Rural power grid weak link identification and reinforcement method and system

Abstract

The invention relates to the technical field of power grid reinforcement, in particular to a rural power grid weak link identification and reinforcement method and system, which can improve the scientificity, efficiency and real-time performance of rural power grid management, and can improve the power supply capability and stability of a power grid through intelligent analysis and customized improvement strategies. The method comprises the following steps: collecting geographical distribution information, power distribution equipment configuration information and historical operation data of a rural power grid; analyzing natural environment and social economic conditions of a region where the rural power grid is located according to geographical distribution information of the rural power grid, and extracting power grid performance influence parameters; checking the health condition, service life and technical parameters of the power distribution equipment according to the configuration information of the power distribution equipment, and obtaining a health state set of the power distribution equipment; and carrying out load analysis on the historical operation data, predicting a future power load change trend, and obtaining a predicted load trend.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid reinforcement, and in particular to a method and system for identifying and reinforcing weak links in a rural power grid. Background Art

[0002] With the continuous development of rural economy and the improvement of people's living standards, the demand for electricity in rural areas is growing, which puts higher requirements on the power supply capacity and stability of rural power grids. However, due to historical reasons and geographical limitations, there are generally some weak links in rural power grids, which not only affect the power supply efficiency of rural power grids, but also threaten the stable operation of the power grid.

[0003] The traditional method of identifying and strengthening the weak links of rural power grids mainly relies on manual inspection and experience judgment, which has the problems of strong subjectivity and low efficiency. Therefore, a more scientific and efficient method is needed to identify and strengthen the weak links of rural power grids. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for identifying and strengthening weak links in rural power grids, which can improve the scientificity, efficiency and real-time performance of rural power grid management, and can also enhance the power supply capacity and stability of the power grid through intelligent analysis and customized improvement strategies.

[0005] In a first aspect, the present invention provides a method for identifying and strengthening weak links in rural power grids, the method comprising:

[0006] Collect geographical distribution information, distribution equipment configuration information and historical operation data of rural power grids;

[0007] According to the geographical distribution information of rural power grids, the natural environment and socio-economic conditions of the areas where rural power grids are located are analyzed to extract the parameters affecting power grid performance;

[0008] According to the configuration information of the power distribution equipment, check the health status, service life and technical parameters of the power distribution equipment, and obtain the health status set of the power distribution equipment;

[0009] Conduct load analysis on historical operation data, predict future power load change trends, and obtain predicted load trends;

[0010] Input the parameters affecting power grid performance, the health status of distribution equipment and the predicted load trend into the pre-built weak link identification model, and the weak link identification model outputs the weak links of the rural power grid;

[0011] Aiming at the weak links in rural power grids, rural power grid improvement strategies are formulated to strengthen the rural power grids.

[0012] Furthermore, the parameters affecting the performance of the power grid include load characteristic parameters, equipment status parameters, natural environment parameters, social and economic parameters, and operating efficiency parameters.

[0013] Furthermore, the method for obtaining the health status set of the power distribution equipment includes:

[0014] Collect the operating status information of the power distribution equipment and obtain the maintenance records of the equipment;

[0015] Conduct on-site inspections of power distribution equipment and obtain on-site inspection results;

[0016] Evaluate the health status of the equipment based on its operating status information, maintenance records, and on-site inspection results to obtain a health status score;

[0017] Check the service life of the equipment and, based on the equipment's operating status information and maintenance records, determine whether the equipment needs to be replaced in advance and obtain service life analysis results;

[0018] Check the consistency between the actual technical parameters of the equipment and the design parameters;

[0019] For devices whose technical parameters do not meet the requirements, output the technical parameters that do not meet the requirements and obtain the technical parameter comparison results;

[0020] The health status score, service life analysis results and technical parameter comparison results are integrated into a health status set of distribution equipment.

[0021] Furthermore, the method for obtaining the predicted load trend includes:

[0022] Clean and preprocess historical operation data to remove outliers, missing values, and duplicate data;

[0023] Organize the data into time series to form a load data set and identify long-term and short-term trends in power load;

[0024] Select the time series analysis model as the basic architecture, use the load data set to train the model, and obtain the load forecasting model;

[0025] Use the load forecasting model to predict the power load within a preset time period in the future and generate a predicted load trend.

[0026] Furthermore, the method for constructing the weak link identification model includes:

[0027] Preprocess the collected parameters affecting power grid performance, distribution equipment health status set and predicted load trend, including data cleaning, data normalization and data encoding;

[0028] Divide the preprocessed data set into training set and test set;

[0029] Selecting a deep learning model as the model's infrastructure, the deep learning model includes logistic regression, decision tree, random forest, gradient boosting machine, support vector machine, and neural network;

[0030] Use the training set to train the selected model and adjust the model's hyperparameters;

[0031] Use the test set to evaluate the trained model and adjust and optimize the model;

[0032] The trained and optimized models are deployed in actual production environments to identify weak links in rural power grids.

[0033] Furthermore, the rural power grid improvement strategy includes equipment renewal and upgrading, network structure optimization, strengthening disaster prevention and mitigation capabilities, intelligent construction, information construction, distributed energy and microgrids.

[0034] Furthermore, the geographic distribution information includes topography, landforms, vegetation coverage, hydrological conditions and population density.

[0035] On the other hand, the present application also provides a rural power grid weak link identification and reinforcement system, the system comprising:

[0036] Data collection module, which collects geographical distribution information of rural power grids, configuration information of power distribution equipment, and historical operation data;

[0037] The environmental and economic analysis module analyzes the natural environment and socio-economic conditions of the areas where rural power grids are located based on the geographical distribution information of rural power grids, and extracts the parameters that affect power grid performance;

[0038] The equipment health assessment module checks the health status, service life and technical parameters of the power distribution equipment according to the configuration information of the power distribution equipment, and obtains the health status set of the power distribution equipment;

[0039] The load analysis and prediction module performs load analysis on historical operation data, predicts future power load change trends, and obtains predicted load trends;

[0040] The weak link identification module inputs the parameters affecting the power grid performance, the health status set of the power distribution equipment and the predicted load trend into the pre-built weak link identification model, and the weak link identification model outputs the weak links of the rural power grid;

[0041] The improvement strategy formulation module sets rural power grid improvement strategies based on the weak links in the rural power grid and strengthens the rural power grid.

[0042] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0043] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0044] Compared with the prior art, the invention has the following beneficial effects: the method reduces the subjectivity of human judgment by comprehensively analyzing multi-source information such as geography, equipment, and operation data, based on data-driven, and improves the accuracy and scientificity of identifying weak links;

[0045] Automated data collection and analysis processes replace time-consuming and labor-intensive manual inspections, greatly improving work efficiency, reducing maintenance costs, and making grid management more efficient;

[0046] This method conducts a comprehensive assessment from multiple dimensions, including natural environment, socio-economic conditions, equipment health status, and load forecasting, ensuring a comprehensive understanding and grasp of the power grid status;

[0047] Using historical data to predict future load trends provides a scientific basis for long-term planning and expansion of the power grid, avoids blind investment, and enhances the grid's ability to adapt to future load growth;

[0048] The pre-built weak link identification model is applied to realize the automatic identification of weak links, improve the identification speed and accuracy, and create conditions for rapid response and problem handling; specific improvement strategies are formulated for the identified weak links, and problem-oriented precise reinforcement is achieved, which avoids the ineffective deployment of resources and improves the pertinence and effectiveness of power grid reinforcement;

[0049] In summary, this method can improve the scientificity, efficiency and real-time management of rural power grids by integrating multiple technologies and data analysis methods. It can also effectively improve the power supply capacity and stability of the power grid through intelligent analysis and customized improvement strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of the present invention;

[0051] Figure 2 It is a structural diagram of the rural power grid weak link identification and reinforcement system. DETAILED DESCRIPTION

[0052] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0053] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0054] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0055] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0056] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0057] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0058] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0059] The present application is described below in conjunction with the drawings in the present application.

[0060] Embodiment 1: Figure 1 As shown, the method for identifying and strengthening weak links in rural power grids of the present invention specifically includes the following steps:

[0061] S1. Collect geographical distribution information, distribution equipment configuration information and historical operation data of rural power grids;

[0062] Geographical distribution information collection:

[0063] Satellite remote sensing images and geographic information systems are used to analyze natural environmental factors such as topography, landforms, vegetation cover, and hydrological conditions in rural areas, while recording the geographic coordinates of key facilities such as the direction of power grid lines, substation locations, and transformer distribution; this helps assess the potential impact of natural disasters on the power grid;

[0064] Collect social and economic information such as population density, industrial development, and electricity demand growth trends in rural areas through government public data and social and economic surveys;

[0065] Power distribution equipment configuration information collection:

[0066] Record detailed information such as model, capacity, installation date, maintenance records, etc. of distribution transformers, lines, switchgear, etc. through handheld terminal devices;

[0067] Promote the use of smart meters, online monitoring equipment and Internet of Things technology to collect the operating parameters of power distribution equipment in real time, realize remote monitoring of equipment status and fault warning; these data are automatically uploaded to the data center, reducing manual intervention and improving data accuracy and timeliness;

[0068] Historical operation data analysis:

[0069] Access to the existing power grid operation big data platform to aggregate power supply and demand data, fault records, maintenance logs, etc. over the years to form a comprehensive historical operation database;

[0070] Preprocess the massive amount of data collected, including missing value filling, outlier detection and removal, data standardization, etc., to ensure data quality and provide a reliable basis for subsequent analysis;

[0071] Use statistical analysis, time series analysis and other methods to identify seasonal changes in power load, impact patterns of abnormal events, etc., to provide input for the forecasting model;

[0072] Through the implementation of step S1, a multi-dimensional database including geography, equipment, and operating conditions can be built, providing solid data support for the subsequent identification and strengthening of weak links in rural power grids.

[0073] S2. Analyze the natural environment and socio-economic conditions of the areas where rural power grids are located based on the geographical distribution information of rural power grids, and extract the parameters affecting power grid performance;

[0074] Natural environment analysis:

[0075] Topographic survey: Analyze the topographic characteristics of the area covered by the power grid, because different topography has a direct impact on the difficulty of line installation, maintenance costs and resistance to natural disasters;

[0076] Climate condition assessment: Consider the regional climate type and frequency of extreme weather. These factors affect the selection and protection level of power grid facilities and are also an important basis for assessing the power grid's ability to resist natural disasters.

[0077] Ecological and environmental considerations: Assess the impact of power grid construction and operation on the local ecological environment, including animal and plant protection, water source protection, etc., to ensure that power grid planning meets ecological and environmental protection requirements;

[0078] Analysis of Socio-Economic Conditions:

[0079] Population distribution and growth: Understand and predict the population size, distribution and growth trend in the region. Densely populated areas often have high electricity demand, so it is important to ensure the stability and reliability of power supply;

[0080] Economic development level: Analyze the development of agriculture, industry, service industry and other industries, especially the layout of high-energy-consuming industries, which directly determine the structure and trend of power demand and affect the load characteristics of the power grid;

[0081] Urban and rural development planning: Combined with the local government's urban and rural construction planning, we can foresee the possible construction of new residential areas, industrial parks, etc. in the next few years, and plan the expansion of the power grid and optimize the layout in advance;

[0082] Extract parameters affecting grid performance:

[0083] Load density: predict the power load density in the region based on population distribution and industrial development;

[0084] Reliability requirements: Determine the specific requirements for power supply reliability based on the importance of social and economic activities in different regions;

[0085] Environmental adaptability index: according to specific natural environmental conditions, set the performance index of power grid equipment such as wind resistance, ice resistance, earthquake resistance, etc.

[0086] Future development coefficient: Combined with urban and rural planning and economic development forecasts, set a coefficient that reflects the future growth rate of electricity demand;

[0087] The parameters affecting the power grid performance include:

[0088] Load characteristic parameters:

[0089] Maximum load: the maximum power demand that the power grid needs to bear within a certain period of time;

[0090] Load growth rate: predict the growth rate of electricity demand in the future;

[0091] Load peak-to-valley difference: the difference between the highest load and the lowest load in a day, reflecting the severity of load changes;

[0092] Load density: The power load per unit area, which is important for planning grid capacity and wiring;

[0093] Device status parameters:

[0094] Equipment health index: Evaluate the operating status and life expectancy of equipment such as transformers, lines, and switches through monitoring data;

[0095] Equipment service life: the number of years the equipment has been in use, used to determine whether it is approaching or reaching its designed service life;

[0096] Failure rate: The probability of failure calculated from the historical failure data of the equipment, which is used to evaluate the reliability of the equipment;

[0097] Natural environment parameters:

[0098] Climate conditions: including average temperature, extreme temperature, precipitation, wind speed, freezing conditions, etc., which affect equipment selection and maintenance cycle;

[0099] Geographical features: such as topography and soil type, which affect line design and construction difficulty;

[0100] Disaster risk: probability and impact of natural disasters such as earthquakes, floods, and typhoons;

[0101] Socio-economic parameters:

[0102] Population distribution: Population size, density and its changing trend directly affect electricity demand;

[0103] Economic development level: changes in regional GDP and industrial structure affect the structure and growth rate of electricity demand;

[0104] Urban and rural planning: Future land use planning, including planning of new development zones, industrial zones or residential areas, affects the distribution of grid load;

[0105] Operation efficiency parameters:

[0106] Line loss rate: the proportion of energy loss during power grid transmission, reflecting the operating efficiency of the power grid;

[0107] Power supply reliability: measures the probability that users receive continuous and stable power supply, usually expressed in terms of the average annual power outage time or number;

[0108] Through the analysis of step S2, we can have a deeper understanding of the natural environment and socio-economic conditions of the area where the power grid is located, providing strong support for the subsequent identification and strengthening of weak links.

[0109] S3. Check the health status, service life and technical parameters of the power distribution equipment according to the configuration information of the power distribution equipment, and obtain a health status set of the power distribution equipment;

[0110] The method for obtaining the health status set of the power distribution equipment includes:

[0111] Collect the operating status information of the power distribution equipment and obtain the maintenance records of the equipment, including the details of all inspections, repairs and replacement parts;

[0112] Conduct on-site inspections of power distribution equipment and obtain on-site inspection results, including the equipment's appearance, wiring, insulation conditions, and operating sounds;

[0113] Evaluate the health status of the equipment based on its operating status information, maintenance records, and on-site inspection results to obtain a health status score;

[0114] Check the service life of the equipment and, based on the equipment's operating status information and maintenance records, determine whether the equipment needs to be replaced in advance and obtain service life analysis results;

[0115] Check the consistency between the actual technical parameters of the equipment and the design parameters;

[0116] For equipment whose technical parameters do not meet the requirements, analyze the reasons and take corresponding measures, output the technical parameters that do not meet the requirements, and obtain the technical parameter comparison results;

[0117] Integrate the health status score, service life analysis results and technical parameter comparison results into a health status set of power distribution equipment;

[0118] Step S3 uses a combination of modern monitoring technology, data analysis models, and standardized equipment management processes to systematically assess the health of distribution equipment, identify potential problems in a timely manner, and provide accurate data support for identifying weak links in the power grid and strengthening them in a targeted manner.

[0119] S4. Perform load analysis on historical operation data, predict future power load change trends, and obtain predicted load trends;

[0120] The method for obtaining the predicted load trend includes:

[0121] Clean and preprocess historical operation data to remove outliers, missing values ​​or duplicate data to ensure data accuracy and reliability;

[0122] Through time series analysis methods, we can identify the long-term and short-term trends of power load, understand the patterns and laws of load changes, and organize the data according to time series to form a continuous and complete load data set;

[0123] Select the time series analysis model as the basic architecture, use the load data set to train the model, and obtain the load forecasting model;

[0124] Use the load forecasting model to forecast the power load within a preset time period in the future and generate a forecast load trend;

[0125] Through the above steps, it is possible to scientifically and objectively analyze historical operating data, predict future power load trends in rural power grids, and provide data support for identifying weak links in the power grid and subsequent strengthening measures.

[0126] S5, inputting the parameters affecting power grid performance, the health status set of distribution equipment and the predicted load trend into a pre-built weak link identification model, and the weak link identification model outputs the weak links of the rural power grid;

[0127] The method for constructing the weak link identification model includes:

[0128] Preprocess the collected parameters affecting power grid performance, distribution equipment health status set and predicted load trend, including data cleaning, data normalization and data encoding;

[0129] The preprocessed data set is divided into a training set and a test set; the training set is used to train the model, and the test set is used to evaluate the generalization ability of the model;

[0130] Selecting a deep learning model as the model's infrastructure, the deep learning model includes logistic regression, decision tree, random forest, gradient boosting machine, support vector machine, and neural network;

[0131] Use the training set to train the selected model and adjust the model's hyperparameters to optimize the model's performance on the validation set;

[0132] Use the test set to evaluate the trained model, understand the performance of the model on unseen data, and adjust and optimize the model accordingly;

[0133] Deploy trained and optimized models into actual production environments to identify weak links in rural power grids;

[0134] Through the above steps, the weak link identification model can scientifically and systematically analyze the current status of rural power grids and provide data support and decision-making basis for the strengthening and optimization of power grids.

[0135] S6. Targeting the weak links in rural power grids, formulate rural power grid improvement strategies and strengthen rural power grids;

[0136] The rural grid improvement strategies include:

[0137] Equipment renewal and upgrade: For transformers, lines, switches and other equipment that have reached their design life or frequently malfunction, priority should be given to replacing them with newer, more durable and efficient equipment; energy-saving and intelligent power grid equipment should be used to improve the intelligence level and interactivity of the power grid;

[0138] Network structure optimization: According to the load distribution and growth trend, optimize the line layout, reduce circuitous lines, shorten the power supply radius, improve the power quality and transmission efficiency; add substations in load-intensive areas or key nodes to enhance power supply capacity; increase interconnection lines to improve the flexibility and reliability of the power grid;

[0139] Strengthen disaster prevention and mitigation capabilities: In view of the fact that rural areas are vulnerable to natural disasters, strengthen the wind, flood and lightning resistance capabilities of power grid facilities; establish a rapid response repair team equipped with necessary emergency equipment and materials to ensure that power supply can be quickly restored when a disaster occurs;

[0140] Intelligent construction: Using the Internet of Things and big data technologies to achieve remote real-time monitoring and intelligent diagnosis of power grid equipment, improve operation and maintenance efficiency, and reduce reliance on manual inspections;

[0141] Informatization construction: Promote the application of smart meters to realize real-time collection and analysis of user electricity consumption data, promote user participation in demand-side management, and optimize electricity load;

[0142] Distributed energy: Encourage and support the use of renewable energy such as solar energy and wind energy in rural areas, and increase the proportion of green energy in the power grid through the distributed power supply access system;

[0143] Microgrid: In remote or unstable power supply areas, build microgrid systems to enhance the self-sufficiency of local power grids and improve power supply reliability;

[0144] Through the implementation of the above comprehensive strategy, not only can the current weak links of rural power grids be solved, but also their long-term operating efficiency and stability can be improved, providing a solid power guarantee for the sustainable development of the rural economy.

[0145] Embodiment 2: Figure 2 As shown, the rural power grid weak link identification and strengthening system of the present invention specifically includes the following modules:

[0146] Data collection module, which collects geographical distribution information of rural power grids, configuration information of power distribution equipment, and historical operation data;

[0147] The environmental and economic analysis module analyzes the natural environment and socio-economic conditions of the areas where rural power grids are located based on the geographical distribution information of rural power grids, and extracts the parameters that affect power grid performance;

[0148] The equipment health assessment module checks the health status, service life and technical parameters of the power distribution equipment according to the configuration information of the power distribution equipment, and obtains the health status set of the power distribution equipment;

[0149] The load analysis and prediction module performs load analysis on historical operation data, predicts future power load change trends, and obtains predicted load trends;

[0150] The weak link identification module inputs the parameters affecting the power grid performance, the health status set of the power distribution equipment and the predicted load trend into the pre-built weak link identification model, and the weak link identification model outputs the weak links of the rural power grid;

[0151] The improvement strategy formulation module sets rural power grid improvement strategies based on the weak links in the rural power grid and strengthens the rural power grid.

[0152] The system can objectively analyze the power grid status through data-driven methods, reduce the subjectivity caused by manual inspections and experience-based judgments, and improve the accuracy and scientificity of identifying weak links. The automated data collection and analysis process greatly shortens the time to identify and handle problems, greatly improves efficiency compared to manual operations, and can respond faster to changes and needs in the power grid, and promptly discover and solve potential problems.

[0153] The system takes into account multiple dimensions such as geographical environment, socio-economic conditions, equipment status and load forecasting, which helps to understand the current status of the power grid at a deeper level and formulate more comprehensive improvement strategies;

[0154] The load analysis and prediction module enables the system to foresee future power load trends, realize the transition from passive response to active prevention, reduce power outages caused by sudden load increases, and ensure stable operation of the power grid;

[0155] The weak link identification module combines advanced technologies such as machine learning to automatically identify problem areas, while the improvement strategy formulation module provides the best data-based solutions to provide strong support for decision makers and ensure the rational allocation and efficient use of resources;

[0156] As the system continues to collect data and learn, its ability to identify and handle problems will continue to improve, and it will be able to better adapt to new needs of rural economic development and changes in the grid structure;

[0157] In summary, the system can improve the scientificity, efficiency and real-time nature of rural power grid management, and can also effectively enhance the power supply capacity and stability of the power grid through intelligent analysis and customized improvement strategies.

[0158] The various variations and specific embodiments of the rural power grid weak link identification and reinforcement method in the aforementioned embodiment 1 are also applicable to the rural power grid weak link identification and reinforcement system in this embodiment. Through the aforementioned detailed description of the rural power grid weak link identification and reinforcement method, technical personnel in this field can clearly know the implementation method of the rural power grid weak link identification and reinforcement system in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0159] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and run on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method embodiment for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0160] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for identifying and strengthening weak links in rural power grids, characterized in that: The method comprises: Collect geographical distribution information, distribution equipment configuration information and historical operation data of rural power grids; According to the geographical distribution information of rural power grids, the natural environment and socio-economic conditions of the areas where rural power grids are located are analyzed to extract the parameters affecting power grid performance; According to the configuration information of the power distribution equipment, check the health status, service life and technical parameters of the power distribution equipment, and obtain the health status set of the power distribution equipment; Conduct load analysis on historical operation data, predict future power load change trends, and obtain predicted load trends; Input the parameters affecting power grid performance, the health status of distribution equipment and the predicted load trend into the pre-built weak link identification model, and the weak link identification model outputs the weak links of the rural power grid; Aiming at the weak links in rural power grids, rural power grid improvement strategies are formulated to strengthen the rural power grids.

2. The method for identifying and strengthening weak links in rural power grids according to claim 1, characterized in that: The power grid performance influencing parameters include load characteristic parameters, equipment status parameters, natural environment parameters, social and economic parameters, and operation efficiency parameters.

3. The method for identifying and strengthening weak links in rural power grids according to claim 1, characterized in that: The method for obtaining the health status set of the power distribution equipment includes: Collect the operating status information of the power distribution equipment and obtain the maintenance records of the equipment; Conduct on-site inspections of power distribution equipment and obtain on-site inspection results; Evaluate the health status of the equipment based on its operating status information, maintenance records, and on-site inspection results to obtain a health status score; Check the service life of the equipment and, based on the equipment's operating status information and maintenance records, determine whether the equipment needs to be replaced in advance and obtain service life analysis results; Check the consistency between the actual technical parameters of the equipment and the design parameters; For devices whose technical parameters do not meet the requirements, output the technical parameters that do not meet the requirements and obtain the technical parameter comparison results; The health status score, service life analysis results and technical parameter comparison results are integrated into a health status set of distribution equipment.

4. The method for identifying and strengthening weak links in rural power grids according to claim 1, characterized in that: The method for obtaining the predicted load trend includes: Clean and preprocess historical operation data to remove outliers, missing values, and duplicate data; Organize the data into time series to form a load data set and identify long-term and short-term trends in power load; Select the time series analysis model as the basic architecture, use the load data set to train the model, and obtain the load forecasting model; Use the load forecasting model to predict the power load within a preset time period in the future and generate a predicted load trend.

5. The method for identifying and strengthening weak links in rural power grids according to claim 1, characterized in that: The method for constructing the weak link identification model includes: Preprocess the collected parameters affecting power grid performance, distribution equipment health status set and predicted load trend, including data cleaning, data normalization and data encoding; Divide the preprocessed data set into training set and test set; Selecting a deep learning model as the model's infrastructure, the deep learning model includes logistic regression, decision tree, random forest, gradient boosting machine, support vector machine, and neural network; Use the training set to train the selected model and adjust the model's hyperparameters; Use the test set to evaluate the trained model and adjust and optimize the model; The trained and optimized models are deployed in actual production environments to identify weak links in rural power grids.

6. The method for identifying and strengthening weak links in rural power grids according to claim 1, characterized in that: The rural power grid improvement strategies include equipment renewal and upgrading, network structure optimization, strengthening disaster prevention and mitigation capabilities, intelligent construction, information construction, distributed energy and microgrids.

7. The method for identifying and strengthening weak links in rural power grids according to claim 1, characterized in that: The geographic distribution information includes topography, landforms, vegetation coverage, hydrological conditions and population density.

8. A rural power grid weak link identification and strengthening system, characterized in that: The system comprises: Data collection module, which collects geographical distribution information of rural power grids, configuration information of power distribution equipment, and historical operation data; The environmental and economic analysis module analyzes the natural environment and socio-economic conditions of the areas where rural power grids are located based on the geographical distribution information of rural power grids, and extracts the parameters that affect power grid performance; The equipment health assessment module checks the health status, service life and technical parameters of the power distribution equipment according to the configuration information of the power distribution equipment, and obtains the health status set of the power distribution equipment; The load analysis and prediction module performs load analysis on historical operation data, predicts future power load change trends, and obtains predicted load trends; The weak link identification module inputs the parameters affecting the power grid performance, the health status set of the power distribution equipment and the predicted load trend into the pre-built weak link identification model, and the weak link identification model outputs the weak links of the rural power grid; The improvement strategy formulation module sets rural power grid improvement strategies based on the weak links in the rural power grid and strengthens the rural power grid.

9. An electronic device for identifying and strengthening weak links in rural power grids, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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

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