Method and device for acquiring spatio-temporal distribution characteristics of voltage

Abstract

A method and device for acquiring voltage dynamic space-time distribution characteristics, the method comprising: acquiring voltage amplitude and voltage phase angle of a plurality of nodes at a plurality of sampling time points; obtaining voltage phase trajectory dynamic observation data set based on the voltage amplitude and voltage phase angle; obtaining voltage relative change and disturbance contribution index based on the voltage phase trajectory dynamic observation data set, to describe the voltage dynamic space-time distribution characteristics. The method and device provided by the embodiment of the application numerically reveal the influence difference of the disturbance on different nodes and the characteristics of the disturbance propagation, solve the problem that the dynamic security analysis of the large power grid becomes difficult with the deepening of the complexity of the power grid, provide auxiliary decision for constructing a stable situation intelligent evaluator and realizing power grid safety scheduling and preventive control, and provide a guarantee for the safe operation of the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system dynamic analysis, in particular to a method and device for obtaining voltage dynamic spatiotemporal distribution characteristics and a computer storage medium. BACKGROUND

[0002] With the access of large-scale new energy and power electronic devices, and the influence of various random uncertain factors, the analysis and control of large power grid dynamic security face a series of problems such as complex grid structure, large geographical span, large number of component types, and complex dynamic behavior. Therefore, it is of great significance to study the spatiotemporal dynamics of complex power grids.

[0003] Wide area measurement system (WAMS) has been widely used in power systems due to its high-precision synchronous phasor measurement, fast communication and other characteristics, and has brought new opportunities for online assessment of voltage stability. Through WAMS, we can obtain real-time recorded disturbance data, and describe the dynamic process of voltage through these data, and quantitatively describe the dynamic spatiotemporal distribution characteristics of voltage and related similarity analysis, to provide effective information for constructing disturbance propagation model and characterizing disturbance propagation characteristics.

[0004] So far, the dynamic spatiotemporal distribution characteristics of power systems mainly focus on the dynamic spatiotemporal distribution characteristics of frequency, and the dynamic spatiotemporal distribution characteristics of voltage trajectory are still in a technical blank, which makes it impossible to accurately and comprehensively describe the spatiotemporal dynamics of complex power grids, especially to accurately reflect and predict the node state of large power grid, resulting in the inability to accurately and comprehensively realize the analysis and control of large power grid dynamic security. SUMMARY

[0005] In view of this, the present application provides a method and device for obtaining voltage dynamic spatiotemporal distribution characteristics and a computer storage medium, aiming to solve the problem that the analysis of large power grid dynamic security becomes difficult with the deepening of the complexity of power grid.

[0006] In a first aspect, the embodiments of the present application provide a method for obtaining voltage dynamic spatiotemporal distribution characteristics, which comprises: obtaining voltage amplitude and voltage phase angle of a plurality of nodes at a plurality of sampling time points; obtaining voltage phase trajectory dynamic observation data set based on the voltage amplitude and voltage phase angle; and obtaining voltage relative change and disturbance contribution index based on the voltage phase trajectory dynamic observation data set, to describe the voltage dynamic spatiotemporal distribution characteristics.

[0007] Further, the obtaining the voltage phase trajectory dynamic observation dataset based on the voltage amplitude and the voltage phase angle comprises: obtaining voltage phase trajectory coordinates based on the voltage amplitude and the voltage phase angle; and constructing a matrix based on the voltage phase trajectory coordinates to obtain the voltage phase trajectory dynamic observation dataset.

[0008] Further, the obtaining the voltage phase relative change amount based on the voltage phase trajectory dynamic observation dataset comprises: obtaining distances between each two nodes at each time point based on the voltage phase trajectory dynamic observation dataset; performing first sliding window processing on the distances between each two nodes at each time point to obtain trajectory distances of a plurality of sliding windows; and calculating an average value of the trajectory distances of the plurality of sliding windows to obtain the voltage phase relative change amount.

[0009] Further, the obtaining the disturbance contribution index based on the voltage phase trajectory dynamic observation dataset comprises: obtaining a covariance matrix of each node and a covariance matrix of the voltage phase trajectory dynamic observation dataset based on the voltage phase trajectory dynamic observation dataset; and obtaining the disturbance contribution index of each node based on the covariance matrix of each node and the covariance matrix of the voltage phase trajectory dynamic observation dataset.

[0010] In a second aspect, an embodiment of the present application further provides a method for obtaining voltage dynamic space-time distribution characteristics, the method comprising: obtaining voltage amplitudes and voltage phase angles of a plurality of nodes at a plurality of sampling time points; obtaining a voltage phase trajectory dynamic observation dataset based on the voltage amplitudes and the voltage phase angles; obtaining a voltage motion feature trajectory group based on the voltage phase trajectory dynamic observation dataset; and performing clustering on the voltage motion feature trajectory group to obtain a clustering result for describing the voltage dynamic space-time distribution characteristics.

[0011] Further, the obtaining the voltage phase trajectory dynamic observation dataset based on the voltage amplitude and the voltage phase angle comprises: obtaining voltage phase trajectory coordinates based on the voltage amplitude and the voltage phase angle; and constructing a matrix based on the voltage phase trajectory coordinates to obtain the voltage phase trajectory dynamic observation dataset.

[0012] Further, the obtaining the voltage motion feature trajectory group based on the voltage phase trajectory dynamic observation dataset comprises: performing second sliding window processing on each node at each time point to obtain a plurality of sliding window phase trajectories; and obtaining each voltage motion feature point of each node based on three continuous phase trajectories of each node to form the voltage motion feature trajectory group.

[0013] In a third aspect, the embodiments of the present application further provide a device for acquiring voltage dynamic spatiotemporal distribution characteristics, the device comprising: a first data acquisition unit configured to acquire voltage amplitudes and voltage phase angles of a plurality of nodes at a plurality of sampling time instants; a first voltage phase locus generation unit configured to obtain a voltage phase locus dynamic observation data set based on the voltage amplitudes and the voltage phase angles; and a first calculation unit configured to obtain a voltage relative variation and a perturbation contribution index based on the voltage phase locus dynamic observation data set, so as to describe the voltage dynamic spatiotemporal distribution characteristics.

[0014] Further, the first voltage phase locus generation unit is further configured to: obtain voltage phase locus coordinates based on the voltage amplitudes and the voltage phase angles; and construct a matrix based on the voltage phase locus coordinates, so as to obtain the voltage phase locus dynamic observation data set.

[0015] Further, the obtaining of the voltage relative variation based on the voltage phase locus dynamic observation data set comprises: obtaining distances between each two nodes at each time instant based on the voltage phase locus dynamic observation data set; performing first sliding window processing on the distances between each two nodes at each time instant, so as to obtain a plurality of sliding window locus distances; and calculating an average value of the plurality of sliding window locus distances, so as to obtain the voltage relative variation.

[0016] Further, the obtaining of the perturbation contribution index based on the voltage phase locus dynamic observation data set comprises: obtaining a covariance matrix of each node and a covariance matrix of the voltage phase locus dynamic observation data set based on the voltage phase locus dynamic observation data set; and obtaining the perturbation contribution index of each node based on the covariance matrix of each node and the covariance matrix of the voltage phase locus dynamic observation data set.

[0017] In a fourth aspect, the embodiments of the present application further provide a device for acquiring voltage dynamic spatiotemporal distribution characteristics, the device comprising: a second data acquisition unit configured to acquire voltage amplitudes and voltage phase angles of a plurality of nodes at a plurality of sampling time instants; a second voltage phase locus generation unit configured to obtain a voltage phase locus dynamic observation data set based on the voltage amplitudes and the voltage phase angles; a voltage motion characteristic locus group generation unit configured to obtain a voltage motion characteristic locus group based on the voltage phase locus dynamic observation data set; and a clustering unit configured to cluster the voltage motion characteristic locus group, so as to obtain a clustering result, so as to describe the voltage dynamic spatiotemporal distribution characteristics.

[0018] Further, the second voltage phase locus generation unit is further configured to: obtain voltage phase locus coordinates based on the voltage amplitudes and the voltage phase angles; and construct a matrix based on the voltage phase locus coordinates, so as to obtain the voltage phase locus dynamic observation data set.

[0019] Further, the voltage motion feature trajectory group generating unit is further configured to: perform second sliding window processing on each node at each time to obtain a plurality of sliding window phase trajectories; and obtain each voltage motion feature point of each node based on three continuous phase trajectories of each node to form a voltage motion feature trajectory group.

[0020] In a fifth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. When the computer program is executed by a processor, the method provided in the above embodiments is implemented.

[0021] The method, device and computer readable storage medium for obtaining voltage dynamic space-time distribution characteristics provided in the embodiments of the present application obtain voltage phase trajectory dynamic observation data sets based on voltage amplitudes and voltage phase angles, process the voltage phase trajectory dynamic observation data sets, obtain voltage relative change amounts and disturbance contribution indexes, provide a data-driven method for obtaining voltage dynamic space-time distribution characteristics, numerically reveal the influence difference of disturbances on different nodes and the characteristics of disturbance propagation, solve the problem that dynamic security analysis for large power grids becomes difficult as the complexity of power grids deepens, provide auxiliary decision-making for constructing a stable situation intelligent evaluator and realizing power grid safety scheduling and preventive control, and provide a guarantee for safe operation of power grids.

[0022] The method, device and computer readable storage medium for obtaining voltage dynamic space-time distribution characteristics provided in the embodiments of the present application obtain voltage phase trajectory dynamic observation data sets based on voltage amplitudes and voltage phase angles, process the voltage phase trajectory dynamic observation data sets, obtain voltage motion feature trajectory groups, perform clustering on the voltage motion feature trajectory groups to obtain clustering results, use the clustering results to describe voltage dynamic space-time distribution characteristics, provide a data-driven method for obtaining voltage dynamic space-time distribution characteristics, numerically reveal the influence difference of disturbances on different nodes and the characteristics of disturbance propagation, solve the problem that dynamic security analysis for large power grids becomes difficult as the complexity of power grids deepens, provide auxiliary decision-making for constructing a stable situation intelligent evaluator and realizing power grid safety scheduling and preventive control, and provide a guarantee for safe operation of power grids. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 An exemplary flowchart of the method for obtaining voltage dynamic space-time distribution characteristics according to the embodiments of the present application is shown;

[0024] Figure 2 A 0.11s to 0.62s voltage phase trajectory diagram according to the embodiments of the present application is shown;

[0025] Figure 3 A spatial distribution of voltage relative change amounts on a 39-node topology diagram according to the embodiments of the present application is shown;

[0026] Figure 4 A spatial distribution of perturbation contribution indices on a 39-node topology graph is shown according to an embodiment of the present application;

[0027] Figure 5 An exemplary flow chart of a method for acquiring voltage dynamic spatiotemporal distribution characteristics is shown according to an embodiment of the present application.

[0028] Figure 6 A 0.11s to 0.41s voltage phase trajectory chain graph is shown according to an embodiment of the present application.

[0029] Figure 7 A voltage motion feature trajectory group graph is shown according to an embodiment of the present application.

[0030] Figure 8 A motion feature distance from near to far topology graph is shown according to an embodiment of the present application.

[0031] Figure 9 A clustering result of a voltage motion feature trajectory group is shown according to an embodiment of the present application.

[0032] Figure 10 A structural schematic diagram of an apparatus for acquiring voltage dynamic spatiotemporal distribution characteristics is shown according to an embodiment of the present application.

[0033] Figure 11 A structural schematic diagram of an apparatus for acquiring voltage dynamic spatiotemporal distribution characteristics is shown according to an embodiment of the present application. DETAILED DESCRIPTION

[0034] Reference will now be made to the exemplary embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout. The embodiments of the present application are not limited to the examples described herein, but can be applied to other embodiments.

[0035] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0036] Figure 1 An exemplary flow chart of a method for acquiring voltage dynamic spatiotemporal distribution characteristics is shown according to an embodiment of the present application.

[0037] As Figure 1As shown, the method comprises:

[0038] Step S101: Obtain voltage amplitude and voltage phase angle of a plurality of nodes at a plurality of sampling time points.

[0039] The time sequence T composed of sampling time points is (t1, t2, …, t m , the voltage amplitude data V and the voltage phase angle data A of n nodes at T sampling time points, T and n are positive integers, as follows:

[0040]

[0041] Among them, v i,T is the voltage amplitude data sequence of node i, θ i,T is the voltage phase angle data sequence of node i.

[0042] Step S102: Obtain voltage phase trajectory dynamic observation data set based on voltage amplitude and voltage phase angle.

[0043] Further, step S102 comprises:

[0044] Based on the voltage amplitude and the voltage phase angle, the voltage phase trajectory coordinates obtained are:

[0045] Based on the voltage phase trajectory coordinates, a matrix is constructed to obtain the voltage phase trajectory dynamic observation data set.

[0046] The voltage phase trajectory is the directed trajectory of the voltage phasor in the complex plane, and the real part and the imaginary part of the voltage phase trajectory coordinates, i.e. the real part and the imaginary part of the voltage phase trajectory, are respectively:

[0047]

[0048] Among them,

[0049] Based on X and Y, a matrix is constructed to obtain the voltage phase trajectory dynamic observation data set, i.e. the matrix D is as follows:

[0050] D = X + Yi;

[0051] Where i is the imaginary unit.

[0052] Step S103: Obtain voltage relative change and disturbance contribution index based on the voltage phase trajectory dynamic observation data set, to describe the voltage dynamic space-time distribution characteristics.

[0053] Further, based on the voltage phase trajectory dynamic observation data set, the voltage relative change is obtained, including:

[0054] Based on the voltage phase trajectory dynamic observation data set, the distance between each two nodes at each time point is obtained;

[0055] At each time step, the distance between every two nodes is processed by the first sliding window to obtain the trajectory distance of several sliding windows;

[0056] Calculate the average distance of the trajectory of several sliding windows to obtain the relative change in voltage.

[0057] The distance between points in the voltage phase trajectory is calculated using the following formula:

[0058]

[0059] Where x represents the real part of the voltage phase trajectory and y represents the imaginary part of the voltage phase trajectory;

[0060] For T sampling times, divide the time into Q segments with P time intervals to obtain Q sliding windows. Within each sliding window, there are P distances between every two nodes. Calculate the sum of the P distances to obtain the trajectory distance of the sliding window; P and Q are both positive integers.

[0061] Calculate the average trajectory distance of all sliding windows to obtain the relative change in voltage.

[0062] Furthermore, based on the dynamic observation dataset of voltage phase trajectories, the disturbance contribution index is obtained, including:

[0063] Based on the voltage phase trajectory dynamic observation dataset, the covariance matrix of each node and the covariance matrix of the voltage phase trajectory dynamic observation dataset are obtained.

[0064] Based on the covariance matrix of each node and the covariance matrix of the voltage phase trajectory dynamic observation dataset, the disturbance contribution index of each node is obtained.

[0065] The covariance matrix of each node is calculated using the following formula:

[0066]

[0067] The covariance matrix of the voltage phase trajectory dynamic observation dataset is calculated using the following formula:

[0068]

[0069] Among them, D i D represents the data in the i-th column of the phase trajectory dynamic observation dataset, i.e., the data of the i-th node; H This represents the conjugate transpose of D.

[0070] The Disturbance Contribution Index (DCI) is calculated using the following formula:

[0071]

[0072] where, for a matrix Z of dimension n*m, the energy can be represented by the Frobenius norm, i.e.,

[0073]

[0074] where, z ij represents an element of the matrix Z.

[0075] Specifically, the measured data of IEEE39 node system is adopted, the disturbance node is the 15th node, the fault is three-phase short circuit, the fault occurrence time is 0.1s, the fault clearance time is 0.74s, 0.01s is taken as a time cycle, the time period of 0.11s to 0.62s is taken, and the voltage is normalized.

[0076] Firstly, the voltage amplitude and voltage phase angle of 39 nodes in the time range of 0.11s to 0.62s are obtained, and the voltage phase trajectory of 0.11s to 0.62s is as shown in Figure 2 .

[0077] For the voltage relative change amount, the sliding window is selected as 0.05s, starting from 0.11s, including (11-15), (12-16), (13-17)……, a total of 48 sliding windows, and then the distance between points (the corresponding points of two trajectories) is calculated, and the formula is as follows:

[0078]

[0079] where, x represents the real part of the voltage phase trajectory, and y represents the imaginary part of the voltage phase trajectory.

[0080] Then the trajectory distance of each sliding window, i.e. the distance between two curves, is calculated, and the formula is as follows:

[0081]

[0082] The average value of the trajectory distance of the obtained 48 sliding windows is taken to obtain the voltage relative change amount. The voltage relative change amount of each node is sorted from small to large, and is distinguished by color and size, and is drawn on the 39 node topology graph, as shown in Figure 3 .

[0083] For the disturbance contribution index, the covariance matrix C sys can be calculated with N data samples D, and the calculation formula is as follows:

[0084]

[0085] where, D H represents the conjugate transpose of D, and N is 52.

[0086] And for a matrix Z with dimension n*m, its energy can be represented by Frobenius norm, that is: Z = Csys

[0087]

[0088] Where, z ij represents the element of matrix Z, C sys norm is the total energy generated by the system, and for a certain node, its covariance matrix can be represented as:

[0089]

[0090] Where, D i represents the i-th column of the phase trajectory dynamic observation data set, that is, the data of the i-th node, and N is 52.

[0091] The energy generated by a certain node can also be obtained by Frobenius norm. Therefore, the ratio of the energy generated by the i-th node to the total energy generated by the whole system can be used as an index to measure the contribution of the i-th node to the disturbance of the whole system, which is called disturbance contribution index DCI (disturbance contribution index), and its expression is:

[0092]

[0093] The disturbance contribution index of each node is sorted from small to large, and is distinguished by color and size, and is drawn on the 39-node topology graph, as shown in Figure 4 .

[0094] It can be seen that the disturbance propagation starts from the fault node and presents a state of outward divergence. The farther the distance from the 15-node electrical appliance, the smaller the influence degree, and it reaches a relatively small value at the generator. This is because the voltage in the power grid is a dynamic balance process, and the generator functions to supply power to the load. Therefore, when the disturbance occurs, the generator in the power grid should hinder the voltage change caused by the disturbance, so the relative change amount of voltage at the generator node is larger than that at the 15-node, which is consistent with the characteristics of the power grid and the law of disturbance propagation.

[0095] The above embodiment obtains voltage phase trajectory dynamic observation data set based on voltage amplitude and voltage phase angle, processes the voltage phase trajectory dynamic observation data set, obtains voltage phase change and disturbance contribution index, and provides a data-driven method for obtaining voltage dynamic space-time distribution characteristics. The method numerically reveals the influence difference of the disturbance on different nodes and the characteristics of the disturbance propagation, solves the problem that the dynamic security analysis of the large power grid becomes difficult as the complexity of the power grid deepens, provides auxiliary decision for constructing a stable situation intelligent evaluator and realizing power grid safety scheduling and preventive control, and provides a guarantee for the safe operation of the power grid.

[0096] Figure 5 An exemplary flowchart of a method for obtaining voltage dynamic space-time distribution characteristics according to an embodiment of the application is shown.

[0097] As shown in Figure 5 , the method comprises:

[0098] Step S501: Obtain voltage amplitudes and voltage phase angles of a plurality of nodes at a plurality of sampling time points.

[0099] The time sequence T=(t1, t2, …, t m ) is formed by the sampling time, and the voltage amplitude data V and the voltage phase angle data A of the n nodes at the T sampling time points, T and n are positive integers, as shown below:

[0100]

[0101] Where, v i,T is the voltage amplitude data sequence of node i, and θ i,T is the voltage phase angle data sequence of node i.

[0102] Step S502: Obtain voltage phase trajectory dynamic observation data set based on voltage amplitude and voltage phase angle.

[0103] Further, step S502 comprises:

[0104] The voltage phase trajectory coordinates obtained based on the voltage amplitude and the voltage phase angle are:

[0105] Based on the voltage phase trajectory coordinates, a matrix is constructed to obtain the voltage phase trajectory dynamic observation data set.

[0106] The voltage phase trajectory is a directed trajectory of the voltage phasor in the complex plane, and the real part and the imaginary part of the voltage phase trajectory coordinates, i.e., the real part and the imaginary part of the voltage phase trajectory, are respectively represented as:

[0107]

[0108] Where,

[0109] Based on X and Y, a matrix is ​​constructed to obtain the dynamic observation dataset of voltage phase trajectory, i.e., matrix D, as follows:

[0110] D = X + Yi;

[0111] Where i is the imaginary unit.

[0112] Step S503: Based on the voltage phase trajectory dynamic observation dataset, obtain the voltage motion characteristic trajectory group.

[0113] Further, step S503 includes:

[0114] At each time point, a second sliding window process is performed on each node to obtain the phase trajectories of several sliding windows;

[0115] For each node, based on three continuous phase trajectories, each voltage motion feature point of each node is obtained, forming a voltage motion feature trajectory group.

[0116] The trajectory distance of the voltage phase trajectory is defined as follows:

[0117]

[0118] In the formula, Indicates length and distance. Indicates location distance.

[0119] The length distance is defined as follows:

[0120]

[0121] Location distance is defined as follows:

[0122]

[0123] In the formula, Indicates vertical distance. Indicates parallel distance. Projecting the beginning and end to p i and p j You can get Both ends to Projection distance l ⊥1 l ⊥2 p i arrive Distance l from the beginning ||1和 p j arrive Distance l at the end ||2 The definitions of perpendicular distance and parallel distance are as follows:

[0124]

[0125]

[0126] The voltage motion feature trajectory is defined as follows:

[0127] Take three consecutive sliding windows of a node with interval Δt to form a phase trajectory chain and Define the length distance d of the trajectory mode between two sliding windows θm and the length distance d lm , and the formula is as follows:

[0128]

[0129]

[0130] Where θ represents the included angle between the two trajectories.

[0131] Calculate the length distance and the angle distance of the first two phase trajectories and the last two phase trajectories and respectively, to obtain d θm , d θ(m+1) , d lm , d l(m+1) , which are marked as motion feature points on the voltage motion feature plane to form the voltage motion feature trajectory.

[0132] Step S504: Cluster the voltage motion feature trajectory group to obtain a clustering result for describing the voltage dynamic spatiotemporal distribution characteristics.

[0133] DBSCAN clustering is performed on the voltage motion feature trajectory group, and the steps are as follows:

[0134] (1) First, initialize the clustering label, and mark all objects as unvisited;

[0135] (2) Randomly select a node p from the voltage motion feature trajectory group and mark it as visited;

[0136] (3) If the number of objects contained in the ε neighborhood of p is at least MinPts, then create a new cluster C and add p to C

[0137] (4) Let N be the set of objects in the ε neighborhood of p, and take each point pi in N;

[0138] (5) If pi is unvisited, mark pi as visited, and if the e-neighborhood of pi has at least MinPts objects, add all of these objects to N, and if pi is not already a member of any cluster, add pi to cluster C;

[0139] (6) Let the number of clusters n = n + 1;

[0140] (7) Output C, and mark the remaining unvisited nodes as noise.

[0141] It needs to be understood that the similarity of the voltage motion characteristic trajectory is represented by the trajectory distance, the closer the distance, the higher the similarity, and the higher the trajectory similarity, the higher the influence degree of the disturbance node.

[0142] Specifically, IEEE39 node system is used for analysis. In this example, three-phase short-circuit fault occurs at 0.1s in 15 nodes, and the fault is removed at 0.74s. According to the proportion of 1 cycle to 0.01s, 1000 cycles are given, that is, the data within 10s. In this paper, the data of T=[11, 41] are selected, and Δt is set to 10 cycles to form a group of voltage phase trajectory groups, as shown in Figure 6 , and then the voltage motion characteristic trajectory group is calculated according to the algorithm in this chapter as the original set of clustering, as shown in Figure 7 .

[0143] When selecting parameters, considering that there are 46 branches in the 39-node system, the value of MinPts is set to 3, and according to the trajectory distance between each trajectory, the value of ε is set to 0.08. After clustering, the distance ranking of 15 nodes from small to large in the 39-node topology is shown in Figure 8 , and the clustering visualization result is shown in Figure 9 .

[0144] Figure 8 The distance from the disturbance node (15 node) is represented by the color and size of the rectangle, the color from red to green, the color from dark to light, and the size from large to small. In this way, the distance from the 15 node can be more intuitively represented. It can be seen that the distance from the 15 node begins to present a divergent shape, and according to the research in this chapter, the similarity of the trajectory is represented by the trajectory distance, the closer the distance, the higher the similarity, and the higher the trajectory similarity, the higher the influence degree of the disturbance node. Therefore, it can be shown that the disturbance propagation begins to present a divergent shape from the disturbance node.

[0145] Figure 9For the clustering result, black represents that the node is classified as a noise point in the DBSCAN cluster, including 15 nodes, 35 nodes and 36 nodes, while 1 node, 39 nodes and 9 nodes are separately clustered, 28 nodes, 29 nodes and 38 nodes are separately clustered, and the remaining nodes are clustered. In combination with the topology graph from far to near distance, it can be seen that the noise points and the nodes with fewer members in the cluster are nodes far away from the 15 nodes, which also confirms the divergent propagation mode of the disturbance.

[0146] In combination with Figure 8 and Figure 9 , it can also be seen that the generator nodes are generally far away from the disturbance nodes, and the nodes around the 39 generator nodes and the 38 generator nodes are clustered, because the role of the generator is to provide energy for the power grid, and its trend is to hinder voltage instability caused by node failure. Therefore, compared with the voltage motion characteristic trajectory of the nodes near the 15 nodes, the voltage motion characteristic trajectory of the generator nodes and the nodes near the generator nodes is far away from the 15 nodes, and the similarity is low. In combination with the voltage amplitude graph, it can also be seen that the degree of change of these nodes is small, which is in line with the law of power grid disturbance propagation.

[0147] The above embodiment obtains a voltage phase trajectory dynamic observation data set based on the voltage amplitude and the voltage phase angle, processes the voltage phase trajectory dynamic observation data set, obtains a voltage motion characteristic trajectory group, clusters the voltage motion characteristic trajectory group, and obtains a clustering result. The clustering result is used to describe the voltage dynamic space-time distribution characteristics, and provides a data-driven method for obtaining the voltage dynamic space-time distribution characteristics. The influence difference of the disturbance on different nodes and the characteristics of the disturbance propagation are numerically revealed, the problem that dynamic security analysis of a large power grid becomes difficult as the complexity of the power grid deepens is solved, auxiliary decision-making for constructing a stable situation intelligent evaluator and realizing power grid safety scheduling and preventive control is provided, and the safe operation of the power grid is ensured.

[0148] Figure 10 A structural schematic diagram of an apparatus for obtaining voltage dynamic space-time distribution characteristics according to an embodiment of the present application is shown.

[0149] As Figure 10 shown, the apparatus comprises:

[0150] A first data acquisition unit 1001 is configured to acquire voltage amplitudes and voltage phase angles of a plurality of nodes at a plurality of sampling time points.

[0151] A time sequence T=(t1, t2, …, t m ) is formed by the sampling time points, and voltage amplitude data V and voltage phase angle data A of n nodes at T sampling time points, T and n are positive integers, and are as follows:

[0152]

[0153] wherein v i,T is the voltage amplitude data sequence of node i, θ i,T is the voltage phase angle data sequence of node i.

[0154] The first voltage phase locus generating unit 1002 is configured to obtain a voltage phase locus dynamic observation data set based on the voltage amplitude and the voltage phase angle.

[0155] Further, the first voltage phase locus generating unit 1002 is further configured to:

[0156] The voltage phase locus coordinates obtained based on the voltage amplitude and the voltage phase angle are:

[0157] Based on the voltage phase locus coordinates, a matrix is constructed to obtain the voltage phase locus dynamic observation data set.

[0158] The voltage phase locus is a directed locus of the voltage phasor in the complex plane, and the voltage phase locus coordinates, i.e. the real part and the imaginary part of the voltage phase locus, are respectively represented as:

[0159]

[0160] wherein,

[0161] Based on the X and the Y, a matrix is constructed to obtain the voltage phase locus dynamic observation data set, i.e. the matrix D is as follows:

[0162] D = X + Yi;

[0163] wherein i is an imaginary unit.

[0164] The first calculating unit 1003 is configured to obtain a voltage relative change amount and a disturbance contribution index based on the voltage phase locus dynamic observation data set, so as to describe the voltage dynamic space-time distribution characteristics.

[0165] Further, the voltage relative change amount obtained based on the voltage phase locus dynamic observation data set includes:

[0166] The distance between each two nodes at each time point is obtained based on the voltage phase locus dynamic observation data set.

[0167] The distance between each two nodes at each time point is subjected to first sliding window processing to obtain a plurality of sliding window locus distances.

[0168] The average value of the plurality of sliding window locus distances is calculated to obtain the voltage relative change amount.

[0169] The distance between the midpoint of the voltage phase locus and a point is calculated by using the following formula:

[0170]

[0171] wherein x represents the real part of the voltage phase trajectory, and y represents the imaginary part of the voltage phase trajectory;

[0172] At each P time interval, the voltage phase trajectory is divided into Q segments, obtaining Q sliding windows, and the distance between each two nodes in each sliding window is P, and the sum of P distances is obtained to obtain the trajectory distance of the sliding window; P and Q are positive integers;

[0173] The average of the trajectory distances of all sliding windows is calculated to obtain the voltage phase relative change.

[0174] Further, based on the voltage phase trajectory dynamic observation data set, the disturbance contribution index is obtained, including:

[0175] Based on the voltage phase trajectory dynamic observation data set, the covariance matrix of each node and the covariance matrix of the voltage phase trajectory dynamic observation data set are obtained.

[0176] Based on the covariance matrix of each node and the covariance matrix of the voltage phase trajectory dynamic observation data set, the disturbance contribution index of each node is obtained.

[0177] The covariance matrix of each node is calculated by the following formula:

[0178]

[0179] The covariance matrix of the voltage phase trajectory dynamic observation data set is calculated by the following formula:

[0180]

[0181] wherein D i represents the i-th column of the phase trajectory dynamic observation data set, that is, the data of the i-th node; D H represents the conjugate transpose of D.

[0182] The disturbance contribution index DCI is calculated by the following formula:

[0183]

[0184] wherein for a matrix Z with dimension n*m, the energy can be represented by the Frobenius norm, that is:

[0185]

[0186] wherein z ij represents the element of matrix Z.

[0187] Specifically, the measured data of IEEE39 node system is adopted, the disturbance node is 15 node, the fault is three-phase short circuit, the fault occurs at 0.1s, the fault is removed at 0.74s, 0.01s is taken as a time cycle, the time period of 0.11s to 0.62s is taken, and the voltage is normalized.

[0188] Firstly, the voltage amplitude and voltage phase angle of 39 nodes in the time range of 0.11s to 0.62s are obtained, and the voltage phase trajectory of 0.11s to 0.62s is as shown in Figure 2

[0189] For the voltage relative change amount, the sliding window is selected as 0.05s, starting from 0.11s, including (11-15), (12-16), (13-17)……, a total of 48 sliding windows, and then the distance between points (the corresponding points of two trajectories) is calculated, and the formula is as follows:

[0190]

[0191] Wherein, x represents the real part of the voltage phase trajectory, and y represents the imaginary part of the voltage phase trajectory.

[0192] Then the trajectory distance of each sliding window, that is, the distance between two curves, is calculated, and the formula is as follows:

[0193]

[0194] The average value of the trajectory distance of the 48 sliding windows is taken, and the voltage relative change amount is obtained. The voltage relative change amount of each node is sorted from small to large, and is distinguished by color and size, and is drawn on the 39 node topology graph, as shown in Figure 3

[0195] For the disturbance contribution index, the covariance matrix C sys can be calculated with N data samples D, and the calculation formula is as follows:

[0196]

[0197] Wherein, D H represents the conjugate transpose of D, and N is 52.

[0198] And for a matrix Z with dimension n*m, the energy can be represented by the Frobenius norm, that is: Z=Csys

[0199]

[0200] Wherein, z ij represents the element of matrix Z, and C sys ​​The norm of is the total energy generated by the system, and similarly, the covariance matrix of a node can be expressed as:

[0201]

[0202] where D i represents the i-th column of the phase trajectory dynamic observation data set, that is, the data of the i-th node, and N is 52.

[0203] The energy generated by a node can also be obtained by the Frobenius norm. Therefore, the ratio of the energy generated by the i-th node to the total energy generated by the entire system can be used as an index to measure the contribution degree of the i-th node to the disturbance of the entire system, which is called disturbance contribution index DCI (disturbance contribution index), and its expression is:

[0204]

[0205] The disturbance contribution indexes of each node are sorted from small to large, and are distinguished by color and size, and are drawn on the 39-node topology graph as shown in Figure 4 .

[0206] It can be seen that the disturbance propagation presents a state of divergence outward from the fault node, and the farther the distance from the 15-node electrical appliance, the smaller the influence degree, and reaches a relatively minimum value at the generator, which is due to the fact that the voltage in the power grid is a dynamic balance process, and the generator functions to supply power to the load, so when the disturbance occurs, the generator in the power grid should hinder the voltage change caused by the disturbance, so the relative voltage change at the generator node is relatively large compared with the 15-node voltage, which is consistent with the characteristics of the power grid and the disturbance propagation law.

[0207] The above embodiment obtains the voltage phase trajectory dynamic observation data set based on the voltage amplitude and the voltage phase angle, processes the voltage phase trajectory dynamic observation data set, obtains the voltage relative change and the disturbance contribution index, and provides a data-driven method for obtaining the voltage dynamic spatiotemporal distribution characteristics, numerically reveals the influence difference of the disturbance on different nodes and the characteristics of the disturbance propagation, solves the problem that the dynamic security analysis of a large power grid becomes difficult as the complexity of the power grid deepens, provides auxiliary decision-making for constructing a stable situation intelligent evaluator and realizing power grid safety scheduling and preventive control, and provides a guarantee for the safe operation of the power grid.

[0208] Figure 11 Fig. 1 shows a structural schematic diagram of an apparatus for obtaining voltage dynamic spatiotemporal distribution characteristics according to an embodiment of the present application.

[0209] As shown in Figure 11 , the apparatus comprises:

[0210] The second data acquisition unit 1101 is configured to acquire voltage amplitude and voltage phase angle of a plurality of nodes at a plurality of sampling time points.

[0211] The time sequence T composed of the sampling time is T=(t1, t2, …, t m The voltage amplitude data V and the voltage phase angle data A of the n nodes at the T sampling time points, T and n are positive integers, and are as follows:

[0212]

[0213] Wherein, v i,T is the voltage amplitude data sequence of the node i, θ i,T is the voltage phase angle data sequence of the node i.

[0214] The second voltage phase trajectory generation unit 1102 is configured to obtain a voltage phase trajectory dynamic observation data set based on the voltage amplitude and the voltage phase angle.

[0215] Further, the second voltage phase trajectory generation unit 1102 is further configured to:

[0216] The voltage phase trajectory coordinates obtained based on the voltage amplitude and the voltage phase angle are:

[0217] Based on the voltage phase trajectory coordinates, a matrix is constructed to obtain the voltage phase trajectory dynamic observation data set.

[0218] The voltage phase trajectory is a directed trajectory of the voltage phasor in the complex plane, and the real part and the imaginary part of the voltage phase trajectory coordinates, i.e. the real part and the imaginary part of the voltage phase trajectory, are respectively:

[0219]

[0220] Wherein,

[0221] Based on X and Y, a matrix is constructed to obtain the voltage phase trajectory dynamic observation data set, i.e. the matrix D is as follows:

[0222] D=X+Yi;

[0223] Wherein, i is an imaginary unit.

[0224] The voltage motion characteristic trajectory group generation unit 1103 is configured to obtain a voltage motion characteristic trajectory group based on the voltage phase trajectory dynamic observation data set.

[0225] Further, the voltage motion characteristic trajectory group generation unit 1103 is further configured to:

[0226] The second sliding window processing is performed on each node at each time point to obtain a plurality of sliding window phase trajectories.

[0227] For each node, based on three continuous phase trajectories, each voltage motion feature point of each node is obtained, forming a voltage motion feature trajectory group.

[0228] The trajectory distance of the voltage phase trajectory is defined as follows:

[0229]

[0230] In the formula, Indicates length and distance. Indicates location distance.

[0231] The length distance is defined as follows:

[0232]

[0233] Location distance is defined as follows:

[0234]

[0235] In the formula, Indicates vertical distance. Indicates parallel distance. Projecting the beginning and end to p i and p j You can get Both ends to Projection distance l ⊥1 l ⊥2 p i arrive Distance l from the beginning ||1 and p j arrive Distance l at the end ||2 The definitions of perpendicular distance and parallel distance are as follows:

[0236]

[0237]

[0238] The characteristic trajectory of voltage motion is defined as follows:

[0239] A phase trajectory chain is formed by three consecutive sliding windows with an interval of Δt at a certain node. and Define the length d of the trajectory model between two sliding windows. θm and length distance d lm The formula is as follows:

[0240]

[0241]

[0242] wherein θ represents the included angle between two trajectories.

[0243] for the first two phase trajectories and the last two phase trajectories and respectively calculate the length distance and the angle distance, to obtain d θm , d θ(m+1) , d lm , d l(m+1) , which are marked as motion feature points on the voltage motion feature plane, to form a voltage motion feature trajectory.

[0244] The clustering unit 1104 is configured to cluster the voltage motion feature trajectory group to obtain a clustering result, which is used to describe the voltage dynamic spatiotemporal distribution characteristics.

[0245] The DBSCAN clustering is performed on the voltage motion feature trajectory group, and the steps are as follows:

[0246] (1) Firstly, initialize the clustering label, and mark all objects as unvisited;

[0247] (2) Randomly select a node p from the voltage motion feature trajectory group, and mark it as visited;

[0248] (3) If the number of objects contained in the ε neighborhood of p is at least MinPts, then create a new cluster C, and add p to C

[0249] (4) Let N be the set of objects in the ε neighborhood of p, and take each point pi in N;

[0250] (5) If pi is unvisited, mark pi as visited, and if the ε neighborhood of pi has at least MinPts objects, then add these objects to N, and if pi is not an object of any cluster, add pi to the cluster C;

[0251] (6) Let the number of clusters C be n = n + 1;

[0252] (7) Output C, and mark the remaining unmarked nodes as noise.

[0253] It should be understood that the similarity of the voltage motion feature trajectory is represented by the trajectory distance, and the closer the distance, the higher the similarity degree, and the higher the trajectory similarity, the higher the influence degree of the disturbance node.

[0254] Specifically, IEEE39 node system is adopted for analysis. In this example, three-phase short-circuit fault occurs at 0.1s in 15 nodes, and is removed at 0.74s, and 1000 cycles are given according to the proportion of 0.01s per cycle, that is, 10s of data. This paper selects T = [11, 41] data, sets Δt to 10 cycles, and forms a group of voltage phase trajectory groups, as shown in Figure 6 , and then calculates the voltage motion characteristic trajectory group as the original set of clustering according to the algorithm in this chapter, as shown in Figure 7 .

[0255] When selecting parameters, considering that there are 46 branches in the 39 node system, the value of MinPts is set to 3, and according to the trajectory distance between each trajectory, the value of ε is set to 0.08. After clustering, the distance ranking from small to large in the 39 node topology graph of 15 nodes can be obtained, as shown in Figure 8 , and the clustering visualization result is as shown in Figure 9 .

[0256] Figure 8 The distance from the 15 node is represented by the color and size of the rectangle, and the color is from red to green, from dark to light, and the size is from large to small. In this way, the distance from the 15 node can be more intuitively represented. It can be seen that the distance from the 15 node begins to present a divergent shape, and according to the research in this chapter, the similarity of the trajectory is represented by the trajectory distance, and the closer the distance, the higher the similarity. The higher the trajectory similarity, the higher the degree of influence of the disturbance node. Therefore, it can be shown that the disturbance propagation begins to present a divergent shape from the disturbance node.

[0257] Figure 9 The clustering result is shown in the figure, where black represents that the node is classified as a noise point in DBSCAN clustering, including the 15 node, the 35 node and the 36 node, while the 1 node, the 39 node and the 9 node are separately classified into a cluster, the 28 node, the 29 node and the 38 node are separately classified into a cluster, and the remaining nodes are a cluster. Combined with the topology graph from far to near motion characteristic distance, it can be seen that the noise points and the nodes with fewer members in the cluster are nodes far from the 15 node, which also confirms the divergent propagation mode of the disturbance.

[0258] Combined with Figure 8 and Figure 9It can also be seen that the generator nodes are generally far away from the disturbance nodes, and the nodes around the 39th generator node and the 38th generator node are clustered, because the role of the generator is to provide energy for the power grid, and its trend is to hinder voltage instability caused by node failure, so the voltage movement characteristic trajectory of the generator node and the nodes around it is far away from the 15th node, and the similarity is low. In combination with the voltage amplitude diagram, it can also be seen that the degree of change of these nodes is small, which is in line with the law of power grid disturbance propagation.

[0259] The above embodiment obtains a voltage phase trajectory dynamic observation data set based on the voltage amplitude and the voltage phase angle, processes the voltage phase trajectory dynamic observation data set, obtains a voltage movement characteristic trajectory group, clusters the voltage movement characteristic trajectory group, and obtains a clustering result, so as to describe the voltage dynamic space-time distribution characteristics, and provide a data-driven method for obtaining the voltage dynamic space-time distribution characteristics. The method numerically reveals the influence difference of the disturbance on different nodes and the characteristics of the disturbance propagation, solves the problem that the dynamic safety analysis of the large power grid becomes difficult as the complexity of the power grid deepens, provides auxiliary decision-making for constructing a stable situation intelligent evaluator and realizing power grid safety scheduling and preventive control, and provides a guarantee for the safe operation of the power grid.

[0260] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the method for obtaining the voltage dynamic space-time distribution characteristics provided by each of the above embodiments.

[0261] The present application has been described by referring to a few embodiments. However, it is well known to those skilled in the art that, as defined in the attached patent claims, other embodiments equivalent to the above disclosed embodiments fall within the scope of the present application.

[0262] Generally, all terms used in the claims are interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise in the specification. All references to "a" or "an" means "at least one", unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.

[0263] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0264] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0265] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0266] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0267] Finally, it should be noted that the above-mentioned embodiments are only intended to illustrate the technical solutions of the present application, and are not intended to limit the present application. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and any modifications or equivalent replacements should be covered within the protection scope of the claims of the present application.

Claims

1. A method for acquiring voltage dynamic spatio-temporal distribution characteristics, characterized in that, The method comprises: obtaining voltage amplitudes and voltage phase angles of a plurality of nodes at a plurality of sampling time points; based on the voltage amplitudes and voltage phase angles, obtaining a voltage phase trajectory dynamic observation data set; based on the voltage phase trajectory dynamic observation data set, obtaining a voltage relative change quantity and a disturbance contribution index for describing the voltage dynamic spatiotemporal distribution characteristics; wherein, based on the voltage phase trajectory dynamic observation data set, obtaining the voltage relative change quantity comprises: based on the voltage phase trajectory dynamic observation data set, obtaining the distance between each two nodes at each time point; performing first sliding window processing on the distance between each two nodes at each time point to obtain trajectory distances of a plurality of sliding windows; calculating the average of the trajectory distances of the plurality of sliding windows to obtain the voltage relative change quantity.

2. The method of claim 1, wherein, The voltage phase trajectory dynamic observation data set is obtained based on the voltage amplitudes and voltage phase angles, comprising: based on the voltage amplitude and voltage phase angle, the voltage phase trajectory coordinate obtained is: based on the voltage phase trajectory coordinate, a matrix is constructed to obtain the voltage phase trajectory dynamic observation data set.

3. The method of claim 1, wherein, The disturbance contribution index is obtained based on the voltage phase trajectory dynamic observation data set, comprising: based on the voltage phase trajectory dynamic observation data set, obtaining a covariance matrix of each node and a covariance matrix of the voltage phase trajectory dynamic observation data set; based on the covariance matrix of each node and the covariance matrix of the voltage phase trajectory dynamic observation data set, obtaining the disturbance contribution index of each node.

4. A method for acquiring the spatio-temporal distribution characteristics of voltage, characterized in that, The method comprises: obtaining voltage amplitudes and voltage phase angles of a plurality of nodes at a plurality of sampling time points; based on the voltage amplitudes and voltage phase angles, obtaining a voltage phase trajectory dynamic observation data set; based on the voltage phase trajectory dynamic observation data set, obtaining a voltage motion feature trajectory group; performing clustering on the voltage motion feature trajectory group to obtain a clustering result for describing the voltage dynamic spatiotemporal distribution characteristics; wherein, based on the voltage phase trajectory dynamic observation data set, obtaining the voltage motion feature trajectory group comprises: performing second sliding window processing on each node at each time point to obtain a plurality of sliding window phase trajectories; for each node, based on three consecutive phase trajectories, obtaining each voltage motion feature point of each node to form a voltage motion feature trajectory group.

5. The method of claim 4, wherein, The voltage phase trajectory dynamic observation data set is obtained based on the voltage amplitudes and voltage phase angles, comprising: based on the voltage amplitude and voltage phase angle, the voltage phase trajectory coordinate obtained is: based on the voltage phase trajectory coordinate, a matrix is constructed to obtain the voltage phase trajectory dynamic observation data set.

6. An apparatus for acquiring voltage dynamic spatio-temporal distribution characteristics, characterized in that, The device comprises: a first data acquisition unit for obtaining voltage amplitudes and voltage phase angles of a plurality of nodes at a plurality of sampling time points; a first voltage phase trajectory generation unit for obtaining a voltage phase trajectory dynamic observation data set based on the voltage amplitudes and voltage phase angles; a first calculation unit for obtaining a voltage relative change quantity and a disturbance contribution index based on the voltage phase trajectory dynamic observation data set for describing the voltage dynamic spatiotemporal distribution characteristics; wherein, based on the voltage phase trajectory dynamic observation data set, obtaining the voltage relative change quantity comprises: Based on the voltage phase trajectory dynamic observation data set, the distance between each two nodes at each time is obtained; The distance between each two nodes at each time is subjected to first sliding window processing, and the trajectory distance of several sliding windows is obtained; The average value of the trajectory distance of the several sliding windows is calculated, and the voltage phase change amount is obtained.

7. The apparatus of claim 6, wherein, The first voltage phase trajectory generation unit is further configured to: Based on the voltage amplitude and voltage phase angle, the voltage phase trajectory coordinates are obtained: Based on the voltage phase trajectory coordinates, a matrix is constructed, and the voltage phase trajectory dynamic observation data set is obtained.

8. The apparatus of claim 6, wherein, The disturbance contribution index based on the voltage phase trajectory dynamic observation data set includes: Based on the voltage phase trajectory dynamic observation data set, the covariance matrix of each node and the covariance matrix of the voltage phase trajectory dynamic observation data set are obtained; Based on the covariance matrix of each node and the covariance matrix of the voltage phase trajectory dynamic observation data set, the disturbance contribution index of each node is obtained.

9. An apparatus for acquiring voltage dynamic spatio-temporal distribution characteristics, characterized in that, The device comprises: A second data acquisition unit is configured to acquire the voltage amplitude and voltage phase angle of a plurality of nodes at a plurality of sampling times; A second voltage phase trajectory generation unit is configured to obtain a voltage phase trajectory dynamic observation data set based on the voltage amplitude and voltage phase angle; A voltage motion characteristic trajectory group generation unit is configured to obtain a voltage motion characteristic trajectory group based on the voltage phase trajectory dynamic observation data set; A clustering unit is configured to cluster the voltage motion characteristic trajectory group to obtain a clustering result for describing the voltage dynamic space-time distribution characteristics; The voltage motion characteristic trajectory group generation unit is configured to: Each node at each time is subjected to second sliding window processing, and the phase trajectory of several sliding windows is obtained; For each node, based on three consecutive phase trajectories, each voltage motion characteristic point of each node is obtained to constitute a voltage motion characteristic trajectory group.

10. The apparatus of claim 9, wherein, The second voltage phase trajectory generation unit is further configured to: Based on the voltage amplitude and voltage phase angle, the voltage phase trajectory coordinates are obtained: Based on the voltage phase trajectory coordinates, a matrix is constructed, and the voltage phase trajectory dynamic observation data set is obtained.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method of any one of claims 1-3 or 4-5.

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