A scenario-adaptive fault repair scheduling method and system

Abstract

The present application provides a scenario-adaptive fault repair scheduling method and system, which relates to the field of fault detection technology, including: real-time monitoring of the target power grid operation status, identifying multiple operating fault scenario parameters, performing fault detection, traversing the fault detection data set, determining multiple fault points, evaluating multiple fault points, generating a fault repair scheduling plan, performing dynamic feedback and adaptive adjustment, and generating a fault repair scheduling optimization plan. This application can solve the technical problem of low efficiency of fault repair in the prior art due to the complex and changeable situation of power grid faults and the lack of a dynamic feedback mechanism. By integrating multiple data sources for real-time monitoring, the generated fault repair scheduling plan is dynamically fed back and adaptively adjusted, achieving the technical effect of improving the efficiency of power grid fault repair.

Description

Technical Field

[0001] The present application relates to the field of fault detection technology, and in particular to a scenario-adaptive fault repair scheduling method and system. Background Art

[0002] With the continuous development and increasing intelligence of power systems, power grid failures have become one of the main problems affecting the stable operation and reliability of power systems. Therefore, power grid fault diagnosis technology has become an important tool for power system operation management and maintenance. Power grid failures may be caused by a variety of factors, such as weather changes, equipment aging, operational errors, etc., resulting in different types of failures and degrees of impact. At present, the existing power grid fault detection methods are complex and changeable, and a fixed emergency repair plan may not be able to adapt to all situations.

[0003] In summary, the prior art has the technical problem of low efficiency in fault repair due to the complex and changeable conditions of power grid faults and the lack of a dynamic feedback mechanism. Summary of the invention

[0004] The purpose of this application is to provide a scenario-adaptive fault repair scheduling method and system to solve the technical problem in the prior art that the fault repair efficiency is low due to the complex and changeable conditions of power grid faults and the lack of a dynamic feedback mechanism.

[0005] In view of the above problems, the present application provides a scenario-adaptive fault repair scheduling method and system.

[0006] In a first aspect, the present application provides a scenario-adaptive fault repair scheduling method, which is implemented by a scenario-adaptive fault repair scheduling system, wherein the scenario-adaptive fault repair scheduling method includes: real-time monitoring of the target power grid operation status through multiple data sources, identifying multiple operation fault scenario parameters according to a real-time operation information set; performing fault detection on the target power grid based on the real-time operation information set to generate a fault detection data set; traversing the fault detection data set to locate the fault, determine multiple fault points, perform fault impact assessment based on the multiple fault points, and generate a fault repair sequence according to the assessment results; based on the multiple operation fault scenario parameters, generate a fault repair scheduling plan in combination with the fault repair sequence; executing the fault repair scheduling plan to perform dynamic feedback according to the multiple operation fault scenario parameters, adaptively adjust according to the feedback results, and generate a fault repair scheduling optimization plan.

[0007] In the second aspect, the present application also provides a scenario-adaptive fault repair scheduling system, which is used to execute a scenario-adaptive fault repair scheduling method as described in the first aspect, wherein the scenario-adaptive fault repair scheduling system includes: a real-time monitoring module, the real-time monitoring module is used to monitor the operating status of the target power grid in real time through multiple data sources, and identify multiple operating fault scenario parameters according to the real-time operating information set; a fault detection module, the fault detection module is used to perform fault detection on the target power grid based on the real-time operating information set, and generate a fault detection data set; a fault assessment module, the fault assessment module is used to traverse the fault detection data set to locate the fault, determine multiple fault points, perform fault impact assessment based on the multiple fault points, and generate a fault repair sequence according to the assessment results; a scheduling plan generation module, the scheduling plan generation module is used to generate a fault repair scheduling plan based on the multiple operating fault scenario parameters and in combination with the fault repair sequence; a scheduling plan optimization module, the scheduling plan optimization module is used to execute the fault repair scheduling plan according to the multiple operating fault scenario parameters for dynamic feedback, adaptively adjust according to the feedback results, and generate a fault repair scheduling optimization plan.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] Monitor the target power grid's operating status in real time through multiple data sources, identify multiple operating fault scenario parameters based on the real-time operating information set; perform fault detection on the target power grid based on the real-time operating information set to generate a fault detection data set; traverse the fault detection data set to locate the fault, determine multiple fault points, perform fault impact assessment based on the multiple fault points, and generate a fault repair sequence based on the assessment results; generate a fault repair scheduling plan based on the multiple operating fault scenario parameters and in combination with the fault repair sequence; execute the fault repair scheduling plan, perform dynamic feedback according to the multiple operating fault scenario parameters, perform adaptive adjustment according to the feedback results, and generate a fault repair scheduling optimization plan. In other words, by integrating multiple data sources for real-time monitoring, dynamically feedback and adaptively adjust the generated fault repair scheduling plan, the technical effect of improving the efficiency of power grid fault repair is achieved.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 A flowchart of a scenario-adaptive fault repair scheduling method for this application;

[0013] Figure 2 A schematic diagram of the structure of a scenario-adaptive fault repair and dispatching system for this application.

[0014] Explanation of the reference numerals: real-time monitoring module 11 , fault detection module 12 , fault assessment module 13 , scheduling scheme generation module 14 , scheduling scheme optimization module 15 . DETAILED DESCRIPTION

[0015] This application provides a scenario-adaptive fault repair scheduling method and system to solve the technical problem in the prior art that the fault repair efficiency is low due to the complex and changeable situation of power grid faults and the lack of a dynamic feedback mechanism. By integrating multiple data sources for real-time monitoring, the generated fault repair scheduling plan is dynamically fed back and adaptively adjusted, achieving the technical effect of improving the efficiency of power grid fault repair.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] For example, please refer to the attached Figure 1 The present application provides a scenario-adaptive fault repair scheduling method, wherein the scenario-adaptive fault repair scheduling method is applied to a scenario-adaptive fault repair scheduling system, and the scenario-adaptive fault repair scheduling method specifically includes the following steps:

[0018] Step 1: Monitor the target power grid operation status in real time through multiple data sources, and identify multiple operation fault scenario parameters based on the real-time operation information set.

[0019] Specifically, the operation data of the power grid is collected in real time through multiple different data sources, including various sensors, monitoring equipment, historical data records, etc., to ensure that the operation status of the power grid can be obtained in real time, including key parameters such as voltage, current, temperature, and load. The collected real-time data is analyzed to identify possible fault scenarios, including fault type, fault level, fault location, fault impact range, etc. Based on the historical fault data and real-time data analysis results, key parameters related to the fault scenario are extracted, such as fault current size, fault duration, equipment temperature, etc., to obtain multiple operating fault scenario parameters. By monitoring the operating status of the power grid in real time and identifying multiple operating fault scenario parameters, the system can more accurately predict and diagnose potential faults.

[0020] Step 2: Perform fault detection on the target power grid based on the real-time operation information set to generate a fault detection data set.

[0021] Specifically, the target power grid is detected for faults through real-time operation information sets collected in real time by smart sensors and other devices deployed at key nodes of the power grid, and the collected real-time data is analyzed to identify potential faults. Fault detection involves real-time monitoring of parameters such as voltage, current, and frequency, and comparison with normal operating parameters. If data abnormalities are found, they are marked as potential faults. The operating status of the power grid is monitored in real time, and possible faults are detected to generate a fault detection data set. Fault detection of the target power grid based on the real-time operation information set and the generation of a fault detection data set help the system to respond quickly when a fault occurs and take appropriate measures to repair the fault.

[0022] Step three: traverse the fault detection data set to locate the fault, determine multiple fault points, perform fault impact assessment based on the multiple fault points, and generate a fault repair sequence according to the assessment result.

[0023] Specifically, first, fault features are extracted from the historical fault data set, and a fault feature library is constructed by combining the historical fault type set. The feature library is used to traverse the fault detection data set, and the fault point is determined by matching. Based on the fault point, the fault location information, fault type information, and fault event information are extracted. Secondly, according to the fault type information, the fault point is clustered and analyzed to generate multiple clusters. Based on the fault location information and fault time information, the clusters are weighted and multiple weight coefficients are generated. Then, a fault impact assessment function is constructed, and the fault impact assessment calculation is performed through the function to generate multiple fault impact assessment values. Finally, according to the fault impact assessment value, the fault points are sorted in descending order, the fault repair priority is determined, and the fault repair sequence is generated. Through this process, the repair resources are effectively allocated, and the faults with the greatest impact on the operation of the power grid are given priority, thereby reducing the power outage time.

[0024] Step 4: Generate a fault repair scheduling plan based on the multiple operating fault scenario parameters and in combination with the fault repair sequence.

[0025] Specifically, based on the real-time operation information set and combined with the historical fault data set, the key parameters of multiple operation fault scenarios are determined, such as fault current size, fault duration, equipment temperature, etc. According to the fault repair sequence and operation fault scenario parameters, a detailed repair plan is formulated, including the allocation of repair teams, the use of repair tools and accessories, and the estimated repair time. Considering actual limiting factors, such as the geographical location of the repair team, traffic conditions, weather effects, etc., a fault repair scheduling plan is generated, a specific repair plan and resource allocation plan are formulated, and repair resources and manpower are reasonably allocated to ensure the smooth progress of the repair work. Based on multiple operation fault scenario parameters and fault repair sequences, a fault repair scheduling plan is generated, which helps to respond quickly when a fault occurs and take appropriate measures to repair the fault.

[0026] Step 5: Execute the fault repair scheduling plan, perform dynamic feedback according to the multiple operating fault scenario parameters, perform adaptive adjustment according to the feedback results, and generate a fault repair scheduling optimization plan.

[0027] Specifically, the fault repair scheduling plan is implemented to monitor the operation status of the power grid, the repair status of the fault points, the utilization efficiency of the repair resources, etc. in real time, and the repair process is dynamically fed back based on the real-time monitoring of the operating fault scenario parameters. Based on the data and information collected through dynamic feedback, the repair strategy is adaptively adjusted to change the path of the repair team, adjust the allocation of repair resources, change the repair sequence, etc., to optimize the repair process. Based on the results of the adaptive adjustment, the fault repair scheduling plan is optimized to generate a fault repair scheduling optimization plan to improve the repair efficiency and the reliability of the power grid.

[0028] Furthermore, step one of this application includes:

[0029] Configure multiple data sources based on power grid operation demand information; collect data from the target power grid according to the multiple data sources through multiple intelligent sensors; retrieve historical fault data sets, perform operation analysis on the real-time operation information set in combination with the historical fault data sets, and generate multiple operation fault parameters; traverse the multiple operation fault parameters and match and identify them with preset fault scenario parameters, and determine the multiple operation fault scenario parameters.

[0030] Specifically, first, we need to deeply understand the operation requirements of the power grid, including the scale, structure, key nodes, normal operating parameters, etc. of the power grid. According to the operation requirements of the power grid, we select data sources that can provide necessary information, including real-time sensor data, historical operation data, weather forecasts, power grid load forecasts, etc., to ensure that the operation status of the power grid can be fully and accurately reflected. We configure the selected data sources, set the data collection frequency, data transmission format, data storage method, etc., to ensure that data can be collected and transmitted as required. Secondly, according to the operation characteristics and requirements of the power grid, we select appropriate smart sensors and deploy these sensors at the key nodes of the power grid, such as voltage sensors, current sensors, temperature sensors, etc. Each smart sensor collects data from different data sources according to its configuration and function, including the power grid equipment itself, environmental factors, power grid load, etc. Data collection is carried out according to the predetermined frequency and standards to ensure the continuity and consistency of the data. The collected data is preprocessed, including data cleaning, denoising, normalization, etc., to improve the quality of the data.

[0031] Next, obtain the historical fault data set from the database or data storage system, including past fault events, fault causes, fault impacts, repair measures, etc. Combine the real-time operation information set with the historical fault data set for comprehensive analysis, compare the current operation status with the historical fault status, identify potential fault modes, predict possible fault development, etc. Based on the analysis results, generate multiple operation fault parameters, such as fault probability, fault type, fault impact range, recommended repair measures, etc. Finally, according to the characteristics of the power grid and historical fault data, pre-set a series of possible fault scenario parameters, such as short circuit fault, open circuit fault, overload fault, equipment aging fault, etc. Compare and match the obtained operation fault parameters with the preset fault scenario parameters one by one to identify and determine the current operation fault scenario. Extract key parameters related to the fault scenario, such as fault current size, fault duration, equipment temperature, etc. Through matching and identification, determine multiple fault scenarios that occur in the current power grid operation. By combining real-time data and historical fault data, the system can more accurately identify parameters that may cause faults, thereby improving the accuracy of fault identification.

[0032] Further, step three of this application includes:

[0033] The historical fault data set is retrieved for feature extraction to generate multiple fault feature data sets; a fault feature library is constructed based on the multiple fault feature data sets combined with a historical fault type set; the fault detection data set is traversed according to the fault feature library for matching, and the multiple fault points are determined according to the matching results.

[0034] Specifically, the historical fault data set contains records of past fault events, including detailed information such as the type of fault, time of occurrence, location, scope of impact, and repair measures. Data mining techniques, such as cluster analysis and association rule mining, can be used to identify common patterns and characteristics of faults. The information in the historical fault data set is analyzed and processed to extract key features related to the fault, and multiple fault feature data sets are generated, each of which corresponds to a specific fault type or pattern. Multiple fault feature data sets are combined with the historical fault type set to form a comprehensive fault feature library. In this library, each fault type corresponds to a set of features that describe the characteristic patterns of this type of fault. The fault feature library is traversed to match the fault detection data set, and the fault features in the fault feature library are compared and matched with the real-time data in the fault detection data set. The matching process involves the application of algorithms, such as pattern recognition and machine learning. Through matching, the system can identify real-time data that matches the features described in the fault feature library, thereby determining the fault point. Retrieve historical fault data sets for feature extraction, generate multiple fault feature data sets, build a fault feature library based on historical fault type sets, traverse the fault detection data sets for matching, and determine multiple fault points based on the matching results, which helps to improve the accuracy and efficiency of fault detection and location.

[0035] Furthermore, the present application also includes the following steps:

[0036] Extract multiple fault location information, multiple fault type information, and multiple fault time information based on the multiple fault points; perform cluster analysis on the multiple fault points according to the multiple fault type information to generate multiple cluster clusters; perform fault impact assessment calculations on the multiple cluster clusters based on the multiple fault location information and the multiple fault time information to generate multiple fault impact assessment values; sort the multiple fault points in descending order according to the multiple fault impact assessment values ​​to determine a fault repair priority; and generate a fault repair sequence according to the fault repair priority.

[0037] Specifically, based on multiple fault points, determine the exact location of the fault, including the specific location of the equipment, the section of the line, the area of ​​the substation, etc. Identify and classify the types of faults, including short circuit faults, open circuit faults, overload faults, equipment aging faults, etc. Record the events of the faults, analyze the patterns and trends of the faults, and evaluate the impact of the faults on the operation of the power grid. Using the fault type information, perform cluster analysis on multiple fault points, and group the fault points according to the fault type information. Cluster analysis is an unsupervised learning technology that divides fault points with similar characteristics into the same cluster. Clustering algorithms such as K-means and DBSCAN can be used. These clusters represent similar fault modes or fault characteristics. Generating multiple clusters is the process of classifying and grouping fault points according to their characteristics and properties. Each cluster contains a group of fault points with similar characteristics.

[0038] According to multiple fault location information and multiple fault time information, a fault impact assessment function is used. For each cluster, a fault impact assessment value is generated through the above calculation, which reflects the overall impact of the fault event in the cluster. All fault points are sorted from high to low according to their fault impact assessment values, and the fault points with the greatest impact will be ranked first, while the fault points with the least impact will be ranked last. According to the sorting results, a priority is assigned to each fault point. Fault points with higher rankings will be assigned higher priorities, which means that they should get repair resources first. When determining the priority, in addition to the fault impact assessment value, other practical factors should also be considered, such as the availability of repair resources, the geographical location of repair personnel, traffic conditions, etc. Based on the priority sorting, a fault repair sequence is generated, which will guide the repair team to carry out repair work in the order of priority, ensuring that the faults with the greatest impact on the power grid are handled first. Comprehensively considering the location, type, time and impact of the fault, as well as through cluster analysis and impact assessment, ensure that the repair work is carried out in the established priority order, so as to effectively manage power grid faults.

[0039] Furthermore, the present application also includes the following steps:

[0040] Based on the multiple fault location information and the multiple fault time information, weights are assigned to the multiple clusters to generate multiple weight coefficients; a fault impact assessment function is constructed: fault =w1·L+w2·U+w3·T; where I fault is the fault impact assessment value, L is the electric load loss, U is the fault location information, T is the fault duration, w1, w2, w3 are all included in multiple weight coefficients; the fault impact assessment calculation is performed through the fault impact assessment function to generate the multiple fault impact assessment values.

[0041] Specifically, different types of faults are prioritized based on the fault location information and fault time information. Weight allocation is a quantitative method that assigns a weight coefficient to each cluster based on factors such as the severity, impact range, and frequency of the fault. These weight coefficients reflect the importance and urgency of different types of faults. Multiple weight coefficients are generated, each of which represents the priority and importance of the fault type. The weight coefficient needs to be dynamically adjusted according to the real-time situation. If a large-scale fault occurs in a certain area, the weight of the area will be temporarily increased. In order to quantify the impact of faults on power grid operation, a fault impact assessment function is constructed: I fault =w1·L+w2·U+w3·T; where I fault is the fault impact assessment value, L is the electric load loss, U is the fault location information, T is the fault duration, w1, w2, w3 are multiple weight coefficients, which can be adjusted according to historical data and expert experience to adapt to different fault conditions and grid operation requirements.

[0042] Generally speaking, the greater the loss of electric load, the more serious the impact of the fault on the operation of the power grid, so the weight of this item is usually higher; the location information of the fault is also important, because different locations may have different impacts on different parts of the power grid. Faults occurring in the city center will affect more users than faults occurring in the suburbs; the longer the fault lasts, the greater the impact on the power grid. By calculating the I fault The value can determine which faults have a greater impact on the power grid, so that these faults can be handled first. The impact of each factor is reflected by multiplying its corresponding weight coefficient. For example, if the value of w1 is large, the impact of the electric load loss L on the fault impact assessment value is greater.

[0043] According to the real-time monitored fault feature data and the information in the fault feature library, the fault impact assessment function is used to calculate the impact assessment value of each fault, which involves mathematical operations and algorithm applications, such as weighted summation and numerical analysis. The calculation results are summarized and recorded to generate multiple fault impact assessment values, which reflect the degree of impact of different faults on the operation of the power grid. Based on the fault location information and fault time information, multiple clusters are weighted, multiple weight coefficients are generated, and the fault impact assessment value is calculated, which helps the system give priority to fault types with greater impact and higher urgency when formulating emergency repair plans and resource allocation, thereby improving emergency repair efficiency and power grid reliability.

[0044] Further, step five of this application includes:

[0045] A dynamic feedback mechanism is constructed to execute the fault repair scheduling plan for real-time monitoring and generate a dynamic repair progress parameter set; a repair data array is constructed according to the dynamic repair progress parameters; the repair data array is adaptively adjusted based on the multiple operating fault scenarios to generate feedback results; the fault repair scheduling plan is optimized according to the feedback results to generate a fault repair scheduling optimization plan.

[0046] Specifically, a dynamic feedback mechanism is built to collect and analyze data and information during the repair process in real time, such as the progress of the repair team, the repair status of the fault point, etc. According to the previously generated fault repair scheduling plan, the repair team is guided to start the repair work, and the progress of the repair team and the repair status of the fault point are tracked in real time. The real-time data during the repair process is aggregated and recorded to generate a dynamic repair progress parameter set, including the current location of the repair team, the number of completed repair tasks, and the estimated completion time.

[0047] The real-time data collected during the repair process is summarized and structured to construct a repair data array, including the current location of the repair team, the number of completed repair tasks, and the expected completion time. The repair strategy is dynamically adjusted according to the operating fault scenario parameters and the real-time data in the repair data array. Adaptive adjustment includes changing the path of the repair team, adjusting the allocation of repair resources, changing the repair order, etc. The results of the adaptive adjustment are summarized and recorded to generate feedback results, including the adjustment of the repair strategy, the improvement of repair efficiency, and the shortening of fault recovery time. According to the feedback results, the constraints affecting the repair scheduling are considered, the repair work that has been executed is evaluated, the problems and deficiencies in the repair process are understood, and the evaluation results are generated. According to the evaluation results, the repair priority is updated, the fault repair scheduling time node is adjusted, and the fault repair scheduling plan is optimized. By real-time monitoring of the fault repair scheduling plan, the repair data array is adaptively adjusted, and the plan is optimized according to the feedback results to improve the repair efficiency.

[0048] Furthermore, the present application also includes the following steps:

[0049] Introduce emergency repair scheduling constraint conditions, perform retrospective evaluation on the multiple fault points according to the feedback results, and generate retrospective evaluation results; update the fault repair priorities of the multiple fault points according to the retrospective evaluation results, and generate priority update results; adjust multiple fault repair scheduling time nodes according to the priority update results, and generate timing adjustment results; correct the scheduling allocation data of the fault repair scheduling plan according to the timing adjustment results, and determine the fault repair scheduling optimization plan.

[0050] Specifically, first, based on the characteristics of power grid operation and the limitations of emergency repair resources, emergency repair work is restricted and guided, and emergency repair scheduling constraints are introduced, such as the availability of the emergency repair team, the limitations of emergency repair resources, and the accessibility of the fault point. According to the feedback results, combined with the emergency repair data array, the emergency repair work that has been executed is evaluated, the completion of the emergency repair work, the effectiveness of the emergency repair strategy, the utilization efficiency of the emergency repair resources, etc., to understand the problems and deficiencies in the emergency repair process, and generate retrospective evaluation results. According to the evaluation results, the emergency repair priority of the fault point is re-evaluated and adjusted, and the priority update results are generated, including the latest emergency repair priority of the fault point, the adjustment of the emergency repair sequence, etc., reflecting the latest urgency and impact range of the fault point.

[0051] According to the updated emergency repair priority, the scheduling time of fault repair is rearranged, multiple fault repair scheduling time nodes are adjusted, and the timing adjustment results are generated to ensure that the high-priority points can be repaired as soon as possible and reduce the impact of the fault on the operation of the power grid. The timing adjustment results are obtained based on the rearrangement of the fault repair scheduling time nodes, reflecting the latest scheduling arrangements for fault repair, including the new scheduling time of the repair team, the reallocation of repair resources, etc. It is necessary to update the fault repair scheduling plan according to the adjusted scheduling time nodes, modify the scheduling plan of the repair team, adjust the allocation of repair resources, update the repair order, etc. Based on the revised scheduling allocation data, determine the fault repair scheduling optimization plan. According to the feedback results, the repair scheduling plan is optimized to ensure that the repair work is always carried out according to the most optimized plan. At the same time, the repair strategy is adjusted according to the real-time situation to improve the repair efficiency and reduce the power outage time.

[0052] In summary, the scenario-adaptive fault repair scheduling method provided by the present application has the following technical effects:

[0053] Monitor the target power grid's operating status in real time through multiple data sources, identify multiple operating fault scenario parameters based on the real-time operating information set; perform fault detection on the target power grid based on the real-time operating information set to generate a fault detection data set; traverse the fault detection data set to locate the fault, determine multiple fault points, perform fault impact assessment based on the multiple fault points, and generate a fault repair sequence based on the assessment results; generate a fault repair scheduling plan based on the multiple operating fault scenario parameters and in combination with the fault repair sequence; execute the fault repair scheduling plan, perform dynamic feedback according to the multiple operating fault scenario parameters, perform adaptive adjustment according to the feedback results, and generate a fault repair scheduling optimization plan. In other words, by integrating multiple data sources for real-time monitoring, dynamically feedback and adaptively adjust the generated fault repair scheduling plan, the technical effect of improving the efficiency of power grid fault repair is achieved.

[0054] Embodiment 2: Based on the scenario-adaptive fault repair scheduling method in the above embodiment, the present application also provides a scenario-adaptive fault repair scheduling system, see the attached Figure 2 The scenario-adaptive fault repair dispatching system includes:

[0055] The real-time monitoring module 11 is used to monitor the target power grid operation status in real time through multiple data sources, and identify multiple operation fault scenario parameters according to the real-time operation information set.

[0056] The fault detection module 12 is used to perform fault detection on the target power grid based on the real-time operation information set to generate a fault detection data set.

[0057] The fault assessment module 13 is used to traverse the fault detection data set to locate the fault, determine multiple fault points, perform fault impact assessment based on the multiple fault points, and generate a fault repair sequence according to the assessment result.

[0058] The scheduling plan generating module 14 is used to generate a fault repair scheduling plan based on the multiple operating fault scenario parameters and in combination with the fault repair sequence.

[0059] The scheduling scheme optimization module 15 is used to execute the fault repair scheduling scheme, perform dynamic feedback according to the multiple operating fault scenario parameters, perform adaptive adjustment according to the feedback results, and generate a fault repair scheduling optimization scheme.

[0060] Furthermore, the real-time monitoring module 11 in the scenario-adaptive fault repair dispatching system is also used for:

[0061] Configure multiple data sources based on power grid operation demand information; collect data from the target power grid according to the multiple data sources through multiple intelligent sensors; retrieve historical fault data sets, perform operation analysis on the real-time operation information set in combination with the historical fault data sets, and generate multiple operation fault parameters; traverse the multiple operation fault parameters and match and identify them with preset fault scenario parameters, and determine the multiple operation fault scenario parameters.

[0062] Furthermore, the fault assessment module 13 in the scenario-adaptive fault repair scheduling system is also used for:

[0063] The historical fault data set is retrieved for feature extraction to generate multiple fault feature data sets; a fault feature library is constructed based on the multiple fault feature data sets combined with a historical fault type set; the fault detection data set is traversed according to the fault feature library for matching, and the multiple fault points are determined according to the matching results.

[0064] Furthermore, the fault assessment module 13 in the scenario-adaptive fault repair scheduling system is also used for:

[0065] Extract multiple fault location information, multiple fault type information, and multiple fault time information based on the multiple fault points; perform cluster analysis on the multiple fault points according to the multiple fault type information to generate multiple cluster clusters; perform fault impact assessment calculations on the multiple cluster clusters based on the multiple fault location information and the multiple fault time information to generate multiple fault impact assessment values; sort the multiple fault points in descending order according to the multiple fault impact assessment values ​​to determine a fault repair priority; and generate a fault repair sequence according to the fault repair priority.

[0066] Furthermore, the scenario-adaptive fault repair and dispatching system further includes a fault assessment value generating module, which is used to:

[0067] Based on the multiple fault location information and the multiple fault time information, weights are assigned to the multiple clusters to generate multiple weight coefficients; a fault impact assessment function is constructed: fault =w1·L+w2·U+w3·T; where I fault is the fault impact assessment value, L is the electric load loss, U is the fault location information, T is the fault duration, w1, w2, w3 are all included in multiple weight coefficients; the fault impact assessment calculation is performed through the fault impact assessment function to generate the multiple fault impact assessment values.

[0068] Furthermore, the scheduling scheme optimization module 15 in the scenario-adaptive fault repair scheduling system is also used for:

[0069] A dynamic feedback mechanism is constructed to execute the fault repair scheduling plan for real-time monitoring and generate a dynamic repair progress parameter set; a repair data array is constructed according to the dynamic repair progress parameters; the repair data array is adaptively adjusted based on the multiple operating fault scenarios to generate feedback results; the fault repair scheduling plan is optimized according to the feedback results to generate a fault repair scheduling optimization plan.

[0070] Furthermore, the scheduling scheme optimization module 15 in the scenario-adaptive fault repair scheduling system is also used for:

[0071] Introduce emergency repair scheduling constraint conditions, perform retrospective evaluation on the multiple fault points according to the feedback results, and generate retrospective evaluation results; update the fault repair priorities of the multiple fault points according to the retrospective evaluation results, and generate priority update results; adjust multiple fault repair scheduling time nodes according to the priority update results, and generate timing adjustment results; correct the scheduling allocation data of the fault repair scheduling plan according to the timing adjustment results, and determine the fault repair scheduling optimization plan.

[0072] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The scenario-adaptive fault repair scheduling method and specific examples in the first embodiment are also applicable to the scenario-adaptive fault repair scheduling system of the present embodiment. Through the above detailed description of the scenario-adaptive fault repair scheduling method, those skilled in the art can clearly know the scenario-adaptive fault repair scheduling system of the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0073] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0074] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A scenario-adaptive fault repair scheduling method, characterized in that: include: Monitor the target power grid operation status in real time through multiple data sources, and identify multiple operation fault scenario parameters based on the real-time operation information set; Performing fault detection on the target power grid based on the real-time operation information set to generate a fault detection data set; Traversing the fault detection data set to locate the fault, determine multiple fault points, perform fault impact assessment based on the multiple fault points, and generate a fault repair sequence according to the assessment result; Based on the multiple operating fault scenario parameters, generating a fault repair scheduling plan in combination with the fault repair sequence; Execute the fault repair scheduling plan, perform dynamic feedback according to the multiple operating fault scenario parameters, perform adaptive adjustment according to the feedback results, and generate a fault repair scheduling optimization plan; Performing a fault impact assessment based on the multiple fault points and generating a fault repair sequence according to the assessment result includes: Extracting multiple fault location information, multiple fault type information, and multiple fault time information based on the multiple fault points; Performing cluster analysis on the multiple fault points according to the multiple fault type information to generate multiple clusters; Performing fault impact assessment calculations on the multiple clusters based on the multiple fault location information and the multiple fault time information to generate multiple fault impact assessment values; Sorting the multiple fault points in descending order according to the multiple fault impact assessment values ​​to determine the fault repair priority; Generate a fault repair sequence according to the fault repair priority; Executing the fault repair scheduling plan, dynamically feeding back the multiple operating fault scenario parameters, and adaptively adjusting according to the feedback results to generate a fault repair scheduling optimization plan, including: Construct a dynamic feedback mechanism, execute the fault repair scheduling plan for real-time monitoring, and generate a dynamic repair progress parameter set; Constructing a repair data array according to the dynamic repair progress parameter; Adaptively adjusting the emergency repair data array based on the multiple operational fault scenarios to generate feedback results; The fault repair scheduling plan is optimized according to the feedback result to generate a fault repair scheduling optimization plan.

2. A scenario-adaptive fault repair scheduling method as claimed in claim 1, characterized in that: The target power grid operation status is monitored in real time through multiple data sources, and multiple operation fault scenario parameters are identified based on the real-time operation information set, including: Configure multiple data sources based on power grid operation demand information; Collecting data from the target power grid according to the multiple data sources through multiple intelligent sensors; Retrieving a historical fault data set, performing operation analysis on the real-time operation information set in combination with the historical fault data set, and generating a plurality of operation fault parameters; The multiple operating fault parameters are traversed and matched with preset fault scenario parameters to identify the multiple operating fault scenario parameters.

3. A scenario-adaptive fault repair scheduling method as claimed in claim 2, characterized in that: Traversing the fault detection data set to locate the fault and determine multiple fault points, including: Retrieving the historical fault data set to perform feature extraction and generate multiple fault feature data sets; Building a fault feature library based on the multiple fault feature data sets combined with a historical fault type set; The fault detection data set is traversed according to the fault feature library for matching, and the multiple fault points are determined according to the matching results.

4. A scenario-adaptive fault repair scheduling method as claimed in claim 1, characterized in that Based on the multiple fault location information and the multiple fault time information, a fault impact assessment calculation is performed on the multiple clusters to generate multiple fault impact assessment values, including: Based on the multiple fault location information and the multiple fault time information, weights are assigned to the multiple clusters to generate multiple weight coefficients; Construct a fault impact assessment function: = ； in, is the fault impact assessment value, L is the electric load loss, U is the fault location information, T is the fault duration, , , are included in multiple weight coefficients; The fault impact assessment function is used to perform a fault impact assessment calculation to generate the plurality of fault impact assessment values.

5. The scenario-adaptive fault repair scheduling method according to claim 1, characterized in that: The fault repair scheduling plan is optimized according to the feedback result to generate a fault repair scheduling optimization plan, including: Introducing emergency repair scheduling constraint conditions, performing retrospective evaluation on the multiple fault points according to the feedback results, and generating retrospective evaluation results; Updating the fault repair priorities of the multiple fault points according to the backtracking evaluation results to generate a priority update result; Adjust multiple fault repair scheduling time nodes according to the priority update result to generate a timing adjustment result; The scheduling allocation data of the fault repair scheduling plan is corrected according to the timing adjustment result to determine the fault repair scheduling optimization plan.

6. A scenario-adaptive fault repair dispatch system, characterized in that: The method for implementing a scenario-adaptive fault repair scheduling method according to any one of claims 1 to 5, wherein the scenario-adaptive fault repair scheduling system comprises: A real-time monitoring module, which is used to monitor the target power grid operation status in real time through multiple data sources and identify multiple operation fault scenario parameters according to the real-time operation information set; A fault detection module, the fault detection module is used to perform fault detection on the target power grid based on the real-time operation information set to generate a fault detection data set; A fault assessment module, the fault assessment module is used to traverse the fault detection data set to locate the fault, determine multiple fault points, perform fault impact assessment based on the multiple fault points, and generate a fault repair sequence according to the assessment result; A scheduling scheme generating module, the scheduling scheme generating module is used to generate a fault repair scheduling scheme based on the multiple operating fault scenario parameters and in combination with the fault repair sequence; The scheduling scheme optimization module is used to execute the fault repair scheduling scheme, perform dynamic feedback according to the multiple operating fault scenario parameters, perform adaptive adjustment according to the feedback results, and generate a fault repair scheduling optimization plan.

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