0 )=f”(x n )=0.

[0112] Combining the above equations and two boundary conditions, the voltage phasor trajectory X can be obtained. i With Y i The cubic spline interpolation function S i (x). This function can smoothly fit the change of voltage phasor and maintain the continuity of the second-order derivative, thus providing higher accuracy and stability in dynamic change scenarios.

[0113] S4.4. Use the coefficients obtained to construct the cubic spline function S on each interval. i (x), and splice them into an overall trajectory fitting function S(x), so as to achieve smooth fitting of the voltage phasor trajectory and represent its changing trend on the complex plane.

[0114] Based on the above scheme, the present invention has significant tracking and evaluation advantages under large disturbance conditions, and can effectively predict system instability and take corresponding emergency control measures. This method provides an efficient scenario reduction method and an accurate power angle stability evaluation method combined with the fluctuation characteristics of wind farms, which significantly improves the calculation efficiency and evaluation efficiency of power angle stability analysis and is suitable for wide application in modern power systems.

[0115] Reference Figure 5 , is the voltage phasor trajectory diagram when the system is stable. The stable scene clustered by S3 is fitted by cubic interpolation. The voltage trajectory turns back at a specific point, and the turnback is continuous and stable, which indicates that the system can quickly grayscale to a stable state and has transient stability.

[0116] Reference Figure 6 , is the voltage phasor trajectory diagram when the system is unstable. The unstable scene clustered by S3 is fitted by three-sample interpolation. The trajectory does not converge and return at a certain position, but continues to extend outward, indicating that the system fails to recover synchronization after the disturbance, the power angle has no stable equilibrium point, and the system fails to maintain transient stability.

[0117] like Figure 7 As shown, a power system power angle stability assessment system based on scenario reduction includes:

[0118] A first scenario matrix construction module is used to obtain load scenario data and construct a load scenario data matrix;

[0119] A second scene matrix construction module performs regularization processing and mirror processing based on the load scene data matrix to generate a work angle scene data matrix;

[0120] A clustering module performs clustering and reduction processing based on the power angle scene data matrix to obtain a clustering result;

[0121] A trajectory fitting module, which divides the interval and fits the voltage phasor trajectory based on the clustering result;

[0122] An evaluation module is used to evaluate the transient stability of the power angle of the power system according to the voltage phasor trajectory.

[0123] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0124] A power system power angle stability assessment device based on scenario reduction:

[0125] at least one processor;

[0126] at least one memory for storing at least one program;

[0127] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method for evaluating the power angle stability of a power system based on scenario reduction.

[0128] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0129] A storage medium stores processor-executable instructions, which are used to implement a method for evaluating the power angle stability of an electric power system based on scenario reduction when executed by the processor.

[0130] The contents of the above method embodiments are all applicable to the present storage medium embodiments. The functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0131] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for evaluating the power angle stability of a power system based on scenario reduction, characterized in that: The following steps are involved: Obtain load scenario data and construct a load scenario data matrix; Performing regularization processing and mirror processing based on the load scenario data matrix to generate a power angle scenario data matrix; Based on the power angle scenario data matrix, clustering and reduction processing are performed to obtain a clustering result; Based on the clustering results, the intervals are divided and the voltage phasor trajectory is fitted; The power angle transient stability of the power system is evaluated according to the voltage phasor trajectory.

2. According to claim 1, a method for evaluating the power angle stability of a power system based on scenario reduction, characterized in that: The step of obtaining load scenario data and constructing a load scenario data matrix specifically includes: Obtain load scenario data; Generate an original data matrix based on the vector form of the load scenario data; The original data matrix is ​​subjected to abnormality determination and correction processing to obtain a load scenario data matrix.

3. According to claim 2, a method for evaluating the power angle stability of a power system based on scenario reduction, characterized in that: The load scenario data matrix is ​​expressed as follows: Among them, ξ 1,1 represents the first eigenvalue in the first scene; ξ nt,n0 represents the n0th eigenvalue of the ntth scene; P s represents the weight distribution vector of the scenes after reduction; p1 represents the weight of the first scene.

4. According to claim 1, a method for evaluating the power angle stability of a power system based on scenario reduction, characterized in that: The step of performing regularization processing and mirror processing based on the load scenario data matrix to generate the angle scenario data matrix specifically includes: Regularizing the load scenario data matrix to obtain a regularized matrix; A scene fluctuation parameter is introduced to divide the regularized matrix into matrix blocks of a preset size to obtain an original sub-matrix; Constructing a mirror matrix based on the original submatrix to obtain a mirror submatrix; The dynamic temporal distance between the submatrix and the mirror submatrix is ​​calculated and the alignment is optimized to generate the corner scene data matrix.

5. According to claim 1, a method for evaluating the power angle stability of a power system based on scenario reduction, characterized in that: The step of performing clustering and reduction processing based on the power angle scene data matrix to obtain a clustering result specifically includes: Introducing a dynamic adjustment parameter of fuzziness, and constructing fuzzy membership based on the power angle scene data matrix; Updating the cluster center of each cluster based on the fuzzy membership; Iteratively update the fuzzy membership and cluster center until the maximum number of iterations is met, the best clustering result is found, and the clustering result is obtained.

6. According to claim 5, a method for evaluating the power angle stability of a power system based on scenario reduction, characterized in that: Also includes: The XBI index was calculated and the clustering results were evaluated.

7. The method for evaluating the power system power angle stability based on scenario reduction according to claim 1, characterized in that: The step of dividing the interval and fitting the voltage phasor trajectory based on the clustering result specifically includes: Based on the clustering results, selecting time points of the scene voltage phasor trajectory data and performing interval division; Construct a cubic polynomial on each interval and compute the cubic spline coefficients; A cubic spline function is constructed on each interval based on the coefficients, and the voltage phasor trajectory is obtained by splicing.

8. A power system power angle stability assessment system based on scenario reduction, characterized in that: include: A first scenario matrix construction module is used to obtain load scenario data and construct a load scenario data matrix; A second scene matrix construction module performs regularization processing and mirror processing based on the load scene data matrix to generate a work angle scene data matrix; A clustering module performs clustering and reduction processing based on the power angle scene data matrix to obtain a clustering result; A trajectory fitting module, which divides the interval and fits the voltage phasor trajectory based on the clustering result; An evaluation module is used to evaluate the transient stability of the power angle of the power system according to the voltage phasor trajectory.

9. A device for evaluating the power angle stability of a power system based on scenario reduction, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method for evaluating power angle stability of a power system based on scenario reduction as described in any one of claims 1 to 7.

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