Power grid fault early warning system based on topology change scene

Abstract

The invention discloses a power grid fault early warning system based on a topology change scene, and relates to the technical field of power grid fault early warning. The topological graph construction module constructs a network topological graph; for each element in the network topological graph, the abnormity positioning module determines whether the element is abnormal or not according to the contrast parameter and the actual parameter; the topology reconstruction module is used for obtaining a setting topological graph according to the element abnormity, and substituting the setting topological graph into the Bayesian compressed sensing model to obtain a target network topological graph; the fault probability calculation module is used for calculating the fault probability of each node; and the setting value determining module is used for determining the setting value of each node according to the fault probability of each node. Network topology reconstruction is carried out through the Bayesian compressed sensing model, the adaptability and flexibility of the early warning system are improved, the efficiency and reliability of power grid fault early warning are improved by calculating the fault probability of each node and quantifying the fault risk of each node in a power grid, and safe and stable operation of a power system is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid fault early warning, and in particular relates to a power grid fault early warning system based on a topology change scenario. Background Art

[0002] As the cornerstone of modern society, the stability and security of the power system are directly related to economic development and social stability. However, power grid failures are inevitable. Once they occur, they will not only cause huge economic losses, but also may cause serious social impacts. Therefore, early warning of power grid failures is particularly important.

[0003] The invention patent announcement number CN113376479B proposes a distribution network fault detection system, including a fault indicator, a master station and a mobile terminal; each time a new fault occurs, the fault record module searches and compares the fault records in the previously set time period A to find out whether there have been faults in the same area. If there have been faults in the previous time period A in the same area, a warning message is displayed on the display module so that the fault area of ​​the power grid can be quickly located. However, this method cannot effectively respond to changes in the topology of the power grid. This method mainly relies on static monitoring data. This method can only play a good role when the topology of the power grid is stable. However, the topology of the current power grid is becoming more and more complex and changes frequently, which makes it difficult for the fault detection method based on static data to cope with the ever-changing power grid environment, resulting in low efficiency and reliability of the fault warning system. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the traditional fault detection method based on static data is difficult to cope with the ever-changing power grid environment, resulting in low efficiency and reliability of the fault warning system, and to propose a power grid fault warning system based on topology change scenarios.

[0005] In the implementation of the present invention, a power grid fault early warning system based on a topology change scenario is proposed, and the system includes:

[0006] A topology map construction module is used to construct a network topology map according to the power grid structure of the target power grid area, and obtain an adjacency matrix according to the network topology map;

[0007] The abnormality positioning module is used to obtain the environmental data corresponding to each element in the network topology diagram in real time, substitute all the environmental data into the preset model to obtain the control parameters corresponding to each element, and determine whether the element is abnormal according to the control parameters and actual parameters for each element; each element in the network topology diagram is any node or any edge in the network topology diagram; the environmental data is the voltage amplitude, voltage phase, current, phase angle of each node in the network topology diagram, and the resistance and reactance of each line; the actual parameters are the environmental data of the element;

[0008] A topology reconstruction module is used to remove abnormal elements in the network topology map to obtain a set topology map, and substitute the set topology map into a Bayesian compressed sensing model to perform network topology reconstruction to obtain a target network topology map;

[0009] A failure probability calculation module, used to calculate the failure probability of each node in the target network graph;

[0010] The setting value determination module is used to determine the setting value of each node in the target network diagram according to the failure probability of each node.

[0011] Optionally, the abnormality locating module includes:

[0012] The data preprocessing module is used to detect the environmental data of each element in real time and preprocess all environmental data to obtain the target data set;

[0013] An admittance calculation module is used to calculate the admittance of each line according to the resistance and reactance of each line, and obtain a conductivity matrix and a susceptance matrix according to the admittance of each line;

[0014] An equation building module, used to build an active power equation, a reactive power equation, a current equation and a phase angle equation according to the target data set, the conductivity matrix and the susceptance matrix;

[0015] The preset model generation module is used to replace the corresponding formulas in the power flow calculation model according to the active power equation, reactive power equation, current equation and phase angle equation to obtain the preset model.

[0016] Optionally, the equation building module includes:

[0017] Active power equation building module, used to pass the formula

[0018] Get active power;

[0019] in, is the active power of node i between adjacent nodes j, g ij is the conductivity between node i and node j, b ijis the susceptance between node i and node j, υ i and v j are the voltage amplitudes of nodes i and j, respectively, i and θ j are the voltage phase angles at node i and node j respectively.

[0020] Optionally, the equation building module further includes:

[0021] Reactive power equation building module, used to pass the formula

[0022] Get reactive power;

[0023] in, is the reactive power between node i and adjacent node j, g ij is the conductivity between node i and node j, b ij is the susceptance between nodes i and j, v i and v j are the voltage amplitudes of nodes i and j, respectively, i and θ j are the voltage phase angles at node i and node j respectively.

[0024] Optionally, the equation building module further includes:

[0025] The current equation building module is used to pass the formula Get current;

[0026] in, is the current between node i and adjacent node j, v i and v j are the voltage amplitudes of nodes i and j, respectively, ij is the resistance between node i and node j.

[0027] Optionally, the equation building module further includes:

[0028] Phase angle equation module, used to calculate the phase angle through the formula σ i =θ i -θ0 to get the phase angle;

[0029] Among them, σ i is the phase angle of node i, θ i and θ0 are the phase angle of node i and the phase angle of the reference waveform of node i, respectively.

[0030] Optionally, the abnormality locating module further includes:

[0031] A judgment difference calculation module is used to calculate the difference between the actual parameter and the reference parameter corresponding to the environmental parameter type of each element to obtain a judgment difference;

[0032] The abnormal element determination module is used to record the element as abnormal if the judgment difference corresponding to all environmental parameter types is greater than a preset threshold; the preset threshold of the environmental parameter type corresponding to each element is different.

[0033] Optionally, the topology reconstruction module includes:

[0034] An initialization module is used to generate an adjacency matrix of a tuning topology map, which is recorded as a tuning adjacency matrix, and substitute the tuning adjacency matrix and the environmental data corresponding to all elements in the tuning topology map into a Bayesian compressed sensing model for initialization to obtain initialization parameters;

[0035] A sparse signal determination module, used for constructing an observation model according to the initialization parameters and the tuning topology map, and performing parameter estimation and signal reconstruction through a Markov chain Monte Carlo method to obtain a sparse signal;

[0036] The target network topology map determining module is used to determine the connection relationship between nodes according to the non-zero elements in the sparse signal to generate the target network topology map.

[0037] Optionally, the failure probability calculation module includes:

[0038] A topology subgraph determination module is used to determine the number of power outage area topology connection subgraphs of each node through a graph theory algorithm according to the target network topology graph;

[0039] A first feature extraction module is used to extract features from all power outage area topology connection subgraphs of each node to obtain a first feature;

[0040] A second feature extraction module is used to obtain the alarm information feature corresponding to the node in the historical fault data and record it as the second feature;

[0041] A fault probability determination module is used to substitute the first feature and the second feature into a preset fault probability model to obtain the fault probability of the node.

[0042] Beneficial effects of the present invention:

[0043] The present invention proposes a power grid fault early warning system based on a topology change scenario, including a topology map construction module, which is used to construct a network topology map according to the power grid structure of a target power grid area, and obtain an adjacency matrix according to the network topology map; an abnormality positioning module, which is used to obtain the environmental data corresponding to each element in the network topology map in real time, substitute all the environmental data into a preset model to obtain the control parameters corresponding to each element, and determine whether the element is abnormal according to the control parameters and actual parameters for each element; a topology reconstruction module, which is used to remove the abnormal positions of the elements in the network topology map to obtain a set topology map, substitute the set topology map into a Bayesian compressed sensing model to perform network topology reconstruction to obtain a target network topology map; a fault probability calculation module, which is used to calculate the fault probability of each node in the target network map; and a set value determination module, which is used to determine the set value of each node in the target network map according to the fault probability of each node. Through the topology map construction module and the abnormality positioning module, the system can obtain the environmental data of each element of the power grid in real time and dynamically update the power grid topology structure. This real-time monitoring and dynamic analysis greatly improves the accuracy and timeliness of fault detection, and avoids the lag and inaccuracy of traditional static monitoring methods when dealing with frequently changing power grid environments. After detecting abnormal elements in the power grid, the network topology is reconstructed through the Bayesian compressed sensing model. The system can effectively cope with complex and changeable power grid environments, improve the adaptability and flexibility of the early warning system, and then quantify the failure risk of each node in the power grid by calculating the failure probability of each node, and determine the setting value of each node through the failure probability, so that the power grid can be adaptively adjusted according to the actual operating status, thereby improving the efficiency and reliability of power grid fault warning and ensuring the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention will be further described below in conjunction with the accompanying drawings.

[0045] Figure 1 A framework diagram of a power grid fault early warning system based on a topology change scenario is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the present invention, the description of "first", "second", etc. is only used for descriptive purposes, and cannot be understood as indicating or implying its relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0047] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0048] The embodiment of the present invention provides a power grid fault early warning system based on topology change scenarios. Figure 1 , Figure 1 A framework diagram of a power grid fault early warning system based on a topology change scenario provided by an embodiment of the present invention. The system includes the following steps:

[0049] A topology map construction module is used to construct a network topology map according to the power grid structure of the target power grid area, and obtain an adjacency matrix according to the network topology map;

[0050] The abnormality location module is used to obtain the environmental data corresponding to each element in the network topology map in real time, substitute all environmental data into the preset model to obtain the control parameters corresponding to each element, and determine whether the element is abnormal based on the control parameters and actual parameters;

[0051] The topology reconstruction module is used to remove abnormal elements in the network topology map to obtain a set topology map, and substitute the set topology map into the Bayesian compressed sensing model to reconstruct the network topology to obtain the target network topology map;

[0052] A failure probability calculation module is used to calculate the failure probability of each node in the target network graph;

[0053] The setting value determination module is used to determine the setting value of each node in the target network diagram according to the failure probability of each node.

[0054] Among them, each element in the network topology diagram is any node or any edge in the network topology diagram; the environmental data is the voltage amplitude, voltage phase, current, phase angle of each node in the network topology diagram and the resistance and reactance of each line; the actual parameter is the environmental data of the element.

[0055] Based on a power grid fault early warning system based on a topology change scenario provided by an embodiment of the present invention, the system can obtain the environmental data of each element of the power grid in real time and dynamically update the power grid topology structure through a topology map construction module and an abnormality positioning module. This real-time monitoring and dynamic analysis greatly improves the accuracy and timeliness of fault detection, avoids the lag and inaccuracy of traditional static monitoring methods when dealing with frequently changing power grid environments, and after detecting abnormal elements in the power grid, reconstructs the network topology through a Bayesian compressed sensing model. The system can effectively cope with complex and changeable power grid environments, improves the adaptability and flexibility of the early warning system, and then quantifies the failure risk of each node in the power grid by calculating the failure probability of each node, and determines the setting value of each node through the failure probability so that the power grid can be adaptively adjusted according to the actual operating status, thereby improving the efficiency and reliability of power grid fault early warning and ensuring the safe and stable operation of the power system.

[0056] In one implementation, the nodes of the network topology diagram are composed of distribution transformers, switch stations, power plants, substations, power users, etc., and the edges are determined by actual connection relationships.

[0057] In one implementation, the preset model is constructed by first preprocessing the environmental data of each element in the power grid, including voltage amplitude, voltage phase, current, phase angle, and resistance and reactance of each line, to remove noise and outliers, and organize them into a target data set in a unified format; then calculate the admittance of each line based on the resistance and reactance of each line. This step converts each line in the power grid into a representation of its conductive performance, summarizes the admittance information of all lines, generates a conductivity matrix and an susceptance matrix, and establishes basic equations related to power grid operation based on the preprocessed target data set and the admittance matrix, including active power equation, reactive power equation, current equation and phase angle equation. These equations are used to describe the electrical relationship between each node and line in the power grid, and the above basic equations are integrated together to form an overall preset model. This model is used to replace the traditional power flow calculation model and reflect the dynamic electrical relationship between each node and line in the power grid.

[0058] In one implementation, by constructing and updating a network topology map and an adjacency matrix, the system can reflect changes in the grid structure in real time, thereby improving the speed and accuracy of response to changes in the grid state.

[0059] In one implementation, real-time acquisition and processing of environmental data of various elements of the power grid can quickly identify and locate faults when the power grid environment changes dynamically, thereby enhancing the sensitivity and early warning capabilities of the system.

[0060] In one implementation, by substituting environmental data into a preset model to obtain control parameters, and comparing them with actual parameters, it is precisely determined whether the power grid elements are abnormal, thereby improving the accuracy of fault detection.

[0061] In one implementation, after detecting abnormal elements in the power grid, the faulty elements can be quickly removed and the network topology can be reconstructed, and the Bayesian compressed sensing model is used for reconstruction to ensure that the system can still quickly adjust and recover in the event of a fault, maintaining the continuity and stability of the power grid.

[0062] In one implementation, the set value of each node is dynamically adjusted according to the failure probability to ensure the stable operation of the power grid in a dynamically changing environment. Dynamic adjustment is thus performed, and the system can adapt to the actual operating status of the power grid and improve operating efficiency and reliability.

[0063] In one embodiment, the anomaly location module includes:

[0064] The data preprocessing module is used to detect the environmental data of each element in real time and preprocess all environmental data to obtain the target data set;

[0065] An admittance calculation module is used to calculate the admittance of each line according to the resistance and reactance of each line, and obtain a conductivity matrix and a susceptance matrix according to the admittance of each line;

[0066] An equation building module, used to build an active power equation, a reactive power equation, a current equation and a phase angle equation according to a target data set, a conductivity matrix and a susceptance matrix;

[0067] The preset model generation module is used to obtain the preset model by replacing the corresponding formulas in the power flow calculation model according to the active power equation, reactive power equation, current equation and phase angle equation.

[0068] In one implementation, preprocessing includes noise removal, outlier detection, data correction, and missing value processing, and the preprocessing adopts existing technologies.

[0069] In one implementation, the admittance is calculated based on the resistance and reactance of each line to generate the conductivity matrix and the susceptance matrix, which accurately describe the conductive characteristics of each line in the power grid and are important inputs for subsequent equation establishment and preset model generation.

[0070] In one implementation, the target data set and the admittance matrix are used to establish an active power equation, a reactive power equation, a current equation, and a phase angle equation to fully reflect the electrical characteristics of the power grid.

[0071] In one implementation, the traditional power flow calculation model is replaced by the established active power equation, reactive power equation, current equation and phase angle equation to generate a more accurate and dynamically responsive preset model, thereby more efficiently performing fault detection and early warning, adapting to the dynamic changes of the power grid, and improving the efficiency of calculation and analysis.

[0072] In one embodiment, the equation building module includes:

[0073] Active power equation building module, used to pass the formula

[0074] Get active power;

[0075] in, is the active power of node i between adjacent nodes j, g ij is the conductivity between node i and node j, b ij is the susceptance between nodes i and j, v i and v j are the voltage amplitudes of nodes i and j, respectively, i and θ j are the voltage phase angles at node i and node j respectively.

[0076] In one embodiment, the equation building module further includes:

[0077] Reactive power equation building module, used to pass the formula

[0078] Get reactive power;

[0079] in, is the reactive power between node i and adjacent node j, g ij is the conductivity between node i and node j, b ij is the susceptance between nodes i and j, v i and v j are the voltage amplitudes of nodes i and j, respectively, i and θ j are the voltage phase angles at node i and node j respectively.

[0080] In one embodiment, the equation building module further includes:

[0081] The current equation building module is used to Get current;

[0082] in, is the current between node i and adjacent node j, v i and v jare the voltage amplitudes of nodes i and j, respectively, ij is the resistance between node i and node j.

[0083] In one embodiment, the equation building module further includes:

[0084] Phase angle equation module, used to calculate the phase angle through the formula σ i =θ i -θ0 to get the phase angle;

[0085] Among them, σ i is the phase angle of node i, θ i and θ0 are the phase angle of node i and the phase angle of the reference waveform of node i, respectively.

[0086] In one embodiment, the anomaly location module further includes:

[0087] A judgment difference calculation module is used to calculate the difference between the actual parameter and the reference parameter corresponding to the environmental parameter type of each element to obtain a judgment difference;

[0088] The abnormal element determination module is used to record the element as abnormal if the judgment difference corresponding to all environmental parameter types is greater than a preset threshold; the preset threshold of the environmental parameter type corresponding to each element is different.

[0089] In one implementation, by calculating the difference, the system can quantify the degree of deviation of the environmental data and provide a more objective and quantitative basis for abnormal judgment.

[0090] In one implementation, by determining the difference in judgment of all environmental parameter types, the system can conduct a multi-dimensional comprehensive analysis to avoid misjudgment caused by a single parameter. Each environmental parameter type has a different preset threshold, and the system can set different sensitivities for different types of parameters to improve the flexibility and accuracy of detection.

[0091] In one implementation, the preset threshold of the environmental parameter type corresponding to each element is different. Specifically, the type of each environmental parameter is obtained by classification according to the environmental parameters, such as voltage amplitude, voltage phase, current, phase angle, resistance of each line, and reactance of each line. Data of different environmental parameters are extracted from historical operation records, and statistical analysis is performed on each parameter to determine its statistical characteristics such as mean, standard deviation, maximum value, minimum value, etc. A corresponding threshold is set for each statistical characteristic, all thresholds are mapped to the same length in the corresponding data, and the average value of the threshold corresponding to each statistical characteristic is calculated to obtain the preset threshold of the environmental parameter type corresponding to the element; the corresponding threshold is set for each statistical characteristic and is determined by a technician, such as setting the threshold for data points outside the range of mean ±2 standard deviations, setting the threshold for eighty percent of the maximum value, etc.

[0092] In one implementation, only when the judgment difference of all environmental parameter types is greater than a preset threshold value, the element is recorded as an anomaly, thereby reducing the probability of false alarms and improving the accuracy of anomaly detection.

[0093] In one embodiment, the topology reconstruction module includes:

[0094] An initialization module is used to generate an adjacency matrix of a tuning topology map, which is recorded as a tuning adjacency matrix. The environment data corresponding to all elements in the tuning adjacency matrix and the tuning topology map are substituted into a Bayesian compressed sensing model for initialization to obtain initialization parameters.

[0095] The sparse signal determination module is used to construct an observation model according to the initialization parameters and the tuning topology map, and obtain the sparse signal by performing parameter estimation and signal reconstruction through the Markov chain Monte Carlo method;

[0096] The target network topology map determination module is used to determine the connection relationship between nodes according to the non-zero elements in the sparse signal and generate the target network topology map.

[0097] In one implementation, by generating an adjacency matrix of a set topology graph, the system can accurately reflect the connection relationship of the current power grid and provide accurate initial data for the subsequent Bayesian compressed sensing model.

[0098] In one implementation, the signal is reconstructed to obtain a sparse signal

[0099] In one implementation, the environmental data is combined with the set adjacency matrix to initialize the model, so that the model can more comprehensively reflect the actual operating status of the power grid and ensure the accuracy of the initialization parameters.

[0100] In one implementation, parameter estimation and signal reconstruction are performed using the Markov Chain Monte Carlo method, which can handle complex probability distributions, provide high-quality parameter estimation results, and improve the accuracy of the model.

[0101] In one implementation, a topology reconstruction method based on sparse signals can quickly determine the actual connection structure of the power grid, ensuring that the system can respond and adjust in a timely manner when the power grid structure changes.

[0102] In one embodiment, the failure probability calculation module includes:

[0103] A topology subgraph determination module is used to determine the number of topology connection subgraphs of the power outage area of ​​each node through a graph theory algorithm according to the target network topology graph;

[0104] A first feature extraction module is used to extract features from all power outage area topology connection subgraphs of each node to obtain a first feature;

[0105] A second feature extraction module is used to obtain the alarm information feature corresponding to the node in the historical fault data and record it as the second feature;

[0106] The fault probability determination module is used to substitute the first feature and the second feature into a preset fault probability model to obtain the fault probability of the node.

[0107] In one implementation, a graph theory algorithm is used to determine the number of topologically connected subgraphs of the power outage area of ​​each node, which can accurately identify the areas in the power grid that may be affected, ensuring rapid location of the fault point and the scope of impact.

[0108] In one implementation, for each node, the features of the topological connection subgraphs of all power outage areas of the node are extracted, and the operating status and potential failure risks of each node in the power grid can be comprehensively analyzed.

[0109] In one implementation, historical fault data records various faults that occurred during the past operation of the power grid and their detailed information, including fault occurrence time, fault location, fault type, environmental data, fault duration, fault impact range, fault cause analysis, etc.; fault information is a further analysis and refinement of historical fault data, including alarm information, recovery measures, fault consequences, fault statistics, etc.

[0110] In one implementation, by analyzing the alarm information characteristics in historical fault data, the system can identify common fault modes in the power grid and enhance prediction capabilities.

[0111] In one implementation, the first feature and the second feature are substituted into a preset fault probability model, which can comprehensively evaluate the failure probability of each node, provide a more comprehensive fault prediction result, and calculate personalized failure probability based on the characteristics and historical data of different nodes, thereby improving the accuracy and pertinence of the prediction.

[0112] In one implementation, the fault probability model is generated by collecting past fault data and alarm information of the power grid, removing noise and outliers from the fault data and alarm information, filling missing values, and normalizing the data to obtain a historical data set, dividing the historical data set into a validation set and a training set, training the training set with a random forest algorithm to obtain a training model, validating the model with the validation set, and then adjusting the model through cross-validation and hyperparameter tuning until the difference between the results obtained with the validation set and the training set is within a preset range to obtain a fault probability model; the preset range is determined by a technician.

[0113] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A power grid fault early warning system based on topology change scenarios, characterized in that: The system comprises: A topology map construction module is used to construct a network topology map according to the power grid structure of the target power grid area, and obtain an adjacency matrix according to the network topology map; The abnormality positioning module is used to obtain the environmental data corresponding to each element in the network topology diagram in real time, substitute all the environmental data into the preset model to obtain the control parameters corresponding to each element, and determine whether the element is abnormal according to the control parameters and actual parameters for each element; each element is any node or any edge in the network topology diagram; the environmental data is the voltage amplitude, voltage phase, current, phase angle of each node and the resistance and reactance of each line; The actual parameter is the environment data of the element A topology reconstruction module is used to remove abnormal elements in the network topology map to obtain a set topology map, and substitute the set topology map into a Bayesian compressed sensing model to perform network topology reconstruction to obtain a target network topology map; A failure probability calculation module, used to calculate the failure probability of each node in the target network graph; The setting value determination module is used to determine the setting value of each node in the target network diagram according to the failure probability of each node.

2. A power grid fault early warning system based on topology change scenario according to claim 1, characterized in that: The abnormality positioning module includes: The data preprocessing module is used to detect the environmental data of each element in real time and preprocess all environmental data to obtain the target data set; An admittance calculation module is used to calculate the admittance of each line according to the resistance and reactance of each line, and obtain a conductivity matrix and a susceptance matrix according to the admittance of each line; An equation building module, used to build an active power equation, a reactive power equation, a current equation and a phase angle equation according to the target data set, the conductivity matrix and the susceptance matrix; The preset model generation module is used to replace the corresponding formulas in the power flow calculation model according to the active power equation, reactive power equation, current equation and phase angle equation to obtain the preset model.

3. A power grid fault early warning system based on topology change scenario according to claim 2, characterized in that: The equation building module includes: Active power equation building module, used to pass the formula Get active power; in, is the active power of node i between adjacent nodes j, g ij is the conductivity between node i and node j, b ij is the susceptance between node i and node j, υ i and j are the voltage amplitudes of nodes i and j, respectively, i and θ j are the voltage phase angles at node i and node j respectively.

4. A power grid fault early warning system based on topology change scenario according to claim 2, characterized in that: The equation building module also includes: Reactive power equation building module, used to pass the formula Get reactive power; in, is the reactive power between node i and adjacent node j, g ij is the conductivity between node i and node j, b ij is the susceptance between node i and node j, υ i and j are the voltage amplitudes of nodes i and j, respectively, i and θ j are the voltage phase angles at node i and node j respectively.

5. A power grid fault early warning system based on topology change scenario according to claim 2, characterized in that: The equation building module also includes: The current equation building module is used to Get current; in, is the current between node i and adjacent node j, υ i and j are the voltage amplitudes of nodes i and j, respectively, ij is the resistance between node i and node j.

6. A power grid fault early warning system based on topology change scenario according to claim 2, characterized in that: The equation building module also includes: Phase angle equation module, used to calculate the phase angle through the formula σ i =θ i -θ0 to get the phase angle; Among them, σ i is the phase angle of node i, θ i and θ0 are the phase angle of node i and the phase angle of the reference waveform of node i, respectively.

7. A power grid fault early warning system based on topology change scenario according to claim 2, characterized in that: The abnormality positioning module also includes: A judgment difference calculation module is used to calculate the difference between the actual parameter and the reference parameter corresponding to the environmental parameter type of each element to obtain a judgment difference; The abnormal element determination module is used to record the element as abnormal if the judgment difference corresponding to all environmental parameter types is greater than a preset threshold; the preset threshold of the environmental parameter type corresponding to each element is different.

8. The power grid fault early warning system based on topology change scenario according to claim 1 is characterized in that: The topology reconstruction module includes: An initialization module is used to generate an adjacency matrix of a tuning topology map, which is recorded as a tuning adjacency matrix, and substitute the tuning adjacency matrix and the environmental data corresponding to all elements in the tuning topology map into a Bayesian compressed sensing model for initialization to obtain initialization parameters; A sparse signal determination module, used for constructing an observation model according to the initialization parameters and the tuning topology map, and performing parameter estimation and signal reconstruction through a Markov chain Monte Carlo method to obtain a sparse signal; The target network topology map determining module is used to determine the connection relationship between nodes according to the non-zero elements in the sparse signal to generate the target network topology map.

9. The power grid fault early warning system based on topology change scenario according to claim 1, characterized in that: The failure probability calculation module includes: A topology subgraph determination module is used to determine the number of power outage area topology connection subgraphs of each node through a graph theory algorithm according to the target network topology graph; A first feature extraction module is used to extract features from all power outage area topology connection subgraphs of each node to obtain a first feature; A second feature extraction module is used to obtain the alarm information feature corresponding to the node in the historical fault data and record it as the second feature; A fault probability determination module is used to substitute the first feature and the second feature into a preset fault probability model to obtain the fault probability of the node.

Images