Distribution line fault positioning method and system based on Beidou system

Abstract

The invention discloses a distribution line fault positioning method and system based on a Beidou system, and relates to the technical field of line fault analysis, and the method comprises the steps: collecting key node data, defining a node state according to the state data of a distributed power supply, a fault indicator and an intelligent electric meter, constructing a data matrix, and calculating a principal component matrix of the state data; constructing a fault mode recognition model for the principal component matrixes of different state data, and performing model fusion; and calculating the state probability of the node at the next moment based on the historical fault data. According to the method, the model is constructed through the principal component matrix based on the three view angles of the distributed power supply, the fault indicator and the intelligent electric meter, the multi-dimensional characteristics of the fault can be reflected more comprehensively, and the overall fault state of the current power system can be represented more accurately through the comprehensive fault mode vector generated through view angle fusion. Deviation and blind areas possibly existing in a single-view-angle model are reduced, and therefore the fault positioning precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of line fault analysis, and in particular to a method and system for locating power distribution line faults based on a Beidou system. Background Art

[0002] With the continuous expansion of the scale of power systems and the increase in the proportion of distributed power generation access, the structure of distribution networks has become increasingly complex, and the difficulty of fault location and diagnosis has increased significantly. Traditional distribution line fault location methods mainly rely on single data sources such as fault indicators and smart meters, such as fault current, switch status, and voltage and current changes.

[0003] However, these methods have the limitations of insufficient positioning accuracy and slow response speed when facing multi-point faults, transient faults and complex grid topologies. At the same time, due to the access of distributed power sources, traditional fault location methods find it difficult to accurately distinguish the difference between power disturbances caused by distributed power fluctuations and actual faults, which can easily lead to misjudgments and missed judgments. Moreover, they only rely on a single or a small number of data sources for analysis, which makes it difficult to fully reflect the complex relationship between various status data in the power system, reducing the accuracy of fault location and is not conducive to rapid recovery after a fault and stable operation of the power system. Summary of the invention

[0004] In view of the problems existing in the above-mentioned existing distribution line fault location method and system based on the Beidou system, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that due to the access of distributed power sources, it is difficult for traditional fault location methods to accurately distinguish the difference between power disturbances caused by distributed power fluctuations and actual faults, which can easily lead to misjudgment and missed judgments. Moreover, it only relies on a single or a small number of data sources for analysis, which makes it difficult to fully reflect the complex relationship between various types of status data in the power system, reducing the accuracy of fault location, and is not conducive to rapid recovery after faults and stable operation of the power system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a distribution line fault location method based on Beidou system, which comprises:

[0007] Collect key node data, define node status according to the status data of distributed power sources, fault indicators and smart meters, build a data matrix, and calculate the principal component matrix of the status data;

[0008] Construct a fault mode recognition model for the principal component matrix of different state data and perform model fusion;

[0009] Calculate the state probability of the node at the next moment based on historical fault data, and calculate the state probability of the current time step by combining the comprehensive fault mode vector with the Bayesian theorem;

[0010] Analyze the failure probability of regional nodes based on key nodes, securely encrypt and transmit data, and perform off-site backup and verification regularly.

[0011] As a preferred solution of the method for locating a distribution line fault based on the Beidou system of the present invention, wherein: the key node data is collected, and the node status is defined according to the status data of the distributed power source, the fault indicator and the smart meter, including:

[0012] Based on the distribution lines, the load center nodes, trunk line branch nodes, substation nodes and fault-prone area nodes are defined as key nodes;

[0013] Deploy integrated monitoring units at key nodes of distribution lines, including integrated Beidou communication modules, AMI smart meters and fault indicators;

[0014] Based on the AMI smart meter, the deployed nodes are polled and detected to collect the status data of the AMI smart meter, including voltage, current, active power, reactive power and meter status;

[0015] Based on the status data of the AMI smart meter, the status of the AMI smart meter is determined. If the meter is online and working normally, the status value is 1; if the meter is disconnected, the status value is 0;

[0016] At the same time, a judgment is made based on the state data of the fault indicator. If the fault indicator detects a fault current, the state value of the indicator is judged to be 1; if the signal of the fault indicator is normal, the state value of the indicator is judged to be 0;

[0017] Collect status data of distributed power sources, including real-time power generation, voltage, frequency deviation and current injection.

[0018] As a preferred solution of the method for locating a distribution line fault based on the Beidou system of the present invention, wherein: the data matrix is ​​constructed and the principal component matrix of the state data is calculated, including:

[0019] A data matrix is ​​constructed based on the state data of distributed power sources, fault indicators and smart meters respectively, where the rows of the matrix represent different observation samples and the columns represent different features;

[0020] Standardize the matrix number and calculate the covariance matrix of the matrix. Perform eigenvalue decomposition to obtain eigenvalue vectors and eigenvector matrices;

[0021] Sort by size of eigenvalue vectors, accumulate the sum of eigenvalue vectors until it accounts for 95% of the sum of total eigenvalue vectors, form a dimension reduction matrix, and multiply it with the standardized data matrix to perform data dimension reduction, expressed as:

[0022] ;

[0023] in represents the principal component matrix of the ith state data of dimensionality reduction, Represents the dimension reduction matrix of the i-th state data, A data matrix representing the data of the i-th state.

[0024] As a preferred solution of the method for locating distribution line faults based on the Beidou system described in the present invention, wherein: the fault mode recognition model is constructed for the principal component matrix of different state data, and the model is fused, including:

[0025] The fault mode recognition model is constructed using a multi-layer perceptron MLP and the principal component matrices from three perspectives, which are expressed as:

[0026] ;

[0027] in represents the model output of the principal component matrix of the i-th state data, represents the ReLU activation function, represents the weight of the i-th state data, Represents the bias term of the i-th state data;

[0028] Use the training set and the cross entropy loss function to calculate the error between the model prediction and the actual fault category, use the Adam optimizer and the gradient descent method to iteratively optimize the model parameters, and stop iterating when the model loss no longer decreases significantly during the continuous iteration process, output the model parameters and update the model;

[0029] Based on the three-perspective fault mode recognition model, the model fusion is performed according to the weighted coefficient, which is expressed as:

[0030] ;

[0031] in represents the comprehensive failure mode vector of the kth sample, represents the total number of viewing angles, Represents the weight coefficient of the principal component matrix of the i-th state data;

[0032] Use the training set and the cross entropy loss function to calculate the error between the model prediction and the actual fault category, calculate the gradient of the loss function for each weighted coefficient, and iteratively optimize the model parameters through the Adam optimizer and the gradient descent method. When the model loss no longer decreases significantly during continuous iterations, stop iterating and output the model parameters to update the model.

[0033] As a preferred solution of the method for locating distribution line faults based on the Beidou system of the present invention, wherein: the state probability of the node at the next moment is calculated based on historical fault data, and the state probability of the current time step is calculated by combining the Bayesian theorem with the comprehensive fault mode vector, including:

[0034] The key nodes of the distribution line are used as nodes of the dynamic Bayesian network DBN, and the node status is used to define whether the node is in a fault state at a certain time point;

[0035] Determine the dependency of DBN nodes according to the topological structure of the distribution line, draw a directed acyclic graph (DAG), represent the state transition relationship between nodes and determine the network structure of the dynamic Bayesian network;

[0036] Based on historical fault data, the number of faults that occur at node i and the number of faults that occur at adjacent node j in the next time step are counted and divided by the total number of faults at node i, the single state transition probability between the two nodes is calculated, and the state transition matrix is ​​constructed based on the state transition probabilities of different nodes;

[0037] At the same time, based on the historical fault data, the prior probability of the node is calculated by dividing the total number of faults in the historical data of the node by the total number of acquisitions of all time steps of the node;

[0038] The state probability of the node at the next moment is calculated based on the state transfer matrix and the state probability at the current moment, which is expressed as:

[0039] ;

[0040] Where n represents the total number of nodes, and They represent the state transition probabilities of node j at time step t+1 and node i at time step t, respectively. Represents the state of node i at time t The state of node j at time t+1 is transferred The transition probability of

[0041] By using Bayesian theorem, the state probability is combined with the comprehensive fault mode vector to update the state probability of each node at the current time step, which is expressed as:

[0042] ;

[0043] in represents the posterior failure probability based on the comprehensive failure mode vector and the node state probability at the current moment, Indicates the node status is The comprehensive failure mode vector of node i is observed The probability of represents the prior state probability of node i, Indicates the status of all nodes Next, observe The total probability of

[0044] The state probability of the node at the next moment of the historical data statistics node is recorded, and the accurate state probability of the node at the next moment is selected according to the actual fault information to calculate the sum of the average value and the standard deviation as the prediction threshold. If the state probability of the node at the next moment of the calculation node is greater than or equal to the prediction threshold, a node fault warning is issued.

[0045] As a preferred solution of the method for locating distribution line faults based on the Beidou system of the present invention, wherein: the failure probability of regional nodes analyzed according to key nodes includes:

[0046] Using the high-precision positioning function of the Beidou system, the real-time location coordinates of each monitoring node are obtained, and the Euclidean distance between the two nodes is calculated through the geographic coordinates of the two nodes. , and based on the key nodes, the maximum distance value between all pairs of nodes in the area is selected according to the geographical location and normalized;

[0047] Based on the posterior fault probability of the current time step and the Euclidean distance of the nodes, the fault location point probability of the regional nodes is calculated, which is expressed as:

[0048] ;

[0049] in represents the probability of fault location point of regional node i, represents the probability of node j based on the posterior state, represents the maximum Euclidean distance of regional nodes, L represents the total number of nodes in the key node area, represents the Euclidean distance between nodes i and j;

[0050] Based on the fault location point probability of the regional node, the sum of the average fault probability of all regional joint points and the double standard deviation is calculated based on historical data as the location threshold. If the calculated fault location point probability of the regional node is greater than the location threshold, it is judged as a regional fault.

[0051] As a preferred solution of the method for locating a distribution line fault based on the Beidou system of the present invention, the secure encryption and transmission of data and regular off-site backup and verification include:

[0052] For monitoring nodes such as distributed power sources, fault indicators and smart meters, the information is sent to the monitoring center in real time through the Beidou system terminal equipment using the TLS transport layer security protocol encrypted channel;

[0053] The detection data AES encryption algorithm is used to encrypt and store the location information of the monitoring node, and the role-based access control RBAC mechanism is used to limit data access rights;

[0054] Store the backup data to an offsite server and verify the data integrity.

[0055] Another object of the present invention is to provide a system for locating a distribution line fault based on the Beidou system, which comprises:

[0056] Data acquisition module, which collects data based on distributed power sources, fault indicators, and smart meters and performs preprocessing;

[0057] The fault mode recognition module calculates the covariance matrix of the state data matrix of different data sources, performs eigenvalue decomposition, builds a single-view fault mode recognition model, and performs model fusion;

[0058] The fault prediction module calculates the state probability prediction at the next moment based on historical fault data and calculates the state probability at the current time step based on Bayesian theorem;

[0059] Regional node failure analysis module calculates the failure probability of all nodes in each key node area and comprehensively analyzes the failure probability distribution of regional nodes;

[0060] The data security management module encrypts and stores the location information and status data of the monitoring nodes, regularly backs up the data to an off-site server, and verifies the data integrity.

[0061] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned distribution line fault location method based on the Beidou system when executing the computer program.

[0062] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned distribution line fault location method based on the Beidou system.

[0063] The beneficial effects of the present invention are as follows: by constructing a model based on the principal component matrix of three perspectives, namely, distributed power supply, fault indicator and smart meter, the multi-dimensional characteristics of the fault can be reflected more comprehensively; the comprehensive fault mode vector generated by perspective fusion can more accurately represent the overall fault state of the current power system, reduce the deviation and blind spots that may exist in a single perspective model, and thus improve the accuracy of fault location; by combining the comprehensive fault mode vector with the state probability of the current time step, the state characteristics of different perspectives are integrated, the operating state of the node is analyzed from multiple perspectives, and the accuracy of fault location is improved by introducing the dual constraints of the posterior state probability and the geographical distance, so that the probability of fault location points in the area can be accurately calculated, and the faulty nodes in the area can be effectively identified. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0065] Figure 1 The figure is a flow chart of the distribution line fault location method based on the Beidou system.

[0066] Figure 2 This is a structural diagram of the distribution line fault location system based on the Beidou system. DETAILED DESCRIPTION

[0067] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0068] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0069] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0070] Example 1, reference Figure 1, which is the first embodiment of the present invention, and provides a distribution line fault location method based on the Beidou system. The distribution line fault location method based on the Beidou system includes:

[0071] S1, collect key node data, define node status according to the status data of distributed power sources, fault indicators and smart meters, build a data matrix, and calculate the principal component matrix of the status data;

[0072] Preferably, key node data is collected, and node status is defined according to status data of distributed power sources, fault indicators and smart meters, including:

[0073] Based on the distribution lines, the load center nodes, trunk line branch nodes, substation nodes and fault-prone area nodes are defined as key nodes;

[0074] Deploy integrated monitoring units at key nodes of distribution lines, including integrated Beidou communication modules, AMI smart meters and fault indicators;

[0075] Based on the AMI smart meter, the deployed nodes are polled and detected to collect the status data of the AMI smart meter, including voltage, current, active power, reactive power and meter status;

[0076] Based on the status data of the AMI smart meter, the status of the AMI smart meter is determined. If the meter is online and working normally, the status value is 1; if the meter is disconnected, the status value is 0;

[0077] At the same time, a judgment is made based on the state data of the fault indicator. If the fault indicator detects a fault current, the state value of the indicator is judged to be 1. If the signal of the fault indicator is normal, the state value of the indicator is judged to be 0.

[0078] Collect status data of distributed power sources, including real-time power generation, voltage, frequency deviation and current injection.

[0079] By defining the load center nodes, trunk line branch nodes, substation nodes and fault-prone area nodes in the distribution line as key nodes and deploying comprehensive monitoring units at these key nodes, accurate monitoring of the key positions of the entire distribution network can be achieved. By collecting multi-dimensional status data (such as voltage, current, power, frequency, etc.) of AMI smart meters, fault indicators and distributed power sources, combined with the working status of meters and indicators, the operating status of the distribution lines can be comprehensively analyzed, and the real-time operating status of each key node can be obtained in a timely and accurate manner. The comprehensive monitoring unit integrates Beidou communication module, AMI smart meter and fault indicator, which can not only transmit high-precision location information, but also comprehensively obtain power parameters and fault status information. By jointly analyzing the monitoring data of distributed power sources and trunk lines, the overall health of the power system can be better evaluated.

[0080] Furthermore, the data matrix is ​​constructed and the principal component matrix of the state data is calculated, including:

[0081] A data matrix is ​​constructed based on the state data of distributed power sources, fault indicators and smart meters respectively, where the rows of the matrix represent different observation samples and the columns represent different features;

[0082] Normalize the matrix number and calculate the covariance matrix of the matrix, expressed as:

[0083] ;

[0084] in represents the covariance matrix of the i-th state data matrix, represents the transpose calculation of the i-th state data matrix, and n represents the number of observation samples of the i-th state data standardized matrix;

[0085] Covariance matrix Perform eigenvalue decomposition to obtain the eigenvalue vector and eigenvector matrix, expressed as:

[0086] ;

[0087] in represents the covariance matrix of the i-th state data, Represents the eigenvector matrix of the i-th state data, where each column vector is an eigenvector, Represents the eigenvalue diagonal matrix of the i-th state data, where the values ​​on the diagonal are the eigenvalue vectors. The eigenvalue vectors are based on the eig algorithm in linear algebra in the NumPy library, which decomposes the covariance matrix to obtain the eigenvalue vectors and eigenvector matrix;

[0088] Sort by size of eigenvalue vectors, accumulate the sum of eigenvalue vectors until it accounts for 95% of the sum of total eigenvalue vectors, form a dimension reduction matrix, and multiply it with the standardized data matrix to perform data dimension reduction, expressed as:

[0089] ;

[0090] in represents the principal component matrix of the ith state data of dimensionality reduction, Represents the dimension reduction matrix of the i-th state data, A data matrix representing the data of the i-th state.

[0091] By standardizing the data matrices of different states, the dimensions of all features (such as voltage, current, power, etc.) are consistent, thereby avoiding analysis deviations caused by differences in the magnitude of different feature values. Data matrices are constructed based on the state data of distributed power sources, fault indicators, and smart meters, respectively, which can effectively integrate the feature information of multiple data sources, so that the fault detection system can fully grasp the state information of each node in the power grid, and can form information complementarity between different data sources (such as power fluctuations of distributed power sources, voltage and current anomalies of smart meters, and fault currents of fault indicators), so as to more accurately identify fault features. By sorting the size of eigenvalue vectors and selecting the main features (accounting for 95%), most of the feature information related to the fault can be retained, and the secondary information not related to the fault can be ignored, thereby improving the ability to identify abnormal states and faults. By calculating the covariance matrix, the correlation structure between different state features can be found. With the use of eigenvector decomposition and dimensionality reduction technology, the data dimension can be effectively reduced, so that the system can still perform data analysis and fault detection efficiently and accurately when facing large-scale, multi-dimensional data.

[0092] S2, constructing a fault mode recognition model for the principal component matrix of different state data and performing model fusion;

[0093] Preferably, a fault mode recognition model is constructed for the principal component matrix of different state data, and model fusion is performed, including:

[0094] The fault mode recognition model is constructed using a multi-layer perceptron MLP and the principal component matrices from three perspectives, which are expressed as:

[0095] ;

[0096] in represents the model output of the principal component matrix of the i-th state data, represents the ReLU activation function, represents the weight of the i-th state data, Represents the bias term of the i-th state data;

[0097] Use the training set and the cross entropy loss function to calculate the error between the model prediction and the actual fault category, use the Adam optimizer and the gradient descent method to iteratively optimize the model parameters, and stop iterating when the model loss no longer decreases significantly during the continuous iteration process, output the model parameters and update the model;

[0098] Based on the three-perspective fault mode recognition model, the model fusion is performed according to the weighted coefficient, which is expressed as:

[0099] ;

[0100] in represents the comprehensive failure mode vector of the kth sample, represents the total number of viewing angles, Represents the weight coefficient of the principal component matrix of the i-th state data;

[0101] Use the training set and the cross entropy loss function to calculate the error between the model prediction and the actual fault category, calculate the gradient of the loss function for each weighted coefficient, and iteratively optimize the model parameters through the Adam optimizer and the gradient descent method. When the model loss no longer decreases significantly during continuous iterations, stop iterating and output the model parameters to update the model.

[0102] The model is constructed by principal component matrices based on three perspectives: distributed power, fault indicator and smart meter. The principal component matrix of each perspective extracts the core information of different status data. At the same time, multi-perspective fusion can more comprehensively reflect the multi-dimensional characteristics of faults and improve the ability to identify complex fault modes. The introduced distributed power status data can improve the distribution network's judgment on the impact of the increasing number of distributed power sources on the operation of the power grid. By performing principal component analysis on distributed power data with different access rates, the operating characteristics of the power grid under different access conditions can be extracted, so that efficient fault identification can be achieved under different distributed power access rates. In scenarios with high distributed power access rates, the complex fault modes caused by power supply fluctuations can be better analyzed. When the access rate is low, the model can pay more attention to the operating status of the load end and the backbone network, improving the system's adaptability to different scenarios.

[0103] The MLP model can learn complex nonlinear relationships through nonlinear transformations of multiple hidden layers (such as ReLU activation function), so as to better model the complex relationship between various state characteristics and fault modes in the power system. Based on the weighted fusion of models from different perspectives, the weights can be automatically adjusted according to the contribution of different state data in fault mode recognition. The comprehensive fault mode vector generated by perspective fusion can more accurately represent the overall fault state of the current power system, reduce the possible deviations and blind spots of a single perspective model, and thus improve the accuracy of fault location. The fault mode recognition model combining the multi-layer perceptron MLP with the principal component matrix can effectively improve the accuracy and efficiency of power system fault detection. At the same time, the model fusion based on the perspective weighting coefficient further improves the robustness and flexibility of the system.

[0104] S3, calculates the state probability of the node at the next moment based on historical fault data, and calculates the state probability of the current time step by combining the comprehensive fault mode vector with the Bayesian theorem;

[0105] Preferably, the state probability of the node at the next moment is calculated based on the historical fault data, and the state probability of the current time step is calculated by combining the Bayesian theorem with the comprehensive fault mode vector, including:

[0106] The key nodes of the distribution line are used as nodes of the dynamic Bayesian network DBN, and the node status is used to define whether the node is in a fault state at a certain time point;

[0107] Determine the dependency of DBN nodes according to the topological structure of the distribution line (for example, if two nodes share a transmission line, they may have a direct state dependency), draw a directed acyclic graph DAG, represent the state transition relationship between nodes and determine the network structure of the dynamic Bayesian network. Each directed edge in the directed acyclic graph DAG It means that the state of node i at time t will affect the state of node j at time t+1;

[0108] Based on historical fault data, the number of faults that occur at node i and the number of faults that occur at adjacent node j in the next time step are counted and divided by the total number of faults at node i, the single state transition probability between the two nodes is calculated, and the state transition matrix is ​​constructed based on the state transition probabilities of different nodes;

[0109] At the same time, based on the historical fault data, the prior probability of the node is calculated by dividing the total number of faults in the historical data of the node by the total number of acquisitions of all time steps of the node;

[0110] The state probability of the node at the next moment is calculated based on the state transfer matrix and the state probability at the current moment, which is expressed as:

[0111] ;

[0112] Where n represents the total number of nodes, and They represent the state transition probabilities of node j at time step t+1 and node i at time step t, respectively. Represents the state of node i at time t The state of node j at time t+1 is transferred The transition probability of

[0113] By using Bayesian theorem, the state probability is combined with the comprehensive fault mode vector to update the state probability of each node at the current time step, which is expressed as:

[0114] ;

[0115] in represents the posterior failure probability based on the comprehensive failure mode vector and the node state probability at the current moment, Indicates the node status is The comprehensive failure mode vector of node i is observed The probability of represents the prior state probability of node i, Indicates the status of all nodes Next, observe The total probability of

[0116] The state probability of the node at the next moment of the historical data statistics node is recorded, and the accurate state probability of the node at the next moment is selected according to the actual fault information to calculate the sum of the average value and the standard deviation as the prediction threshold. If the state probability of the node at the next moment of the calculation node is greater than or equal to the prediction threshold, a node fault warning is issued.

[0117] By using the key nodes of the distribution lines as nodes of the dynamic Bayesian network, the state changes of these nodes that are crucial to the operation of the power grid can be accurately monitored and predicted. By clarifying the state transfer path between nodes through the directed acyclic graph (DAG), the fault propagation characteristics between different nodes in the power grid can be effectively captured. The state transfer matrix calculated based on historical data can reflect the fault transmission law between nodes. By combining the current observation data (comprehensive fault mode vector) with the historical state probability and using the Bayesian theorem to dynamically update the node's failure probability, the state probability can be corrected according to real-time data, making fault detection and prediction more accurate and real-time, and effectively reducing false alarms and missed alarms.

[0118] By utilizing the status data of AMI smart meters, fault indicators and distributed power sources and integrating them into the calculation of DBN, the system's ability to identify different types of faults can be effectively improved. The dynamic Bayesian network can quickly update the node status probability after the real-time observation data (such as the comprehensive fault mode vector) arrives, thereby realizing dynamic early warning of the fault status, ensuring that the system can perceive the changes in node status in a timely manner and quickly issue early warning signals, thereby improving the system's real-time response capability;

[0119] By combining the comprehensive fault mode vector with the state probability of the current time step, the state characteristics of different perspectives are integrated and combined, and the operation status of the node is analyzed from multiple perspectives. Compared with traditional fault detection that often relies on only a single data source, the processing efficiency of massive complex data obtained based on the Beidou system and the ability to identify complex fault modes are improved. The state probability of the node can be dynamically updated when the observation data arrives, and it can be dynamically adjusted without relying on complete historical data, which improves the flexibility and accuracy of fault diagnosis. By combining the comprehensive fault mode vector with the state probability of the current time step, it can make a fine distinction between multiple states, providing a basis for the location of regional faults, distinguishing short-term fluctuations caused by power grid disturbances from long-term anomalies caused by equipment failures, and avoiding false alarms or missed alarms;

[0120] By combining the status update of real-time data with the status prediction of the next moment, the future status can be inferred based on real-time detection, so that abnormal conditions can be warned and handled in a timely manner, and preventive measures can be taken before a failure occurs, thereby reducing the probability and impact of a failure.

[0121] S4, analyzes the failure probability of regional nodes based on key nodes, securely encrypts and transmits data, and regularly performs off-site backup and verification;

[0122] Preferably, the failure probability of regional nodes is analyzed based on key nodes, including:

[0123] Using the high-precision positioning function of the Beidou system, the real-time location coordinates of each monitoring node are obtained, and the Euclidean distance between the two nodes is calculated through the geographic coordinates of the two nodes. , and based on the key nodes, the maximum distance value between all pairs of nodes in the area is selected according to the geographical location and normalized;

[0124] Based on the posterior fault probability of the current time step and the Euclidean distance of the nodes, the fault location point probability of the regional nodes is calculated, which is expressed as:

[0125] ;

[0126] in represents the probability of fault location point of regional node i, represents the probability of node j based on the posterior state, represents the maximum Euclidean distance of regional nodes, L represents the total number of nodes in the key node area, represents the Euclidean distance between nodes i and j;

[0127] Based on the fault location point probability of the regional node, the sum of the average fault probability of all regional joint points and the double standard deviation is calculated based on historical data as the location threshold. If the calculated fault location point probability of the regional node is greater than the location threshold, it is judged as a regional fault.

[0128] The high-precision location coordinates of the monitoring nodes are obtained through the Beidou system, and the Euclidean distance between the nodes is calculated. The high-precision positioning can accurately determine the geographical location of each node, avoiding the fault location deviation caused by the position error, thereby improving the accuracy of fault location. Through the accurate calculation of the probability of fault location points in the area, the faulty nodes in the area can be effectively identified to avoid misjudging the fault as other normal regional nodes. In addition, with the judgment of the positioning threshold, the fault warning signal can be issued in time to avoid the expansion and spread of the fault. By accurately calculating the probability of fault location points in the area, the specific location of the fault can be quickly located, reducing the time and labor cost of troubleshooting.

[0129] The accuracy of fault location is improved by introducing the dual constraints of posterior state probability and geographical distance. Traditional methods only consider the weighted or static prior probability of geographical distance, ignoring the precise description of node status by real-time data. Combining real-time posterior state probability with geographical distance can more accurately judge the actual status of the faulty node. Through Bayesian updating, the posterior state probability can be dynamically adjusted according to new observation data at any time, so that fault location no longer depends on fixed historical patterns, which enhances the flexibility of fault location and enables accurate judgments to be made in different fault scenarios.

[0130] Furthermore, data is securely encrypted and transmitted, and regularly backed up and verified off-site, including,

[0131] For monitoring nodes such as distributed power sources, fault indicators and smart meters, the information is sent to the monitoring center in real time through the Beidou system terminal equipment using the TLS transport layer security protocol encrypted channel;

[0132] The detection data AES encryption algorithm is used to encrypt and store the location information of the monitoring node, and the role-based access control RBAC mechanism is used to limit data access rights;

[0133] Store the backup data to an offsite server and verify the data integrity.

[0134] By using the TLS encryption protocol to encrypt and transmit data from monitoring nodes such as distributed power sources, fault indicators and smart meters, it is possible to effectively prevent eavesdropping, tampering and forgery of data during transmission. The secure storage and transmission of data improves the storage security of data and the defense capabilities of the system. The use of role-based access control mechanisms prevents unauthorized users from arbitrarily accessing or modifying sensitive data, thus avoiding data abuse and illegal operations. By backing up data off-site, the correctness and integrity of the backup data can be ensured.

[0135] Example 2, reference Figure 2 , which is the second embodiment of the present invention, and which is different from the previous embodiment, provides a system for locating a distribution line fault based on the Beidou system, including:

[0136] Data acquisition module, which collects data based on distributed power supply, fault indicator and smart meter and performs pre-processing;

[0137] The fault mode recognition module calculates the covariance matrix of the state data matrix of different data sources, performs eigenvalue decomposition, builds a single-view fault mode recognition model, and performs model fusion;

[0138] The fault prediction module calculates the state probability prediction at the next moment based on historical fault data and calculates the state probability at the current time step based on Bayesian theorem;

[0139] Regional node failure analysis module calculates the failure probability of all nodes in each key node area and comprehensively analyzes the failure probability distribution of regional nodes;

[0140] The data security management module encrypts and stores the location information and status data of the monitoring nodes, regularly backs up the data to an off-site server, and verifies the data integrity.

[0141] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0142] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0143] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0144] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distribution line fault location method based on Beidou system, characterized in that: include, Collect key node data, define node status according to the status data of distributed power sources, fault indicators and smart meters, build a data matrix, and calculate the principal component matrix of the status data; Construct a fault mode recognition model for the principal component matrix of different state data and perform model fusion; Calculate the state probability of the node at the next moment based on historical fault data, and calculate the state probability of the current time step by combining the comprehensive fault mode vector with the Bayesian theorem; Analyze the failure probability of regional nodes based on key nodes, securely encrypt and transmit data, and perform off-site backup and verification regularly.

2. The method for locating a distribution line fault based on the Beidou system according to claim 1, characterized in that: The collecting of key node data and defining the node status according to the status data of the distributed power source, the fault indicator and the smart meter include: Based on the distribution lines, the load center nodes, trunk line branch nodes, substation nodes and fault-prone area nodes are defined as key nodes; Deploy integrated monitoring units at key nodes of distribution lines, including integrated Beidou communication modules, AMI smart meters and fault indicators; Based on the AMI smart meter, the deployed nodes are polled and detected to collect the status data of the AMI smart meter, including voltage, current, active power, reactive power and meter status; Based on the status data of the AMI smart meter, the status of the AMI smart meter is determined. If the meter is online and working normally, the status value is 1; if the meter is disconnected, the status value is 0; At the same time, a judgment is made based on the state data of the fault indicator. If the fault indicator detects a fault current, the state value of the indicator is judged to be 1; if the signal of the fault indicator is normal, the state value of the indicator is judged to be 0; Collect status data of distributed power sources, including real-time power generation, voltage, frequency deviation and current injection.

3. The method for locating a distribution line fault based on the Beidou system as claimed in claim 2, characterized in that: The data matrix is ​​constructed, and the principal component matrix of the state data is calculated. include, A data matrix is ​​constructed based on the state data of distributed power sources, fault indicators and smart meters respectively, where the rows of the matrix represent different observation samples and the columns represent different features; Standardize the matrix number and calculate the covariance matrix of the matrix. Perform eigenvalue decomposition to obtain eigenvalue vectors and eigenvector matrices; Sort by size of eigenvalue vectors, accumulate the sum of eigenvalue vectors until it accounts for 95% of the sum of total eigenvalue vectors, form a dimension reduction matrix, and multiply it with the standardized data matrix to perform data dimension reduction, expressed as: ； in represents the principal component matrix of the ith state data of the reduced dimension, Represents the dimension reduction matrix of the i-th state data, A data matrix representing the data of the i-th state.

4. The method for locating a distribution line fault based on the Beidou system as claimed in claim 3, characterized in that: The method of constructing a fault mode recognition model for the principal component matrix of different state data and performing model fusion includes: The fault mode recognition model is constructed using a multi-layer perceptron MLP and the principal component matrices from three perspectives, which are expressed as: ； in represents the model output of the principal component matrix of the i-th state data, represents the ReLU activation function, represents the weight of the i-th state data, Represents the bias term of the i-th state data; Use the training set and the cross entropy loss function to calculate the error between the model prediction and the actual fault category, use the Adam optimizer and the gradient descent method to iteratively optimize the model parameters, and stop iterating when the model loss no longer decreases significantly during the continuous iteration process, output the model parameters and update the model; Based on the three-perspective fault mode recognition model, the model fusion is performed according to the weighted coefficient, which is expressed as: ； in represents the comprehensive failure mode vector of the kth sample, represents the total number of viewing angles, Represents the weight coefficient of the principal component matrix of the i-th state data; Use the training set and the cross entropy loss function to calculate the error between the model prediction and the actual fault category, calculate the gradient of the loss function for each weighted coefficient, and iteratively optimize the model parameters through the Adam optimizer and the gradient descent method. When the model loss no longer decreases significantly during continuous iterations, stop iterating and output the model parameters to update the model.

5. The method for locating a distribution line fault based on the Beidou system as claimed in claim 4, characterized in that: The state probability of the node at the next moment is calculated based on the historical fault data, and the state probability of the current time step is calculated by combining the Bayesian theorem with the comprehensive fault mode vector, including: The key nodes of the distribution line are used as nodes of the dynamic Bayesian network DBN, and the node status is used to define whether the node is in a fault state at a certain time point; Determine the dependency of DBN nodes according to the topological structure of the distribution line, draw a directed acyclic graph (DAG), represent the state transition relationship between nodes and determine the network structure of the dynamic Bayesian network; Based on historical fault data, the number of faults that occur at node i and the number of faults that occur at adjacent node j in the next time step are counted and divided by the total number of faults at node i, the single state transition probability between the two nodes is calculated, and the state transition matrix is ​​constructed based on the state transition probabilities of different nodes; At the same time, based on the historical fault data, the prior probability of the node is calculated by dividing the total number of faults in the historical data of the node by the total number of acquisitions of all time steps of the node; The state probability of the node at the next moment is calculated based on the state transfer matrix and the state probability at the current moment, which is expressed as: ； Where n represents the total number of nodes, and They represent the state transition probabilities of node j at time step t+1 and node i at time step t, respectively. Represents the state of node i at time t The state of node j at time t+1 is transferred The transition probability of By using Bayesian theorem, the state probability is combined with the comprehensive fault mode vector to update the state probability of each node at the current time step, which is expressed as: ； in represents the posterior failure probability based on the comprehensive failure mode vector and the node state probability at the current moment, Indicates the node status is The comprehensive failure mode vector of node i is observed The probability of represents the prior state probability of node i, Indicates the status of all nodes Next, observe The total probability of The state probability of the node at the next moment of the historical data statistics node is recorded, and the accurate state probability of the node at the next moment is selected according to the actual fault information to calculate the sum of the average value and the standard deviation as the prediction threshold. If the state probability of the node at the next moment of the calculation node is greater than or equal to the prediction threshold, a node fault warning is issued.

6. The method for locating a distribution line fault based on the Beidou system as claimed in claim 5, characterized in that: Analyzing the failure probability of regional nodes according to key nodes includes: Using the high-precision positioning function of the Beidou system, the real-time location coordinates of each monitoring node are obtained, and the Euclidean distance between the two nodes is calculated through the geographic coordinates of the two nodes. , and based on the key nodes, the maximum distance value between all pairs of nodes in the area is selected according to the geographical location and normalized; Based on the posterior fault probability of the current time step and the Euclidean distance of the nodes, the fault location point probability of the regional nodes is calculated, which is expressed as: ； in represents the probability of fault location point of regional node i, represents the probability of node j based on the posterior state, represents the maximum Euclidean distance of regional nodes, L represents the total number of nodes in the key node area, represents the Euclidean distance between nodes i and j; Based on the fault location point probability of the regional node, the sum of the average fault probability of all regional joint points and the double standard deviation is calculated based on historical data as the location threshold. If the calculated fault location point probability of the regional node is greater than the location threshold, it is judged as a regional fault.

7. The method for locating a distribution line fault based on the Beidou system as claimed in claim 6, characterized in that: The data is securely encrypted and transmitted, and regularly backed up and verified off-site, including: For monitoring nodes such as distributed power sources, fault indicators and smart meters, the information is sent to the monitoring center in real time through the Beidou system terminal equipment using the TLS transport layer security protocol encrypted channel; The detection data AES encryption algorithm is used to encrypt and store the location information of the monitoring node, and the role-based access control RBAC mechanism is used to limit data access rights; Store the backup data to an offsite server and verify the data integrity.

8. A system based on the Beidou system-based distribution line fault location method according to any one of claims 1 to 7, characterized in that: include, Data acquisition module, which collects data based on distributed power supply, fault indicator and smart meter and performs pre-processing; The fault mode recognition module calculates the covariance matrix of the state data matrix of different data sources, performs eigenvalue decomposition, builds a single-view fault mode recognition model, and performs model fusion; The fault prediction module calculates the state probability prediction at the next moment based on historical fault data and calculates the state probability at the current time step based on Bayesian theorem; Regional node failure analysis module calculates the failure probability of all nodes in each key node area and comprehensively analyzes the failure probability distribution of regional nodes; The data security management module encrypts and stores the location information and status data of the monitoring nodes, regularly backs up the data to an off-site server, and verifies the data integrity.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the distribution line fault location method based on the Beidou system described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for locating a distribution line fault based on the Beidou system according to any one of claims 1 to 7 are implemented.

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