A distribution network line fault research and judgment disposal system
By integrating multi-source data and using inspection robot technology, the system has achieved accurate location and rapid recovery of faults in distribution network lines, solving the problem of poor multi-source data acquisition in existing systems and improving power supply reliability and intelligence.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUANGGANG POWER SUPPLY COMPANY HUBEI ELECTRIC POWER
- Filing Date
- 2026-03-13
- Publication Date
- 2026-06-19
Smart Images
Figure CN122247018A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the technical field of distribution network line handling systems, and in particular to a distribution network line fault analysis and handling system. Background Technology
[0002] Improving the ability to analyze and handle faults in distribution network lines is an important approach to improving power supply reliability management and a key task for improving county-level distribution dispatch management. Given the current weakness of the distribution network, further improving the ability to analyze and handle faults in distribution network lines has profound and far-reaching significance.
[0003] In terms of power supply reliability, significantly reducing power outage time can transform the traditional time-consuming model of "full-line power outage, manual line inspection, and troubleshooting" into an automated model of "second-level location, minute-level isolation and restoration," significantly shortening the average power outage time for users. At the same time, accurate fault location allows inspection personnel to go directly to the fault point, avoiding large-scale, long-term "human wave tactics" of line inspection, greatly saving manpower, material resources, and time costs. Pre-judging the nature and approximate location of the fault allows the repair team to carry the appropriate equipment and components and go to the optimal route, improving repair efficiency. Currently, the average fault isolation time is more than 2.5 hours, and the fault handling capability urgently needs to be greatly improved.
[0004] Currently, in existing power distribution line fault handling systems, such as the patent with announcement number CN102262201B, an invention discloses a fault detection method and system for overhead power distribution lines, involving detection technology. This aims to solve the problem of low real-time performance in existing technologies for detecting faults in overhead power distribution lines.
[0005] During the use of the existing system, it was found that the system has poor performance in collecting multi-source data and is not convenient for providing suggestions on fault isolation and power restoration for non-faulty areas, which reduces its effectiveness. Summary of the Invention
[0006] To address the aforementioned technical issues, this invention provides a power distribution line fault analysis and handling system that breaks down information silos through multi-source data fusion, integrates multi-dimensional data from power distribution automation, GIS, and meteorology, provides comprehensive support for fault assessment, shortens assessment time and avoids the risk of misoperation, improves on-site handling efficiency, and enables non-contact temperature measurement and foreign object removal by inspection robots, thereby reducing human risk.
[0007] The present invention provides a distribution network line fault analysis and handling system, comprising a multi-source data acquisition unit, an intelligent fault analysis unit, a collaborative handling unit, a system management unit, and an inspection and troubleshooting unit; Multi-source data acquisition unit: Used to acquire fault-related data in real time from external multi-source heterogeneous systems, and to perform standardized processing and fusion to form a unified data pool. The multi-source heterogeneous systems include power distribution automation systems, electricity consumption information acquisition systems, geographic information systems, production management systems and meteorological information systems. The data collected includes switch position information, protection action information, voltage and current telemetry data, user power outage reporting information, line topology data, equipment ledger data and real-time meteorological data. Intelligent fault assessment unit: connected to the multi-source data acquisition unit, used to receive the fused data and execute the fault assessment preset logic to automatically generate the optimal fault isolation and non-faulty area power restoration suggestion scheme; Collaborative handling unit: Connected to the intelligent fault analysis unit, it is used to transform the analysis results and handling strategies into executable tasks, including: The work order generation and dispatch module automatically generates standardized work orders from the disposal plan and accurately dispatches them to the corresponding on-site maintenance and repair personnel through a mobile application. Visual monitoring module for handling process: Displays the fault point, power outage area, location of handling personnel, and work order execution progress in real time on the geographic information system, realizing panoramic visual monitoring; Multi-party collaborative communication module: Integrates voice, messaging, and video call functions, providing a unified collaborative work platform for dispatchers, field personnel, and management personnel; System Management Unit: Used to support the configuration, monitoring, and analysis of the entire system, including: Knowledge base management module: used to maintain and update fault assessment rules, emergency response plans, equipment parameters and historical case database, supporting both manual and self-learning methods; Permissions and Workflow Management Module: Defines user permissions for different roles, and allows customization of approval and workflow processes for fault handling; The comprehensive analysis and reporting module statistically analyzes key indicators such as historical fault data, judgment accuracy, and handling time, and automatically generates analysis reports to support network optimization and decision-making. Inspection and Troubleshooting Unit: This unit utilizes inspection robots to perform non-contact temperature measurement on distribution network lines, detect overheating defects, and remove foreign objects from the lines. By fusing multi-source data, it breaks down information silos, integrating distribution automation, GIS, and meteorological data to provide comprehensive support for fault assessment. The intelligent assessment unit employs an improved matrix algorithm and Bayesian network fusion positioning, combined with simulation and pre-analysis, to achieve precise location of faulty sections and safe verification of recovery plans, shortening assessment time and mitigating the risk of misoperation. The collaborative handling unit automatically generates and dispatches work orders, utilizing panoramic visualization to monitor repair progress and coordinating with real-time communication to improve on-site handling efficiency. The inspection robot performs non-contact temperature measurement and foreign object removal, reducing human risk. Overall, it achieves rapid response, accurate location, stable operation, and optimal decision-making for distribution network faults, comprehensively improving power supply reliability and intelligence.
[0008] Preferably, the intelligent fault assessment unit includes a fault initiation module, a fault location module, a power outage impact range analysis module, and a handling strategy generation module; Fault Initiation Module: Based on preset rules or models, it performs correlation analysis on multi-source alarm information, confirms that the fault event has actually occurred, and triggers the judgment process; Fault location module: Based on real-time topology, switch status and electrical quantity information, it uses rule reasoning and artificial intelligence algorithms to automatically infer the section or equipment where the fault is most likely to occur and generate probabilistic location results; Power outage impact analysis module: Based on fault location results and network topology, it automatically analyzes and lists the affected substations, lines, transformers and users, and estimates the outage load; The fault isolation and power restoration strategy generation module automatically generates optimal fault isolation and power restoration suggestions for non-faulty areas based on fault location results, real-time network operation mode, and a pre-set fault isolation plan library.
[0009] Preferably, it also includes a simulation and deduction module; Simulation and deduction module: Located between the intelligent fault assessment unit and the collaborative handling unit, it is used to simulate the execution of the generated handling plan before the intelligent fault assessment unit officially issues the strategy, to pre-simulate the network topology changes and load transfer after the plan is executed, to verify the feasibility and security of the plan, and to provide early warning.
[0010] Preferably, the fault location module employs a multi-source information fusion location method based on an improved matrix algorithm, specifically: The distribution network topology is abstracted into a node and branch association matrix. By combining the switch action sequence and protection signal, the candidate fault sections are quickly narrowed down. Then, the fine-grained information of fault indicators and user voltage sags is integrated. The fault probability of each candidate section is calculated using a Bayesian network model, and the section with the highest probability is output as the location result.
[0011] Preferably, the disposal strategy generation module incorporates a plan priority and dynamic adjustment mechanism. First, it matches the historical disposal plan with the highest similarity in the plan library, and then dynamically verifies and corrects the plan in combination with the current real-time network topology to ensure that the generated strategy is executable and optimal.
[0012] Preferably, the inspection robot includes a drive unit, a support unit, a marking device, an unmanned body, a bracket, support wheels, a debris removal shovel, a first camera, an infrared camera, and a second camera; The bracket is installed on the top of the drone body; Two sets of support wheels are mounted on the bracket and powered by a drive unit to rotate. Both sets of obstacle removal shovels are mounted on a support device, which is used to move and adjust the two sets of obstacle removal shovels. The first camera and the infrared camera are respectively mounted on the outer wall of the drone body; The first set of second cameras is installed on the outer wall of the drone body, and the second set of second cameras is installed on the outer wall of the bracket; A marking device is installed on the drone body to mark the distribution network lines. When the distribution network lines need to be inspected, the drone body is controlled to fly to the set inspection position. The drone body then lands on the distribution network lines using two sets of support wheels. A drive mechanism then rotates the two sets of support wheels, causing the drone body to move along the distribution network lines. This reduces the energy consumption of the inspection robot and extends its working time. While moving, the drone body uses an infrared camera to capture infrared images of the distribution network lines, and two second cameras simultaneously capture images of both sides of the distribution network lines. The captured images are transmitted to the backend in real time for analysis and inspection of the distribution network lines. The robot is used to detect high-temperature faults and surface damage. Once a defect is detected, the location is marked with a marking device to facilitate the location by maintenance personnel, improving work efficiency and convenience. During the inspection and movement, two sets of obstacle removal shovels rub the distribution network lines together to remove dirt and foreign objects from the line surface, reducing the complexity of manual cleaning. The first camera captures the position of the support wheels, improving the ease of operation for operators when landing the inspection robot on the distribution network lines. When inspecting distribution network lines in other sections, the robot can fly and move, improving the convenience of crossing utility poles and towers.
[0013] Preferably, the support device includes a guide frame, a first electric cylinder, a guide seat, a second electric cylinder, a support arm, a push block, a guide column, and a spring; The guide frame is installed on the outer wall of the UAV body; The fixed end of the first electric cylinder is mounted on the guide frame; The guide seat is slidably mounted on the guide frame, the moving end of the first electric cylinder is connected to the guide seat, and the lower parts of the two sets of obstacle removal shovels are slidably mounted on the guide seat. The second electric cylinder is mounted on the guide seat; The bottom ends of the two sets of support arms are rotatably connected to the moving end of the second electric cylinder, and the top ends of the two sets of support arms are rotatably connected to the two sets of push blocks respectively. Two sets of push blocks are slidably mounted on the guide seat, and the two sets of push blocks are in contact with the lower part of the two sets of obstacle removal shovels respectively; Two sets of guide posts are slidably installed on the guide seats, and the ends of the two sets of guide posts are connected to two sets of obstacle removal shovels respectively. Two sets of springs are respectively fitted onto two sets of guide posts. When it is necessary to clean the surface of the power distribution line, the second electric cylinder pushes the two sets of support arms upward, so that the two sets of support arms support the two sets of push blocks to slide away from each other, thereby causing the two sets of push blocks to push the two sets of obstacle removal shovels away from each other. Then, the first electric cylinder drives the guide seat to move upward, so that the two sets of obstacle removal shovels move to both sides of the line. After that, the second electric cylinder drives the two sets of support arms to move downward and stop pushing the two sets of obstacle removal shovels. At this time, the two sets of obstacle removal shovels are pushed closer to each other and closed by the two sets of springs, so that the two sets of obstacle removal shovels contact the surface of the line. When the drone moves forward, the two sets of obstacle removal shovels remove dirt and foreign objects from the surface of the power distribution line.
[0014] Preferably, the marking device includes a storage tank, a nozzle, a first valve, a second valve, and a third valve; The storage tank is installed on the outer wall of the drone. The bottom of the nozzle extends into the storage tank; The first valve is connected to the nozzle; The second and third valves are respectively connected to the storage tank. The colored paint is put into the storage tank through the second valve and stored inside. The storage tank is pressurized through the third valve. When it is necessary to spray markings on the surface of the distribution network line, the first valve is opened to squeeze the paint out by the pressure inside the storage tank. The discharged paint is sprayed onto the surface of the distribution network line through the nozzle.
[0015] Preferably, the drive device includes a synchronous pulley, a synchronous belt, and a motor; Two sets of synchronous pulleys are respectively installed on the rotating ends of two sets of support pulleys; The timing belt is fitted between two sets of timing pulleys; The motor is mounted on the bracket, and the motor output is connected to one of the support wheels. The rotation of the support wheels is driven by the motor, and the two sets of support wheels are connected by two sets of synchronous pulleys and synchronous belts, which improves the convenience of the inspection robot moving on the line.
[0016] Preferably, it also includes a base; The base is located at the bottom of the drone body; this improves the stability of the drone body when it lands on the ground.
[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: By breaking down information silos through multi-source data fusion, integrating multi-dimensional data from power distribution automation, GIS, and meteorology, it provides comprehensive support for fault assessment. The intelligent assessment unit adopts an improved matrix algorithm and Bayesian network fusion positioning, combined with simulation and pre-drilling, to achieve accurate location of fault sections and safe verification of recovery plans, shortening assessment time and avoiding the risk of misoperation. The collaborative handling unit automatically generates and dispatches work orders, utilizes panoramic visualization to monitor the repair progress, and simultaneously improves on-site handling efficiency with real-time communication. The inspection robot achieves non-contact temperature measurement and foreign object removal, reducing human risk. Overall, it realizes fast response, accurate location, stable operation, and optimal decision-making for distribution network faults, comprehensively improving power supply reliability and intelligence level. Attached Figure Description
[0018] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram showing the connection between the multi-source data acquisition unit and the collaborative processing unit; Figure 3 This is a structural diagram showing the connection between the system management unit and the inspection and troubleshooting unit, etc. Figure 4 This is an isometric structural diagram of the connection between the drone body and the support frame, etc. Figure 5 This is an isometric structural diagram of the connection between the bracket and the support wheels, etc. Figure 6 This is an isometric structural diagram of the connection between the unmanned aerial vehicle body and the guide frame, etc. Figure 7 This is a partial isometric structural diagram showing the connection between the guide seat and the second electric cylinder, etc. Figure 8 This is an isometric structural diagram of the connection between the unmanned aerial vehicle body and the storage tank, etc. Figure 9 This is a partial isometric structural diagram of the connection between the storage tank and the third valve, etc. Figure 10 This is an isometric structural diagram showing the connection between the bracket and the motor, etc.
[0019] The attached diagram shows the following markings: 101, Unmanned Aerial Vehicle (UAV) body; 102, Support frame; 103, Support wheel; 104, Obstacle removal shovel; 105, First camera; 106, Infrared camera; 107, Second camera; 201, Guide frame; 202, First electric cylinder; 203, Guide seat; 204, Second electric cylinder; 205, Support arm; 206, Push block; 207, Guide column; 208, Spring; 301, Storage tank; 302, Nozzle; 303, First valve; 304, Second valve; 305, Third valve; 401, Synchronous pulley; 402, Synchronous belt; 403, Motor; 501, Base. Detailed Implementation
[0020] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
[0021] Example 1 like Figures 1 to 3 As shown, the present invention provides a distribution network line fault analysis and handling system, which includes a multi-source data acquisition unit, an intelligent fault analysis unit, a collaborative handling unit, a system management unit, and an inspection and troubleshooting unit. Multi-source data acquisition unit: Used to acquire fault-related data in real time from external multi-source heterogeneous systems, and to perform standardized processing and fusion to form a unified data pool. The multi-source heterogeneous systems include power distribution automation systems, electricity consumption information acquisition systems, geographic information systems, production management systems and meteorological information systems. The data collected includes switch position information, protection action information, voltage and current telemetry data, user power outage reporting information, line topology data, equipment ledger data and real-time meteorological data. Intelligent fault assessment unit: connected to the multi-source data acquisition unit, used to receive the fused data and execute the fault assessment preset logic to automatically generate the optimal fault isolation and non-faulty area power restoration suggestion scheme; Collaborative handling unit: Connected to the intelligent fault analysis unit, it is used to transform the analysis results and handling strategies into executable tasks, including: The work order generation and dispatch module automatically generates standardized work orders from the disposal plan and accurately dispatches them to the corresponding on-site maintenance and repair personnel through a mobile application. Visual monitoring module for handling process: Displays the fault point, power outage area, location of handling personnel, and work order execution progress in real time on the geographic information system, realizing panoramic visual monitoring; Multi-party collaborative communication module: Integrates voice, messaging, and video call functions, providing a unified collaborative work platform for dispatchers, field personnel, and management personnel; System Management Unit: Used to support the configuration, monitoring, and analysis of the entire system, including: Knowledge base management module: used to maintain and update fault assessment rules, emergency response plans, equipment parameters and historical case database, supporting both manual and self-learning methods; Permissions and Workflow Management Module: Defines user permissions for different roles, and allows customization of approval and workflow processes for fault handling; The comprehensive analysis and reporting module statistically analyzes key indicators such as historical fault data, judgment accuracy, and handling time, and automatically generates analysis reports to support network optimization and decision-making. Inspection and Troubleshooting Unit: Used to perform non-contact temperature measurement on power distribution lines using inspection robots, detect overheating defects, and remove foreign objects from the power distribution lines. The intelligent fault assessment unit includes a fault initiation module, a fault location module, a power outage impact range analysis module, and a handling strategy generation module. Fault Initiation Module: Based on preset rules or models, it performs correlation analysis on multi-source alarm information, confirms that the fault event has actually occurred, and triggers the judgment process; Fault location module: Based on real-time topology, switch status and electrical quantity information, it uses rule reasoning and artificial intelligence algorithms to automatically infer the section or equipment where the fault is most likely to occur and generate probabilistic location results; Power outage impact analysis module: Based on fault location results and network topology, it automatically analyzes and lists the affected substations, lines, transformers and users, and estimates the outage load; The fault isolation and power restoration strategy generation module automatically generates optimal fault isolation and power restoration suggestions for non-faulty areas based on fault location results, real-time network operation mode, and a pre-set fault isolation plan library. It also includes a simulation and deduction module; Simulation and simulation module: Located between the intelligent fault assessment unit and the collaborative handling unit, it is used to simulate the execution of the generated handling plan before the intelligent fault assessment unit officially issues the strategy, to pre-simulate the network topology changes and load transfer after the plan is executed, to verify the feasibility and security of the plan, and to provide early warning. The fault location module employs a multi-source information fusion location method based on an improved matrix algorithm, specifically: The distribution network topology is abstracted into a node and branch association matrix. By combining the switch action sequence and protection signal, the candidate fault sections are quickly narrowed down. Then, the fine-grained information of fault indicators and user voltage sags is integrated. The Bayesian network model is used to calculate the fault probability of each candidate section, and the section with the highest probability is output as the location result. The disposal strategy generation module has an embedded contingency plan priority and dynamic adjustment mechanism. First, it matches the historical disposal contingency plan with the highest similarity in the contingency plan library, and then dynamically checks and corrects the contingency plan in combination with the current real-time network topology. In this embodiment, information silos are broken down by multi-source data fusion, integrating multi-dimensional data from power distribution automation, GIS, and meteorology to provide comprehensive support for fault assessment. The intelligent assessment unit uses an improved matrix algorithm and Bayesian network fusion positioning, combined with simulation and pre-drilling, to achieve accurate location of the fault section and safe verification of the recovery plan, shortening the assessment time and avoiding the risk of misoperation. The collaborative handling unit automatically generates and dispatches work orders, uses panoramic visualization to monitor the repair progress, and coordinates with real-time communication to improve on-site handling efficiency. The inspection robot achieves non-contact temperature measurement and foreign object removal, reducing human risk. Overall, it realizes fast response, accurate location, stable operation, and optimal decision-making for power distribution network faults, comprehensively improving the reliability and intelligence level of power supply.
[0022] Example 2 Based on Example 1, such as Figures 4 to 10 As shown, this invention provides a distribution network line fault assessment and handling system. The inspection robot includes a drive unit, a support unit, a marking device, an unmanned body 101, a bracket 102, support wheels 103, an obstacle removal shovel 104, a first camera 105, an infrared camera 106, and a second camera 107. The bracket 102 is installed on the top of the unmanned aerial vehicle body 101; Two sets of support wheels 103 are mounted on the bracket 102 by means of a drive device to provide power for rotation. Both sets of obstacle removal shovels 104 are mounted on a support device, which is used to move and adjust the two sets of obstacle removal shovels 104. The first camera 105 and the infrared camera 106 are respectively installed on the outer wall of the unmanned body 101; The first set of second cameras 107 is installed on the outer wall of the drone body 101, and the second set of second cameras 107 is installed on the outer wall of the bracket 102. The marking device is installed on the unmanned aerial vehicle body 101, and the marking device is used to spray markings on the power distribution lines; The support device includes a guide frame 201, a first electric cylinder 202, a guide seat 203, a second electric cylinder 204, a support arm 205, a push block 206, a guide column 207, and a spring 208; Guide frame 201 is installed on the outer wall of the unmanned aerial vehicle body 101; The fixed end of the first electric cylinder 202 is mounted on the guide frame 201; The guide seat 203 is slidably mounted on the guide frame 201. The moving end of the first electric cylinder 202 is connected to the guide seat 203. The lower parts of the two sets of obstacle removal shovels 104 are slidably mounted on the guide seat 203. The second electric cylinder 204 is mounted on the guide seat 203; The bottom ends of the two sets of support arms 205 are rotatably connected to the moving end of the second electric cylinder 204, and the top ends of the two sets of support arms 205 are rotatably connected to the two sets of push blocks 206 respectively. Two sets of push blocks 206 are slidably mounted on the guide seat 203, and the two sets of push blocks 206 are in contact with the lower part of the two sets of obstacle removal shovels 104 respectively; Two sets of guide posts 207 are slidably mounted on guide seats 203, and the ends of the two sets of guide posts 207 are respectively connected to two sets of obstacle removal shovels 104; Two sets of springs 208 are respectively fitted onto two sets of guide posts 207; The marking device includes a storage tank 301, a nozzle 302, a first valve 303, a second valve 304, and a third valve 305; Storage tank 301 is installed on the outer wall of the unmanned aerial vehicle body 101; The bottom end of the nozzle 302 extends into the storage tank 301; The first valve 303 is connected to the nozzle 302; The second valve 304 and the third valve 305 are respectively connected to the storage tank 301; The drive device includes a synchronous pulley 401, a synchronous belt 402, and a motor 403; Two sets of synchronous pulleys 401 are respectively installed on the rotating ends of two sets of support pulleys 103; The timing belt 402 is fitted between the two sets of timing pulleys 401; Motor 403 is mounted on bracket 102, and the output end of motor 403 is connected to one of the support wheels 103; Also includes base 501; The base 501 is located at the bottom of the unmanned aerial vehicle body 101; In this embodiment, when the surface of the distribution network line needs to be cleaned, the second electric cylinder 204 pushes the two sets of support arms 205 upward, causing the two sets of support arms 205 to support the two sets of push blocks 206 to slide away from each other. This causes the two sets of push blocks 206 to push the two sets of obstacle removal shovels 104 away from each other. Then, the first electric cylinder 202 drives the guide seat 203 to move upward, causing the two sets of obstacle removal shovels 104 to move to both sides of the line. After that, the second electric cylinder 204 drives the two sets of support arms 205 to move downward, stopping the push on the two sets of obstacle removal shovels 104. At this time, the two sets of obstacle removal shovels 104 are respectively controlled by two sets of springs 2 08. Push them closer together to close, so that the two sets of obstacle removal shovels 104 come into contact with the surface of the power distribution line. When the UAV body 101 moves forward, the two sets of obstacle removal shovels 104 remove dirt and foreign objects from the surface of the power distribution line. The colored paint is put into the storage tank 301 through the second valve 304 and stored. The storage tank 301 is pressurized through the third valve 305. When it is necessary to spray markings on the surface of the power distribution line, the first valve 303 is opened, so that the pressure in the storage tank 301 squeezes the paint out. The discharged paint is sprayed onto the surface of the power distribution line through the nozzle 302.
[0023] The main functions achieved by this invention are: 1. By breaking down information silos through multi-source data fusion, comprehensive support is provided for fault assessment. The intelligent assessment unit adopts an improved matrix algorithm and Bayesian network fusion positioning, combined with simulation and pre-drilling, to achieve accurate positioning of faulty sections and safe verification of recovery plans, shortening assessment time and avoiding the risk of misoperation. 2. By using inspection robots to transmit the captured images to the backend in real time, high-temperature faults and surface damage defects of the distribution network lines can be detected. When a defect is detected, the location is marked by a marking device, which makes it easier for maintenance personnel to find the location and improves work efficiency and convenience. 3. During the inspection and movement process, two sets of obstacle removal shovels 104 are used to rub the distribution network line around it, which makes it easier to remove dirt and foreign objects from the surface of the line and reduces the complexity of personnel cleaning.
[0024] The unmanned aerial vehicle body 101, first camera 105, infrared camera 106, second camera 107, first electric cylinder 202, second electric cylinder 204 and motor 403 of the power distribution line fault analysis and handling system of the present invention are commercially available. Technical personnel in this industry only need to install and operate them according to the accompanying instruction manual, without requiring any creative work from those skilled in the art.
[0025] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A distribution network line fault analysis and handling system, characterized in that, It includes a multi-source data acquisition unit, an intelligent fault analysis unit, a collaborative handling unit, a system management unit, and an inspection and troubleshooting unit; Multi-source data acquisition unit: Used to acquire fault-related data in real time from external multi-source heterogeneous systems, and to perform standardized processing and fusion to form a unified data pool. The multi-source heterogeneous systems include power distribution automation systems, electricity consumption information acquisition systems, geographic information systems, production management systems and meteorological information systems. The data collected includes switch position information, protection action information, voltage and current telemetry data, user power outage reporting information, line topology data, equipment ledger data and real-time meteorological data. Intelligent fault assessment unit: connected to the multi-source data acquisition unit, used to receive the fused data and execute the fault assessment preset logic to automatically generate the optimal fault isolation and non-faulty area power restoration suggestion scheme; Collaborative handling unit: Connected to the intelligent fault analysis unit, it is used to transform the analysis results and handling strategies into executable tasks, including: The work order generation and dispatch module automatically generates standardized work orders from the disposal plan and accurately dispatches them to the corresponding on-site maintenance and repair personnel through a mobile application. Visual monitoring module for handling process: Displays the fault point, power outage area, location of handling personnel, and work order execution progress in real time on the geographic information system, realizing panoramic visual monitoring; Multi-party collaborative communication module: Integrates voice, messaging, and video call functions, providing a unified collaborative work platform for dispatchers, field personnel, and management personnel; System Management Unit: Used to support the configuration, monitoring, and analysis of the entire system, including: Knowledge base management module: used to maintain and update fault assessment rules, emergency response plans, equipment parameters and historical case database, supporting both manual and self-learning methods; Permissions and Workflow Management Module: Defines user permissions for different roles, and allows customization of approval and workflow processes for fault handling; The comprehensive analysis and reporting module statistically analyzes key indicators such as historical fault data, judgment accuracy, and handling time, and automatically generates analysis reports to support network optimization and decision-making. Inspection and troubleshooting unit: Used to perform non-contact temperature measurement on power distribution lines using inspection robots, detect overheating defects, and remove foreign objects from the power distribution lines.
2. The distribution network line fault analysis and handling system as described in claim 1, characterized in that, The intelligent fault assessment unit includes a fault initiation module, a fault location module, a power outage impact range analysis module, and a handling strategy generation module. Fault Initiation Module: Based on preset rules or models, it performs correlation analysis on multi-source alarm information, confirms that the fault event has actually occurred, and triggers the judgment process; Fault location module: Based on real-time topology, switch status and electrical quantity information, it uses rule reasoning and artificial intelligence algorithms to automatically infer the section or equipment where the fault is most likely to occur and generate probabilistic location results; Power outage impact analysis module: Based on fault location results and network topology, it automatically analyzes and lists the affected substations, lines, transformers and users, and estimates the outage load; The fault isolation and power restoration strategy generation module automatically generates optimal fault isolation and power restoration suggestions for non-faulty areas based on fault location results, real-time network operation mode, and a pre-set fault isolation plan library.
3. The distribution network line fault assessment and handling system as described in claim 1, characterized in that, It also includes a simulation and deduction module; Simulation and deduction module: Located between the intelligent fault assessment unit and the collaborative handling unit, it is used to simulate the execution of the generated handling plan before the intelligent fault assessment unit officially issues the strategy, to pre-simulate the network topology changes and load transfer after the plan is executed, to verify the feasibility and security of the plan, and to provide early warning.
4. The distribution network line fault analysis and handling system as described in claim 2, characterized in that, The fault location module employs a multi-source information fusion location method based on an improved matrix algorithm, specifically: The distribution network topology is abstracted into a node and branch association matrix. By combining the switch action sequence and protection signal, the candidate fault sections are quickly narrowed down. Then, the fine-grained information of fault indicators and user voltage sags is integrated. The fault probability of each candidate section is calculated using a Bayesian network model, and the section with the highest probability is output as the location result.
5. The distribution network line fault assessment and handling system as described in claim 2, characterized in that, The disposal strategy generation module has an embedded contingency plan priority and dynamic adjustment mechanism. First, it matches the historical disposal contingency plan with the highest similarity in the contingency plan library, and then dynamically verifies and corrects the contingency plan in combination with the current real-time network topology.
6. The distribution network line fault analysis and handling system as described in claim 1, characterized in that, The inspection robot includes a drive unit, a support unit, a marking unit, an unmanned body (101), a bracket (102), support wheels (103), an obstacle removal shovel (104), a first camera (105), an infrared camera (106), and a second camera (107). The bracket (102) is installed on the top of the unmanned aerial vehicle (101); Two sets of support wheels (103) are mounted on the bracket (102) by means of a drive device to provide power for rotation; Both sets of obstacle removal shovels (104) are mounted on a support device, which is used to move and adjust the two sets of obstacle removal shovels (104); The first camera (105) and the infrared camera (106) are respectively installed on the outer wall of the unmanned vehicle body (101); The first set of second cameras (107) is installed on the outer wall of the unmanned vehicle body (101), and the second set of second cameras (107) is installed on the outer wall of the bracket (102); The marking device is installed on the unmanned aerial vehicle (101) and is used to spray markings on the power distribution lines.
7. The distribution network line fault assessment and handling system as described in claim 6, characterized in that, The support device includes a guide frame (201), a first electric cylinder (202), a guide seat (203), a second electric cylinder (204), a support arm (205), a push block (206), a guide column (207), and a spring (208). The guide frame (201) is installed on the outer wall of the unmanned aerial vehicle body (101); The fixed end of the first electric cylinder (202) is mounted on the guide frame (201); The guide seat (203) is slidably mounted on the guide frame (201), the moving end of the first electric cylinder (202) is connected to the guide seat (203), and the lower parts of the two sets of obstacle removal shovels (104) are slidably mounted on the guide seat (203); The second electric cylinder (204) is mounted on the guide seat (203); The bottom ends of the two sets of support arms (205) are rotatably connected to the moving end of the second electric cylinder (204), and the top ends of the two sets of support arms (205) are rotatably connected to the two sets of push blocks (206) respectively. Two sets of push blocks (206) are slidably mounted on the guide seat (203), and the two sets of push blocks (206) are in contact with the lower part of the two sets of obstacle removal shovels (104); Two sets of guide posts (207) are slidably installed on guide seats (203), and the ends of the two sets of guide posts (207) are connected to two sets of obstacle removal shovels (104) respectively; Two sets of springs (208) are respectively fitted onto two sets of guide posts (207).
8. The distribution network line fault assessment and handling system as described in claim 6, characterized in that, The marking device includes a storage tank (301), a nozzle (302), a first valve (303), a second valve (304), and a third valve (305). The storage tank (301) is installed on the outer wall of the unmanned aerial vehicle (101); The bottom end of the nozzle (302) extends into the storage tank (301); The first valve (303) is connected to the nozzle (302); The second valve (304) and the third valve (305) are respectively connected to the storage tank (301).
9. The distribution network line fault assessment and handling system as described in claim 6, characterized in that, The drive device includes a synchronous pulley (401), a synchronous belt (402), and a motor (403). Two sets of synchronous pulleys (401) are respectively installed on the rotating ends of two sets of support pulleys (103); The timing belt (402) is fitted between two sets of timing pulleys (401); The motor (403) is mounted on the bracket (102), and the output end of the motor (403) is connected to one of the support wheels (103).
10. The distribution network line fault assessment and handling system as described in claim 6, characterized in that, It also includes the base (501); The base (501) is located at the bottom of the unmanned aircraft body (101).