Simulation method of combined weak power grid simulation device

Abstract

The invention relates to the technical field of combined weak power grid simulation, in particular to a simulation method of a combined weak power grid simulation device, and the method comprises the steps: obtaining a graph model of a combined weak power grid, and collecting the current data, voltage data and power data of each key node in the graph model in real time; extracting each abnormal node; obtaining correlation factors between any abnormal node and other key nodes, and selecting related nodes of any abnormal node; obtaining the fault contribution degree of each related node, allocating a weight to each related node, and obtaining the fault connection weight of each related node in combination with the correlation factor between any abnormal node and each related node; and constructing a weighted graph model, and obtaining a fault area of any abnormal node by analyzing a connection relationship between nodes in the weighted graph model. The invention aims to improve the accuracy of fault area positioning.

Description

Technical Field

[0001] The present application relates to the technical field of combined weak power grid simulation, and in particular to a simulation method of a combined weak power grid simulation device. Background Art

[0002] Combined weak grids refer to grid structures composed of multiple small, dispersed power sources and loads in distributed energy, microgrids, and complex power systems. Currently, combined weak grid simulation devices are commonly used to simultaneously simulate complex operating conditions such as the integration of multiple distributed power sources, the changes in loads, and the dynamic adjustment of grid topology, providing a controllable and repeatable experimental platform for weak grid research.

[0003] However, the combined weak power grid is composed of multiple distributed power sources and loads, and its power supply direction and power flow are multi-directional and dynamic. When simulating power supply, due to the diverse distribution and connection methods of the power sources, the fault current may flow into or out of the fault point from multiple directions, making the judgment of the fault direction complicated. In the operation of the weak power grid, accurately judging the fault direction is the key link to quickly locate the fault, cut off the fault area and restore power supply. If the fault direction cannot be accurately judged, the fault isolation will not be timely, the fault range will be expanded, and the power supply reliability of more users will be affected. The existing technology will produce misjudgment when locating faults in multi-power supply, which may cause malfunction of the relay protection device and even affect the stability of the combined weak power grid, increasing the operation risk of the power grid. Summary of the Invention

[0004] In view of the above, it is necessary to provide a simulation method of a combined weak power grid simulation device, which improves the accuracy of fault area positioning compared with the traditional simulation method of a combined weak power grid simulation device: The present invention provides a method for simulating a combined weak power grid simulation device, the method comprising the following steps: A combined weak power grid simulation device is used to obtain a graphical model of the combined weak power grid, and current data, voltage data, and power data of each key node in the graphical model are collected in real time; By comprehensively analyzing the distribution of the time when the current data, voltage data and power data of each key node suddenly change, each abnormal node is extracted from all key nodes; By analyzing the correlation of current changes, voltage changes, and power changes between any abnormal node and the remaining key nodes, and combining the differences in current change complexity, voltage change complexity, and power change complexity between the abnormal node and the remaining key nodes, the correlation factors between the abnormal node and the remaining key nodes are obtained, and the related nodes of the abnormal node are selected from all the key nodes; The weight distribution of each related node in the low-dimensional features at the current acquisition moment is evaluated using a non-negative matrix decomposition algorithm to obtain the fault contribution of each related node. Based on the connection relationship between key nodes in the graph model and the correlation factor between any abnormal node and its related nodes, a weight is assigned to each related node. The fault connection weight of each related node is obtained by combining the correlation factor between any abnormal node and its related nodes, the fault contribution, and the weight. Based on the fault connection weights, a weighted graph model is constructed, and the fault area of ​​any abnormal node is obtained by analyzing the connection relationship between nodes in the weighted graph model; The combined weak power grid simulation device includes: a power supply module, an impedance matching module, a load simulation module and a control and monitoring system. Among them, the transformer in the power supply module is designed with dual voltage levels; the inductor in the impedance matching module is used to adjust the impedance to achieve a specific short-circuit ratio; the load simulation module is used to adjust the load size and type; the control and monitoring system uses power system simulation software to simulate the combined weak power grid and obtain a graphical model of the combined weak power grid.

[0005] In one embodiment, the process of extracting each abnormal node is as follows: All current data, voltage data, and power data collected at each key node are arranged in time sequence to form the current sequence, voltage sequence, and power sequence of each key node; The mutation point detection algorithm is used to obtain the mutation points in each current series, voltage series and power series respectively; The occurrence times of all mutation points in the current sequence, voltage sequence, and power sequence of any key node are counted. If mutation points are detected in the current sequence, voltage sequence, and power sequence of any key node, and when the discreteness of the time intervals between the occurrence times of any two adjacent mutation points is less than a preset threshold, the key node is regarded as an abnormal node.

[0006] In one embodiment, the process of obtaining the correlation factor is: Calculating the correlation coefficient of the current sequence, the correlation coefficient of the voltage sequence, and the correlation coefficient of the power sequence between any abnormal node and the remaining key nodes respectively; calculating the fractal dimension of the current sequence, the fractal dimension of the voltage sequence, and the fractal dimension of the power sequence of each key node respectively; respectively calculating the fractal dimension difference of the current sequence, the fractal dimension difference of the voltage sequence, and the fractal dimension difference of the power sequence between any abnormal node and the remaining key nodes; The correlation factors are respectively positively correlated with the correlation coefficient of the current sequence, the correlation coefficient of the voltage sequence, and the correlation coefficient of the power sequence; and are respectively negatively correlated with the fractal dimension difference of the current sequence, the fractal dimension difference of the voltage sequence, and the fractal dimension difference of the power sequence.

[0007] In one embodiment, the expression of the correlation factor is: Where, represents the correlation factor between the vth abnormal node and the ith key node; w represents the number of types of data collected at each key node; 、 、 They represent the correlation coefficient of the current sequence, the voltage sequence, and the power sequence between the vth abnormal node and the ith key node respectively; 、 、 They represent the fractal dimension difference of the current sequence, the fractal dimension difference of the voltage sequence, and the fractal dimension difference of the power sequence between the vth abnormal node and the ith key node respectively; e represents an exponential function with a natural constant as the base.

[0008] In one embodiment, the method for selecting the relevant nodes is: using key nodes whose correlation factors with any abnormal node are greater than a preset correlation factor threshold as relevant nodes of any abnormal node.

[0009] In one embodiment, the method for obtaining the fault contribution is: The key nodes are used as column indicators, and the current, voltage, and power are used as row indicators to construct a feature matrix. The data in the columns corresponding to any abnormal node in the feature matrix are retained, and the data in the remaining columns are assigned to 0 to obtain a reset feature matrix. The non-negative matrix factorization algorithm is used to decompose the reset feature matrix to obtain the coefficient matrix, and the sum of the data in each column of the coefficient matrix is ​​used as the fault contribution of the key node corresponding to each column.

[0010] In one embodiment, allocating weights to the relevant nodes includes: Any abnormal node and its related nodes in the graph model are marked, the marked graph model is input into the link analysis algorithm, the weight vectors of the related nodes are output, and the entropy of the weight vectors of the related nodes is used as the weight of the related nodes, wherein the normalized value of the correlation factor between any abnormal node and its related nodes is used as the initial weight of the related nodes.

[0011] In one embodiment, the expression of the fault connection weight is: Where, represents the fault connection weight of the jth related node of the vth abnormal node; represents the correlation factor between the vth abnormal node and its jth related node; 、 They represent the fault contribution and weight of the jth related node of the vth abnormal node respectively; ϵ represents a preset positive number.

[0012] In one embodiment, the method for obtaining the fault area is: The node weight of each related node of any abnormal node in the weighted graph model is its fault connection weight, and the node weight of each other key node is 0; a graph traversal algorithm is used to obtain each fault propagation path with any abnormal node as the root node in the weighted graph model; Calculate the sum of the node weights of all key nodes on each fault propagation path; For the fault propagation path with the largest sum, count the number of edges connected to each key node on the fault propagation path, calculate the product of the node weight of each key node on the fault propagation path and the number of edges, and combine all the key nodes and edges passed through from any abnormal node to the key node with the largest product to form the fault area of ​​any abnormal node.

[0013] This application has at least the following beneficial effects: This application uses a mutation point detection algorithm to detect mutation points in current, voltage, and power sequences, and filters abnormal nodes based on the distribution of the mutation point occurrence time combined with the time threshold; through the comprehensive weight of linear correlation and fractal dimension difference, the problem that the traditional single correlation coefficient is easily interfered by noise is solved, and the electrical correlation between nodes is accurately reflected; for the problem of interference from non-correlated nodes, low-dimensional features are extracted from the reset feature matrix through non-negative matrix decomposition, and the fault connection weight is designed in combination with the link analysis algorithm. The node correlation and fault propagation contribution are combined to eliminate the influence of non-correlated nodes and redundant paths, and the key propagation paths that are strongly associated with the fault are screened out; for the problem of fault location ambiguity, the fault propagation path is generated based on the weighted graph model and graph traversal algorithm. The product of the fault connection weight and the electrical connection density is used to solve the problem that the traditional method ignores the dynamic propagation characteristics of the topological structure, accurately locate the fault area, and realize the improvement of fault location from "single-dimensional detection" to "multi-dimensional correlation analysis", significantly improving the accuracy and robustness of fault location, and avoiding the expansion of the fault range caused by misjudgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0015] Figure 1 A block diagram of a combined weak power grid simulation device provided in one embodiment of the present application; Figure 2 A flowchart of a simulation method for a combined weak power grid simulation device provided in one embodiment of the present application; Figure 3 Get the flow chart for the fault area. DETAILED DESCRIPTION

[0016] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application relates. The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. It should be understood that, unless otherwise indicated, " / " represents or.

[0018] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0019] The components of a combined weak power grid simulation device provided by the present application are described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1, which shows a block diagram of a combined weak power grid simulation device provided by an embodiment of the present application. In order to improve the function of the combined weak power grid simulation device, a combined weak power grid simulation generating device is set. Specifically, the combined weak power grid simulation generating device is mainly composed of a power supply module, an impedance matching module, a load simulation module and a control and monitoring system. In this embodiment, the initial output power of the configured power supply module is 3MW, and the transformer in the power supply module is designed with dual voltage levels, 10KV and 35kV, with a withstand voltage capacity of 45.5kV, which is used to simulate grid interconnection scenarios of different voltage levels. The reactor in the impedance matching module adjusts impedance to achieve the simulated short-circuit ratio (SCR) of 1.0, effectively suppressing current fluctuations and simulating the low short-circuit capacity characteristics of a weak grid. The load simulation module adjusts the load size and type to match the output of the power module, simulating the load conditions in an actual grid. The control and monitoring system uses Simulink to simulate the combined weak grid. The Simulink toolbox is a widely used tool in power system simulation, providing a rich library of power system modules that can be used to build the topology of combined weak grids and simulate the dynamic behavior of distributed power sources (such as photovoltaic and wind power), loads, and the grid. During the simulation, the grid is abstracted into a graphical model. The nodes in the graphical model represent the source access points of the distributed power sources, the load access points, and the intersection points of three or more lines. The edges in the graphical model represent the lines connecting these nodes. The construction of the graphical model reflects the grid topology and can intuitively display the connections between sources, loads, and lines. It also provides a basic framework for subsequent data collection, fault analysis, and protection strategy design. By setting the above parameters, the combined weak power grid simulation generator can realize the simulation function of short-circuit ratio SCR=1.0 of 3MW and above.

[0021] The specific scheme of the simulation method of the combined weak power grid simulation device provided by the present application is described in detail below with reference to the accompanying drawings.

[0022] like Figure 2 As shown, it shows a flowchart of a simulation method of a combined weak power grid simulation device provided by an embodiment of the present application, the method comprising the following steps: Step S1 : collecting the current data, voltage data and power data of each key node in the graph model in real time.

[0023] In the combined weak power grid, nodes that are of great significance to the grid operation status and fault location are defined as key nodes. Current sensors, voltage sensors and power sensors are installed at each key node to measure the current data, voltage data and power data of each key node in real time. All current data, voltage data and power data collected at each key node are normalized to eliminate the influence of dimension.

[0024] In this embodiment, key nodes include: each power access point of a distributed power source, each load access point, and a line intersection point of three or more lines.

[0025] In this embodiment, the acquisition frequency of current data, voltage data and power data is 10 kHz. The value of the acquisition frequency is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0026] In this embodiment, the softmax function is used to normalize the current data, voltage data, and power data respectively.

[0027] All current, voltage, and power data at each key node are arranged in time series to form the current, voltage, and power sequences at each key node. A feature matrix is ​​constructed using the key nodes as column indices and current, voltage, and power as row indices.

[0028] In this embodiment, the first row of the characteristic matrix is ​​current data, the second row is voltage data, and the third row is power data. The number of columns of the characteristic matrix is ​​equal to the number of key nodes.

[0029] Step S2 , extracting abnormal nodes from all key nodes by comprehensively analyzing the distribution of time when sudden changes occur in the current data, voltage data, and power data of each key node.

[0030] Real-time fault monitoring of the combined weak grid, using current, voltage, and power at key nodes, provides a basis for subsequent fault location and isolation. Failure to promptly monitor faults can lead to a wider range of faults, increasing repair difficulty, power outage duration, and even more serious power outages.

[0031] A mutation point detection algorithm is used to detect mutation points in the current, voltage, and power sequences of each key node in the combined weak power grid. The occurrence times of all mutation points in the current, voltage, and power sequences of any key node are counted. If mutation points are detected in the current, voltage, and power sequences of any key node, and the standard deviation of the time intervals between any two adjacent mutation points is less than a preset threshold, the key node is determined to be connected to a fault point and is designated as an abnormal node. For example, a sudden increase in current or a sudden drop in voltage at an abnormal node may indicate a short circuit, while a sudden power surge may indicate a power supply or load abnormality.

[0032] In this embodiment, a Cumulative Sum Control Chart (CUSUM) algorithm is used to obtain each mutation point in each current sequence, voltage sequence, and power sequence. The CUSUM algorithm is a well-known technology and will not be described in detail in this application. As other implementation methods, on the basis of being able to obtain each mutation point in each current sequence, voltage sequence, and power sequence, the implementer may adopt other existing technologies, such as the Bayesian mutation point detection algorithm, the Mann-Kendall mutation point detection algorithm, etc., and this application does not impose any special restrictions.

[0033] In this embodiment, the value of the preset threshold is 0.02. The value of the preset threshold is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0034] Step S3, by analyzing the correlation of current changes, voltage changes, and power changes between any abnormal node and the remaining key nodes, combined with the difference in current change complexity, voltage change complexity, and power change complexity between the any abnormal node and the remaining key nodes, obtain the correlation factor between the any abnormal node and the remaining key nodes, and select the related nodes of the any abnormal node from all key nodes.

[0035] In a combined weak power grid, fault currents can flow into or out of the fault point from multiple directions, making it difficult to determine the fault direction. By screening nodes directly connected to the fault point, the fault scope can be narrowed, avoiding misjudgments and malfunctioning of relay protection devices. In a circuit, current at any node follows Kirchhoff's current law, which states that the sum of the currents flowing into a node equals the sum of the currents flowing out of the node. If two nodes are directly electrically connected, such as through a wire or transformer, a change in the current at one node will cause a corresponding change in the current at the other connected node. For example, if the current at one node suddenly increases, the current at the next connected node will also increase accordingly. Therefore, when the current, voltage, or power at one node changes, nodes that are electrically closer or directly connected to it will also be affected, exhibiting a correlation.

[0036] Taking any abnormal node as an example, the correlation coefficient of the current sequence between any abnormal node and the remaining key nodes is calculated respectively, and the correlation coefficient of the voltage sequence between any abnormal node and the remaining key nodes is calculated respectively; the correlation coefficient of the power sequence between any abnormal node and the remaining key nodes is calculated respectively; the fractal dimension of the current sequence, the fractal dimension of the voltage sequence, and the fractal dimension of the power sequence of each key node are calculated respectively.

[0037] In this embodiment, the correlation coefficients between current sequences, the correlation coefficients between voltage sequences, and the correlation coefficients between power sequences are all Pearson correlation coefficients. The Pearson correlation coefficient is a well-known technology and will not be described in detail in this application. As other implementation methods, on the basis of being able to calculate the correlation coefficients between current sequences, the correlation coefficients between voltage sequences, and the correlation coefficients between power sequences, the implementer may adopt other existing technologies, such as the Spearman correlation coefficient, the Kendall rank correlation coefficient, etc., and this application does not impose any special restrictions.

[0038] In this embodiment, the box counting method is used to calculate the fractal dimension of the current sequence, the fractal dimension of the voltage sequence, and the fractal dimension of the power sequence respectively. The box counting method is a well-known technology and will not be described in detail in this application. As other implementation methods, on the basis of being able to calculate the fractal dimension of the current sequence, the fractal dimension of the voltage sequence, and the fractal dimension of the power sequence, the implementer may adopt other existing technologies, such as the Hurst exponent method, the Higuchi algorithm, etc., and this application does not impose any special restrictions.

[0039] Based on the above analysis, the correlation coefficients of the current sequence, voltage sequence, and power sequence between any abnormal node and the remaining key nodes are combined with the fractal dimension differences of the current sequence, voltage sequence, and power sequence between any abnormal node and the remaining key nodes to obtain the correlation factor between the abnormal node and the remaining key nodes. The expression is: Where, represents the correlation factor between the vth abnormal node and the ith key node; w represents the number of types of data collected by each key node; 、 、 They represent the correlation coefficient of the current sequence, the voltage sequence, and the power sequence between the vth abnormal node and the ith key node respectively; 、 、 They represent the fractal dimension difference of the current sequence, the fractal dimension difference of the voltage sequence, and the fractal dimension difference of the power sequence between the vth abnormal node and the ith key node respectively; e represents an exponential function with a natural constant as the base, which is used to convert 、 、 In this embodiment, the value of w is 3.

[0040] In this embodiment, in the process of calculating the correlation factor, the differences involved are all absolute values ​​of the differences. As other implementation methods, on the basis of being able to measure the differences between fractal dimensions, the implementer can use other calculation methods for measurement, such as the square of the difference, the ratio, etc., and this application does not impose any special restrictions.

[0041] It should be noted that: It reflects the synchronization of current changes between the vth abnormal node and the ith key node, It reflects the difference in current change between the vth abnormal node and the ith key node. When the value is larger, the complexity of the current change between the vth abnormal node and the ith key node will be more different, and the correlation of the current change between the vth abnormal node and the ith key node will be weakened; when The larger the value is, the stronger the consistency of the current change trend between the vth abnormal node and the ith key node is, and the more similar the complexity of the current change is, the more similar the current is to the i-th key node. Based on the above analysis, the greater the contribution of and right Analyze the contribution degree. When is larger, the correlation between the vth abnormal node and the ith key node is stronger, indicating that the ith key node is more likely to be affected by the fault point or directly connected to the fault point.

[0042] Since the correlation factor can directly reflect the electrical connection relationship between any abnormal node and the remaining key nodes, a correlation coefficient threshold is set, and each key node whose correlation factor with any abnormal node is greater than the correlation factor threshold is used as the relevant node of any abnormal node. The relevant nodes have a strong correlation with the abnormal node in terms of electrical connection and are more likely to be directly affected by the fault point or directly connected to the fault point.

[0043] In this embodiment, the correlation coefficient threshold is 0.6. The value of the correlation coefficient threshold is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0044] Step S4: evaluate the weight distribution of the relevant nodes in the low-dimensional features at the current acquisition moment through a non-negative matrix decomposition algorithm, obtain the fault contribution of the relevant nodes, and assign weights to the relevant nodes based on the connection relationship between the key nodes in the graph model and the correlation factors between any abnormal node and its relevant nodes; obtain the fault connection weight of the relevant nodes by combining the correlation factors between any abnormal node and its relevant nodes, the fault contribution and the weight.

[0045] In order to more intuitively analyze the electrical connection relationship between any abnormal node and its related nodes, the data in the columns corresponding to all related nodes of any abnormal node in the feature matrix are retained, and the data in the remaining columns are assigned to 0 to obtain a reset feature matrix.

[0046] The reset feature matrix is ​​used as the input of the non-negative matrix factorization algorithm, the rank r=2 is set, and two non-negative matrices are output, including a size of The basis matrix and size are The coefficient matrix is ​​constructed, where w represents the number of data types collected by each key node, and N represents the number of key nodes. The sum of the data in each column of the coefficient matrix is ​​used as the fault contribution of the key node corresponding to each column. Each column of the coefficient matrix corresponds to a key node, and each row corresponds to a low-dimensional feature. The low-dimensional feature can reflect the similarities and differences between key nodes. The size of the key node on the low-dimensional feature can reflect the correlation between the key node and the fault signal. The larger the value of any key node on the low-dimensional feature, the more important the key node may be in the propagation of the fault signal. Adding the data in each column of the coefficient matrix can quantify the total contribution of the key node corresponding to each column on all low-dimensional features.

[0047] Furthermore, any abnormal node and its related nodes in the graph model are marked, the marked graph model is input into a link analysis algorithm, the weight vectors of the related nodes are output, and the entropy of the weight vectors of the related nodes is used as the weight of the related nodes. The normalized value of the correlation factor between the abnormal node and its related nodes is used as the initial weight of the related nodes. The entropy calculation method is well known in the art and will not be described in detail in this application.

[0048] In this embodiment, the PageRank algorithm is used to output the weight vectors of the relevant nodes. The PageRank algorithm is a well-known technology and will not be described in detail in this application. The damping factor of the PageRank algorithm is 0.85. As other implementation methods, the implementer can choose other feasible methods on the basis of being able to measure the importance of each key node in the graph model.

[0049] In this embodiment, the softmax function is used to normalize the correlation factors.

[0050] Based on the above analysis, the fault connection weights of the related nodes of any abnormal node are obtained by combining the correlation factor between the abnormal node and its related nodes, the fault contribution, and the weight. The expression is: Where, represents the fault connection weight of the jth related node of the vth abnormal node; represents the correlation factor between the vth abnormal node and its jth related node; 、 They represent the fault contribution and weight of the j-th related node of the v-th abnormal node, respectively; ϵ represents a preset positive number used to avoid the denominator being 0. The value of ϵ is preset manually and can be set by the implementer. In this embodiment, the value of ϵ is 0.01.

[0051] It should be noted that: It reflects the overall correlation between the vth abnormal node and its jth related node in terms of current, voltage and power change trends, and measures the electrical connection strength and synchronization between the jth related node and the fault point. The larger the value is, the stronger the correlation between the jth related node and the fault point is, and the more likely it is to be directly connected to the fault point or directly affected by the fault power. The bigger; Measures the importance of the jth related node in the fault propagation process, The larger the value is, the more important the jth related node is in fault propagation, and the more likely the fault is to occur between the vth abnormal node and its jth related node. The bigger; It measures the influence and stability of the jth related node in the grid topology, The smaller it is, the more concentrated the weight distribution of the jth related node is, the more likely it is a key node in the power grid, and the stronger its influence on fault propagation is. The bigger. Used to quantify the degree of association between the jth related node and the fault point, The larger it is, the stronger the connection between the jth related node and the fault point is, and the more important its role in fault propagation is.

[0052] Step S5: construct a weighted graph model based on the fault connection weights, and obtain the fault area of ​​any abnormal node by analyzing the connection relationship between nodes in the weighted graph model.

[0053] In a combined weak power grid, an abnormal node only represents a node related to a fault, but the exact location of the fault may be located in a line or a key node near the abnormal node. Therefore, it is necessary to accurately locate the fault location and construct a weighted graph model based on the graph model. The node weight of each related node of any abnormal node in the weighted graph model is its fault connection weight, and the node weight of the remaining key nodes is 0.

[0054] Since faults usually propagate from the fault point to the surrounding key nodes, the propagation path can be represented by the edges in the weighted graph model, and the strength of fault propagation is related to the node weights of the relevant nodes. The key nodes with larger weights are more likely to be important nodes for fault propagation. Taking any abnormal node as the root node, a graph traversal algorithm is used to obtain each fault propagation path with any abnormal node as the root node in the weighted graph model; calculate the sum of the node weights of all key nodes on each fault propagation path; for the fault propagation path with the largest sum, count the number of edges connected to each key node on the fault propagation path, calculate the product of the node weight of each key node on the fault propagation path and the number of edges, and combine all the key nodes and edges from any abnormal node to the key node with the largest product to form the fault area of ​​any abnormal node. The flow chart for obtaining the fault area is as follows: Figure 3 shown.

[0055] It should be noted that the node weight is used to reflect the importance of the key node in fault propagation. The larger the node weight, the more likely the key node is directly connected to the fault point, or is directly affected by the fault point, and the more important its role in the fault propagation process. The number of edges connected to the key node indicates the number of electrical connections of the key node in the power grid. The larger the number of edges, the more other key nodes the key node is connected to, and the higher the possibility that the fault signal will propagate through these connections.

[0056] In this embodiment, the breadth-first search algorithm is used to obtain the fault propagation paths with any abnormal node as the root node in the weighted graph model. As other implementation methods, based on the ability to obtain the fault propagation paths with any abnormal node as the root node in the weighted graph model, the implementer can select other feasible algorithms at his or her discretion.

[0057] In summary, the present application uses a mutation point detection algorithm to detect the mutation points of current, voltage, and power sequences, and screens abnormal nodes based on the distribution of the time when the mutation points occur, combined with the time threshold; through the comprehensive weight of linear correlation and fractal dimension difference, the problem that the traditional single correlation coefficient is easily interfered by noise is solved, and the electrical correlation between nodes is accurately reflected; for the problem of interference from non-correlated nodes, the reset feature matrix is ​​extracted with low-dimensional features through non-negative matrix decomposition, and the fault connection weight is designed in combination with the link analysis algorithm. The node correlation and fault propagation contribution are combined to eliminate the influence of non-correlated nodes and redundant paths, and the key propagation paths that are strongly associated with the fault are screened out; for the problem of ambiguity in fault location, the fault propagation path is generated based on the weighted graph model and graph traversal algorithm. The product of the fault connection weight and the electrical connection density solves the problem that the traditional method ignores the dynamic propagation characteristics of the topological structure, accurately locates the fault area, and realizes the improvement of fault location from "single-dimensional detection" to "multi-dimensional correlation analysis", significantly improving the accuracy and robustness of fault location, and avoiding the expansion of the fault range caused by misjudgment.

[0058] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the systems, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0059] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic characteristics of the present application. Therefore, from all perspectives, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

Claims

1. A simulation method for a combined weak power grid simulation device, characterized in that: The method comprises the following steps: A combined weak power grid simulation device is used to obtain a graphical model of the combined weak power grid, and current data, voltage data, and power data of each key node in the graphical model are collected in real time; By comprehensively analyzing the distribution of the time when the current data, voltage data and power data of each key node suddenly change, each abnormal node is extracted from all key nodes; By analyzing the correlation of current changes, voltage changes, and power changes between any abnormal node and the remaining key nodes, and combining the differences in current change complexity, voltage change complexity, and power change complexity between the abnormal node and the remaining key nodes, the correlation factors between the abnormal node and the remaining key nodes are obtained, and the related nodes of the abnormal node are selected from all the key nodes; The weight distribution of each related node in the low-dimensional features at the current acquisition moment is evaluated using a non-negative matrix decomposition algorithm to obtain the fault contribution of each related node. Based on the connection relationship between key nodes in the graph model and the correlation factor between any abnormal node and its related nodes, a weight is assigned to each related node. The fault connection weight of each related node is obtained by combining the correlation factor between any abnormal node and its related nodes, the fault contribution, and the weight. Based on the fault connection weights, a weighted graph model is constructed, and the fault area of ​​any abnormal node is obtained by analyzing the connection relationship between nodes in the weighted graph model; The combined weak power grid simulation device includes: a power supply module, an impedance matching module, a load simulation module and a control and monitoring system. Among them, the transformer in the power supply module is designed with dual voltage levels; the inductor in the impedance matching module is used to adjust the impedance to achieve a specific short-circuit ratio; the load simulation module is used to adjust the load size and type; the control and monitoring system uses power system simulation software to simulate the combined weak power grid and obtain a graphical model of the combined weak power grid.

2. The simulation method of a combined weak power grid simulation device according to claim 1, characterized in that: The extraction process of each abnormal node is as follows: All current data, voltage data, and power data collected at each key node are arranged in time sequence to form the current sequence, voltage sequence, and power sequence of each key node; The mutation point detection algorithm is used to obtain the mutation points in each current series, voltage series and power series respectively; The occurrence times of all mutation points in the current sequence, voltage sequence, and power sequence of any key node are counted. If mutation points are detected in the current sequence, voltage sequence, and power sequence of any key node, and when the discreteness of the time intervals between the occurrence times of any two adjacent mutation points is less than a preset threshold, the key node is regarded as an abnormal node.

3. The simulation method of a combined weak power grid simulation device according to claim 1, characterized in that: The process of obtaining the correlation factor is as follows: Calculating the correlation coefficient of the current sequence, the correlation coefficient of the voltage sequence, and the correlation coefficient of the power sequence between any abnormal node and the remaining key nodes respectively; calculating the fractal dimension of the current sequence, the fractal dimension of the voltage sequence, and the fractal dimension of the power sequence of each key node respectively; respectively calculating the fractal dimension difference of the current sequence, the fractal dimension difference of the voltage sequence, and the fractal dimension difference of the power sequence between any abnormal node and the remaining key nodes; The correlation factors are respectively positively correlated with the correlation coefficient of the current sequence, the correlation coefficient of the voltage sequence, and the correlation coefficient of the power sequence; They are negatively correlated with the fractal dimension differences of current series, voltage series, and power series, respectively.

4. The simulation method of the combined weak power grid simulation device according to claim 3, characterized in that: The expression of the correlation factor is: Where, represents the correlation factor between the vth abnormal node and the ith key node; w represents the number of types of data collected at each key node; 、 、 They represent the correlation coefficient of the current sequence, the voltage sequence, and the power sequence between the vth abnormal node and the ith key node respectively; 、 、 They represent the fractal dimension difference of the current sequence, the fractal dimension difference of the voltage sequence, and the fractal dimension difference of the power sequence between the vth abnormal node and the ith key node respectively; e represents an exponential function with a natural constant as the base.

5. The simulation method of a combined weak power grid simulation device according to claim 1, characterized in that: The method for selecting the relevant nodes is: using the key nodes whose correlation factors with any abnormal node are greater than a preset correlation factor threshold as the relevant nodes of any abnormal node.

6. The simulation method of a combined weak power grid simulation device according to claim 1, characterized in that: The method for obtaining the fault contribution is: The key nodes are used as column indicators, and the current, voltage and power are used as row indicators to construct a feature matrix; The data of the columns corresponding to all related nodes of any abnormal node in the feature matrix are retained, and the data of the remaining columns are assigned to 0 to obtain a reset feature matrix; The non-negative matrix factorization algorithm is used to decompose the reset feature matrix to obtain the coefficient matrix, and the sum of the data in each column of the coefficient matrix is ​​used as the fault contribution of the key node corresponding to each column.

7. The simulation method of a combined weak power grid simulation device according to claim 1, characterized in that: The allocating weights to the relevant nodes includes: Any abnormal node and its related nodes in the graph model are marked, the marked graph model is input into the link analysis algorithm, the weight vectors of the related nodes are output, and the entropy of the weight vectors of the related nodes is used as the weight of the related nodes, wherein the normalized value of the correlation factor between any abnormal node and its related nodes is used as the initial weight of the related nodes.

8. The simulation method of a combined weak power grid simulation device according to claim 1, characterized in that: The expression of the fault connection weight is: Where, represents the fault connection weight of the jth related node of the vth abnormal node; represents the correlation factor between the vth abnormal node and its jth related node; 、 They represent the fault contribution and weight of the jth related node of the vth abnormal node respectively; ϵ represents a preset positive number.

9. The simulation method of a combined weak power grid simulation device according to claim 1, characterized in that: The method for obtaining the fault area is: The node weight of each related node of any abnormal node in the weighted graph model is its fault connection weight, and the node weight of each other key node is 0; a graph traversal algorithm is used to obtain each fault propagation path with any abnormal node as the root node in the weighted graph model; Calculate the sum of the node weights of all key nodes on each fault propagation path; For the fault propagation path with the largest sum, count the number of edges connected to each key node on the fault propagation path, calculate the product of the node weight of each key node on the fault propagation path and the number of edges, and combine all the key nodes and edges passed through from any abnormal node to the key node with the largest product to form the fault area of ​​any abnormal node.

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