A method and system for evaluating power system power angle stability based on scenario reduction

Abstract

The present invention discloses a method and system for evaluating power angle stability in a power system based on scenario reduction. The method comprises: obtaining load scenario data for a large-scale power system and constructing a multivariate load scenario data matrix; performing regularization, submatrix partitioning, and mirroring on the multivariate load scenario data matrix to generate a power angle scenario data matrix; performing clustering and reduction on the power angle scenario data matrix to obtain clustering results; fitting voltage phasor trajectories using cubic spline interpolation based on the clustering results; and evaluating the power angle transient stability of the power system based on the voltage phasor trajectories. The system comprises: a first scenario matrix construction module, a second scenario matrix construction module, a clustering module, a trajectory fitting module, and an evaluation module. By using the present invention, the computational efficiency and evaluation efficiency of power angle stability analysis are improved. The present invention can be widely applied in 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] Large-scale integration of renewable energy sources, particularly wind power, into the power grid is underway. However, the randomness and volatility of wind power, as well as its lower noise immunity compared to traditional synchronous generators, pose significant challenges to power angle stability in power systems. Because wind turbines lack the rotational inertia of traditional synchronous generators, they cannot effectively smooth power angle oscillations through inertial response. The volatility and intermittent nature of wind power further complicate its impact on grid stability. Large-scale grid integration of wind farms increases the equivalent impedance of the power system, altering power flow distribution and increasing the electrical distance between the sending and receiving grids, severely impacting power angle stability. If a transmission channel fails, unbalanced power in the grid can trigger power angle oscillations in synchronous generators. In severe cases, this can lead to power angle instability and, in turn, system disconnection. Furthermore, during major grid disturbances, the malfunction or failure of emergency control measures such as relay protection devices, automatic generator tripping, and load shedding can trigger even larger power angle instability, leading to a chain reaction, such as large-scale wind turbine disconnection. 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 in existing power angle stability assessment methods of dealing with the complexity and volatility of scenarios caused by large-scale wind power integration, which leads to low accuracy of power angle stability assessment, in a first aspect, the present invention proposes a power system power angle stability assessment method 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 multi-dimensional load scenario data matrix;

[0005] Regularization processing, sub-matrix partitioning processing and mirror processing are performed based on the multi-load scenario data matrix 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 power system power angle transient stability 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 multi-element load scenario data matrix specifically includes:

[0010] Obtain multi-dimensional load scenario data;

[0011] Generate the original data matrix using 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 decision rule 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 and introduce the time weight function to obtain the regularized matrix;

[0016] The scene fluctuation parameter is introduced to divide the regularization 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 fuzzy membership;

[0022] The fuzzy membership and cluster centers are updated iteratively 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 multi-element 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 clustering results;

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

[0032] Evaluation module, used to evaluate the transient stability of power system power angle based on 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 existing technology, 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 deals with the scene complexity and volatility brought about 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 This is a flowchart of the steps of a method for evaluating power system power angle stability based on scenario reduction according to the present invention;

[0035] Figure 2 This is a schematic diagram of the box diagram structure of a specific embodiment of the present invention;

[0036] Figure 3 This 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 This is a voltage phasor trajectory diagram when the voltage is stable according to a specific embodiment of the present invention;

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

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

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

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

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

[0044] As used in this application and the claims, unless the context clearly indicates an exception, the terms "a," "an," "an," and / or "the" are not intended to refer to the singular and may include the plural, unless the context clearly indicates otherwise. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements. The phrase "comprises a..." does not preclude the presence of additional identical elements in the process, method, product, or apparatus that includes the elements.

[0045] In the description of the embodiments of this application, "plurality" refers to two or more than two. The terms "first" and "second" below are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include one or more of the features.

[0046] In addition, flow charts are used in this 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 in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more 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 power system power angle stability based on scenario reduction proposed in the present invention. This method can be applied to computer devices. 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: dividing the intervals and fitting the voltage phasor trajectory based on the clustering result;

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

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

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

[0055] S1.2. Generate an original 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 row vectors within a specified time period constitute the original data matrix.

[0057] S1.3. Use the box plot method to perform abnormality determination and correction on the original data matrix 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; p1 represents 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 matrix O, each data point is judged one by one to be 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 outlier data processing, a new data matrix is ​​generated:

[0068]

[0069] In some feasible embodiments, 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 1×dim small matrix blocks, 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 Estimation is performed based on historical data of wind power fluctuations, reflecting the amplitude of wind power fluctuations 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 range.

[0077] S2.3. Construct the mirror matrix O′ of matrix O′ m , the matrix is ​​split 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, optimize the alignment, and generate the angle 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, and the time weight parameter w(t) is added to enhance the processing ability of time dynamic 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 Represents 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 optimal 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 shows a scenario display of grid data (voltage and power angle) after variable matrix optimization. In this example, grid data for 21 wind power scenarios was sampled by controlling different wind power penetration rates. This data was then visualized after the matrix optimization described above.

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

[0087] S3.1. Introducing dynamic fuzzy adjustment parameters and constructing fuzzy membership based on the power angle scenario data matrix;

[0088] Initialize the number of clusters c and the fuzzification parameter m, randomly assign the initial value, 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] At 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, the number of clusters c and the fuzzification parameter m are adaptively adjusted 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] Compute the least square distance between cluster centers:

[0100]

[0101] Calculate the Xie-Beni index:

[0102]

[0103] The smaller the index, 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, step S4 specifically includes:

[0106] S4.1, determine the nodes and intervals: for the clustering results of S3 above, select the time points of the scene voltage phasor trajectory data and divide it into intervals. Let the partition interval be [a, b], and select n+1 nodes x0, x1, ..., x in this interval. n , satisfying a=x0 <x1<…<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 (xx i ) 2 +c i (xx i )+di ,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 each interval requires four coefficients, a total of 4n unknowns must 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. Using interpolation and continuity, a system of equations is established to calculate the cubic spline coefficients, totaling (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”(x0)=f”(x0) and S”(x n )=f”(x n ), in the special case f”(x0)=f”(x n )=0.

[0112] Combining the above equations and the 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 changes in the voltage phasor and maintain the continuity of the second-order derivative, thereby providing higher accuracy and stability in dynamic change scenarios.

[0113] S4.4. Using the coefficients obtained, construct the cubic spline function S on each interval. i (x), and splice them into an overall trajectory fitting function S(x), thereby achieving smooth fitting of the voltage phasor trajectory and expressing its changing trend on the complex plane.

[0114] Based on the above scheme, the present invention offers significant tracking and assessment advantages under large disturbance conditions, effectively predicting system instability and enabling the implementation of appropriate emergency control measures. This method provides an efficient scenario reduction method and an accurate power angle stability assessment method that incorporates the fluctuating characteristics of wind farms. This significantly improves the computational and assessment efficiency of power angle stability analysis, making it suitable for widespread 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 with 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 scenario clustered by S3 is fitted with cubic 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 scenario matrix construction module, performing regularization processing and mirror processing based on the load scenario data matrix to generate a work angle scenario data matrix;

[0120] A clustering module, performing 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 intervals and fits the voltage phasor trajectory based on the clustering results;

[0122] An evaluation module is used to evaluate the power system power angle transient stability 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 power system power angle stability 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, when executed by the processor, are used to implement the above-mentioned scenario-based power system power angle stability assessment method.

[0130] The contents of the above method embodiments are all applicable to the present storage medium embodiment. The functions specifically implemented by the present storage medium embodiment 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 can 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 and mirroring processing on the load scenario data matrix to generate a work 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, dividing the intervals and fitting the voltage phasor trajectory; evaluating the power system power angle transient stability according to the voltage phasor trajectory; 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: Performing regularization processing on the load scenario data matrix to obtain a regularized matrix; A scenario fluctuation parameter is introduced to divide the regularized matrix into matrix blocks of a preset size to obtain an original submatrix; the scenario fluctuation parameter is estimated based on historical data of wind power fluctuations to reflect the amplitude of wind power fluctuations at each moment; Constructing a mirror matrix based on the original sub-matrix to obtain a mirror sub-matrix; Calculate the dynamic time distance between the sub-matrix and the mirror sub-matrix, optimize the alignment, and generate the angle scene data matrix; For any i, j, the optimization objective function is: Among them, K i,j represents the submatrix, K' i,j represents the corresponding mirror submatrix, a i and b j Represents the matrix K i,j and K' i,j The elements in w(t k ) represents the weight of each time point, and L represents the length of the path after optimal alignment; The step of dividing the intervals 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 calculate 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.

2. The method for evaluating power system power angle stability based on scenario reduction according to claim 1, characterized in that: The step of obtaining load scenario data and constructing a load scenario data matrix specifically includes: Obtain load scenario data; generating 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. The method for evaluating the power system power angle stability based on scenario reduction according to claim 2, characterized in that: The load scenario data matrix is ​​represented 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 scene after reduction; p1 represents the weight of the first scene.

4. The method for evaluating the power system power angle stability based on scenario reduction according to claim 1, 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 fuzzy dynamic adjustment parameter and constructing a 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.

5. The method for evaluating the power system power angle stability based on scenario reduction according to claim 4, characterized in that: Also includes: The XBI index was calculated and the clustering results were evaluated.

6. A power system power angle stability assessment system based on scenario reduction, characterized in that: The method for evaluating the power angle stability of a power system based on scenario reduction according to claim 1 comprises: A first scenario matrix construction module is used to obtain load scenario data and construct a load scenario data matrix; a second scenario matrix construction module, performing regularization processing and mirror processing based on the load scenario data matrix to generate a work angle scenario data matrix; A clustering module, performing clustering and reduction processing based on the power angle scene data matrix to obtain a clustering result; A trajectory fitting module, which divides the intervals and fits the voltage phasor trajectory based on the clustering results; An evaluation module is used to evaluate the power system power angle transient stability according to the voltage phasor trajectory.

7. 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 system power angle stability based on scenario reduction as described in any one of claims 1 to 5.

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