Power distribution network operation and maintenance method, system and equipment based on multi-source data

Abstract

The invention discloses a power distribution network operation and maintenance method, system and equipment based on multi-source data, and relates to the technical field of power distribution network operation and maintenance. The power distribution network operation and maintenance method based on the multi-source data comprises the following steps of fault operation and maintenance detection, node risk analysis and fault transmission operation and maintenance. According to the invention, fault operation and maintenance detection is carried out on the power transmission line in the power distribution network based on the obtained multi-source operation and maintenance data to judge whether to execute power transmission line node data analysis, and if the power transmission line node data analysis is executed, risk level updating is carried out in combination with an initial node risk level to obtain a real-time node risk level; if the power transmission line node data analysis is not executed, power transmission line node fault transmission operation and maintenance are carried out based on the current real-time node risk level, so that the effect of carrying out power transmission line fault detection and positioning more timely is achieved; the problem of high fault detection positioning response delay of the power transmission line in the operation and maintenance process of the power distribution network in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network operation and maintenance, and particularly to a distribution network operation and maintenance method, system and device based on multi-source data. Background Technique

[0002] The operation and maintenance (operation and maintenance) of the distribution network is a key part of ensuring the safe, stable and efficient operation of the power system. With the continuous increase in energy demand and the gradual development of the power system towards intelligence and automation, the traditional distribution network operation and maintenance methods are facing many challenges. The state monitoring means of traditional distribution network equipment mainly rely on manual inspections or regular detections, with low efficiency and prone to missed detections. By introducing intelligent sensors and automated monitoring devices to integrate multi-source data, the distribution network can monitor the operation status of the power grid in real time, quickly identify potential faults and give early warnings, avoiding major faults caused by long-term ineffective operation of equipment, thereby improving the operation and maintenance efficiency and safety, and further enhancing the safety, stability and economy of the power grid operation, providing strong support for the sustainable development of the power industry.

[0003] Existing distribution network operation and maintenance usually rely on the integration of a variety of advanced technologies, including multiple links such as data collection, data transmission, data processing and analysis, and intelligent decision support. A large number of intelligent meters, sensors and monitoring devices are deployed in the distribution network to monitor the status of power equipment (such as current, voltage, frequency, temperature, etc.) in real time. By integrating data from different sources, the analysis based on multi-source data can provide decision support for the operation and maintenance of the distribution network, and use machine learning and deep learning algorithms to perform pattern recognition and anomaly detection on the real-time monitoring data. Through the combination of data collection, transmission, analysis and decision support systems, refined management, intelligent operation and maintenance and fault prediction of the distribution network are realized to ensure the safe, stable and efficient operation of the distribution network.

[0004] For example, the grid operation status monitoring method and system based on multi-source data analysis disclosed in the patent application with the publication number of: CN118971339A includes: collecting the operation parameter data of the power grid through different data sources and sensors; preprocessing the collected operation parameter data of the power grid; batch-processing the operation parameter data of the power grid collected by different data sources and sensors after preprocessing to form batch data, and integrating each batch of data using data fusion technology to form a multi-source data set; analyzing the multi-source data set to identify abnormal patterns in the grid operation status; generating detection signals and alarm signals respectively according to the analysis results, the detection signals detect the abnormal patterns, and the alarm signals notify the operation and maintenance personnel according to the detection results.

[0005] For example, a method and system for detecting the stable operation of distribution network equipment announced in the invention patent announcement with the announcement number of CN118249515B includes: obtaining the topological structure of the distribution network part under the current power grid and associating the distribution network equipment with the obtained topological structure; obtaining historical equipment monitoring data and associating the segmented monitoring data with the main extended topology in chronological order; and determining the influence range of the associated distribution network equipment according to the covering equipment of each node of the main extended topology; for each sub-extended topology after data association, extracting sub-topology features; constructing a multi-layer perceptron network model; adding labels to the extracted topology features according to the alarm data in the historical equipment monitoring data and training the multi-layer perceptron network model; and determining the detection result of the stable operation of the distribution network equipment based on the trained multi-layer perceptron network model.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it is found that the above technology has at least the following technical problems: In a distribution network system, a local fault usually refers to a fault that occurs in a certain part or equipment of the power system, rather than a widespread fault in the entire distribution network or transmission line. Local faults usually do not cause large-scale current and voltage fluctuations, and may be misjudged or missed by the monitoring system. Due to the lack of timely feedback information, the fault location time may be greatly extended.

[0007] In addition, the environment around the distribution line may interfere with the fault detection of the transmission line. For example, strong electromagnetic fields, weather factors (such as lightning, sand and dust, rain and snow, etc.) may cause delays or errors in fault detection, and there is a problem of high response delay in fault detection and location of transmission lines during the operation and maintenance of the distribution network. Summary of the Invention

[0008] The embodiments of the present application provide a method, system and device for distribution network operation and maintenance based on multi-source data, which solve the problem of high response delay in fault detection and location of transmission lines during the operation and maintenance of the distribution network in the prior art, and realize more timely fault detection and location of transmission lines.

[0009] The embodiments of the present application provide a method for distribution network operation and maintenance based on multi-source data, including the following steps: S1, performing fault operation and maintenance detection on the transmission lines in the distribution network based on the obtained multi-source operation and maintenance data, and determining whether to perform data analysis on the nodes of the transmission lines; S2, if performing data analysis on the nodes of the transmission lines, then updating the risk level in combination with the initial node risk level to obtain the real-time node risk level; S3, if not performing data analysis on the nodes of the transmission lines, then performing fault transmission operation and maintenance on the nodes of the transmission lines based on the current real-time node risk level.

[0010] Further, the specific process of performing fault operation and maintenance detection on transmission lines in the distribution network based on the obtained multi-source operation and maintenance data is as follows: Obtain the operation and maintenance meteorological data within a preset time interval, where the operation and maintenance meteorological data includes electromagnetic pulse width, electromagnetic noise level, thermal expansion friction heat, and friction power loss; perform electromagnetic correction on the electromagnetic noise level difference analysis result according to the analysis result of the ratio of the electromagnetic pulse width to the reference electromagnetic pulse width, perform friction correction on the friction power loss through the analysis result of the ratio of the thermal expansion friction heat to the reference friction heat, and respectively perform weighted operations on the results of the electromagnetic correction and the friction correction by combining the correction influence weights and then couple them to obtain the operation and maintenance meteorological correction factor; obtain the multi-source operation and maintenance data within a preset time interval, perform weighted operations on the obtained multi-source operation and maintenance data and the corresponding detection and evaluation weights and then couple and process them, and perform meteorological difference correction on the result of the coupled processing through the operation and maintenance meteorological correction factor to obtain the operation and maintenance detection and evaluation value, where the multi-source operation and maintenance data includes voltage volatility, current volatility, and harmonic distortion rate; the operation and maintenance detection and evaluation value is used to quantitatively evaluate the fault compliance degree of performing operation and maintenance detection on transmission lines in the distribution network.

[0011] Further, the correction influence weights include lightning strike correction influence weight and wind correction influence weight; the electromagnetic correction is used to correct the influence degree of the electromagnetic pulse width on the electromagnetic noise level; the friction correction is used to correct the influence degree of the thermal expansion friction heat on the friction power loss; the operation and maintenance meteorological correction factor is used to correct the influence degree of lightning strike electromagnetic interference and wind friction interference on the operation and maintenance detection and evaluation value; the operation and maintenance meteorological correction factor represents the quantitative data of the joint effect of the electromagnetic pulse width, electromagnetic noise level, thermal expansion friction heat, and friction power loss on the operation and maintenance meteorological correction factor; the detection and evaluation weights include voltage detection and evaluation weight, current detection and evaluation weight, and harmonic detection and evaluation weight; the operation and maintenance detection and evaluation value represents the quantitative data of the joint effect of the voltage volatility, current volatility, and harmonic distortion rate on the fault compliance degree of performing operation and maintenance detection on transmission lines in the distribution network.

[0012] Further, the specific process of determining whether to perform data analysis on transmission line nodes is as follows: According to the preset operation and maintenance detection threshold range obtained from the preset database, determine whether the obtained operation and maintenance detection and evaluation value is within the preset operation and maintenance detection threshold range; if the operation and maintenance detection and evaluation value is within the preset operation and maintenance detection threshold range obtained from the preset database, perform data analysis on transmission line nodes; if the operation and maintenance detection and evaluation value is not within the preset operation and maintenance detection threshold range obtained from the preset database, do not perform data analysis on transmission line nodes.

[0013] Furthermore, the specific steps for analyzing the data of the transmission line nodes are as follows: Obtain the node operation and maintenance analysis data of the nodes with the preset proportional order levels in the initial node risk level of the transmission line based on the deviation degree between the operation and maintenance detection and evaluation value and the operation and maintenance detection reference threshold; the node operation and maintenance analysis data includes the node corrosion depth, node insulation resistance, node oxide layer thickness, node grounding resistance, and node grounding current; perform corrosion difference correction on the approaching operation result of the node insulation resistance according to the analysis result of the node corrosion depth difference ratio to obtain the corrosion difference correction result, perform oxidation difference correction on the analysis result of the node oxide layer thickness difference ratio, the node grounding resistance ratio result, and the approaching operation result of the node grounding current to obtain the oxidation difference correction result, and couple the corrosion difference correction result and the oxidation difference correction result through weighted operation with the corresponding risk detection weights to obtain the node operation and maintenance evaluation value; the node operation and maintenance evaluation value is used to quantitatively evaluate the compliance degree of the fault probability of the transmission line node risk detection.

[0014] Furthermore, the risk detection weights include insulation risk detection weights and grounding performance detection weights; the corrosion difference correction is used to correct the influence degree of the node corrosion depth on the node insulation resistance; the oxidation difference correction is used to correct the influence degree of the node oxide layer thickness on the node grounding resistance and the node grounding current; the node operation and maintenance evaluation value represents the quantitative data of the compliance degree of the fault probability of the transmission line node risk detection jointly by the node corrosion depth, node insulation resistance, node oxide layer thickness, node grounding resistance, and node grounding current.

[0015] Further, the specific steps for updating the risk level by combining the initial node risk level to obtain the real-time node risk level are as follows: Obtain the node operation and maintenance evaluation value of the sequential level node, and map the deviation degree between the node operation and maintenance evaluation value of the sequential level node and the operation and maintenance evaluation reference value to obtain the node operation and maintenance detection score; comprehensively map the initial node risk level score of the sequential level node and the node operation and maintenance detection score to obtain the updated node risk level score, and the initial node risk level score is mapped based on the initial node risk level; combine the updated node risk level score to judge whether the initial sequential level node in the sequential level node performs a level ascending order. If it performs a level ascending order, update the initial node risk level of the initial sequential level node to the initial node risk level of the corresponding node to obtain the real-time node risk level of the initial sequential level node; if it does not perform a level ascending order, judge whether the initial sequential level node in the sequential level node performs a level retention. If it performs a level retention, update the initial node risk level of the initial sequential level node to the real-time node risk level of this node; otherwise, obtain the pre-updated initial node risk level node and the updated node risk level scores of all nodes from the pre-updated initial node risk level node to the initial sequential level node, and compare the updated node risk level score of the initial sequential level node with the updated node risk level scores of the pre-updated initial node risk level node and all nodes from the pre-updated initial node risk level node to the initial sequential level node; update the initial node risk level of the initial sequential level node to the initial node risk level of the node corresponding to the updated node risk level score less than the initial sequential level node to obtain the real-time node risk level of the initial sequential level node; sequentially judge the real-time node risk levels of all nodes in the sequential level node.

[0016] Further, the specific process for power transmission line node fault transmission operation and maintenance based on the current real-time node risk level is as follows: Obtain the current real-time node risk level, and sequentially obtain the node operation and maintenance evaluation values of the corresponding nodes based on the real-time node risk level; until the node operation and maintenance evaluation value of the corresponding node is not within the preset risk detection threshold range obtained from the preset database; sequentially perform node fault operation and maintenance data transmission in combination with the real-time node risk level, and the fault operation and maintenance data includes node operation and maintenance analysis data and node operation and maintenance evaluation values.

[0017] The embodiment of the present application provides a distribution network operation and maintenance system based on multi-source data, including: a fault operation and maintenance detection module, a node risk analysis module, and a fault transmission operation and maintenance module; the fault operation and maintenance detection module is used to perform fault operation and maintenance detection on the transmission lines in the distribution network based on the obtained multi-source operation and maintenance data, and determine whether to perform data analysis on the transmission line nodes; the node risk analysis module is used to update the risk level in combination with the initial node risk level to obtain the real-time node risk level if the data analysis on the transmission line nodes is performed; the fault transmission operation and maintenance module is used to perform fault transmission operation and maintenance on the transmission line nodes based on the current real-time node risk level if the data analysis on the transmission line nodes is not performed.

[0018] The embodiment of the present application provides an electronic device, which includes a memory for storing computer program instructions and a processor for executing the program instructions. Wherein, when the computer program instructions are executed by the processor, the electronic device is triggered to execute the distribution network operation and maintenance method based on multi-source data.

[0019] One or more technical solutions provided in the embodiment of the present application have at least the following technical effects or advantages: 1. By performing fault operation and maintenance detection on the transmission lines in the distribution network through the obtained multi-source operation and maintenance data to determine whether to perform data analysis on the transmission line nodes, if it is performed, the risk level is updated to obtain the real-time node risk level, otherwise, fault transmission operation and maintenance on the transmission line nodes is performed based on the current real-time node risk level, thereby realizing data analysis and judgment on the transmission line nodes in the fault operation and maintenance detection of the transmission lines, and further realizing more timely fault detection and positioning of the transmission lines, effectively solving the problem of high response delay in fault detection and positioning of transmission lines in the existing distribution network operation and maintenance process.

[0020] 2. By mapping the deviation degree between the node operation and maintenance evaluation value of the sequential level node and the operation and maintenance evaluation reference value to obtain the node operation and maintenance detection score, then comprehensively mapping the initial node risk level score of the sequential level node and the node operation and maintenance detection score to obtain the updated node risk level score, and then judging the real-time node risk level of all nodes in the sequential level node in turn in combination with the updated node risk level score, thereby realizing the improvement of the reliability of the transmission line node risk level evaluation, and further realizing more accurate fault detection and positioning of the transmission lines in the distribution network operation and maintenance.

[0021] 3. Obtain the node operation and maintenance evaluation value of the corresponding node in sequence according to the real-time node risk level until the node operation and maintenance evaluation value of the corresponding node is not within the preset risk detection threshold range obtained from the preset database. Then, perform the transmission of fault operation and maintenance data for the node in sequence in combination with the real-time node risk level, thereby improving the transmission efficiency of node fault operation and maintenance data based on the real-time node risk level, and further reducing the response delay of the transmission line node fault detection. Brief Description of the Drawings

[0022] Figure 1 The flowchart of the distribution network operation and maintenance method based on multi-source data provided by the embodiment of the present application; Figure 2 The structural schematic diagram of the distribution network operation and maintenance system based on multi-source data provided by the embodiment of the present application. Detailed Embodiment

[0023] The embodiment of the present application provides a distribution network operation and maintenance method, system and device based on multi-source data, which solves the problem of high response delay in fault detection and location of transmission lines during the operation and maintenance of the distribution network in the prior art. Through the obtained multi-source operation and maintenance data, the fault operation and maintenance detection of the transmission lines in the distribution network is carried out to judge whether to perform the data analysis of the transmission line nodes. If the data analysis of the transmission line nodes is performed, the risk level is updated in combination with the initial node risk level to obtain the real-time node risk level. If the data analysis of the transmission line nodes is not performed, the fault transmission operation and maintenance of the transmission line nodes is carried out based on the current real-time node risk level, realizing more timely fault detection and location of the transmission lines.

[0024] The technical solution in the embodiment of the present application is to solve the problem of high response delay in fault detection and location of transmission lines during the operation and maintenance of the distribution network. The general idea is as follows: Through the fault operation and maintenance detection of the transmission lines in the distribution network to judge whether to perform the data analysis of the transmission line nodes. If so, the risk level is updated to obtain the real-time node risk level. Otherwise, the fault transmission operation and maintenance of the transmission line nodes is carried out based on the current real-time node risk level, achieving the effect of more timely fault detection and location of the transmission lines during the operation and maintenance of the distribution network.

[0025] In order to better understand the above technical solution, the above technical solution will be described in detail below in combination with the accompanying drawings of the specification and specific embodiments.

[0026] Such as Figure 1As shown in the figure, it is a flowchart of the distribution network operation and maintenance method based on multi-source data provided by the embodiments of the present application. The method includes the following steps: S1, perform fault operation and maintenance detection on the transmission lines in the distribution network based on the obtained multi-source operation and maintenance data, and determine whether to perform data analysis on the transmission line nodes; S2, if data analysis on the transmission line nodes is performed, update the risk level in combination with the initial node risk level to obtain the real-time node risk level; S3, if data analysis on the transmission line nodes is not performed, perform transmission line node fault transmission operation and maintenance based on the current real-time node risk level.

[0027] In this embodiment, the operation and maintenance of the distribution network is an important part of the power system, involving the monitoring and management of a large number of equipment and facilities. The transmission line is the backbone of the power system, carrying a large number of power transmission tasks. Due to its frequent crossing of complex geographical environments (such as mountains, forests, lakes, etc.) and exposure to harsh weather conditions (such as thunderstorms, hailstorms, strong winds, etc.), transmission line faults occur frequently and are difficult to quickly locate and repair.

[0028] In extreme weather (such as thunderstorm weather), the transmission line may be damaged, resulting in power outages. The faults may be caused by various reasons such as electrical equipment damage and line short circuits. At this time, it is necessary to accurately judge the location, type and possible expansion range of the fault through an intelligent monitoring system combined with multi-source data. Although the intelligent level of power system operation and maintenance is constantly improving, there are still certain technical bottlenecks when facing local faults. This application effectively reduces the delay of fault detection and location response for transmission lines in the process of distribution network operation and maintenance by optimizing the processing and transmission processes in the transmission line fault detection process, thereby realizing more refined operation and maintenance management and promoting the power industry to develop in a more intelligent and digital direction.

[0029] Further, the specific process of performing fault operation and maintenance detection on the transmission lines in the distribution network based on the obtained multi-source operation and maintenance data is as follows: obtain the operation and maintenance meteorological data within a preset time interval; perform lightning electromagnetic correction on the electromagnetic noise level difference analysis result according to the analysis result of the ratio of the electromagnetic pulse width to the reference electromagnetic pulse width, perform wind friction correction on the friction power loss through the analysis result of the ratio of the thermal expansion friction heat to the reference friction heat, and perform weighted operations on the results of the lightning electromagnetic correction and the wind friction correction respectively by combining the correction influence weights, and then couple them to obtain the operation and maintenance meteorological correction factor (i.e., in the operation and maintenance detection evaluation value ); obtain the multi-source operation and maintenance data within a preset time interval, perform weighted operations on the obtained multi-source operation and maintenance data and the corresponding detection evaluation weights and then couple and process them, and perform meteorological difference correction on the result of the coupling process through the operation and maintenance meteorological correction factor to obtain the operation and maintenance detection evaluation value.

[0030] The method for obtaining the operation and maintenance detection evaluation value is as follows: ; In the formula, represents the operation and maintenance detection and evaluation value, represents the operation and maintenance meteorological correction factor, represents the voltage detection and evaluation weight, represents the current detection and evaluation weight, represents the harmonic detection and evaluation weight, represents the voltage volatility, represents the current volatility, represents the harmonic distortion rate.

[0031] Among them, the method for obtaining the operation and maintenance meteorological correction factor is as follows: ; In the formula, represents the lightning strike correction influence weight, represents the wind force correction influence weight, represents the electromagnetic pulse width, represents the reference electromagnetic pulse width, represents the reference maximum noise level, represents the electromagnetic noise level, represents the heat of thermal expansion friction, represents the reference friction heat, represents the reference maximum power loss, represents the friction power loss.

[0032] It should be added that the operation and maintenance meteorological data include the electromagnetic pulse width, electromagnetic noise level, heat of thermal expansion friction, and friction power loss; among them, the electromagnetic pulse width at the transmission line node is obtained through an oscilloscope, and the electromagnetic pulse width is in the same unit as the reference electromagnetic pulse width, both in seconds. The reference electromagnetic pulse width is represented by the result of summing and averaging the collected historical electromagnetic pulse widths; the electromagnetic noise level at the transmission line node is obtained through a spectrum analyzer, and the electromagnetic noise level is in the same unit as the reference maximum noise level, both in decibels. The reference maximum noise level is represented by the maximum value of summing and averaging the collected historical electromagnetic noise levels.

[0033] Obtain the thermal expansion friction heat at the transmission line node within a preset time interval through a heat flow sensor. The thermal expansion friction heat is in the same unit as the reference friction heat, both in joules. The reference friction heat is represented by the result of summing and averaging the collected historical thermal expansion friction heat; obtain the friction power loss at the transmission line node within a preset time interval through a power analyzer in combination with a sound wave sensor (used to detect friction sounds), that is, in the area where power loss occurs, the sound wave sensor also detects friction sounds. The friction power loss is in the same unit as the reference maximum power loss, both in watts. The reference maximum power loss is represented by the result of summing and averaging the collected historical friction power losses.

[0034] The correction influence weights include the lightning strike correction influence weight and the wind force correction influence weight; the lightning strike correction influence weight and the wind force correction influence weight respectively describe the influence degrees of lightning electromagnetic interference (that is, the result of lightning electromagnetic correction of the electromagnetic noise level difference analysis result by the ratio analysis result of the electromagnetic pulse width to the reference electromagnetic pulse width) and wind force friction interference (that is, the result of wind force friction correction of the friction power loss by the ratio analysis result of the thermal expansion friction heat to the reference friction heat) on the operation and maintenance meteorological correction factor, and their value ranges are both [0, 1] and the sum is 1; for example, input the real-time lightning electromagnetic interference and wind force friction interference into the mapping set of the preset lightning electromagnetic interference, wind force friction interference and their respective weight factors in the database to obtain the corresponding weights.

[0035] The multi-source operation and maintenance data include voltage volatility, current volatility, and harmonic distortion rate; obtain the voltage volatility at the transmission line node within a preset time interval through a voltage monitor, obtain the current volatility at the transmission line node within a preset time interval through a current monitor, and obtain the harmonic distortion rate at the transmission line node within a preset time interval through a harmonic analyzer.

[0036] The detection and evaluation weights include voltage detection and evaluation weight, current detection and evaluation weight, and harmonic detection and evaluation weight; they are obtained from a preset database and their value ranges are all [0, 1]. For example, input the real-time voltage volatility, current volatility, and harmonic distortion rate into the mapping set of the preset voltage volatility, current volatility, harmonic distortion rate and their respective weight factors in the database to obtain the corresponding weights; the voltage detection and evaluation weight, current detection and evaluation weight, and harmonic detection and evaluation weight respectively describe the influence degrees of voltage volatility, current volatility, and harmonic distortion rate on the operation and maintenance detection and evaluation value.

[0037] It should be understood that the operation and maintenance meteorological correction factor represents the quantitative data of the combined effects of electromagnetic pulse width, electromagnetic noise level, heat generated by thermal expansion friction, and frictional power loss on the operation and maintenance meteorological correction factor. Among them, the electromagnetic correction is used to correct the influence degree of the electromagnetic pulse width on the electromagnetic noise level, and the friction correction is used to correct the influence degree of the heat generated by thermal expansion friction on the frictional power loss.

[0038] It should also be understood that the operation and maintenance detection and evaluation value is used to quantitatively evaluate the fault compliance degree of the transmission line in the distribution network during operation and maintenance detection. The operation and maintenance detection and evaluation value includes multiple aspects of parameters. Specifically, the operation and maintenance meteorological correction factor is used to correct the influence degree of lightning electromagnetic interference and wind friction interference on the operation and maintenance detection and evaluation value. As the voltage volatility, current volatility, and harmonic distortion rate increase, the operation and maintenance detection and evaluation value increases accordingly.

[0039] In addition, the operation and maintenance detection and evaluation value represents the quantitative data of the combined effects of voltage volatility, current volatility, and harmonic distortion rate on the fault compliance degree of the transmission line in the distribution network during operation and maintenance detection. This algorithm takes into account the correlation and mutual influence among the parameters in the operation and maintenance detection and evaluation value, and conducts comprehensive analysis through quantification. For example, electromagnetic pulses are usually caused by external electromagnetic interference sources (such as lightning, etc.). The longer the electromagnetic pulse width, the more significant the interference of the electromagnetic signal on the transmission line, resulting in an increase in the electromagnetic noise level. The increase in the electromagnetic noise level further leads to current fluctuations and vibrations inside the transmission line, and may also further trigger the generation of frictional heat. At the same time, as the electromagnetic noise level increases, the non-linear effect in the transmission line may be stronger, thereby triggering more harmonic distortions and causing the harmonic distortion rate to increase accordingly.

[0040] Through the numerical evaluation of the fault operation and maintenance detection compliance degree of the transmission line in the distribution network, the reliability of the fault operation and maintenance detection of the transmission line is improved, and further, the accuracy of the fault detection of the transmission line during the operation and maintenance of the distribution network is improved.

[0041] Furthermore, the specific process for determining whether to perform data analysis on the transmission line node is as follows: According to the preset operation and maintenance detection threshold range obtained from the preset database, determine whether the obtained operation and maintenance detection and evaluation value is within the preset operation and maintenance detection threshold range. If the operation and maintenance detection and evaluation value is within the preset operation and maintenance detection threshold range obtained from the preset database, perform data analysis on the transmission line node; if the operation and maintenance detection and evaluation value is not within the preset operation and maintenance detection threshold range obtained from the preset database, do not perform data analysis on the transmission line node.

[0042] Among them, the preset operation and maintenance detection threshold range is obtained from the preset database and is set by professionals according to the standards in the field. For example, the preset operation and maintenance detection threshold range is set to be from 1.0 to 2.0.

[0043] It should be added that the specific steps for analyzing the data of transmission line nodes are as follows: Obtain the node operation and maintenance analysis data of the nodes at the preset proportional order level in the initial node risk level of the transmission line based on the deviation degree between the operation and maintenance detection and evaluation value and the operation and maintenance detection reference threshold; Perform corrosion difference correction on the node insulation resistance approximation result according to the analysis result of the proportion of node corrosion depth difference to obtain the corrosion difference correction result, and perform oxidation difference correction on the analysis result of the proportion of node oxide layer thickness difference, the node grounding resistance proportion result, and the node grounding current approximation result to obtain the oxidation difference correction result. After combining the corrosion difference correction result and the oxidation difference correction result with the corresponding risk detection weights for weighted operation, couple them to obtain the node operation and maintenance evaluation value.

[0044] The method for obtaining the node operation and maintenance evaluation value is as follows: ; In the formula, represents the node operation and maintenance evaluation value, represents the insulation risk detection weight, represents the grounding performance detection weight, represents the reference maximum corrosion depth, represents the node corrosion depth, represents the reference minimum insulation resistance, represents the node insulation resistance, represents the reference maximum oxide layer thickness, represents the node oxide layer thickness, represents the node grounding resistance, represents the reference grounding resistance, represents the reference grounding current, represents the node grounding current.

[0045] It should be understood that the corrosion difference correction is used to correct the influence degree of the node corrosion depth on the node insulation resistance; the oxidation difference correction is used to correct the influence degree of the node oxide layer thickness on the node grounding resistance and the node grounding current; the node operation and maintenance evaluation value represents the quantitative data of the degree of compliance of the node corrosion depth, the node insulation resistance, the node oxide layer thickness, the node grounding resistance, and the node grounding current with the transmission line node risk detection failure probability, and is used to quantitatively evaluate the degree of compliance of the transmission line node risk detection failure probability.

[0046] Among them, the operation and maintenance detection reference threshold is the maximum value of the preset operation and maintenance detection threshold range; the node operation and maintenance analysis data includes the node corrosion depth, the node insulation resistance, the node oxide layer thickness, the node grounding resistance, and the node grounding current; specifically, the node corrosion depth is obtained by an ultrasonic thickness gauge, and the unit of the node corrosion depth is the same as that of the reference maximum corrosion depth, both being millimeters. The reference maximum corrosion depth is represented by the maximum value obtained by summing and averaging the collected historical node corrosion depths.

[0047] The node insulation resistance at the nodes of the transmission line is obtained through an insulation resistance tester. The unit of the node insulation resistance is the same as that of the reference minimum insulation resistance, both being megohms. The result of summing and averaging the collected historical node insulation resistances represents the reference minimum insulation resistance. The node oxide layer thickness at the nodes of the transmission line is obtained through a coating thickness gauge. The unit of the node oxide layer thickness is the same as that of the reference maximum oxide layer thickness, both being micrometers. The result of summing and averaging the collected historical node oxide layer thicknesses represents the reference maximum oxide layer thickness.

[0048] The node grounding resistance at the nodes of the transmission line is obtained through a grounding resistance tester. The unit of the node grounding resistance is the same as that of the reference grounding resistance, both being ohms. The result of summing and averaging the collected historical node grounding resistances represents the reference grounding resistance. The node grounding current at the nodes of the transmission line is obtained through a grounding current tester. The unit of the node grounding current is the same as that of the reference grounding current, both being amperes. The result of summing and averaging the collected historical node grounding currents represents the reference grounding current.

[0049] The risk detection weights include the insulation risk detection weight and the grounding performance detection weight. The insulation risk detection weight and the grounding performance detection weight respectively correspond to describing the influence degree of the node insulation risk detection performance (i.e., the result of correcting the corrosion difference of the node insulation resistance approaching operation result by the analysis result of the proportion of node corrosion depth difference) and the node grounding risk detection performance (i.e., the result of correcting the oxidation difference of the analysis result of the proportion of node oxide layer thickness difference, the proportion result of node grounding resistance, and the node grounding current approaching operation result) on the node operation and maintenance evaluation value. For example, inputting the real-time node insulation risk detection performance and the node grounding risk detection performance into the mapping set of the preset node insulation risk detection performance, node grounding risk detection performance and their respective weight factors in the database to obtain the corresponding weights. The sum of the insulation risk detection weight and the grounding performance detection weight is 1.

[0050] It should also be understood that the node operation and maintenance evaluation value includes parameters in multiple aspects, and there are connections among the various parameters and they do not exist independently. For example, corrosion usually damages the physical structure of the equipment, especially the metal parts. An increase in the corrosion depth usually leads to damage to the surface of the node or the housing of the electrical equipment, which may cause damage or degradation of the insulation layer. Corrosion will cause a decrease in electrical insulation, thereby reducing the insulation resistance of the node. That is, as the corrosion depth of the node increases, the insulation resistance of the node decreases, meaning there is more leakage current, which in turn affects the stability of the node grounding resistance and the change of the node grounding current. At the same time, the corrosion of the node may change the formation process of the oxide layer. Usually, corrosion will damage or change the originally normally formed oxide layer. When the oxide layer is too thick, that is, when the thickness of the node oxide layer increases, it may hinder effective grounding, resulting in an increase in the node grounding resistance. The greater the node grounding resistance, the more restricted the current flow between the node and the ground, and the node grounding current may be smaller. In summary, through a quantitative method, a quantitative evaluation of the compliance degree of the fault detection failure probability of the transmission line node is realized, and thus a more accurate evaluation of the fault detection of the transmission line during the operation and maintenance of the distribution network is achieved.

[0051] Furthermore, the specific steps for updating the risk level in combination with the initial node risk level to obtain the real-time node risk level are as follows: Obtain the node operation and maintenance evaluation value of the sequential level node, and map the deviation degree between the node operation and maintenance evaluation value of the sequential level node and the operation and maintenance evaluation reference value to obtain the node operation and maintenance detection score; comprehensively map the initial node risk level score of the sequential level node and the node operation and maintenance detection score to obtain the updated node risk level score, and the initial node risk level score is mapped based on the initial node risk level.

[0052] Judge whether the initial sequential level node in the sequential level node performs a level ascending in combination with the updated node risk level score. If a level ascending is performed, update the initial node risk level of the initial sequential level node to the initial node risk level of the corresponding node to obtain the real-time node risk level of the initial sequential level node.

[0053] If a level ascending is not performed, judge whether the initial sequential level node in the sequential level node performs a level retention (that is, the level of the initial sequential level node in the sequential level node remains unchanged after the risk level is updated). If a level retention is performed, update the initial node risk level of the initial sequential level node to the real-time node risk level of this node; otherwise, obtain the pre-updated initial node risk level node and the updated node risk level scores of all nodes from the pre-updated initial node risk level node to the initial sequential level node, and compare the updated node risk level score of the initial sequential level node with the updated node risk level scores of the pre-updated initial node risk level node and all nodes from the pre-updated initial node risk level node to the initial sequential level node.

[0054] Update the initial node risk level of the initial sequential level node to be less than the initial node risk level of the node corresponding to the updated node risk level score of the initial sequential level node to obtain the real-time node risk level of the initial sequential level node; sequentially judge the real-time node risk levels of all nodes in the sequential level node.

[0055] Among them, the operation and maintenance evaluation reference value is the result of summing and averaging the historical node operation and maintenance evaluation values. The initial node risk level is obtained through the time delay measurement method (frequency response method); the real-time node risk level is obtained by combining the initial node risk level for risk level update, realizing a more accurate evaluation of the fault probability of the transmission line node risk detection, and further realizing an orderly response in the process of transmission line node fault detection based on the real-time node risk level.

[0056] Furthermore, the specific process of transmission line node fault transmission operation and maintenance based on the current real-time node risk level is as follows: obtain the current real-time node risk level, and sequentially obtain the node operation and maintenance evaluation values of the corresponding nodes based on the real-time node risk level; until the node operation and maintenance evaluation value of the corresponding node is not within the preset risk detection threshold range obtained from the preset database; combine the real-time node risk level to sequentially perform node fault operation and maintenance data transmission, and the fault operation and maintenance data includes node operation and maintenance analysis data and node operation and maintenance evaluation values.

[0057] In this embodiment, the preset risk detection threshold range is obtained from the preset database and is set by professionals according to the standards in the field. For example, the preset risk detection threshold range is set to be from 1.0 to 3.0; among them, the fault operation and maintenance data transmission is realized through wired transmission (such as optical fiber transmission), and the node fault operation and maintenance data transmission is sequentially performed according to the real-time node risk level by sorting the real-time node risk levels; by combining the preset risk detection threshold range for judgment and sequentially performing node fault operation and maintenance data transmission according to the real-time node risk level, the improvement of the fault operation and maintenance data transmission efficiency in the process of transmission line node fault detection is realized.

[0058] Such as Figure 2As shown in the figure, it is a schematic structural diagram of a distribution network operation and maintenance system based on multi-source data provided by an embodiment of the present application. The distribution network operation and maintenance system based on multi-source data provided by the embodiment of the present application includes: a fault operation and maintenance detection module, a node risk analysis module, and a fault transmission operation and maintenance module; the fault operation and maintenance detection module is used to perform fault operation and maintenance detection on the transmission lines in the distribution network based on the acquired multi-source operation and maintenance data, and determine whether to perform data analysis on the transmission line nodes; the node risk analysis module is used to update the risk level in combination with the initial node risk level to obtain the real-time node risk level if the data analysis on the transmission line nodes is performed; the fault transmission operation and maintenance module is used to perform fault transmission operation and maintenance on the transmission line nodes based on the current real-time node risk level if the data analysis on the transmission line nodes is not performed.

[0059] In this embodiment, through the fault operation and maintenance detection of the transmission lines in the distribution network and the corresponding node fault analysis and processing of the transmission lines according to the detection results, the fault operation and maintenance response corresponding to the real-time node risk level of the transmission line nodes is realized, and further, the fault detection and positioning of the transmission lines in the distribution network operation and maintenance process are carried out more accurately and timely.

[0060] An embodiment of the present application also provides an electronic device, which includes a memory for storing computer program instructions and a processor for executing the program instructions. Among them, when the computer program instructions are executed by the processor, the electronic device is triggered to execute the distribution network operation and maintenance method based on multi-source data.

[0061] In summary, in the embodiment of the present application, the fault operation and maintenance detection of the transmission lines in the distribution network is performed through the acquired multi-source operation and maintenance data to determine whether to perform data analysis on the transmission line nodes. If it is executed, the risk level is updated to obtain the real-time node risk level. Otherwise, the fault transmission operation and maintenance of the transmission line nodes are performed based on the current real-time node risk level, thereby realizing the data analysis and judgment of the transmission line nodes in the fault operation and maintenance detection of the transmission lines, and further realizing the more timely fault detection and positioning of the transmission lines, effectively solving the problem of high response delay in fault detection and positioning of transmission lines in the distribution network operation and maintenance process in the prior art.

[0062] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] The present invention is described with reference to flowchart illustrations and / or block diagram illustrations of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowchart illustrations and / or block diagram illustrations, and combinations of flows and / or blocks in the flowchart illustrations and / or block diagram illustrations, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing apparatus create means for implementing the functions specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0064] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0066] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0067] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A distribution network operation and maintenance method based on multi-source data, characterized in that It includes the following steps: S1. Based on the acquired multi-source operation and maintenance data, conduct fault operation and maintenance detection on the transmission lines in the distribution network, and determine whether to perform data analysis on the transmission line nodes; S2. If data analysis on the transmission line nodes is performed, update the risk level in combination with the initial node risk level to obtain the real-time node risk level; S3. If data analysis on the transmission line nodes is not performed, conduct fault transmission operation and maintenance on the transmission line nodes based on the current real-time node risk level.

2. The method for operation and maintenance of a distribution network based on multi-source data according to claim 1, wherein The specific process of conducting fault operation and maintenance detection on the transmission lines in the distribution network based on the acquired multi-source operation and maintenance data is as follows: Obtain the operation and maintenance meteorological data within a preset time interval, where the operation and maintenance meteorological data includes electromagnetic pulse width, electromagnetic noise level, heat expansion friction heat, and friction power loss; Conduct electromagnetic correction on the electromagnetic noise level difference analysis result according to the analysis result of the ratio of the electromagnetic pulse width to the reference electromagnetic pulse width, conduct friction correction on the friction power loss through the analysis result of the ratio of the heat expansion friction heat to the reference friction heat, and perform weighted operations on the results of the electromagnetic correction and the friction correction respectively in combination with the correction influence weight, and then couple them to obtain the operation and maintenance meteorological correction factor; Obtain the multi-source operation and maintenance data within a preset time interval, perform weighted operations on the acquired multi-source operation and maintenance data and the corresponding detection and evaluation weights and then couple them for processing, and conduct meteorological difference correction on the result of the coupled processing through the operation and maintenance meteorological correction factor to obtain the operation and maintenance detection and evaluation value. The multi-source operation and maintenance data includes voltage volatility, current volatility, and harmonic distortion rate; The operation and maintenance detection and evaluation value is used to quantitatively evaluate the fault compliance degree of the operation and maintenance detection of the transmission lines in the distribution network.

3. The method for maintaining and operating a distribution network based on multi-source data according to claim 2, wherein The correction influence weight includes lightning strike correction influence weight and wind correction influence weight; The electromagnetic correction is used to correct the influence degree of the electromagnetic pulse width on the electromagnetic noise level; The friction correction is used to correct the influence degree of the heat expansion friction heat on the friction power loss; The operation and maintenance meteorological correction factor is used to correct the influence degree of lightning electromagnetic interference and wind friction interference on the operation and maintenance detection and evaluation value; The operation and maintenance meteorological correction factor represents the quantitative data of the combined effect of the electromagnetic pulse width, electromagnetic noise level, heat expansion friction heat, and friction power loss on the operation and maintenance meteorological correction factor; The detection and evaluation weight includes voltage detection and evaluation weight, current detection and evaluation weight, and harmonic detection and evaluation weight; The operation and maintenance detection and evaluation value represents the quantitative data of the combined effect of voltage volatility, current volatility, and harmonic distortion rate on the fault compliance degree of the operation and maintenance detection of the transmission lines in the distribution network.

4. The method for operation and maintenance of a distribution network based on multi-source data according to claim 2, wherein, The specific process of determining whether to perform data analysis on the transmission line nodes is as follows: According to the preset operation and maintenance detection threshold range obtained from the preset database, determine whether the obtained operation and maintenance detection and evaluation value is within the preset operation and maintenance detection threshold range; If the operation and maintenance detection and evaluation value is within the preset operation and maintenance detection threshold range obtained from the preset database, perform data analysis on the transmission line nodes; If the operation and maintenance detection and evaluation value is not within the preset operation and maintenance detection threshold range obtained from the preset database, do not perform data analysis on the transmission line nodes.

5. The method for operation and maintenance of a distribution network based on multi-source data according to claim 4, wherein The specific steps of the data analysis on the transmission line nodes are as follows: Obtain the node operation and maintenance analysis data of the nodes with the preset proportional order levels in the initial node risk level of the transmission line based on the deviation degree between the operation and maintenance detection evaluation value and the operation and maintenance detection reference threshold; The node operation and maintenance analysis data includes the node corrosion depth, node insulation resistance, node oxide layer thickness, node grounding resistance, and node grounding current; According to the analysis result of the proportion difference of the node corrosion depth, perform corrosion difference correction on the approaching operation result of the node insulation resistance to obtain the corrosion difference correction result. Perform oxidation difference correction on the analysis result of the proportion difference of the node oxide layer thickness, the proportion result of the node grounding resistance, and the approaching operation result of the node grounding current to obtain the oxidation difference correction result. After weighting operation with the corresponding risk detection weights, couple them to obtain the node operation and maintenance evaluation value; The node operation and maintenance evaluation value is used to quantitatively evaluate the compliance degree of the fault probability of the risk detection of the transmission line nodes.

6. The method for operation and maintenance of a distribution network based on multi-source data according to claim 5, wherein, The risk detection weights include insulation risk detection weight and grounding performance detection weight; The corrosion difference correction is used to correct the influence degree of the node corrosion depth on the node insulation resistance; The oxidation difference correction is used to correct the influence degree of the node oxide layer thickness on the node grounding resistance and the node grounding current; The node operation and maintenance evaluation value represents the quantitative data of the compliance degree of the fault probability of the risk detection of the transmission line nodes jointly by the node corrosion depth, node insulation resistance, node oxide layer thickness, node grounding resistance, and node grounding current.

7. The method for operation and maintenance of a distribution network based on multi-source data according to claim 5, characterized in that, The specific steps for updating the risk level by combining the initial node risk level to obtain the real-time node risk level are as follows: Obtain the node operation and maintenance evaluation value of the sequential level nodes, and map the deviation degree between the node operation and maintenance evaluation value of the sequential level nodes and the operation and maintenance evaluation reference value to obtain the node operation and maintenance detection score; Perform comprehensive mapping on the initial node risk level score of the sequential level nodes and the node operation and maintenance detection score to obtain the updated node risk level score. The initial node risk level score is mapped based on the initial node risk level; Combine the updated node risk level score to judge whether the initial sequential level nodes in the sequential level nodes perform a level ascending. If a level ascending is performed, update the initial node risk level of the initial sequential level nodes to the initial node risk level of the corresponding nodes to obtain the real-time node risk level of the initial sequential level nodes; If a level ascending is not performed, judge whether the initial sequential level nodes in the sequential level nodes perform a level retention. If a level retention is performed, update the initial node risk level of the initial sequential level nodes to the real-time node risk level of these nodes; Otherwise, obtain the updated node risk level scores of the pre-updated initial node risk level nodes and all nodes from the pre-updated initial node risk level nodes to the initial sequential level nodes, and compare the updated node risk level score of the initial sequential level nodes with the updated node risk level scores of the pre-updated initial node risk level nodes and all nodes from the pre-updated initial node risk level nodes to the initial sequential level nodes; Update the initial node risk level of the initial sequential level node to be less than the initial node risk level of the node corresponding to the updated node risk level score of the initial sequential level node to obtain the real-time node risk level of the initial sequential level node; Sequentially judge the real-time node risk levels of all nodes in the sequential level nodes.

8. The method for operation and maintenance of a distribution network based on multi-source data according to claim 7, wherein, The specific process of power transmission line node fault transmission operation and maintenance based on the current real-time node risk level is as follows: Obtain the current real-time node risk level, and sequentially obtain the node operation and maintenance evaluation values of the corresponding nodes based on the real-time node risk level; Until the node operation and maintenance evaluation value of the corresponding node is not within the preset risk detection threshold range obtained from the preset database; Combined with the real-time node risk level, perform node fault operation and maintenance data transmission in sequence, and the fault operation and maintenance data includes node operation and maintenance analysis data and node operation and maintenance evaluation values.

9. A distribution network operation and maintenance system based on multi-source data, characterized in that, Including: A fault operation and maintenance detection module, a node risk analysis module, and a fault transmission operation and maintenance module; The fault operation and maintenance detection module is used to perform fault operation and maintenance detection on the power transmission line in the distribution network based on the obtained multi-source operation and maintenance data, and judge whether to perform power transmission line node data analysis; The node risk analysis module is used to update the risk level in combination with the initial node risk level to obtain the real-time node risk level if power transmission line node data analysis is performed; The fault transmission operation and maintenance module is used to perform power transmission line node fault transmission operation and maintenance based on the current real-time node risk level if power transmission line node data analysis is not performed.

10. An electronic device, the electronic device includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein, When the computer program instructions are executed by the processor, trigger the electronic device to execute the distribution network operation and maintenance method based on multi-source data according to any one of claims 1-8.

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