Power distribution network line fault diagnosis system and method

Abstract

The invention provides a power distribution network line fault diagnosis system and method, and belongs to the technical field of power system data processing, and the system comprises a data collection module which divides a power distribution network into different physical regions, and deploys a preset sensor group in each physical region to collect the related data of the power distribution network; the map construction module is used for determining the health state of each physical region and further constructing a regional health map; the data analysis module is used for deploying a monitoring tool, acquiring monitoring data of each physical area and analyzing the monitoring data so as to acquire a fault influence coefficient of each line of the power distribution network; the strategy making module is used for determining a corresponding prevention and control strategy and a self-healing strategy based on the fault influence coefficient of each line; and the load optimization module is used for determining a load distribution coefficient of each line based on the fault influence coefficient of each line so as to optimize power grid distribution. Effective power distribution network line fault diagnosis and analysis are achieved, load distribution of a power grid is optimized, and the self-healing capacity is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system data processing, and in particular to a distribution network line fault diagnosis system and method. Background Art

[0002] As the scale of modern power grids continues to expand and become more complex, the operation status monitoring and fault diagnosis of distribution networks have become increasingly important. Existing distribution network fault diagnosis systems are usually based on traditional electrical parameter detection methods, which makes it difficult to accurately locate the fault area in a complex power grid environment.

[0003] The closest existing technical solution is to install a certain number of sensors in the distribution network to monitor the electrical parameters such as current and voltage of the line to identify faults. However, this type of solution is easily limited by data redundancy and processing efficiency issues when dealing with large-scale power grids, and it is difficult to effectively combine regional health status for comprehensive analysis. In addition, existing technologies also have limitations in optimizing load distribution and improving the self-healing ability of the power grid.

[0004] Therefore, the present invention provides a distribution network line fault diagnosis system and method. Summary of the invention

[0005] The present invention provides a distribution network line fault diagnosis system, which is used to construct a regional health map by dividing physical areas and combining sensor data, thereby realizing accurate monitoring and fault location of distribution network lines, introducing fault influence coefficients, and combining regional health status analysis to formulate targeted prevention and self-healing strategies, thereby improving the accuracy of fault diagnosis and self-healing ability, improving the accuracy of distribution network fault location, and realizing more effective monitoring and analysis through comprehensive regional health status, thereby optimizing the load distribution of the power grid and improving the self-healing ability.

[0006] The present invention provides a distribution network line fault diagnosis system, comprising: Data acquisition module: divides the distribution network into different physical areas, and deploys a preset sensor group in each physical area to collect distribution network related data; Graph construction module: Use distribution network related data to determine the health status of each physical area and then build a regional health graph; Data analysis module: Deploy monitoring tools based on regional health maps, obtain and analyze monitoring data of each physical area based on the monitoring tools, and then obtain the fault impact coefficient of each line in the distribution network; Strategy formulation module: Determines the corresponding prevention control strategy and self-healing strategy based on the fault impact coefficient of each line; Load optimization module: Determine the load distribution coefficient of each line based on the fault impact coefficient of each line, and then optimize the power grid distribution.

[0007] The present invention provides a distribution network line fault diagnosis system, a data acquisition module, comprising: A first division unit: acquiring geographic information of the distribution network, extracting geographic features of the distribution network based on the geographic information of the distribution network, performing a first division on the distribution network based on the geographic features of the distribution network, and acquiring a plurality of first areas; A level determination unit: acquiring historical maintenance data and equipment service life data of equipment in the first area, and then determining the classification level of the equipment in the first area; A second division unit: performing a second division on the first area based on the division levels of the devices in the first area, thereby obtaining a plurality of second areas; A load determination unit: acquiring historical load data of each line in each second area, and then determining a load level of each second area; A third division unit: performing a third division on the second area based on the load level of each second area, thereby obtaining a plurality of third areas, and further determining the third areas as physical areas for distribution network division; Regional deployment unit: a preset sensor group is deployed in each third area to collect distribution network related data.

[0008] The present invention provides a distribution network line fault diagnosis system, a graph construction module, comprising: Data analysis unit: Analyze the distribution network related data to obtain the line health related data in each physical area and the equipment health related data in each physical area; A first acquisition unit: determining a health coefficient of a line in each physical area based on line health related data in each physical area; A second acquisition unit: determining a health coefficient of the equipment in each physical area based on the equipment health related data in each physical area; A state determination unit: determining a health state of each physical area based on a health coefficient of a line and a health coefficient of a device in each physical area; Map construction unit: Builds a regional health map based on the health status of each physical area and a preset visualization method.

[0009] The present invention provides a distribution network line fault diagnosis system, a first acquisition unit, comprising: Analyze the line health related data in each physical area to obtain several sub-health coefficients; A combined function of the sub-health coefficients of the lines in each physical area is determined based on the sub-health coefficients of the lines in each physical area: ;in, is the qth sub-health factor of the line in the ith physical area, The number of sub-health coefficients of the line in the ith physical area, is the preset weight corresponding to the qth sub-health coefficient of the line in the ith physical area, is the preset adjustment weight corresponding to the sub-health coefficient, is the interaction weight corresponding to the sub-health coefficient, is the p-th sub-health coefficient of the line in the ith physical area except the p-th sub-health coefficient, It is the preset dynamic fluctuation adjustment coefficient.

[0010] Determine the health factor of the line in each physical area based on the line health related data in each physical area: ;in, is the health factor of the line in the ith physical area, is the preset global health coefficient benchmark of the ith physical area, and , is the voltage fluctuation frequency of the line in the i-th physical area in the j-th voltage interval, n1 is the number of voltage intervals of the preset voltage fluctuation, is the adjustment coefficient corresponding to the combined function of the sub-health coefficients, k is the constant of the time decay rate, t is the current time, The last time of major maintenance. is the preset attenuation control coefficient, The present invention provides a distribution network line fault diagnosis system, a second acquisition unit, comprising: Determine the health factor of the equipment in each physical area based on the equipment health related data in each physical area: ; ; in, is the health factor of the equipment in the ith physical area, is the hth operating parameter of the uth device in the i-th physical area, is the number of devices in the ith physical area, is the corresponding weight of the u-th device in the i-th physical area, is the operation function of the u-th device in the i-th physical area, is the historical failure coefficient of the equipment in the ith physical area, is the preset interaction weight, is the hth operating parameter of the rth device in the i-th physical area, is the operation function of the rth device in the i-th physical area, is the interaction coefficient of the equipment in the ith physical area, is the number of operating parameters of the u-th device in the i-th physical area, is the number of operating parameters of the rth device in the i-th physical area, is a logarithmic function.

[0011] The present invention provides a distribution network line fault diagnosis system, a strategy formulation module, comprising: Priority determination unit: prioritizes lines according to the magnitude of the fault impact coefficient, and then determines the priority of each line; Fault assessment unit: obtains historical data and monitoring data of each line, identifies the possible fault types of each line, and conducts risk assessment on the fault types to obtain the risk coefficient of each fault type; Strategy determination unit: according to the priority of each line and the risk factor of the fault type, the corresponding preventive control strategy is determined based on the preset priority-fault type risk factor-control strategy database; Strategy startup unit: When the fault impact coefficient exceeds the preset self-healing strategy trigger value, the corresponding self-healing strategy is started.

[0012] The present invention provides a method for diagnosing faults in a power distribution network, comprising: Step 1: Divide the distribution network into different physical areas, and deploy a preset sensor group in each physical area to collect distribution network related data; Step 2: Use distribution network related data to determine the health status of each physical area and then build a regional health map; Step 3: Deploy monitoring tools based on the regional health map, obtain and analyze the monitoring data of each physical area based on the monitoring tools, and then obtain the fault impact coefficient of each line in the distribution network; Step 4: Determine the corresponding prevention control strategy and self-healing strategy based on the fault impact coefficient of each line; Step 5: Determine the load distribution coefficient of each line based on the fault impact coefficient of each line, and then optimize the power grid distribution.

[0013] Compared with the prior art, the present invention has the following beneficial effects: By dividing the physical areas and combining sensor data to build a regional health map, accurate monitoring and fault location of the distribution network lines are achieved. By introducing the fault impact coefficient and combining it with regional health status analysis, targeted prevention and self-healing strategies are formulated, which improves the accuracy of fault diagnosis and self-healing capabilities, and the accuracy of distribution network fault location. More effective monitoring and analysis are achieved through comprehensive regional health status, thereby optimizing the load distribution of the power grid and improving its self-healing capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0015] Figure 1 It is a structural schematic diagram of a distribution network line fault diagnosis system provided by an embodiment of the present invention.

[0016] Figure 2 It is a flow chart of a distribution network line fault diagnosis method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1 The embodiment of the present invention provides a distribution network line fault diagnosis system, such as Figure 1 As shown, including: Data acquisition module: divides the distribution network into different physical areas, and deploys a preset sensor group in each physical area to collect distribution network related data; Graph construction module: Use distribution network related data to determine the health status of each physical area and then build a regional health graph; Data analysis module: Deploy monitoring tools based on regional health maps, obtain and analyze monitoring data of each physical area based on the monitoring tools, and then obtain the fault impact coefficient of each line in the distribution network; Strategy formulation module: Determines the corresponding prevention control strategy and self-healing strategy based on the fault impact coefficient of each line; Load optimization module: Determine the load distribution coefficient of each line based on the fault impact coefficient of each line, and then optimize the power grid distribution.

[0019] In this embodiment, the physical area refers to the division of the entire distribution network into several smaller geographical or functional units in the distribution network system for better management and monitoring. These areas are usually divided according to the physical layout or load characteristics of the power grid to facilitate the management of equipment or lines in the area. For example, the distribution network of a city can be divided into different physical areas according to urban areas, industrial areas, residential areas, etc., and each area has its own specific power grid structure and power demand; In this embodiment, the preset sensor group refers to a group of sensors deployed in each physical area, which is used to collect data related to the operation status of the distribution network in real time. These sensors are pre-set at specific locations and can monitor voltage, current, temperature, humidity, etc. For example, in a physical area, a group of sensors is installed to measure the current and voltage of the main transformer and the ambient temperature. These sensors monitor the operation of the equipment in real time; In this embodiment, relevant data refers to various data information generated during the operation of the distribution network, which is mainly used to diagnose the health status of the power grid, including voltage, current, power, temperature, humidity, fault alarm information, etc. For example, in a certain physical area, the collected relevant data include cable temperature, current fluctuations, and frequent voltage abnormality data.

[0020] In this embodiment, the health status of a physical area refers to analyzing the operating conditions of the equipment and lines in the area based on the data collected by the sensors to determine whether there is a risk of abnormality or potential failure. For example, by analyzing the data such as the cable temperature and current changes in a certain area, it is determined that the lines in the area are at risk of overload and are in a poor health state. In this embodiment, the regional health map is a chart or model constructed based on the health status of the physical area, which represents the health status of different areas of the entire distribution network. It can be used to visualize the operating status of different areas and facilitate the rapid location of problem areas. For example, by constructing a regional health map, it is found that the health status of the power grid in a certain industrial area is poor, which may be due to aging and overload operation of equipment.

[0021] In this embodiment, the deployment of monitoring tools based on the regional health map is to deploy additional monitoring tools in areas with poor health or high risk of failure according to the health status displayed by the regional health map, so as to improve the monitoring accuracy and track the status changes of the problem areas in real time. For example, if the health map shows that there is an overload risk in a certain area, then the area is equipped with intelligent current monitoring equipment to track the line load changes in real time; In this embodiment, the monitoring tool refers to a device or system used to monitor and collect information about the operating status of the distribution network, such as an ammeter, a voltmeter, a fault detector, etc., to help analyze and diagnose the status of the power grid. For example, a current monitor is installed on the line. When the current exceeds a preset value, the monitoring tool will sound an alarm and transmit the data to the control center. In this embodiment, the fault impact coefficient is an indicator determined through data analysis, which is used to evaluate the impact of each line fault on the overall operation of the distribution network. The higher the coefficient, the greater the impact of the line fault on the system. For example, the fault impact coefficient of a trunk line is very high, indicating that its fault may cause large-scale power outages, and therefore needs to be handled with priority.

[0022] In this embodiment, the preventive control strategy refers to the response measures formulated to avoid potential failures or risks. By analyzing the health map and the failure impact coefficient, measures are taken in advance to reduce the probability of failure. For example, when the health status of a certain area is poor, the load distribution of the area is adjusted to reduce the line load to prevent the occurrence of overload failures. In this embodiment, the self-healing strategy refers to the repair measures automatically taken by the system after a fault occurs, so that the fault area can quickly restore power supply or minimize the impact. It usually relies on smart devices and automated control technology. For example, when a branch fails, the self-healing strategy automatically switches the power supply path so that users in the area can restore power supply in a short time.

[0023] In this embodiment, the load distribution coefficient is a parameter for reasonably distributing the load according to the health status and fault impact coefficient of each line, so as to ensure a more stable and balanced operation of the power grid and reduce the risk of overload operation of a certain line. For example, in a certain area, the load distribution coefficient of a certain line is relatively high, so part of the load is distributed to the line with a better health status to reduce the risk.

[0024] The beneficial effects of the above technical solution are: by dividing the physical areas and combining the sensor data to build a regional health map, accurate monitoring and fault location of the distribution network lines are achieved, the fault impact coefficient is introduced, and combined with the regional health status analysis, targeted prevention and self-healing strategies are formulated, which improves the accuracy of fault diagnosis and self-healing ability, improves the accuracy of distribution network fault location, and achieves more effective monitoring and analysis through comprehensive regional health status, thereby optimizing the load distribution of the power grid and improving the self-healing ability.

[0025] Example 2 The embodiment of the present invention provides a distribution network line fault diagnosis system, a data acquisition module, including: A first division unit: acquiring geographic information of the distribution network, extracting geographic features of the distribution network based on the geographic information of the distribution network, performing a first division on the distribution network based on the geographic features of the distribution network, and acquiring a plurality of first areas; A level determination unit: acquiring historical maintenance data and equipment service life data of equipment in the first area, and then determining the classification level of the equipment in the first area; A second division unit: performing a second division on the first area based on the division levels of the devices in the first area, thereby obtaining a plurality of second areas; A load determination unit: acquiring historical load data of each line in each second area, and then determining a load level of each second area; A third division unit: performing a third division on the second area based on the load level of each second area, thereby obtaining a plurality of third areas, and further determining the third areas as physical areas for distribution network division; Regional deployment unit: a preset sensor group is deployed in each third area to collect distribution network related data.

[0026] In this embodiment, the geographic information of the distribution network refers to the layout, location and related geographic environment information of the distribution network in the actual geographic space, including the location coordinates of the distribution lines, substations, and equipment, as well as natural environment characteristics such as terrain and climate in the region. For example, the coordinates of substation A located in a certain city (35.6895° N, 139.6917° E), and the region is mountainous terrain. This information helps determine the distribution and management strategy of the equipment; In this embodiment, the geographical features are key features extracted from the geographical information of the distribution network, such as terrain type (mountainous, plain), climate conditions, line density, etc., which are helpful for regional division and fault risk assessment of the distribution network. For example, the possible geographical features of a mountain distribution network include high altitude and humid climate, which affect line maintenance and equipment failure risks; In this embodiment, the first division of the distribution network based on geographical features refers to dividing the distribution network into different areas according to conditions such as terrain, climate, and equipment density. In this way, the equipment and lines in different areas can be managed more targeted. For example, the distribution network is divided into high-altitude areas and plain areas according to geographical features. Each area will have different maintenance strategies due to different environments. In this embodiment, the first area is the area obtained after the first division. These areas are the result of the preliminary division of the distribution network and are used to further refine the management and diagnosis strategies. For example, in the distribution network division, the part close to the mountainous area is divided into the first area 1, and the part located in the city is divided into the first area 2, so as to handle their maintenance needs separately.

[0027] In this embodiment, the classification level of the equipment is the health status or risk level assigned to the equipment based on the historical maintenance data, service life and other information of the equipment. The classification level is used to determine the risk priority of the equipment in different areas. For example, a transformer is classified as a lower level because it has not been maintained for a long time and has a long service life, indicating that priority maintenance is required.

[0028] In this embodiment, the second division is to further divide the first area based on the division level of the device, thereby obtaining the second area, so as to monitor and manage the health status of the device more finely. For example, in the first area 1, according to different health conditions of the device, it is divided into the second area 1 and the second area 2, representing healthy and high-risk device areas respectively; In this embodiment, the second area is a more refined management area obtained after the second division, so that targeted operation and maintenance strategies can be adopted according to the risk level of the equipment. For example, in the first area 2, healthy equipment is divided into the second area A, and aging equipment is divided into the second area B, so that status monitoring and priority maintenance can be performed separately.

[0029] In this embodiment, the load level indicates the historical load level of the line in different areas, reflecting the load pressure and potential overload risk of the line. The load level is used to evaluate and allocate line maintenance resources. For example, the historical load data of a line is high, so the line is classified as a high load level and needs to be monitored intensively to avoid failures caused by overload. In this embodiment, the third division is based on the load level of each second area, and the second area is further subdivided into third areas to form a final physical management area. For example, in the second area with a high load level, several third areas are further divided according to the specific load distribution, so as to deploy more sensors for real-time monitoring; In this embodiment, the third area is the final physical area division unit, which is used to install sensors and collect data in the distribution network. The areas are divided after considering multiple factors such as equipment health and load, which facilitates more effective diagnosis and monitoring. For example, temperature and current sensors are installed in the high-load third area to monitor the line health in real time and ensure timely response in case of overload.

[0030] The beneficial effects of the above technical solution are: fine zoning of the distribution network through multi-level division, optimization of zoning based on equipment historical data, load level, etc., deployment of sensor groups in the final physical area to collect data, and hierarchical division of regions based on geographical features and equipment status, which improves the accuracy and real-time performance of data collection and provides more efficient data support for subsequent fault diagnosis and prevention.

[0031] Example 3 The embodiment of the present invention provides a distribution network line fault diagnosis system, a graph construction module, including: Data analysis unit: Analyze the distribution network related data to obtain the line health related data in each physical area and the equipment health related data in each physical area; A first acquisition unit: determining a health coefficient of a line in each physical area based on line health related data in each physical area; A second acquisition unit: determining a health coefficient of the equipment in each physical area based on the equipment health related data in each physical area; A state determination unit: determining a health state of each physical area based on a health coefficient of a line and a health coefficient of a device in each physical area; Map construction unit: Builds a regional health map based on the health status of each physical area and a preset visualization method.

[0032] In this embodiment, the line health-related data refers to data used to evaluate the operating status of each line in the distribution network, including voltage, current, temperature, line load, short-circuit current, insulation status, etc., which are indicators directly related to the safety and stability of line operation. For example, the health-related data of a distribution line may include the current fluctuation frequency of the line, temperature anomaly records, and the number of short circuits that have occurred in history; In this embodiment, the equipment health-related data refers to data used to evaluate the health status of various equipment (such as transformers, switches, circuit breakers, etc.) in the distribution network, including the service life, maintenance records, temperature, vibration, overload history, etc. of the equipment. For example, the equipment health-related data of a transformer may include its operating temperature, oil level, service life, maintenance times, etc.

[0033] In this embodiment, the health coefficient of the line is a quantitative indicator obtained by analyzing the line health-related data, which is used to indicate the health status of the line. The health coefficient is usually a score or grade, reflecting the failure risk of the line. The higher the coefficient, the better the health status of the line. For example, the health coefficient of a distribution line is 90 points, indicating that the line is in good operating condition and has a low failure risk; while the health coefficient of another line is 60 points, which means that it needs to be monitored or repaired.

[0034] In this embodiment, the health coefficient of the equipment is a quantitative indicator calculated based on equipment health-related data, which indicates the operating status and failure risk of the equipment. The higher the equipment health coefficient, the better the operating status of the equipment and the lower the probability of failure. For example, the health coefficient of a circuit breaker is 85 points, indicating that it is in good condition, while the health coefficient of another old circuit breaker is only 40 points, indicating that it needs to be replaced or repaired as soon as possible.

[0035] In this embodiment, the health coefficient of the lines and the health coefficient of the equipment in each physical area determine the health status of each physical area. For example, the overall health status of the area is comprehensively judged by combining the health coefficients of all lines and equipment in the physical area. The higher the health coefficients of the lines and equipment, the better the operating stability of the area; the lower the coefficient, the potential failure risk in the area. For example, the health coefficients of all lines and equipment in a physical area are very high, so the health status of the area is judged to be "good". On the contrary, the health coefficients of multiple lines and equipment in another area are low, and the health status of the area is evaluated as "dangerous", which requires further monitoring and maintenance.

[0036] In this embodiment, the preset visualization method refers to presenting the health status of each physical area of ​​the distribution network in a certain graphical manner, usually through color coding, charts, maps, etc., to facilitate users to intuitively view and analyze the health status of each area, including pie charts, heat maps, bar charts, etc. For example, in the form of a heat map, areas with good health status are represented by green, while areas with poor health status are marked with red or orange, helping operation and maintenance personnel to quickly locate areas that need attention.

[0037] The beneficial effects of the above technical solution are: by constructing a regional health map, integrating line and equipment health data, dynamically evaluating the health status of each physical area, and presenting it in a visual way, it achieves comprehensive integration and intuitive display of the health status of equipment and lines, providing a clear and rapid diagnostic basis for maintenance decisions, and effectively improving fault prevention and response efficiency.

[0038] Example 4 An embodiment of the present invention provides a distribution network line fault diagnosis system, wherein a first acquisition unit includes: Analyze the line health related data in each physical area to obtain several sub-health coefficients; A combined function of the sub-health coefficients of the lines in each physical area is determined based on the sub-health coefficients of the lines in each physical area: ;in, is the qth sub-health factor of the line in the ith physical area, The number of sub-health coefficients of the line in the ith physical area, is the preset weight corresponding to the qth sub-health coefficient of the line in the ith physical area, is the preset adjustment weight corresponding to the sub-health coefficient, is the interaction weight corresponding to the sub-health coefficient, is the p-th sub-health coefficient of the line in the ith physical area except the p-th sub-health coefficient, It is the preset dynamic fluctuation adjustment coefficient.

[0039] Determine the health factor of the line in each physical area based on the line health related data in each physical area: ;in, is the health factor of the line in the ith physical area, is the preset global health coefficient benchmark of the ith physical area, and , is the voltage fluctuation frequency of the line in the i-th physical area in the j-th voltage interval, n1 is the number of voltage intervals of the preset voltage fluctuation, is the adjustment coefficient corresponding to the combined function of the sub-health coefficients, k is the constant of the time decay rate, t is the current time, The last time of major maintenance. is the preset attenuation control coefficient, In this embodiment, the preset global health coefficient benchmark is a reference standard value for evaluating the health status of the distribution network, which usually represents the health level under ideal conditions. Compared with the health coefficient, the benchmark value can help determine whether the health status of the current line or area meets the standard. For example, the preset global health coefficient benchmark of a distribution network system is 100 points. If the health coefficient of a line in a certain area is 80 points, it means that the health status of the area is lower than the ideal standard and may need to be repaired; In this embodiment, the number of voltage intervals indicates the number of sub-intervals into which the voltage value range is divided. Each voltage interval can reflect the degree of different voltage fluctuations, which helps to analyze voltage stability more carefully. For example, in a voltage range from 0 to 240 volts, it is divided into 5 voltage intervals (such as 0-50V, 50-100V, 100-150V, etc.), then the number of voltage intervals is 5. The fluctuation frequency of different intervals helps to determine whether the operation of the line is stable; In this embodiment, the combination function of the sub-health coefficient is a function used to integrate multiple sub-health coefficients. It can combine the values, weights, and interaction factors of different sub-health coefficients to determine the health status of the entire line or area. Assume that a line in a physical area has three sub-health coefficients: temperature coefficient, current coefficient, and insulation coefficient. The combination function obtains the overall health status of the line in the area by adding these coefficients according to their respective weights; In this embodiment, the preset attenuation control coefficient is used to control the rate of decline of the health factor over time, reflecting the gradual aging process of the health condition. Over time, even if there is no major fault, the health factor will gradually decline due to the aging of the equipment or the operating load. For example, the preset attenuation control coefficient of a line in a certain area is 0.9, which means that the health factor will decay at this ratio every once in a while. Assuming that the current health factor is 90, it will become 90×0.9=81 in the next evaluation, which reflects the natural aging of the equipment.

[0040] In this embodiment, the sub-health factor refers to a value that reflects the health status of a specific attribute or sub-indicator in the distribution network line. The sub-health factor can be obtained by quantifying the status of each single indicator such as temperature, current fluctuation, insulation condition, etc. For example, the sub-health factor of a line may include a temperature health factor of 85, a current health factor of 80, and an insulation health factor of 90. These values ​​indicate the health status of the line in these aspects; In this embodiment, the dynamic fluctuation adjustment coefficient is a parameter used to balance the line health coefficient. When the operating status of the line fluctuates significantly, the coefficient can make appropriate adjustments to the health status to reflect the real-time health status. For example, when the temperature of a line suddenly rises, the dynamic fluctuation adjustment coefficient may be set to 1.2 to increase the sensitivity of the line health status, thereby increasing the adjustment range of the health coefficient and reflecting the immediate impact of temperature fluctuations on the line.

[0041] The beneficial effects of the above technical solution are: by establishing a line health coefficient, comprehensively considering the voltage fluctuation frequency, time attenuation and multiple seed health coefficients and their weights and dynamic interaction effects, dynamically adjusting the health assessment, and using multi-dimensional data to achieve accurate line health status assessment, it provides a more reliable basis for fault diagnosis and prevention, and significantly improves the accuracy and timeliness of diagnosis.

[0042] Example 5 An embodiment of the present invention provides a distribution network line fault diagnosis system, wherein a second acquisition unit includes: Determine the health factor of the equipment in each physical area based on the equipment health related data in each physical area: ; ; in, is the health factor of the equipment in the ith physical area, is the hth operating parameter of the uth device in the i-th physical area, is the number of devices in the ith physical area, is the corresponding weight of the u-th device in the i-th physical area, is the operation function of the u-th device in the i-th physical area, is the historical failure coefficient of the equipment in the ith physical area, is the preset interaction weight, is the hth operating parameter of the rth device in the i-th physical area, is the operation function of the rth device in the i-th physical area, is the interaction coefficient of the equipment in the ith physical area, is the number of operating parameters of the u-th device in the i-th physical area, is the number of operating parameters of the rth device in the i-th physical area, is a logarithmic function.

[0043] In this embodiment, the operating parameters refer to the key indicators or status data that need to be monitored during the operation of the device, which reflect the working condition and load status of the device, and are usually used to evaluate the health status and performance of the device. For example, the operating parameters of the transformer equipment in the distribution network may include voltage, current, temperature, power factor, etc. These parameters can help determine whether the transformer is working within the normal range; In this embodiment, the historical failure coefficient is a coefficient determined based on the historical failure data of the device, which reflects the frequency or severity of past failures of the device. This coefficient is used to assess the possibility of future failures of the device and the risk of the current health status. For example, if a cable has experienced three failures in the past year, each of which takes a long time to repair and is a high-risk failure type, then its historical failure coefficient may be set to a higher value (such as 0.8), indicating that the failure risk of the device is relatively high; In this embodiment, the operation function of the device is a mathematical expression that describes the relationship between various parameters and the overall behavior of the device during operation. The operation function integrates various operation parameters of the device. For example, for a transformer, the operation function of the device may represent the relationship between current, temperature and load. For example, when the load increases, the temperature rises and the current increases. The operation function can evaluate the health of the transformer through the change trend of these parameters and adjust the health factor.

[0044] The beneficial effects of the above technical solution are: by calculating the health coefficient of the equipment in each physical area, comprehensively considering the equipment operating parameters, weights, historical failure coefficients and the interaction between equipment, comprehensively evaluating the health status of the equipment, and realizing refined and dynamic health monitoring, it provides a scientific basis for the precise maintenance and prevention of the distribution network, and effectively improves the reliability of the system and the fault warning capability.

[0045] Example 6 The embodiment of the present invention provides a distribution network line fault diagnosis system, a data analysis module, including: Data acquisition unit: obtains historical fault data of each physical area, deploys monitoring tools based on the regional health map, and obtains monitoring data of each physical area; Data analysis unit: obtaining load data of each physical area and attribute data of the line based on the monitoring data of each physical area; Coefficient determination unit: determines the fault impact coefficient of each line of the distribution network based on the historical fault data, load data and attribute data of the line of each physical area.

[0046] In this embodiment, historical fault data refers to the line or equipment fault information recorded in each physical area that has occurred in the past, including the time of the fault, fault type, fault duration, repair process, etc., which is used to analyze the cause and mode of the fault so as to make risk prediction and maintenance planning. For example, a line in a physical area has experienced two faults in the past year. The first one was a short circuit fault caused by overload, and the second one was an insulation failure fault caused by equipment aging. This information will be recorded in the historical fault data for subsequent diagnosis and analysis; In this embodiment, the load data of the physical area refers to the power load level borne by the power distribution lines or equipment in each physical area over a period of time, reflecting the working pressure, operating efficiency and potential overload risk of the equipment. For example, during peak hours, the load data record of a physical area shows obvious high current and high power output. These load data are used to determine whether the equipment in the area is overloaded and help prevent failures caused by overload; In this embodiment, the attribute data of the line includes basic information and physical characteristics related to the distribution line itself, such as the length, material, voltage level, installation location, etc. of the line. For example, the attribute data of a line may include that its length is 5 kilometers, the material is copper wire, the voltage level is 10kV, and it is installed in a mountainous area. Such attribute information helps to evaluate whether the line is suitable for the current load demand or environmental conditions.

[0047] In this embodiment, the fault impact coefficient of each line of the distribution network is determined based on the historical fault data, load data and attribute data of each physical area. For example, in a certain area, the historical fault data of a high-load line is frequent, and the attribute data shows that its material is prone to aging, so its fault impact coefficient is determined to be a high value (for example, 0.85). A high fault impact coefficient means that once a fault occurs in the line, it may cause a large-scale power outage or system instability.

[0048] The beneficial effects of the above technical solution are: by acquiring and analyzing the historical fault data, monitoring data and line attribute data of the physical area, generating a regional health map and determining the fault impact coefficient, it is possible to accurately assess the fault risk of each line, provide intelligent data support for subsequent fault prevention and maintenance, and improve the overall stability and operational reliability of the distribution network.

[0049] Example 7 The embodiment of the present invention provides a distribution network line fault diagnosis system, a strategy formulation module, including: Priority determination unit: prioritizes lines according to the magnitude of the fault impact coefficient, and then determines the priority of each line; Fault assessment unit: obtains historical data and monitoring data of each line, identifies the possible fault types of each line, and conducts risk assessment on the fault types to obtain the risk coefficient of each fault type; Strategy determination unit: according to the priority of each line and the risk factor of the fault type, the corresponding preventive control strategy is determined based on the preset priority-fault type risk factor-control strategy database; Strategy startup unit: When the fault impact coefficient exceeds the preset self-healing strategy trigger value, the corresponding self-healing strategy is started.

[0050] In this embodiment, the priority of each line is divided into levels according to its fault impact coefficient, indicating the degree to which the line needs to be paid attention to in fault diagnosis and prevention. The higher the priority of the line, the greater the impact that may be caused by a fault, so it needs to be maintained or monitored first. If the fault impact coefficient of line A is 0.9 and that of line B is 0.5, the priority of line A will be higher. This means that when resources are limited, line A is maintained and tested first; In this embodiment, the fault type refers to various specific fault modes that may occur on the distribution line or equipment, such as short circuit, overload, ground fault, etc. Each fault type will have different effects on the operation of the line and require different processing methods. For example, the types of faults that a line may face include short circuit faults, overload faults, and insulation failures. Each fault type may occur under specific conditions and has different degrees of impact on the distribution system; In this embodiment, the risk coefficient of a fault type is an indicator for evaluating the probability of occurrence of a specific fault type and its impact on the line. The higher the coefficient, the greater the possibility or impact of the fault type. For example, the short-circuit fault risk coefficient of line A is 0.7, and the overload fault risk coefficient is 0.3. This means that the short-circuit fault is more likely to occur on this line, and its potential impact is more serious, requiring more stringent monitoring and protection measures.

[0051] In this embodiment, the preset priority-fault type risk coefficient-control strategy is a preset control strategy table, which determines specific prevention and control measures based on the line priority and the risk coefficient of the fault type. This strategy table specifies corresponding control measures for each situation (a combination of different priorities and risk coefficients). It is assumed that the preset strategy is: when the line priority is high and the fault type risk coefficient is greater than 0.6, emergency inspections and fault warning measures are taken; when the priority is medium and the risk coefficient is between 0.3 and 0.6, regular monitoring measures are taken.

[0052] In this embodiment, the self-healing strategy includes: Fault isolation and recovery: Design a process for automatically isolating damaged lines to ensure that the fault area can be quickly isolated after a fault occurs to prevent the impact from spreading. At the same time, a recovery mechanism is formulated to automatically switch the load to the backup line or restore power supply through other means. Self-healing control strategy optimization: Based on the experience of historical faults and real-time data, the self-healing control strategy is continuously optimized to ensure that the system can quickly respond to and handle faults.

[0053] The beneficial effects of the above technical solution are: through the priority division of line fault influence coefficients, risk assessment of fault types and comprehensive use of preset control strategies, intelligent and precise fault prevention and control are achieved, and prevention strategies can be dynamically adjusted and self-healing measures can be automatically triggered for different lines and fault types, effectively improving the fault response efficiency and system stability of the distribution network.

[0054] Example 8 The embodiment of the present invention provides a method for diagnosing a fault in a power distribution network. Figure 2 As shown, including: Step 1: Divide the distribution network into different physical areas, and deploy a preset sensor group in each physical area to collect distribution network related data; Step 2: Use distribution network related data to determine the health status of each physical area and then build a regional health map; Step 3: Deploy monitoring tools based on the regional health map, obtain and analyze the monitoring data of each physical area based on the monitoring tools, and then obtain the fault impact coefficient of each line in the distribution network; Step 4: Determine the corresponding prevention control strategy and self-healing strategy based on the fault impact coefficient of each line; Step 5: Determine the load distribution coefficient of each line based on the fault impact coefficient of each line, and then optimize the power grid distribution.

[0055] The beneficial effects of the above technical solution are as follows: by dividing the physical areas and combining the sensor data to construct the regional health map, accurate monitoring and fault location of the distribution network lines are achieved, the fault impact coefficient is introduced, and combined with the regional health status analysis, targeted prevention and self-healing strategies are formulated, which improves the accuracy of fault diagnosis and self-healing ability, improves the accuracy of distribution network fault location, and achieves more effective monitoring and analysis through comprehensive regional health status, thereby optimizing the load distribution of the power grid and improving the self-healing ability.

[0056] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0057] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distribution network line fault diagnosis system, characterized in that: include: Data acquisition module: divides the distribution network into different physical areas, and deploys a preset sensor group in each physical area to collect distribution network related data; Graph construction module: Use distribution network related data to determine the health status of each physical area and then build a regional health graph; Data analysis module: Deploy monitoring tools based on regional health maps, obtain and analyze monitoring data of each physical area based on the monitoring tools, and then obtain the fault impact coefficient of each line in the distribution network; Strategy formulation module: Determines the corresponding prevention control strategy and self-healing strategy based on the fault impact coefficient of each line; Load optimization module: Determine the load distribution coefficient of each line based on the fault impact coefficient of each line, and then optimize the power grid distribution.

2. A distribution network line fault diagnosis system according to claim 1, characterized in that: Data acquisition module, including: A first division unit: acquiring geographic information of the distribution network, extracting geographic features of the distribution network based on the geographic information of the distribution network, performing a first division on the distribution network based on the geographic features of the distribution network, and acquiring a plurality of first areas; A level determination unit: acquiring historical maintenance data and equipment service life data of equipment in the first area, and then determining the classification level of the equipment in the first area; A second division unit: performing a second division on the first area based on the division levels of the devices in the first area, thereby obtaining a plurality of second areas; A load determination unit: acquiring historical load data of each line in each second area, and then determining a load level of each second area; A third division unit: performing a third division on the second area based on the load level of each second area, thereby obtaining a plurality of third areas, and further determining the third areas as physical areas for distribution network division; Regional deployment unit: a preset sensor group is deployed in each third area to collect distribution network related data.

3. A distribution network line fault diagnosis system according to claim 1, characterized in that: Graph building blocks, including: Data analysis unit: Analyze the distribution network related data to obtain the line health related data in each physical area and the equipment health related data in each physical area; A first acquisition unit: determining a health coefficient of a line in each physical area based on line health related data in each physical area; A second acquisition unit: determining a health coefficient of the equipment in each physical area based on the equipment health related data in each physical area; A state determination unit: determining a health state of each physical area based on a health coefficient of a line and a health coefficient of a device in each physical area; Map construction unit: Builds a regional health map based on the health status of each physical area and a preset visualization method.

4. A distribution network line fault diagnosis system according to claim 3, characterized in that: The first acquisition unit includes: Analyze the line health related data in each physical area to obtain several sub-health coefficients; A combined function of the sub-health coefficients of the lines in each physical area is determined based on the sub-health coefficients of the lines in each physical area: ;in, is the qth sub-health factor of the line in the ith physical area, The number of sub-health coefficients of the line in the ith physical area, is the preset weight corresponding to the qth sub-health coefficient of the line in the ith physical area, is the preset adjustment weight corresponding to the sub-health coefficient, is the interaction weight corresponding to the sub-health coefficient, is the p-th sub-health coefficient of the line in the ith physical area, is the preset dynamic fluctuation adjustment coefficient; Determine the health factor of the line in each physical area based on the line health related data in each physical area: ;in, is the health factor of the line in the ith physical area, is the preset global health coefficient benchmark of the ith physical area, and , is the voltage fluctuation frequency of the line in the i-th physical area in the j-th voltage interval, n1 is the number of voltage intervals of the preset voltage fluctuation, is the adjustment coefficient corresponding to the combined function of the sub-health coefficients, k is the constant of the time decay rate, t is the current time, The last time of major maintenance. is the preset attenuation control coefficient, is a logarithmic function.

5. A distribution network line fault diagnosis system according to claim 3, characterized in that: The second acquisition unit includes: Determine the health factor of the equipment in each physical area based on the equipment health related data in each physical area: ； ； in, is the health factor of the equipment in the ith physical area, is the hth operating parameter of the uth device in the i-th physical area, is the number of devices in the ith physical area, is the corresponding weight of the u-th device in the i-th physical area, is the operation function of the u-th device in the i-th physical area, is the historical failure coefficient of the equipment in the ith physical area, is the preset interaction weight, is the hth operating parameter of the rth device in the i-th physical area, is the operating function of the rth device in the i-th physical area, is the interaction coefficient of the equipment in the ith physical area, is the number of operating parameters of the u-th device in the i-th physical area, is the number of operating parameters of the rth device in the i-th physical area, is a logarithmic function.

6. A distribution network line fault diagnosis system according to claim 1, characterized in that: Data analysis modules, including: Data acquisition unit: obtains historical fault data of each physical area, deploys monitoring tools based on the regional health map, and obtains monitoring data of each physical area; Data analysis unit: obtaining load data of each physical area and attribute data of the line based on the monitoring data of each physical area; Coefficient determination unit: determines the fault impact coefficient of each line of the distribution network based on the historical fault data, load data and attribute data of the line in each physical area.

7. A distribution network line fault diagnosis system according to claim 1, characterized in that: Strategy development module, including: Priority determination unit: prioritizes lines according to the magnitude of the fault impact coefficient, and then determines the priority of each line; Fault assessment unit: obtains historical data and monitoring data of each line, identifies the possible fault types of each line, and conducts risk assessment on the fault types to obtain the risk coefficient of each fault type; Strategy determination unit: according to the priority of each line and the risk factor of the fault type, the corresponding preventive control strategy is determined based on the preset priority-fault type risk factor-control strategy database; Strategy startup unit: When the fault impact coefficient exceeds the preset self-healing strategy trigger value, the corresponding self-healing strategy is started.

8. A method for diagnosing faults in a distribution network, characterized in that: include: Step 1: Divide the distribution network into different physical areas, and deploy a preset sensor group in each physical area to collect distribution network related data; Step 2: Use distribution network related data to determine the health status of each physical area and then build a regional health map; Step 3: Deploy monitoring tools based on the regional health map, obtain and analyze the monitoring data of each physical area based on the monitoring tools, and then obtain the fault impact coefficient of each line in the distribution network; Step 4: Determine the corresponding prevention control strategy and self-healing strategy based on the fault impact coefficient of each line; Step 5: Determine the load distribution coefficient of each line based on the fault impact coefficient of each line, and then optimize the power grid distribution.

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