Low-voltage distribution network fault mode analysis method based on dynamic topology

Abstract

The invention belongs to the technical field of low-voltage distribution network fault diagnosis, and particularly relates to a low-voltage distribution network fault mode analysis method based on dynamic topology. The method comprises the following steps: acquiring power line information and equipment information, and establishing a power distribution network data model and a low-voltage distribution network electronic map; obtaining node through-flow information, and performing matching according to the power distribution network data model to obtain monitoring fault information; presetting a fault diagnosis database and defining a fault type according to the monitored fault information, and establishing a fault tree model based on the fault diagnosis database to calculate a fault source probability of a fault event; feedback fault information and fault abnormal information are obtained, and similarity matching is carried out to obtain fault root cause data; and according to the fault source probability and the fault root cause data, obtaining equipment and power lines related to monitoring fault information and feedback fault information, and determining a fault area. According to the method, the fault positioning of the low-voltage distribution network is realized, and the operation quality of the distribution network is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of low-voltage distribution network fault diagnosis, and in particular relates to a low-voltage distribution network fault mode analysis method based on dynamic topology. Background Art

[0002] As the demand for electricity grows, the requirements for power supply quality and reliability are also getting higher and higher. Due to the complexity of the current distribution network, the geographical location of electricity users is dispersed and the number is large, which makes it difficult for electricity users to solve and respond in time after a fault occurs. In the traditional fault area location method, due to the complex structure of the distribution network and the low degree of automation of the electrical equipment in the distribution network, the only way to determine the fault area is to use the switch trial method, which causes many switches to operate repeatedly, resulting in a situation where the load limit of the power line is exceeded. Summary of the invention

[0003] In order to solve the problem existing in the prior art: due to the low degree of automation of electrical equipment in the distribution network, switches are repeatedly operated, resulting in the problem of exceeding the load limit of the power line; the present invention provides a low-voltage distribution network fault mode analysis method based on dynamic topology. The purpose of the present invention can be achieved through the following technical solutions:

[0004] Obtain power line information and equipment information, establish distribution network data model and low-voltage distribution network electronic map;

[0005] Obtain node flow information and match it with the distribution network data model to obtain monitoring fault information;

[0006] Presetting a fault diagnosis database and defining fault types according to the monitored fault information, and establishing a fault tree model based on the fault diagnosis database to calculate the fault source probability of the fault event;

[0007] Obtain feedback fault information and fault abnormality information and perform similarity matching to obtain fault root cause data;

[0008] Based on the fault source probability and fault root cause data, the monitoring fault information and the equipment and power lines involved in the feedback fault information are obtained and the fault area is determined.

[0009] Specifically, the distribution network model includes coding, category, geometric attributes, associated attributes, and management attributes; wherein the coding is the unique identification code of the equipment, the category includes power supply, line, switch, and transformer, the geometric attribute is the geographic spatial information of the equipment, the associated attribute is the topological relationship between the equipment, and the management attribute is a numerical parameter that characterizes whether the equipment is in an operating state or a closed state.

[0010] Specifically, the method for determining the fault area is: obtaining a fault information sequence according to the power line, and synchronously saving the fault information sequence in the attribute database of the distribution network data model; obtaining the fault loop information and connection relationship according to the distribution network topology structure corresponding to the attribute data; marking all the switching devices in the loop according to the obtained loop information, obtaining the area where the faulty device is located according to the switch information, and highlighting the topological elements corresponding to the faulty device on the low-voltage distribution network electronic map.

[0011] Specifically, the method for generating the fault information sequence is as follows: installing a monitoring device at a line node to measure current load data; if the current load is greater than a preset threshold, using 0 to represent the monitoring fault information at the node; if the current load is less than or equal to the preset threshold, using 1 to represent the monitoring fault information at the node; integrating the fault information of all nodes into a fault information sequence. The fault information includes node fault and node normal, 0 represents node fault, and 1 represents node normal.

[0012] Specifically, the method for establishing the distribution network data model is: first transforming the substation, transformer, switch, circuit breaker, and pole tower into point elements, transforming the overhead line and the buried line into line elements, and transforming the regional information into surface elements, and then vectorizing the spatial data of the point elements, the line elements, and the surface elements into a distribution network topology structure; associating the equipment through an attribute database and establishing an attribute layer, and establishing a distribution network data model according to the distribution network topology structure and the attribute layer, wherein the attribute database is a database storing a plurality of fault information sequences, and the fault information sequence is composed of monitoring fault information of a plurality of line nodes.

[0013] Specifically, a fault tree model is established based on the fault diagnosis database to calculate the fault source probability of the fault event, including:

[0014] Define fault types, the fault types include phase-to-phase short circuit fault, low current ground fault, and high resistance fault, and set the phase-to-phase short circuit fault type as a top-level event, the low current ground fault type as a middle-level event, and the high resistance fault as a bottom-level event;

[0015] Establishing a fault tree node, wherein the fault tree node structure includes a node number, a node name, a node type, a number of nodes, a parent node, a child node, a weight, and a description value, and is saved as a linked list and stored in the fault diagnosis database;

[0016] The linked list is traversed to calculate the minimum cut set of the fault tree, and the fault source probability of the corresponding top-level event fault type is calculated according to the minimum cut set. The calculation formula is:

[0017]

[0018] Where Q is the failure probability, T is the top event failure type, r is the number of minimum cut sets, i and j are the minimum cut set counts, p is the probability density function, k i is the number of middle-level events in the minimum cut set, k j is the number of underlying events of the minimum cut set.

[0019] Specifically, the similarity matching calculation method is: extracting the semantic features of the feedback fault information and the fault abnormality information to obtain a semantic feature vector and an abnormality vector, mapping the semantic feature vector and the abnormality vector to a high-dimensional space, and calculating the similarity matrix between the data points in the high-dimensional space. The calculation formula is:

[0020]

[0021] Among them, p j|i is the probability distribution of the abnormal vector ratio under the semantic representation vector, σ i is the standard deviation of the Gaussian distribution, x i is the high-dimensional representation of the semantic feature vector, x j is the high-dimensional representation of the anomaly vector, k is the traversal count of the high-dimensional space data points, n is the total number of high-dimensional data points of the semantic feature vector, and m is the total number of high-dimensional data points of the anomaly vector.

[0022] The beneficial effects of the present invention are:

[0023] (1) By using a function model to reflect the topological structure information of the distribution network, the network topology is divided into several areas for fault analysis based on real-time tracking of the dynamic topological network, and the accurate location information of the distribution network fault is calculated, thereby improving the fault location accuracy;

[0024] (2) By collecting feedback fault information and fault abnormality information, the relationship and dependency between abnormal information are structured, and the fault categories are divided and labeled using clustering algorithms. The fault distribution and occurrence probability are determined by establishing a fault tree model, and the fault location information is graphically displayed. The faulty equipment is located and detected more intuitively, which improves the reliability and management level of the distribution system operation and simplifies the cost and workload of fault location and maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0026] Figure 1 The present invention is a flowchart of a method for analyzing a low-voltage distribution network failure mode based on dynamic topology. DETAILED DESCRIPTION

[0027] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0028] See also Figure 1 ,A low voltage distribution network fault mode analysis method based on dynamic topology,

[0029] S1: Obtain power line information and equipment information, establish distribution network data model and low-voltage distribution network electronic map;

[0030] In this embodiment, the distribution network model is established based on the power line information and the equipment information;

[0031] The method for obtaining the electronic map of the low-voltage distribution network is as follows: topologically transforming the distribution network model to obtain a topological data set model, obtaining attribute data and spatial data of line equipment and substations, integrating the attribute data and the spatial data into the topological data set model to obtain a distribution network data model, and establishing a low-voltage distribution network electronic map based on the distribution network data model;

[0032] S2: Obtain node flow information and match it with the distribution network data model to obtain monitoring fault information;

[0033] The monitoring fault information acquisition method of this embodiment is: parsing the node flow information to obtain node space data and current load data, generating a fault information sequence according to the current load data, and matching the node space data with the distribution network data model according to the fault information sequence to obtain monitoring fault information;

[0034] S3: Preset a fault diagnosis database and define the fault type according to the monitored fault information, and establish a fault tree model based on the fault diagnosis database to calculate the fault source probability of the fault event;

[0035] Specifically, the method for obtaining the fault source probability of a fault event is as follows: establishing a fault tree model based on the fault diagnosis database, obtaining the weight of the fault information in the fault tree model and generating a fault logic view, and calculating the fault source probability of the fault event according to the fault logic view;

[0036] S4: Obtain feedback fault information and fault abnormality information and perform similarity matching to obtain fault root cause data;

[0037] In this embodiment, the method for acquiring the root cause data of the fault includes: extracting the semantic features of the feedback fault information and the fault abnormality information to obtain a semantic feature vector and an abnormality vector, performing similarity matching on the semantic feature vector and the abnormality vector to obtain a similarity matrix, performing data dimension reduction on the similarity matrix to obtain a comparison value, and judging whether the comparison value is greater than a preset threshold value. If so, performing abnormal matching with the fault abnormality template vector of the fault diagnosis database to obtain the root cause data of the fault, and if not, performing entity recognition matching to extract component entities and semantic entities to form a semantic feature representation, calculating the similarity between entities of the semantic feature representation to obtain an abnormal log sequence, and selecting the root cause data of the fault corresponding to the node with the highest similarity in the abnormal log sequence as the matching result;

[0038] S5: Obtain monitoring fault information and feedback the equipment and power lines involved in the fault information and determine the fault area based on the fault source probability and fault root cause data;

[0039] Furthermore, the equipment and power lines involved in the monitored fault information and the feedback fault information are obtained based on the fault source probability and the fault root cause data, the fault area is determined by a method for determining the fault area based on the equipment and the power lines, and is highlighted on the low-voltage distribution network electronic map by updating the distribution network data model.

[0040] By dividing the network topology into several areas for fault analysis, the accurate location information of the distribution network fault is calculated to improve the fault location accuracy and overcome the problem of repeated switch action and exceeding the load limit of the power line due to the low degree of automation of the distribution network electrical equipment.

[0041] In another embodiment, the distribution network model includes codes, categories, geometric attributes, associated attributes, and management attributes; wherein the code is a unique identification code of the device, the categories include power supplies, lines, switches, and transformers, the geometric attributes are the geographic spatial information of the device, the associated attributes are the topological relationships between devices, and the management attributes are numerical parameters that characterize whether the device is in an operating state or a closed state.

[0042] Specifically, the method for establishing the distribution network data model is as follows: transforming substations, transformers, switches, circuit breakers, and pole towers into point elements, transforming overhead lines and underground lines into line elements, and transforming regional information into surface elements, and vectorizing the spatial data of the point elements, the line elements, and the surface elements into a distribution network topology structure; associating the devices through an established attribute database and establishing an attribute layer, and establishing a distribution network data model according to the distribution network topology structure and the attribute layer, wherein the attribute database is a database storing a plurality of fault information sequences, and the fault information sequence is composed of monitoring fault information of a plurality of line nodes.

[0043] In this embodiment, data analysis of the low-voltage distribution network is performed based on GIS to analyze the connection relationship between devices and between devices and lines. By associating the spatial data and attribute data of the power equipment in the distribution network GIS model, the device model connectivity analysis is performed, and the data in the power grid geometry is intuitively displayed and analyzed with the geographic electronic map as the background, realizing two-way query analysis from graph to attribute and from attribute to graph.

[0044] Specifically, the method for generating the fault information sequence is as follows: installing a monitoring device at a line node to measure current load data; if the current load is greater than a preset threshold, using 0 to represent the monitoring fault information at the node; if the current load is less than or equal to the preset threshold, using 1 to represent the monitoring fault information at the node; integrating the fault information of all nodes into a fault information sequence; wherein the fault information includes node fault and node normal, 0 represents node fault, and 1 represents node normal.

[0045] Specifically, a fault tree model is established based on the fault diagnosis database to calculate the fault source probability of the fault event, including:

[0046] Define fault types, the fault types include phase-to-phase short circuit fault, low current ground fault, and high resistance fault, and set the phase-to-phase short circuit fault type as a top-level event, the low current ground fault type as a middle-level event, and the high resistance fault as a bottom-level event;

[0047] Establishing a fault tree node, wherein the fault tree node structure includes a node number, a node name, a node type, a number of nodes, a parent node, a child node, a weight, and a description value, and is saved as a linked list and stored in the fault diagnosis database;

[0048] The linked list is traversed to calculate the minimum cut set of the fault tree, and the fault source probability of the corresponding top-level event fault type is calculated according to the minimum cut set. The calculation formula is:

[0049]

[0050] Where Q is the failure probability, T is the top event failure type, r is the number of minimum cut sets, i and j are the minimum cut set counts, p is the probability density function, k i is the number of middle-level events in the minimum cut set, k j is the number of underlying events of the minimum cut set.

[0051] In another embodiment, deductive modeling is adopted, and the established model is stored in a linked list structure. The cut sets in the fault tree are analyzed by the descending method. The principle is to increase the capacity of the cut set when an AND gate is encountered during the analysis. Starting from the top event, the upper event is replaced by the lower event from top to bottom in layers. When an AND gate is encountered, the events are output side by side. When an OR gate is encountered, the input event is output vertically, until all the bottom events are replaced, all the cut sets are obtained, and then the repeated cut sets are removed to finally obtain the minimum cut set.

[0052] Specifically, the similarity matching calculation method is: extracting the semantic features of the feedback fault information and the fault abnormality information to obtain a semantic feature vector and an abnormality vector, mapping the semantic feature vector and the abnormality vector to a high-dimensional space, and calculating the similarity matrix between the data points in the high-dimensional space. The calculation formula is:

[0053]

[0054] Among them, p j|i is the probability distribution of the abnormal vector ratio under the semantic representation vector, σ i is the standard deviation of the Gaussian distribution, x i is the high-dimensional representation of the semantic feature vector, x j is the high-dimensional representation of the anomaly vector, k is the traversal count of the high-dimensional space data points, n is the total number of high-dimensional data points of the semantic feature vector, and m is the total number of high-dimensional data points of the anomaly vector.

[0055] In another preferred embodiment, a coarse-grained text similarity matching module is used to perform root cause diagnosis for fault information template sequences with large text differences. When the text similarity matching module cannot meet the root cause diagnosis accuracy required by the model, the TF-IDF algorithm is used to extract keywords in the template text, and the importance of each word is quantified by considering the frequency of each word in the log template and the frequency of occurrence in the entire log template sequence. Combined with the component name contained in the current information, the system log knowledge graph (SLKG) is entity matched to perform fine-grained system fault root cause diagnosis. For the threshold setting of the text similarity matching module, the cross-validation method is used to determine the threshold of the text similarity matching module and the fault root cause diagnosis accuracy is used as the evaluation index.

[0056] Specifically, the method for determining the fault area is: obtaining a fault information sequence according to the power line, and synchronously saving the fault information sequence in the attribute database of the distribution network data model; obtaining the fault loop information and connection relationship according to the distribution network topology structure corresponding to the attribute data; marking all the switching devices in the loop according to the obtained loop information, obtaining the fault device according to the switch information, and highlighting the topological elements corresponding to the faulty device on the low-voltage distribution network electronic map.

[0057] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0058] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0059] The program code included in the computer-readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer, including a local area network (LAN) or a wide area network (WAN) through any type of network, or connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0060] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for analyzing low voltage distribution network failure modes based on dynamic topology, characterized in that: include: Obtain power line information and equipment information and establish a distribution network data model; Obtain node flow information and match it with the distribution network data model to obtain monitoring fault information; Presetting a fault diagnosis database and defining fault types according to the monitored fault information, and establishing a fault tree model based on the fault diagnosis database to calculate the fault source probability of the fault event; Obtain feedback fault information and fault abnormality information and perform similarity matching to obtain fault root cause data; Based on the fault source probability and fault root cause data, the monitoring fault information and the equipment and power lines involved in the feedback fault information are obtained and the fault area is determined.

2. A method for analyzing low-voltage distribution network failure modes based on dynamic topology according to claim 1, characterized in that: The distribution network model includes coding, category, geometric attributes, associated attributes, and management attributes; wherein the coding is the unique identification code of the equipment, the categories include power supply, line, switch, and transformer, the geometric attributes are the geographic spatial information of the equipment, the associated attributes are the topological relationship between the equipment, and the management attributes are numerical parameters that characterize whether the equipment is in an operating state or a closed state.

3. A method for analyzing low-voltage distribution network failure modes based on dynamic topology according to claim 1, characterized in that: The method for determining the fault area is: A fault information sequence is obtained according to the power line, and the fault information sequence is synchronously saved in the attribute database of the distribution network data model; fault loop information and connection relationship are obtained according to the distribution network topology structure corresponding to the attribute data; all switch devices in the loop are marked according to the obtained loop information, and the fault area where the faulty device is located is obtained according to the switch information.

4. A method for analyzing low-voltage distribution network failure modes based on dynamic topology according to claim 3, characterized in that: The method for generating the fault information sequence is as follows: installing a monitoring device at a line node to measure current load data; if the current load is greater than a preset threshold, using 0 to represent the monitoring fault information at the node; if the current load is less than or equal to the preset threshold, using 1 to represent the monitoring fault information at the node; The fault information of all nodes is integrated into a fault information sequence.

5. A method for analyzing low-voltage distribution network failure modes based on dynamic topology according to claim 4, characterized in that: The method for establishing the distribution network data model is as follows: firstly, the substation, transformer, switch, circuit breaker, and pole tower are transformed into point elements, the overhead line and the buried line are transformed into line elements, and the regional information is transformed into surface elements, and then the spatial data of the point elements, the line elements, and the surface elements are vectorized into a distribution network topology structure; the devices are associated with an attribute database and an attribute layer is established, and a distribution network data model is established according to the distribution network topology structure and the attribute layer, wherein the attribute database is a database storing a plurality of fault information sequences, and the fault information sequence is composed of monitoring fault information of a plurality of line nodes.

6. A method for analyzing low-voltage distribution network failure modes based on dynamic topology according to claim 1, characterized in that: Establishing a fault tree model based on the fault diagnosis database to calculate the fault source probability of the fault event includes: Define fault types, the fault types include phase-to-phase short circuit fault, low current ground fault, and high resistance fault, and set the phase-to-phase short circuit fault type as a top-level event, the low current ground fault type as a middle-level event, and the high resistance fault as a bottom-level event; Establishing a fault tree node, wherein the fault tree node structure includes a node number, a node name, a node type, a number of nodes, a parent node, a child node, a weight, and a description value, and is saved as a linked list and stored in the fault diagnosis database; The linked list is traversed to calculate the minimum cut set of the fault tree, and the fault source probability of the corresponding top-level event fault type is calculated according to the minimum cut set.

7. A method for analyzing low-voltage distribution network failure modes based on dynamic topology according to claim 1, characterized in that: The calculation method of the similarity matching is: extracting the semantic features of the feedback fault information and the fault abnormality information to obtain a semantic feature vector and an abnormality vector, mapping the semantic feature vector and the abnormality vector to a high-dimensional space, and calculating the similarity matrix between the data points in the high-dimensional space. The calculation formula is: Among them, p j|i is the probability distribution of the abnormal vector ratio under the semantic representation vector, σ i is the standard deviation of the Gaussian distribution, x i is the high-dimensional representation of the semantic feature vector, x j is the high-dimensional representation of the anomaly vector, k is the traversal count of the high-dimensional space data points, n is the total number of high-dimensional data points of the semantic feature vector, and m is the total number of high-dimensional data points of the anomaly vector.

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