Ice disaster under the distribution network repair method, equipment and storage medium

Abstract

The application provides a power distribution network repair method and device under ice disaster, and a storage medium. The method comprises the following steps: based on a meteorological and traffic coupling scheduling model, according to the damaged position distribution of the power distribution network under ice disaster, the initial positions of each repair team, the reserve quantity of each material warehouse, meteorological data and real-time traffic data, the optimal driving path and the repair task sequence of each repair team are determined in a multi-dimensional constraint space; based on a differentiated demand load grading evaluation system, the power outage area corresponding to the damaged position distribution is evaluated in multiple attributes, and the power restoration priority ranking corresponding to each power outage area is obtained; according to the optimal driving path and the repair task sequence, the repair team is dispatched to perform the main network physical repair operation; and before the completion of the main network physical repair operation, according to the power restoration priority ranking, the target power outage area is controlled to restore power. Thus, the repair efficiency is improved, the scientific ranking of the power restoration priority is realized, and the resilience operation ability of the power distribution network under ice disaster is ensured.

Description

Technical Field

[0001] This application relates to the field of power system emergency repair and dispatch technology, and in particular to a method, equipment and storage medium for emergency power restoration of distribution networks under freezing disasters. Background Technology

[0002] In regions frequently affected by extreme low temperatures, rain, snow, and freezing weather, power systems face multiple risks, such as icing of transmission lines, tower collapses, broken conductor strands, and transformer damage, which can easily lead to widespread power outages. Timely restoration of the distribution network after a disaster is crucial for restoring normal production and daily life in affected areas.

[0003] For the emergency repair and restoration of power distribution networks during freezing disasters, the following methods are mainly used: First, damaged locations are identified through manual inspection; second, dispatchers determine the repair sequence for each damaged location based on experience, and then dispatch repair teams to the site to carry out physical repairs according to this sequence; finally, power is restored to the affected area after all or most of the damaged locations have been repaired. However, this method suffers from low repair efficiency and unreasonable power restoration priorities, making it difficult to meet the actual needs of resilient power grid operation under extreme weather conditions. Summary of the Invention

[0004] This application provides a method, equipment, and storage medium for emergency power restoration of power distribution networks under freezing disasters, in order to solve the problems of low emergency repair efficiency, unreasonable power restoration priority, and difficulty in meeting the actual needs of resilient operation of power grids under extreme weather conditions in related technologies.

[0005] Firstly, this application provides a method for emergency power restoration of a power distribution network under freezing disasters, including:

[0006] Acquire the distribution of damaged locations in the power distribution network under freezing disasters, the initial locations of each repair team, the reserve quantities of each material warehouse, meteorological data corresponding to the distribution of damaged locations, and real-time traffic data;

[0007] Based on the meteorological and traffic coupled scheduling model, the optimal driving path and repair task order of each repair team are determined iteratively in a multi-dimensional constrained space according to the distribution of damaged locations, the initial location of each repair team, the reserve of each material warehouse, meteorological data and real-time traffic data. The meteorological and traffic coupled scheduling model is used to dynamically correct the road passage time according to real-time meteorological data and real-time traffic data, with the shortest total repair time as the optimization objective.

[0008] Based on the differentiated demand load classification evaluation system, the power outage areas corresponding to the distribution of damaged locations are evaluated with multiple attributes to obtain the power restoration priority ranking for each power outage area.

[0009] Based on the optimal driving route and the order of emergency repair tasks, dispatch emergency repair teams to carry out physical emergency repairs on the main power grid; and before the physical emergency repairs on the main power grid are completed, control the restoration of power to the target power outage area according to the power restoration priority.

[0010] In one possible implementation, the multiple attributes include topology attributes, reliability attributes, and economic attributes. Based on a differentiated demand load classification evaluation system, the power outage areas corresponding to the distribution of damaged locations are evaluated using multiple attributes to obtain a power restoration priority ranking for each power outage area, including:

[0011] Obtain the differentiated demand weight vector, which reflects the differentiated demands of different regions for topological, reliability, and economic attributes, and is used for the evaluation of all power outage areas;

[0012] For each power outage area corresponding to the distribution of damaged locations, the topological attribute values ​​of the power outage area are determined based on a pre-built topological evaluation model; the reliability attribute values ​​of the power outage area are determined based on a pre-built reliability evaluation model; and the economic attribute values ​​of the power outage area are determined based on a pre-built economic evaluation model.

[0013] The correlation between the differentiated demand weight vector and the attribute feature vector of each power outage area is determined. The attribute feature vector includes topology attribute value, reliability attribute value and economic attribute value.

[0014] The priority for restoring power to each power outage area is determined by ranking the degree of correlation from highest to lowest.

[0015] In one possible implementation, the differentiated demand weight vector is obtained in the following way:

[0016] The fuzzy hierarchical analysis method was used to obtain the initial subjective weights of multiple experts on topological attributes, reliability attributes, and economic attributes.

[0017] Clustering and weighting of the initial subjective weights of multiple experts yields a differentiated demand weight vector.

[0018] In one possible implementation, controlling the restoration of power to the target power outage area according to the power restoration priority includes:

[0019] Based on the priority of power restoration, at least one of the top-ranked power outage areas is selected as the target power outage area.

[0020] Obtain the main grid repair progress corresponding to the target power outage area to determine the estimated repair time of the main grid in the target power outage area;

[0021] When the estimated repair time of the main grid is longer than the user's tolerance time threshold, assess whether the target power outage area meets the islanding operation conditions based on the load demand of the target power outage area and the available emergency resources.

[0022] If the target power outage area meets the conditions for islanded operation, emergency resources will be dispatched to establish a temporary power supply island to supply power to the target power outage area.

[0023] In one possible implementation, emergency resources include distributed power sources and / or mobile energy storage vehicles. Based on the load demand of the target power outage area and the available emergency resources, the assessment includes determining whether the target power outage area meets the conditions for islanded operation, including:

[0024] The total output of the available distributed power sources and / or the power supply capacity of mobile energy storage vehicles within the target power outage area are used as the numerator, and the load demand of the target power outage area is used as the denominator to obtain the emergency power restoration support index for the target power outage area. The emergency power restoration support index represents the degree to which emergency resources meet the needs of high-priority loads.

[0025] When the emergency power restoration support index is greater than or equal to the emergency power restoration support index threshold, the target power outage area is determined to meet the islanding operation conditions.

[0026] In one possible implementation, the emergency power restoration method for power distribution networks under freezing disasters also includes:

[0027] After the physical repair work on the main grid in the target power outage area is completed, the temporary power supply island is smoothly switched to the main grid power supply using quasi-synchronous grid connection technology.

[0028] In one possible implementation, based on the distribution of damaged locations, the initial locations of each repair team, the reserves of each material depot, meteorological data, and real-time traffic data, the optimal travel routes and repair task sequences for each repair team are iteratively determined within a multi-dimensional constrained space, including:

[0029] Based on meteorological data and real-time traffic data, the expected travel time for each road segment is dynamically adjusted.

[0030] An optimization model is constructed based on the distribution of damaged locations, the initial locations of each repair team, the reserves of each material depot, and the expected travel time for each road section.

[0031] The optimization model is solved iteratively using an intelligent optimization algorithm to obtain the optimal driving route and repair task order for each emergency repair team.

[0032] The optimization model takes the shortest total repair time as the objective function and is constrained by the total material demand of each repair team at the damaged location being less than or equal to the corresponding material warehouse reserve, and the priority of repairing the backbone line over the branch line. The total repair time includes the travel time of each repair team and the repair time of each damaged location.

[0033] In one possible implementation, obtaining the location distribution of damage to the power distribution network under a freezing disaster includes:

[0034] Acquire monitoring data for each insulator string on the straight tower, including axial tension, tilt angle, and wind deflection angle;

[0035] Using the successive approximation method, based on the overhead line state equation and tower mechanical equilibrium equation, the vertical specific load of the conductor under icing conditions is determined according to monitoring data.

[0036] Based on historical monitoring data during the same meteorological window period without icing, the vertical load ratio is corrected. The same meteorological window period characterizes the ambient temperature, wind speed and humidity to match the current icing monitoring period.

[0037] Determine the equivalent icing thickness based on the corrected vertical load ratio;

[0038] The affected sections of the line were determined based on the equivalent icing thickness.

[0039] The sections of the power line damaged by icing are used as the distribution locations of the distribution network damaged under freezing disasters.

[0040] Secondly, this application provides emergency power restoration equipment for power distribution networks under freezing disasters, including:

[0041] The acquisition module is used to acquire the distribution of damaged locations of the power distribution network under freezing disasters, the initial locations of each emergency repair team, the reserve quantities of each material warehouse, meteorological data corresponding to the distribution of damaged locations, and real-time traffic data.

[0042] The determination module is used to determine the optimal travel route and repair task order of each repair team in a multi-dimensional constraint space based on the meteorological and traffic coupled scheduling model, according to the distribution of damaged locations, the initial location of each repair team, the reserve of each material warehouse, meteorological data and real-time traffic data. The meteorological and traffic coupled scheduling model is used to dynamically correct the road travel time based on real-time meteorological data and real-time traffic data, with the shortest total repair time as the optimization objective.

[0043] The evaluation module is used to evaluate the power outage areas corresponding to the distribution of damaged locations based on the differentiated demand load classification evaluation system, and obtain the power restoration priority ranking for each power outage area.

[0044] The control module is used to dispatch the emergency repair team to perform physical emergency repairs on the main power grid according to the optimal driving route and the order of emergency repair tasks; and before the physical emergency repairs on the main power grid are completed, it controls the restoration of power to the target power outage area according to the power restoration priority.

[0045] In one possible implementation, the multiple attributes include topology attributes, reliability attributes, and economic attributes. The evaluation module is specifically used to: obtain a differentiated demand weight vector, which reflects the differentiated demands of different regions for topology attributes, reliability attributes, and economic attributes, and is used for the evaluation of all power outage areas; for each power outage area corresponding to the distribution of damaged locations, determine the topology attribute value of the power outage area based on a pre-built topology evaluation model; determine the reliability attribute value of the power outage area based on a pre-built reliability evaluation model; determine the economic attribute value of the power outage area based on a pre-built economic evaluation model; determine the correlation between the differentiated demand weight vector and the attribute feature vector of each power outage area, where the attribute feature vector includes topology attribute values, reliability attribute values, and economic attribute values; and determine the power restoration priority ranking of each power outage area in descending order of correlation.

[0046] In one possible implementation, the differentiated demand weight vector is obtained by: using fuzzy hierarchical analysis to obtain the initial subjective weights of multiple experts on topological, reliability, and economic attributes; and clustering and weighting the initial subjective weights of the multiple experts to obtain the differentiated demand weight vector.

[0047] In one possible implementation, the control module is specifically used to: select at least one outage area ranked high according to the power restoration priority as the target outage area; obtain the main grid repair progress corresponding to the target outage area to determine the estimated main grid repair time for the target outage area; when the estimated main grid repair time is greater than the user's tolerance time threshold, assess whether the target outage area meets the islanding operation conditions based on the load demand of the target outage area and the available emergency resources; if the target outage area meets the islanding operation conditions, schedule emergency resources to establish a temporary power supply island to supply power to the target outage area.

[0048] In one possible implementation, emergency resources include distributed power sources and / or mobile energy storage vehicles. The control module is further configured to: use the total output of the available distributed power sources and / or the power supply capacity of the mobile energy storage vehicles within the target power outage area as the numerator and the load demand of the target power outage area as the denominator to obtain the emergency power restoration support index of the target power outage area. The emergency power restoration support index characterizes the degree to which emergency resources meet the demand for high-priority loads. When the emergency power restoration support index is greater than or equal to the emergency power restoration support index threshold, it is determined that the target power outage area meets the islanding operation conditions.

[0049] In one possible implementation, the control module further includes: after the physical repair work on the main grid in the target power outage area is completed, using quasi-synchronous grid connection technology, smoothly switching the temporary power supply island to the main grid power supply.

[0050] In one possible implementation, the determining module is specifically used to: dynamically adjust the expected travel time of each road segment based on meteorological data and real-time traffic data; construct an optimization model based on the distribution of damaged locations, the initial locations of each repair team, the reserve quantity of each material warehouse, and the expected travel time of each road segment; and use an intelligent optimization algorithm to iteratively solve the optimization model to obtain the optimal travel route and repair task order for each repair team. The optimization model takes the shortest total repair time as its objective function, and is constrained by the total material demand of each repair team at the damaged location being less than or equal to the reserve quantity of the corresponding material warehouse, and the priority of repairing main lines over branch lines. The total repair time includes the travel time of each repair team and the repair time of each damaged location.

[0051] In one possible implementation, the acquisition module is specifically used to: acquire monitoring data of insulator strings of each straight-line tower, including axial tension, tilt angle, and wind deflection angle; determine the vertical load ratio under icing conditions based on the overhead line state equation and tower mechanical equilibrium equation using a successive approximation method and the monitoring data; correct the vertical load ratio based on historical monitoring data during the same meteorological window period without icing, where the same meteorological window period represents ambient temperature, wind speed, and humidity that match the current icing monitoring period; determine the equivalent icing thickness based on the corrected vertical load ratio; determine the icing-damaged sections of the line based on the equivalent icing thickness; and use the icing-damaged sections of the line as the distribution of damaged locations in the distribution network under freezing disasters.

[0052] Thirdly, this application provides an electronic device, including: a memory and a processor;

[0053] The memory stores instructions that the computer executes;

[0054] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0055] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0056] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0057] The method, equipment, and storage medium for emergency power restoration of power distribution networks under freezing disasters provided in this application acquire multi-dimensional data foundations for subsequent dispatching decisions by obtaining the distribution of damaged locations in the power distribution network under freezing disasters, the initial locations of each emergency repair team, the reserve quantities of each material warehouse, meteorological data corresponding to the distribution of damaged locations, and real-time traffic data. Based on this, a meteorological and traffic coupled dispatching model dynamically corrects road travel time according to real-time meteorological and traffic data, and iteratively determines the optimal travel path and repair task order for each emergency repair team within a multi-dimensional constraint space, with the shortest total repair time as the optimization objective. This solves the problems of non-optimal repair paths, misallocation of materials, and difficulty in effectively controlling the overall repair cycle caused by relying on manual experience to determine the repair order in related technologies, significantly improving the efficiency of emergency repair dispatching. Simultaneously, by using a differentiated demand load classification evaluation system, the distribution of damaged locations is analyzed to determine the optimal travel path and repair task order for each emergency repair team within a multi-dimensional constraint space. The system performs multi-attribute evaluations on the corresponding power outage areas to obtain a power restoration priority ranking for each area. This overcomes the shortcomings of related technologies that rely solely on subjective experience to arrange the power restoration sequence due to a lack of systematic quantitative evaluation of load importance, thus avoiding the unreasonable phenomenon of "minor loads being restored first, while core loads wait indefinitely." Furthermore, while dispatching repair teams to perform physical repairs on the main grid according to the optimal travel route and repair task order, the system also controls the restoration of target power outage areas according to the power restoration priority ranking before the main grid physical repairs are completed. This achieves parallel execution of repair and power restoration operations, breaking the traditional serial mode of "repair first, then restore power." This allows core loads to have their power restored ahead of schedule without waiting for the entire main grid to be repaired, effectively solving the problems of low repair dispatch efficiency and unreasonable power restoration priorities in related technologies. This significantly improves the resilience of the power grid under extreme freezing disasters and the power supply guarantee level for core loads. Attached Figure Description

[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0059] Figure 1 A schematic diagram illustrating a scenario for emergency power restoration of a power distribution network under freezing disasters, as provided in an embodiment of this application.

[0060] Figure 2 A flowchart illustrating the emergency repair and power restoration method for a power distribution network under freezing disasters provided in this application embodiment;

[0061] Figure 3 This is a schematic diagram of the structure of the emergency repair and power restoration device for a power distribution network under freezing disasters provided in the embodiments of this application;

[0062] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0063] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0064] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0065] The terms “first,” “second,” etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, products, or apparatus.

[0066] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0067] In related technologies, dispatchers rely on personal experience to determine the repair sequence, lacking quantitative consideration of dynamic factors such as road conditions, weather changes, and material reserves. This leads to significant arbitrariness in repair route selection, resulting in problems such as excessive detours for repair teams, mismatched material carrying, and uneven task allocation among multiple teams. Consequently, the overall repair cycle is difficult to control effectively, and the efficiency of repair resource utilization is low. Furthermore, the "repair first, then restore power" sequential mode means that power can only be restored to the outage area after all or most of the damaged locations have been physically repaired. This model has two major drawbacks: First, the power restoration sequence is entirely dependent on the order in which repairs are completed, i.e., "the areas that are repaired first are restored first," without independently evaluating the importance of the loads in the outage area. If the repair of the core load area is more difficult or the distance is longer, its power restoration time may be later than that of the secondary loads, resulting in the unreasonable phenomenon of "secondary loads being restored first, while core loads wait for a long time." Second, it is impossible to provide temporary power support to the core loads before the repairs are completed, which means that core protection loads such as medical and heating services must passively wait for the main grid repairs to be completed before power can be restored, and the power outage time often far exceeds their tolerance limit.

[0068] In summary, the relevant technologies suffer from low repair efficiency due to reliance on manual inspection and experience-based judgment. Furthermore, the adoption of a sequential "repair first, then restore power" approach and the lack of load importance assessment result in unreasonable power restoration priorities and excessively long outage times for core loads, making it difficult to achieve rapid power restoration for core loads and resilient grid operation under extreme freezing disasters.

[0069] Based on the aforementioned technical issues, the emergency repair and power restoration scheme for distribution networks under freezing disasters provided in this application constructs a comprehensive disaster awareness foundation by acquiring multi-dimensional data. On this basis, "repair path optimization" and "power restoration priority ranking" are decoupled into two independent and parallel decision dimensions. The traditional serial mode of "repair first, then restore power" is replaced by "parallel execution of repair and power restoration". This improves repair efficiency, enhances the rationality of power restoration sequence, significantly shortens the outage time of core loads, and strengthens the resilience of the power grid under extreme freezing disasters.

[0070] Next, we will first introduce the application scenarios of this application.

[0071] This application applies to power distribution network emergency repair scenarios under extreme low-temperature rain, snow, and freezing disasters. In severe weather conditions such as winter cold waves, blizzards, and freezing rain, power distribution network lines and equipment are prone to icing overload, conductor strand breakage, tower tilting, and even tower collapse, leading to large-scale regional power outages. These disasters are typically characterized by their wide impact, long duration, drastic deterioration of road conditions, and complex repair environments, posing severe challenges to fault location, emergency repair scheduling, and power restoration for users.

[0072] This application is particularly applicable to the following typical scenarios: First, power distribution line sections in mountainous and hilly areas with significant micro-meteorological characteristics, where icing occurs frequently and unevenly; second, power distribution networks in urban core areas and peri-urban areas, where load density is high and core loads (such as hospitals, heating stations, and emergency command centers) are concentrated, requiring extremely high power supply reliability; and third, power distribution networks in remote areas where traffic is easily obstructed, making it difficult for repair vehicles to pass and resulting in long dispatch cycles for repair resources. In these scenarios, this application optimizes repair paths by integrating meteorological and traffic dynamic data, determines power restoration priorities by combining load-differentiated demand assessments, and adopts a parallel execution mode for repair and power restoration. This significantly improves repair dispatch efficiency, shortens the outage time of core loads, and ensures the resilient operation of the power distribution network under extreme weather conditions.

[0073] For example, Figure 1 This is a schematic diagram illustrating a scenario for the emergency power restoration method for a power distribution network under freezing disasters provided in an embodiment of this application. Figure 1 As shown, the application scenario includes electronic equipment 11, data acquisition device 12, meteorological service center 13, traffic monitoring center 14, emergency repair team terminal 15, emergency power supply terminal 16, and power distribution network equipment 17.

[0074] The electronic device 11 is communicatively connected to the data acquisition device 12, the meteorological service center 13, the traffic monitoring center 14, the emergency repair team terminal 15, and the emergency power supply terminal 16. The electronic device 11 runs a method for emergency power restoration of the power distribution network under freezing disasters, used for locating damaged locations, planning repair routes, prioritizing power restoration, and parallel scheduling of repair and power restoration. The data acquisition device 12 is deployed at various monitoring nodes of the power distribution network to collect data on the axial tension, tilt angle, and wind deflection angle of insulator strings, and sends the collected data to the electronic device 11. The meteorological service center 13 provides real-time weather forecast data and historical weather data, such as... The system includes wind speed, ambient temperature, snowfall, and icing intensity; the traffic monitoring center 14 provides real-time traffic data and road closure information; the emergency repair team terminal 15, carried by each emergency repair team, receives the repair task sequence and optimal driving route from the electronic equipment 11, and reports the real-time location and repair progress to the electronic equipment 11; the emergency power supply terminal 16 is deployed on mobile energy storage vehicles and distributed power stations, receives emergency power restoration commands from the electronic equipment 11, and reports the available power supply capacity and connection status; the distribution network equipment 17 includes lines, towers, insulator strings, and switchgear, and executes grid connection switching commands from the electronic equipment 11.

[0075] It should be noted that the electronic device 11 interacts with various external devices via wireless communication networks (including 4G, 5G, satellite communication, or IoT communication protocols). There can be one or multiple electronic devices 11. When there are multiple electronic devices 11, they can be deployed in a distributed manner, for example, deployed in a dispatch center, various regional emergency repair command centers, or mobile command vehicles, and jointly complete the emergency repair and power restoration decision-making tasks through data synchronization or master-slave collaboration. The physical form of the electronic device 11 can be a server with a certain computing power, an industrial control computer, a server cluster, a high-performance workstation, or a desktop computer, or it can be integrated into a portable terminal or vehicle-mounted mobile device. This application embodiment does not limit the specific number, physical form, or product type of the electronic devices 11, as long as they can execute the emergency repair and power restoration method for the distribution network under freezing disasters provided in this application. Similarly, the data acquisition device 12 can be deployed at each insulator string of a straight-line tower. Each data acquisition device 12 corresponds to one monitoring point, and its number can be flexibly configured according to the monitoring range and accuracy requirements of the distribution network. This application embodiment does not limit the specific number of data acquisition devices 12.

[0076] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0077] Figure 2 This is a flowchart illustrating the emergency power restoration method for power distribution networks under freezing disasters provided in this application embodiment, as shown below. Figure 2 As shown, the emergency repair and power restoration methods for the power distribution network under this freezing disaster include:

[0078] S201. Obtain the distribution of damaged locations of the power distribution network under freezing disaster, the initial location of each repair team, the reserve quantity of each material warehouse, meteorological data corresponding to the distribution of damaged locations, and real-time traffic data.

[0079] The distribution of damaged locations can be understood as the set of specific geographical locations in the distribution network where faults such as line breaks, tower collapses, and insulator flashovers caused by icing have occurred. For example, data on axial tension, tilt angle, and wind deflection angle can be obtained by data acquisition devices deployed at the insulator strings of each straight-line tower, and the equivalent icing thickness can be determined by back-calculating based on the overhead line state equation and the tower mechanical balance equation. Specifically, when the equivalent icing thickness of a certain section of the line exceeds a preset threshold, that section is marked as a damaged location.

[0080] The initial location of each emergency repair team is the geographical coordinates of its current location, which can be reported in real time, for example, through the built-in GPS module of the mobile terminal carried by the emergency repair team.

[0081] The reserve quantity of each material warehouse refers to the inventory quantity and specifications of emergency repair materials (including wires, insulators, fittings, crimped pipes, tower materials, etc.) stored in each warehouse, which can be obtained, for example, through the material management system interface.

[0082] The meteorological data corresponding to the distribution of damaged locations refers to real-time weather forecast information covering each damaged location area, and is not limited to wind speed, ambient temperature, snowfall intensity and icing intensity. For example, it can be obtained through data interface with the meteorological service center.

[0083] Real-time traffic data refers to real-time traffic information covering the road network between various damaged locations, including road traffic status, congestion level, and closure status, which can be obtained, for example, through data interfaces with traffic monitoring centers.

[0084] By acquiring the aforementioned multi-source data, this step establishes a comprehensive data foundation for subsequent emergency repair scheduling and power restoration decisions, enabling the decision-making process to respond to environmental changes in real time.

[0085] S202. Based on the meteorological and traffic coupled scheduling model, the optimal driving route and repair task order of each repair team are determined iteratively in a multi-dimensional constrained space according to the distribution of damaged locations, the initial location of each repair team, the reserve quantity of each material warehouse, meteorological data and real-time traffic data.

[0086] Among them, the meteorological and traffic coupled scheduling model is used to dynamically correct road travel time based on real-time meteorological data and real-time traffic data, with the shortest total repair time as the optimization objective.

[0087] In this step, the meteorological and traffic coupled scheduling model can be regarded as a decision algorithm that quantitatively models the impact of meteorological and traffic conditions on traffic capacity.

[0088] For example, firstly, based on acquired meteorological data (including wind speed, ambient temperature, snowfall intensity, and icing intensity) and real-time traffic data (including road accessibility and closure status), the expected travel time for each road segment is dynamically adjusted. For instance, when a road segment experiences heavy snow or icing, the expected travel time for that segment will be extended accordingly; when a road segment is closed, it is excluded from the feasible route. Through this dynamic adjustment, road travel time can reflect changes in meteorological conditions in real time, making subsequent route planning more closely aligned with actual road conditions.

[0089] Secondly, based on the distribution of damaged locations, the initial locations of each repair team, the reserves of each material depot, and the dynamically adjusted expected travel time for each road segment, an optimization model for emergency repair scheduling is constructed. This optimization model uses the shortest total repair time as its objective function, where the total repair time includes the cumulative travel time of each repair team from its initial location to each damaged location, as well as the repair time required for on-site operations at each damaged location. Simultaneously, the optimization model introduces multi-dimensional constraints, such as the total material demand of each repair team at its assigned damaged location not exceeding the reserves of its starting material depot, and that repair tasks on main lines take priority over those on branch lines, to ensure the feasibility of the scheduling plan under actual resource constraints and repair priority constraints.

[0090] During the iteration process, the algorithm continuously generates candidate emergency repair task allocation schemes and driving route schemes, calculates the total repair time corresponding to each scheme, and judges the feasibility of the schemes based on constraints. Through operations such as selection, crossover, and mutation, it gradually approaches the optimal solution. When the preset iteration stopping condition is met (such as reaching the maximum number of iterations or the optimal solution no longer improving after several consecutive generations), the algorithm terminates and outputs the current optimal solution, which is the optimal driving route and emergency repair task order for each emergency repair team.

[0091] S203. Based on the differentiated demand load classification evaluation system, multi-attribute evaluation is carried out on the power outage areas corresponding to the distribution of damaged locations to obtain the power restoration priority ranking for each power outage area.

[0092] The power outage areas corresponding to the distribution of damaged locations can be determined by identifying the power supply range affected by each damaged location based on the topology of the distribution network, and marking this power supply range as the corresponding power outage area.

[0093] It should be understood that the differentiated demand load classification evaluation system is a multi-attribute decision-making framework that comprehensively considers the differentiated needs of different regions for power supply reliability, economy, and other aspects. This framework does not limit the specific implementation of evaluation algorithms, but rather provides a configurable evaluation logic: that is, based on pre-built evaluation dimensions and indicators, different calculation methods are used to comprehensively evaluate each power outage area and output a power restoration priority ranking, including dimensions such as topology, reliability, and economy.

[0094] Based on this pre-built evaluation dimension, this embodiment provides multiple methods to prioritize the restoration of power to each power outage area.

[0095] For example, one implementation uses a simplified analytic hierarchy process (AHP) to determine the priority of power restoration. Specifically, key decision-makers at the dispatch center (such as the dispatcher and operation and maintenance experts) compare the topology, reliability, and economy in pairs to construct a judgment matrix and calculate the weight vector for each dimension. Then, for each power outage area, electronic equipment automatically generates a score for each of the three dimensions based on pre-configured scoring rules (e.g., 5 points for power transfer capacity > 50%, 3 points for power transfer capacity between 20% and 50%, 1 point for power transfer capacity < 20%; 5 points for including a tertiary hospital, 3 points for including a community hospital, 1 point for no medical facilities, etc.), using the power grid topology data, load data, and equipment parameters of that outage area. The weight vector and the score vector are weighted and summed to obtain a comprehensive score for each outage area, and the power restoration priority is determined according to the comprehensive score from highest to lowest. This implementation is simple, computationally inefficient, objective, and reproducible, making it suitable for small distribution networks or rapid decision-making scenarios in the initial stages of emergency response.

[0096] In another implementation, principal component analysis (PCA) is used to automatically extract evaluation indicators from the data, achieving an objective quantitative ranking of power restoration priorities. Specifically, historical data for each power outage area on multiple raw indicators are obtained. These raw indicators include average outage frequency, average outage duration, total load loss, unit outage loss, load importance level, and regional economic output. This historical data can be obtained from the power grid dispatching system and load management system. Then, PCA is used to reduce the dimensionality of these raw indicators: the raw indicators are standardized to eliminate dimensional differences; the correlation coefficient matrix is ​​calculated, and eigenvalues ​​and eigenvectors are solved; the top principal components with a cumulative variance contribution rate reaching a preset threshold (e.g., 85%) are selected as evaluation factors. Each principal component is a linear combination of the raw indicators, and its variance contribution rate reflects the amount of information retained by that principal component. For each power outage area, the raw indicator values ​​for that area are substituted into the expressions of each principal component to calculate the score for that area on each principal component; the scores of each principal component are weighted by their variance contribution rate to obtain the comprehensive score for that area. A higher overall score indicates a more urgent need for power restoration in the affected area, and thus warrants a higher restoration priority. Finally, the affected areas are ranked from highest to lowest based on their overall scores to determine the restoration priority ranking.

[0097] Another implementation method employs a rule engine-based rapid grading approach to determine power restoration priority. Specifically, the evaluation dimensions and indicators (such as whether medical facilities, number of households experiencing power outages, estimated repair time, and economic output) in the differentiated demand load grading evaluation system are transformed into executable rule statements. The rule engine then rapidly matches and grades outage areas. For example, several power restoration priority grading rules are pre-defined, such as: outage areas containing tertiary hospitals are automatically assigned the highest priority; outage areas containing heating stations are assigned the second highest priority; areas with more than 1000 households experiencing power outages are given a one-level priority increase; and areas with an estimated repair time exceeding 8 hours are given a one-level priority increase. Then, for each outage area, key attribute information is obtained. This key attribute information includes whether the area contains core load types such as medical facilities, heating facilities, important institutions, and important industrial users, as well as quantitative indicators such as the number of households experiencing power outages, estimated repair time, and economic output. This key attribute information can be obtained from the power grid geographic information system, load management system, and emergency repair progress tracking system. Next, the key attribute information of each power outage area is input into the rule engine. The rule engine matches each rule according to the pre-defined hierarchical rules and automatically outputs the power restoration priority level for each power outage area. This implementation method is fast, logically transparent, easy to understand and execute, and suitable for rapid decision-making at the front line of emergency repairs.

[0098] This embodiment does not limit the specific implementation method for determining the power restoration priority order, and can be flexibly selected according to the data conditions, computing resources and real-time requirements of the actual application scenario.

[0099] It should be noted that steps S202 and S203 do not have a specific execution order; they can be executed sequentially or in parallel.

[0100] S204. Based on the optimal driving route and the order of emergency repair tasks, dispatch the emergency repair team to carry out the physical emergency repair work of the main grid; and before the physical emergency repair work of the main grid is completed, control the restoration of power to the target power outage area according to the power restoration priority.

[0101] The main grid physical repair operation can be understood as the process by which the repair team travels to the damaged locations according to the optimal driving route determined by S202, and performs on-site physical repairs for faults such as broken lines, collapsed towers, and damaged insulators. While the repair team is performing the main grid physical repair operation, the electronic equipment is simultaneously performing power restoration operations.

[0102] For example, in one implementation, if the distribution network where the target power outage area is located has a backup power source or tie switch and the backup line is undamaged, a line transfer method can be used to achieve rapid power restoration. Specifically, electronic equipment identifies whether there is an available transfer path for the target power outage area based on the distribution network topology (e.g., connecting to another feeder powered by a different substation via a normally open tie switch). If a transfer path exists and the remaining capacity of the transfer line is greater than the load demand of the target power outage area, the electronic equipment issues a closing command to the corresponding switching equipment, switching the target power outage area to backup power supply, thereby achieving rapid power restoration. This method does not require the deployment of emergency power supplies, has a fast power restoration speed, and is suitable for areas with a strong network structure and sufficient transfer capacity.

[0103] Another approach involves directly connecting mobile generators to the grid. Specifically, electronic equipment dispatches the mobile generator to a pre-defined connection point (such as the low-voltage side of a distribution transformer or a line branch point) in the target power outage area based on the load demand and grid connection conditions. On-site operators then complete the electrical connection and synchronous grid connection between the generator and the distribution network. The electronic equipment monitors the generator's output power and voltage frequency in real time to ensure it meets the power supply needs of the target power outage area. This method is suitable for areas with a shortage of mobile energy storage vehicles but which do have mobile generators available.

[0104] It should be understood that this step decouples the emergency repair work from the power restoration operation into two parallel execution flows. The emergency repair team focuses on physical repairs, proceeding to the damaged locations to carry out on-site work according to the optimal travel route and repair task sequence determined in step S202. Meanwhile, line transfer and mobile generator connection are independent of the emergency repair work, providing temporary power to core loads before the main grid is fully repaired. In this way, core loads such as medical and heating services can have their power restored ahead of schedule without waiting for the main grid to be fully repaired, shortening the power outage time and significantly reducing the power outage losses of core loads.

[0105] This application provides a multi-dimensional data foundation for subsequent dispatching decisions by acquiring the distribution of damaged locations in the power distribution network under freezing disasters, the initial locations of each repair team, the reserve quantities of each material warehouse, meteorological data corresponding to the distribution of damaged locations, and real-time traffic data. Based on this, a meteorological and traffic coupled dispatching model dynamically corrects road travel time according to real-time meteorological and traffic data, and iteratively determines the optimal travel path and repair task order for each repair team within a multi-dimensional constraint space, with the shortest total repair time as the optimization objective. This solves the problems of suboptimal repair paths, misallocation of materials, and difficulty in effectively controlling the overall repair cycle caused by relying on manual experience to determine the repair order in related technologies, significantly improving the efficiency of repair dispatching. Simultaneously, a multi-attribute evaluation is conducted on the outage areas corresponding to the distribution of damaged locations based on a differentiated demand load classification evaluation system. This method obtains the priority ranking of power restoration for each power outage area, overcoming the shortcomings of related technologies that rely solely on subjective experience to arrange the power restoration sequence due to a lack of systematic quantitative evaluation of load importance. It avoids the unreasonable phenomenon of "secondary loads being restored first, while core loads wait indefinitely." Furthermore, while dispatching repair teams to perform main grid physical repairs based on optimal travel routes and repair task sequences, it also controls the restoration of target power outage areas according to the power restoration priority ranking before the main grid physical repairs are completed. This achieves parallel execution of repair and power restoration operations, breaking the traditional serial mode of "repair first, then restore," allowing core loads to have their power restored ahead of schedule without waiting for the entire main grid to be repaired. This effectively solves the problems of low repair dispatch efficiency and unreasonable power restoration priorities in related technologies, significantly improving the resilience of the power grid and the power supply guarantee level for core loads under extreme freezing disasters.

[0106] In some embodiments, the multiple attributes include topology attributes, reliability attributes, and economic attributes. Based on a differentiated demand load classification evaluation system, the power outage areas corresponding to the distribution of damaged locations are evaluated using multiple attributes to obtain a power restoration priority ranking for each power outage area, including:

[0107] Step 2031: Obtain the differentiated demand weight vector.

[0108] Among them, the differentiated demand weight vector reflects the differentiated needs of different regions for topological, reliability and economic attributes, and is used for the evaluation of all power outage areas to ensure the consistency of evaluation standards.

[0109] For example, the differentiated demand weight vector is obtained by using fuzzy hierarchical analysis to obtain the initial subjective weights of multiple experts on topological, reliability, and economic attributes; and by clustering and weighting the initial subjective weights of the multiple experts to obtain the differentiated demand weight vector.

[0110] In this implementation, multiple experts represent regions with different needs and preferences to reflect differentiated requirements. For example, several experts representing different regional types (such as urban core areas, industrial areas, mountainous areas, and rural areas) are invited. Each expert compares the importance of three attributes—topology, reliability, and economy—pair by pair based on the actual needs of their assigned region. For instance, an expert in an urban core area might consider reliability significantly more important than economy, while an expert in an industrial area might consider economy and reliability equally important, and an expert in a mountainous area might consider topology (i.e., network connectivity) more important than economy. Each expert constructs a judgment matrix using a triangular fuzzy number scale (e.g., a 1 / 9 to 9 scale) from the fuzzy hierarchical analysis method, calculating their initial subjective weight vector to obtain a set of three-dimensional weight vectors. For example, the city expert outputs [0.15, 0.70, 0.15] (i.e., topology 0.15, reliability 0.70, economy 0.15), the industrial zone expert outputs [0.20, 0.40, 0.40], and the mountain area expert outputs [0.50, 0.30, 0.20]. Then, the electronic equipment uses the K-means clustering algorithm to perform cluster analysis on the initial subjective weight schemes of the multiple experts, grouping similar weight schemes into the same category and counting the number of schemes contained in each category. After clustering, categories with a larger number of schemes are assigned larger fusion weights, and categories with a smaller number of schemes are assigned smaller fusion weights. Finally, a weighted average is calculated to obtain a unified differentiated demand weight vector. For example, if the clustering results show that the solutions of urban experts and industrial zone experts belong to the same large category (accounting for 70% of the total number of experts), while the solutions of mountain experts belong to another category (accounting for 30%), then the fused differentiated demand weight vector may be [0.20, 0.55, 0.25], which reflects both the mainstream opinion and the needs of a few regions.

[0111] It should be noted that the aforementioned differentiated demand weight vector only needs to be calculated once and can be reused in the subsequent evaluation of all power outage areas without needing to be recalculated for each power outage area.

[0112] Step 2032: For each power outage area corresponding to the distribution of damaged locations, determine the topology attribute value of the power outage area based on the pre-built topology evaluation model; determine the reliability attribute value of the power outage area based on the pre-built reliability evaluation model; and determine the economic attribute value of the power outage area based on the pre-built economic evaluation model.

[0113] The topology evaluation model is a computational model used to quantify the robustness and load transfer capacity of the distribution network structure in a power outage area. Specifically, the topology attribute value is a weighted composite of two sub-indicators: load transfer capacity and network connectivity. Load transfer capacity refers to the ability of the power outage area to transfer loads from other lines via backup power sources or tie switches. This can be obtained by calculating the ratio of the sum of the remaining capacity of all available load transfer paths in the power outage area to the total load loss in the area; a higher load transfer capacity value indicates stronger load transfer capability. Network connectivity refers to the degree of ring network formation or connectivity of the distribution network in the power outage area. This can be obtained by calculating the ratio of the number of loops to the number of nodes in the network structure in the power outage area using a connectivity algorithm in graph theory; a higher network connectivity value indicates a more robust network. Electronic equipment normalizes the load transfer capacity and network connectivity, then performs a weighted sum to obtain the topology attribute value of the power outage area. The value ranges from 0 to 1, with a higher value indicating a better topology.

[0114] The reliability evaluation model is a computational model used to quantify the power supply reliability level of a power outage area. Specifically, the reliability attribute value is determined by a combination of four sub-indicators: load loss, average outage frequency, average outage time, and power availability. Load loss, expressed in kilowatt-hours, is the total amount of electricity lost due to power outages in the affected area. It can be calculated by multiplying the predicted load value by the actual outage duration. Average outage frequency is the average number of outages per user in the affected area, obtained by dividing the total number of outage users by the total number of users. Average outage time is the average duration of outages per user in the affected area, obtained by dividing the total outage user hours by the total number of users. Power availability is the ratio of actual power supply time to expected power supply time in the affected area, calculated as (total user hours - outage user hours) / total user hours. Of these four indicators, load loss, average outage frequency, and average outage time are negative indicators (lower values ​​are better), while power availability is a positive indicator (higher values ​​are better). The electronic equipment standardizes and normalizes each sub-indicator, and then calculates the weighted sum to obtain the reliability attribute value of the power outage area. The value ranges from 0 to 1, with a higher value indicating a higher level of reliability.

[0115] The economic evaluation model is a calculation model used to quantify the investment costs required to restore power to an outage area and the economic losses caused by the outage. Specifically, the economic attribute value is determined by a combination of three sub-indicators (total investment cost, outage loss, and unit load cost). The total investment cost is the sum of material costs, labor costs, and machinery operating costs required to repair all damaged equipment in the outage area; the outage loss is the economic loss suffered by users in the outage area due to the outage, which can be calculated by multiplying the load loss by the unit outage loss (the unit loss varies depending on the user type, e.g., industrial users have a much higher loss than residential users); and the unit load cost is the total investment cost divided by the total load in the outage area, reflecting the repair cost per kilowatt of load. The above three indicators are all negative indicators (the smaller the value, the better). The electronic equipment performs standardization and normalization on each sub-indicator, then performs a weighted sum, and then converts it into a positive indicator by taking the reciprocal or by subtracting the normalized value from 1. This gives the economic attribute value of the power outage area, which ranges from 0 to 1. The larger the value, the better the economic performance (i.e., the lower the cost and the smaller the loss).

[0116] Using the three evaluation models described above, a set of three-dimensional attribute feature vectors can be obtained for each power outage area. Assuming the attribute feature vectors... =[Topology attribute value, Reliability attribute value, Economic attribute value]. For example, the power outage area where a hospital is located may be [0.65, 0.92, 0.58], the power outage area of ​​an industrial area may be [0.70, 0.55, 0.85], and the power outage area of ​​a residential area may be [0.45, 0.40, 0.50].

[0117] Step 2033: Determine the correlation between the differentiated demand weight vector and the attribute feature vector of each power outage area. The attribute feature vector includes topology attribute value, reliability attribute value and economic attribute value.

[0118] Among them, correlation degree is a quantitative indicator that measures the degree of matching or similarity between two vectors. This embodiment adopts a correlation degree calculation method based on fuzzy entropy and partial entropy, the core principle of which is as follows: First, the correlation degree is calculated by... and Treat them as two fuzzy sets and calculate their respective fuzzy entropies. and Fuzzy entropy reflects the degree of uncertainty of a vector. Entropy is maximum when all components of the vector are close to 0.5, and minimum when all components are close to 0 or 1. Then, two partial entropies are calculated: Indicates from From a perspective The partial entropy, that is, using To measure by weight The degree of ambiguity; Indicates from From a perspective The partial entropy, that is, using To measure by weight The degree of ambiguity. Finally, substitute the above entropy value into the following correlation formula for calculation:

[0119]

[0120] In the formula, For attribute feature vectors Weight vector of differentiated demand The degree of correlation between them.

[0121] when and When there is a high degree of consistency, the sum of partial entropy is small, and the correlation is large; when and When the differences are large, the sum of partial entropy is large, and the correlation is small.

[0122] For example, suppose the differentiated demand weight vector =[0.20,0.55,0.25] (emphasizing reliability), the attribute feature vector of a power outage area in a hospital. =[0.65,0.92,0.58] (outstanding reliability performance), the calculated correlation is high, indicating that the area is highly matched with differentiated needs. Another industrial area's power outage area's attribute feature vector. =[0.70,0.55,0.85] (Outstanding economic performance but average reliability), due to In this region, reliability is given a higher weight and economy a lower weight. The matching degree is relatively low, and the correlation is also relatively low.

[0123] Step 2034: Determine the priority order for power restoration of each power outage area in descending order of correlation.

[0124] For example, the electronic device sorts all the power outage areas calculated in step 2033 in descending order of their correlation scores. The power outage area with the highest correlation score is ranked first and receives the highest power restoration priority; the area with the second highest correlation score is ranked second, and so on. For instance, in a disaster, there are 5 power outage areas with the following calculated correlation scores: hospital area 0.92, heating station area 0.88, emergency command center area 0.85, industrial area 0.62, and residential area 0.45. The power restoration priority would then be: hospital area (highest priority), heating station area (second highest priority), emergency command center area (third priority), industrial area (fourth priority), and residential area (lowest priority).

[0125] In the subsequent step S204, the electronic equipment will select the top-ranked power outage area (such as the hospital area) as the target power outage area based on the ranking result, and restore power in advance by dispatching emergency resources or implementing line transfer.

[0126] In this embodiment, differentiated demands are quantified into unified weights through multi-expert fuzzy hierarchical analysis and clustering weighting. Based on a three-dimensional model of topology, reliability, and economy, each power outage area is objectively scored, and then scientific ranking is achieved through correlation calculation. This overcomes the limitations of subjective experience-based arrangements, ensures priority restoration of core loads, and improves the fairness, transparency, and interpretability of decision-making.

[0127] In some embodiments, power restoration to the target power outage area is controlled according to the power restoration priority, including:

[0128] Step 2041: Based on the power restoration priority, select at least one of the top-ranked power outage areas as the target power outage areas.

[0129] For example, the power restoration priority ranking is a list of power outage areas arranged from highest to lowest relevance, such as [Power Outage Area A (Hospital), Power Outage Area B (Heating Station), Power Outage Area C (Emergency Command Center), Power Outage Area D (Industrial Area), Power Outage Area E (Residential Area)]. Electronic equipment selects one or more outage areas from this list as target outage areas based on the total amount of available emergency resources and scheduling capabilities. For instance, when emergency resources are sufficient, the top two or three ranked outage areas can be selected as target outage areas, and power restoration operations can be carried out in parallel; when emergency resources are limited, only the top-ranked outage area can be selected as the target outage area, and the next priority outage area can be processed after power restoration to that area is completed and emergency resources are released.

[0130] Optionally, the electronic equipment can also merge multiple target power outage areas that are geographically close to each other based on their geographical distribution, in order to reduce the transfer time of emergency resources.

[0131] Step 2042: Obtain the main grid repair progress corresponding to the target power outage area to determine the estimated repair time of the main grid in the target power outage area.

[0132] Among them, the main grid repair progress refers to the physical repair progress of the main lines or superior substations responsible for supplying power to the target power outage area.

[0133] For example, the electronic equipment obtains the main grid repair progress in the following ways: First, it reads the planned repair completion time of the damaged location (such as a section of line or a tower) responsible for supplying power to the target power outage area from the repair plan output in step S202; second, it dynamically updates the actual progress based on the real-time on-site work status (such as "arrived," "under work," "repair completed") reported by the mobile terminals carried by the repair team; third, it uses the changes in the energized state of the line or equipment monitored by the Supervisory Control and Data Acquisition (SCADA) system as an automatic confirmation signal for repair completion; finally, it dynamically adjusts the remaining work time based on real-time meteorological data (e.g., if a sudden blizzard causes the work to be suspended, the estimated repair time is extended accordingly). Based on the above information, the electronic equipment continuously updates the estimated repair time of the main grid for the target power outage area, for example, "repair expected to be completed in 2.5 hours."

[0134] Step 2043: When the estimated repair time of the main grid is greater than the user's tolerance time threshold, assess whether the target power outage area meets the islanding operation conditions based on the load demand of the target power outage area and the available emergency resources.

[0135] Among them, the user tolerance time threshold is the maximum power outage time that different types of users can tolerate. This threshold can be preset according to the load importance level.

[0136] For example, the tolerance time threshold for tertiary-level Class A hospitals can be set to 2 hours, for community hospitals to 4 hours, for heating stations to 6 hours, for emergency command centers to 3 hours, and for general residential users to 12 hours or without restriction. The electronic equipment compares the estimated main grid repair time for the target power outage area determined in step 2042 with the user tolerance time threshold for that area: if the estimated main grid repair time is less than or equal to the user tolerance time threshold, it indicates that the user can tolerate the power outage duration, and there is no need to initiate emergency power restoration; the user can simply wait for the main grid to repair itself naturally. If the estimated main grid repair time is greater than the user tolerance time threshold, it indicates that the user cannot tolerate the power outage duration, and the emergency power restoration assessment process is triggered.

[0137] Correspondingly, in response to triggering the emergency power restoration assessment process, the electronic equipment further assesses whether the target power outage area meets the islanding operation conditions. These islanding operation conditions include the technical feasibility conditions required to establish an independent power supply system using emergency resources.

[0138] Step 2044: If the target power outage area meets the islanding operation conditions, dispatch emergency resources to establish a temporary power supply island to supply power to the target power outage area.

[0139] For example, based on the evaluation results of step 2043, the electronic device generates and issues an emergency dispatch instruction. The emergency dispatch instruction includes: the location coordinates of the target power outage area, the type and quantity of emergency resources to be connected (e.g., dispatching a mobile energy storage vehicle with a specific number to the low-voltage side of a distribution transformer), the suggested driving route (the implementation principle of step S202 can be reused), the power requirements of the core load, and the required power supply duration. Taking a mobile energy storage vehicle as an example, the electronic device sends the above emergency dispatch instruction to the terminal equipment of the corresponding vehicle, and the driver proceeds to the designated access point according to the navigation route. Upon arrival, on-site operators (or automatic grid connection devices) complete the electrical connection between the energy storage vehicle and the distribution network. The electronic device remotely controls the relevant switching equipment to disconnect the target power outage area from the main grid, forming a temporary power supply island powered by the mobile energy storage vehicle. During island operation, the electronic device monitors the output power, voltage, frequency, and remaining power of the energy storage vehicle in real time, and ensures, through control instructions, that the power supply quality within the island meets the requirements of the core load (e.g., voltage deviation not exceeding ±5%, frequency deviation not exceeding ±0.5Hz). When the energy storage vehicle's battery level drops to a preset lower limit, the electronic equipment can dispatch another energy storage vehicle to take over, thus achieving uninterrupted power supply.

[0140] It should be noted that this embodiment does not limit the specific implementation method of islanded operation. For example, it can employ a mobile energy storage vehicle access method, a mobile generator vehicle access method, a regional distributed power supply islanded operation method, or a combination of the above methods. As long as temporary power supply can be provided to the target power outage area before the main grid is repaired, any reasonable islanded operation method falls within the protection scope of this embodiment.

[0141] This embodiment prioritizes and filters target outage areas to ensure that the most critical core loads receive emergency resources first. By comparing the estimated repair time of the main grid with the user's tolerance threshold, emergency power restoration is activated only when necessary to avoid resource waste. The technical feasibility of the emergency dispatch plan is ensured through islanded operation condition assessment. Ultimately, it enables the early restoration of core loads before the main grid is repaired, significantly shortening the outage time for critical users such as medical and heating services, and improving the resilience of the distribution network under extreme freezing disasters.

[0142] In some embodiments, emergency resources include distributed power sources and / or mobile energy storage vehicles. Based on the load demand of the target power outage area and the available emergency resources, an assessment is made as to whether the target power outage area meets the islanding operation conditions. This includes: using the total output of the available distributed power sources and / or the power supply capacity of the mobile energy storage vehicles in the target power outage area as the numerator and the load demand of the target power outage area as the denominator to obtain the emergency power restoration support index of the target power outage area. The emergency power restoration support index characterizes the degree to which emergency resources meet the high-priority load demand. When the emergency power restoration support index is greater than or equal to the emergency power restoration support index threshold, it is determined that the target power outage area meets the islanding operation conditions.

[0143] The emergency power restoration support index guides resource allocation to prioritize power supply to areas with high support indices. Distributed power sources include generation devices located on the user side or at the end of the distribution network, such as, but not limited to, diesel generators, gas generator sets, photovoltaic power plants, small wind turbines, and biogas generator sets. Mobile energy storage vehicles refer to energy storage devices deployed in a vehicle-mounted manner, such as, but not limited to, battery energy storage vehicles, flywheel energy storage vehicles, and supercapacitor energy storage vehicles. Their characteristic is that they can be quickly moved to the target power outage area and connected to the distribution network to provide temporary power. The above emergency resources can be used individually or in combination, and the specific configuration can be flexibly determined based on the availability of resources around the target power outage area.

[0144] For example, electronic devices acquire the total output of available distributed power sources and / or the power supply capacity of mobile energy storage vehicles within the target power outage area, while simultaneously acquiring the load demand of the target power outage area. Taking emergency resources including distributed power sources and mobile energy storage vehicles as an example, the emergency power restoration support index is calculated using the following formula:

[0145]

[0146] In the formula, The emergency power restoration support index; Total output of the callable distributed power source; Power supply capacity for mobile energy storage vehicles; The load demand of the target power outage area.

[0147] This can be understood as the minimum operating power requirement of high-priority loads within the target power outage area, such as primary and secondary backup loads like hospital operating rooms, emergency rooms, ICUs, circulating pumps in heating stations, and electrical equipment in control systems. It can be preset according to the load importance level in the power outage area, or obtained in real time through the load management system.

[0148] Furthermore, in When the threshold value is greater than or equal to the emergency power restoration support index threshold, the target power outage area is determined to meet the islanding operation conditions. The emergency power restoration support index threshold can be set according to the actual emergency strategy. For example, it can be set to 1, 0.8, or 0.6. Different regions can flexibly configure it according to their own emergency resource reserves and core load protection requirements.

[0149] For example, an emergency power restoration support index threshold of 0.5 is used as the criterion for determining islanded operation conditions. When At 0.5, the target power outage area is determined to meet the conditions for islanded operation; when At 0.5, if the target power outage area does not meet the conditions for islanded operation, it is not recommended to establish an island. Priority should be given to accelerating the repair of the main grid or allocating more emergency resources from other areas.

[0150] Furthermore, for situations that meet the conditions for islanded operation (i.e.) 0.5), and a tiered control mechanism can also be introduced to implement differentiated power supply strategies based on the adequacy of emergency resources. For example, a tiered threshold of 1 can be set: when... At 1 o'clock, it indicates that emergency resources are sufficient, and emergency resources are dispatched to establish temporary power supply islands, providing full power to all core loads in the target power outage area; when 0.5 At time 1, it indicates that emergency resources are relatively insufficient. Emergency resources are dispatched to establish temporary power supply islands, while power is supplied after some non-core or secondary core loads are cut off.

[0151] In this embodiment of the application, by constructing an emergency power restoration support index, the output of distributed power sources and the capacity of mobile energy storage vehicles are quantitatively compared with the core load demand, thereby achieving an objective and quantitative assessment of islanded operation conditions and avoiding the subjectivity and uncertainty of relying on human experience judgment.

[0152] In some embodiments, the method for emergency power restoration of the distribution network under freezing disasters also includes: after the physical emergency repair work of the main grid in the target power outage area is completed, using quasi-synchronous grid connection technology, smoothly switching the temporary power supply island to the main grid power supply.

[0153] In this embodiment, the quasi-synchronous grid connection technology should be understood as follows: after the physical repair work of the main grid in the target power outage area is completed, the electronic equipment continuously monitors the voltage amplitude, frequency and phase of the temporary power supply island side and the main grid side. By issuing adjustment commands to the emergency power supply, the parameters on the island side are gradually brought closer to the parameters on the main grid side. When the voltage deviation, frequency deviation and phase angle difference are all less than the preset allowable values, the grid connection switch is closed to realize the smooth parallel operation of the temporary power supply island and the main grid, and to complete the seamless switching of emergency power supply to main grid power supply.

[0154] By utilizing quasi-synchronous grid connection technology, precise matching between the temporary power supply island and the main grid is achieved in three dimensions: voltage amplitude, frequency, and phase. This avoids inrush current at the moment of closing, ensuring a smooth switching process and continuous power supply. Since the temporary power supply island already provides stable power to the core load before grid connection, the core load will not experience power outages or voltage drops during the connection process, achieving a "seamless" power supply guarantee. Compared to the "disconnect-then-reconnect" method of directly disconnecting the temporary power supply island and then reconnecting to the main grid, the quasi-synchronous grid connection method avoids the load experiencing a second power outage during the switching process, making it particularly suitable for core loads with extremely high requirements for power supply continuity, such as medical and heating systems.

[0155] In some embodiments, based on the distribution of damaged locations, the initial locations of each repair team, the reserves of each material depot, meteorological data, and real-time traffic data, the optimal travel path and repair task order of each repair team are iteratively determined within a multi-dimensional constrained space, including:

[0156] Step 2021: Based on meteorological data and real-time traffic data, dynamically adjust the expected travel time for each road segment.

[0157] The meteorological data includes wind speed, ambient temperature, and snowfall intensity (which can be classified into levels, such as level 0 no snowfall, level 1 light snow, level 2 moderate snow, level 3 heavy snow, level 4 blizzard, or expressed as snowfall per unit time). The traffic data includes the real-time traffic status of each road segment (such as smooth traffic, slow traffic, congestion, and closure) and historical average traffic speed.

[0158] For example, the electronic device calculates the road accessibility coefficient for each road segment based on the aforementioned meteorological data and real-time traffic data. The road accessibility coefficient is a dimensionless parameter between 0 and 1, reflecting the degree of impact of severe weather conditions on vehicle traffic capacity. The value of the road accessibility coefficient is determined by wind speed, ambient temperature, and snowfall intensity, and can be determined through a pre-established mapping relationship. For example, the following mapping rule can be established:

[0159] When the wind speed is less than 5 m / s, the temperature is above -5℃, and there is no snowfall or icing, the road accessibility coefficient is 0.9 to 1.0, indicating that traffic is basically normal.

[0160] When the wind speed is 5-10 m / s, the temperature is -10℃ to -5℃, and there is light snow or light icing, the road accessibility coefficient is 0.6 to 0.8, indicating that the traffic capacity is reduced.

[0161] When the wind speed is 10-15 m / s, the temperature is -15℃ to -10℃, and there is moderate snow or moderate icing, the road accessibility coefficient is 0.3 to 0.5, indicating that passage is difficult.

[0162] When the wind speed is greater than 15 m / s, the temperature is below -15℃, or there is heavy snow or severe icing, the road accessibility coefficient is 0.1 to 0.2, indicating that passage is extremely difficult.

[0163] When there is heavy snow or road closure, the road accessibility coefficient is 0, indicating that the road is impassable.

[0164] It should be noted that the numerical ranges in the above mapping rules are merely examples, and those skilled in the art can calibrate and adjust them according to actual road conditions and vehicle performance.

[0165] After determining the road accessibility factor, the electronic equipment calculates the corrected expected travel time for that road segment.

[0166] For example, firstly, according to the formula Calculate the corrected speed of the repair vehicle, where, This is the normal driving speed for this section of road under normal weather conditions (which can be set based on historical data or road speed limits). Due to wind speed Ambient temperature and snowfall intensity The road accessibility coefficient is determined jointly. The corrected speed for the repair vehicles is then calculated. The length of the road segment is then divided by the corrected speed. This gives the expected travel time for that road segment. When When the value is 0, it means that the road segment is impassable. The electronic device sets the expected travel time for the road segment to infinity and automatically avoids the road segment in subsequent route planning.

[0167] Through the above dynamic correction, the expected travel time for each road segment can respond in real time to changes in weather conditions and traffic conditions, providing a dynamic and accurate time cost input for subsequent route optimization.

[0168] Step 2022: Based on the distribution of damaged locations, the initial locations of each repair team, the reserves of each material warehouse, and the expected travel time for each road section, construct an optimization model.

[0169] The optimization model takes the shortest total repair time as the objective function and is constrained by the total material demand of each repair team at the damaged location being less than or equal to the corresponding material warehouse reserve, and the priority of repairing the backbone line over the branch line. The total repair time includes the travel time of each repair team and the repair time of each damaged location.

[0170] For example, the objective function is expressed as:

[0171]

[0172] In the formula, For the emergency repair team to reach the first The dynamic travel time to each damaged location depends on the road travel time dynamically corrected in step 2021 and is affected by the task order of the repair team (i.e., the completion time of the previous damaged location determines the departure time to the next damaged location). For the first The on-site operation time for each damaged location can be preset according to the type of damage (such as broken line or collapsed tower) and severity (such as ice thickness), and can be adjusted according to the impact of ambient temperature on operation efficiency (for example, when the temperature is below -10℃, the efficiency of manual operation decreases and the repair time is extended accordingly).

[0173] Optionally, the constraints may also include all road segments in the planned path. It must be greater than 0, such as >0.2 to ensure basic passage safety.

[0174] The electronic equipment expresses the above objective function and constraints in the form of a mathematical model, forming an optimization problem that can be solved. This model belongs to a constrained combinatorial optimization problem, and the decision variables include: which repair team is assigned to each damaged location (task allocation), and the order in which damaged locations are accessed within each repair team (task sequencing).

[0175] Step 2023: Use intelligent optimization algorithm to iteratively solve the optimization model to obtain the optimal driving route and repair task order for each repair team.

[0176] For example, the optimization model is solved iteratively using a genetic algorithm or a particle swarm optimization algorithm. By simulating the natural evolution process or the cooperative behavior of the group, the model is continuously screened, cross-referenced, and mutated among multiple candidate solutions until the convergence condition is met. Finally, the optimal driving path and the order of emergency repair tasks for each repair team are output.

[0177] Suppose a freezing disaster has 5 damaged locations (F1, F2, F3, F4, F5), 2 repair teams (A, B), and 2 supply depots (W1, W2). After solving using a genetic algorithm, the optimized solution might be: Repair team A starts from supply depot W1 and proceeds sequentially to F1 (backbone line), F3 (branch line), and F5 (branch line), with an estimated total time of 6.5 hours; Repair team B starts from supply depot W2 and proceeds sequentially to F2 (backbone line) and F4 (branch line), with an estimated total time of 5.8 hours; The total repair time is max(6.5, 5.8) = 6.5 hours (i.e., the latest time the two repair teams complete the repair).

[0178] This embodiment incorporates the dynamic impact of meteorological conditions on traffic into the route planning process, enabling repair routes to respond to weather changes in real time. Simultaneously, by constructing an optimization model that includes material and priority constraints and employing an intelligent optimization algorithm for iterative solution, it achieves scientific scheduling of repair tasks under resource-constrained conditions. Compared to traditional methods relying on human experience, this embodiment effectively avoids problems such as suboptimal repair routes, misallocation of materials, and uneven task distribution, significantly improving repair scheduling efficiency.

[0179] In some embodiments, obtaining the location distribution of damage to the power distribution network under freezing disasters includes:

[0180] Step 2011: Obtain monitoring data for each insulator string of the straight tower. The monitoring data includes axial tension, tilt angle and wind deflection angle.

[0181] For example, on a straight-line tower, the upper end of the insulator string is connected to the crossarm via hardware, and the lower end is connected to the conductor via suspension clamps. The insulator string is suspended vertically or nearly vertically. A data acquisition device is deployed on the straight-line tower of the distribution network, including a tension sensor, an angle sensor, and a wind deflection sensor. The tension sensor is installed at the connection point between the insulator string and the crossarm to measure the axial tensile force on the insulator string. Angle sensors are installed on insulator strings to measure the tilt angle θ of the insulator string in the direction along the line (i.e., the direction of the conductor); wind deflection sensors are used to measure the deflection angle of the insulator string in the direction perpendicular to the line (i.e., the direction of the crossarm), i.e., the wind deflection angle. .

[0182] The aforementioned sensors collect data in real time at a preset sampling frequency (e.g., once every 5 minutes, or encrypted to once per minute when weather conditions change drastically), and upload the monitoring data to electronic equipment via a wireless communication network (such as 4G, 5G, or IoT communication protocols). The electronic equipment receives and stores the monitoring data of each insulator string on the straight-line tower, forming time-series data for subsequent calculation of icing thickness.

[0183] It should be noted that the insulator string of the straight tower is selected as the monitoring object in this embodiment because the insulator string on the straight tower is suspended vertically or nearly vertically, mainly bearing the vertical load of the conductor (including the weight of the conductor itself and the weight of the ice). It is sensitive to changes in the vertical load and is suitable for calculating the ice thickness of the conductor.

[0184] Step 2012: Using the successive approximation method, based on the overhead line state equation and the tower mechanical equilibrium equation, determine the vertical specific load of the conductor under icing conditions according to the monitoring data.

[0185] For example, the mechanical equilibrium equations of a tower are expressed as follows:

[0186]

[0187] In the formula, Axial tension The vertical component; For the weight of the insulator string and fittings; The number of wire splits; The cross-sectional area of ​​the conductor; The vertical load to be solved; and These represent the length of conductor carried between the main tower and the lowest points of the conductors on both sides. and It is not a fixed value, but a quantity that varies with the ice load on the conductor; that is, the heavier the ice load, the greater the conductor sag. and It also increases accordingly.

[0188] Because of the equation and , There is a coupling relationship (the conductor sag changes with the ice load), in order to solve accurately... The electronic equipment employs an iterative calculation method using successive approximation. In each iteration, the overhead line state equation is updated based on the current vertical load ratio. and Until it converges.

[0189] For example, the iterative process of the successive approximation method is as follows: First, set an initial estimate of the vertical load ratio. (For example, taking the conductor's own weight as a ratio of load) Then, in the k-th iteration, based on the current vertical load estimate... The conductor sag and tension distribution under this load condition are calculated using the overhead line state equation, thereby updating the conductor lengths on both sides of the tower. and Next, the updated version will be... and Substituting into the tower's mechanical equilibrium equations, a new estimate of the vertical load is obtained by inverse solving. Finally, determine whether the difference between the two estimates is less than the preset convergence threshold. If it is, output the result; otherwise, continue iterating.

[0190] The expression of the state equation for overhead lines can be found in the prior art, and will not be elaborated upon in this embodiment.

[0191] Step 2013: Based on historical monitoring data during the same meteorological window period when there is no icing, the vertical load ratio is corrected. The same meteorological window period characterizes the ambient temperature, wind speed and humidity to match the current icing monitoring period.

[0192] Because the sensor may experience measurement drift in harsh environments such as low temperature, high humidity, and strong winds, and the tension of the conductor itself may also generate additional stress due to temperature changes, directly using the vertical load calculated in step 2012 will result in systematic errors. Therefore, this embodiment introduces a correction mechanism based on historical data.

[0193] For example, data on icing-free periods matching the meteorological conditions of the current monitoring period are retrieved from the historical database. For instance, if the current period is characterized by icing weather with temperatures between -5°C and 0°C, wind speeds of 3-5 m / s, and relative humidity above 80%, the electronic device filters historical data to identify periods with temperatures between -5°C and 0°C, wind speeds of 3-5 m / s, and relative humidity above 80% that have no icing records (e.g., a icing-free window during the same period last year). Furthermore, the average value of the vertical load monitoring data within this historical period is calculated. This average value reflects the specific load measured by the sensor under current meteorological conditions (but without icing). It should be understood that, theoretically, the vertical specific load without icing should be equal to the conductor's self-weight specific load. (A known constant), but due to sensor drift and environmental interference, Possibly with There is a discrepancy.

[0194] The electronic device calculates this deviation. ,like A value greater than 0 indicates a positive drift in the sensor (the measured value is too large). This error is then removed from the current icing monitoring value, resulting in the corrected vertical load ratio. ;like If the value is less than 0, it indicates that the sensor has a negative drift (the measured value is too small). Therefore, this difference should be compensated. (because If the value is negative, subtracting a negative number is equivalent to adding its absolute value; if If the value is 0, no correction is needed.

[0195] The above corrections can effectively eliminate systematic errors such as sensor zero-point drift and wind pressure fluctuations caused by low-temperature environments, making the calculation results of vertical load more accurate.

[0196] Step 2014: Determine the equivalent icing thickness based on the corrected vertical load ratio.

[0197] The equivalent icing thickness represents the thickness of an ice layer that is uniformly distributed around the circumference of a conductor, equating the actual irregular shape of the icing.

[0198] For example, the formula for equivalent icing thickness is expressed as:

[0199]

[0200] In the formula, This represents the equivalent icing thickness. The diameter of the wire; The density of ice accretion on the conductor (usually taken as...) ).

[0201] Step 2015: Based on the equivalent icing thickness, determine the icing-damaged sections of the line and use these icing-damaged sections as the distribution of damaged locations in the distribution network under freezing disasters.

[0202] For example, the calculated equivalent icing thickness at each monitoring point (each linear tower) is compared with a preset icing threshold. For instance, a three-level threshold can be set: when... When the icing thickness is less than 10mm, it is considered light icing, the line is basically safe, and no special treatment is required; when the icing thickness is less than or equal to 10mm... When the icing thickness is less than 20mm, it is considered moderate icing, posing a risk of line breakage; therefore, enhanced monitoring and line patrols are necessary. When the icing thickness is ≥20mm, it is considered severe icing, posing a high risk of line breakage or tower collapse, requiring immediate emergency repairs. For monitoring points identified as severely iced, the electronic equipment marks the line section containing that monitoring point as a "damaged location." Furthermore, the electronic equipment can merge consecutive severely iced sections into a single damaged section based on the icing thickness distribution of multiple adjacent monitoring points.

[0203] In this embodiment, the mechanical equilibrium equations are solved by successive approximation method, which accurately reflects the change of conductor sag with ice load. Combined with historical data under the same meteorological conditions for correction, sensor drift and environmental interference are eliminated, significantly improving the accuracy of ice location and providing accurate disaster damage assessment basis for subsequent emergency repairs. At the same time, compared with traditional manual line inspection, the automatic identification of ice-damaged sections through sensor data greatly shortens the fault location time and improves the efficiency of emergency repairs.

[0204] Figure 3 This is a schematic diagram of the structure of the emergency repair and restoration device for the power distribution network under freezing disasters provided in the embodiments of this application, as shown below. Figure 3 As shown, the power restoration device 30 for power distribution networks under freezing disasters provided in this embodiment includes: an acquisition module 31, a determination module 32, an evaluation module 33, and a control module 34. Wherein:

[0205] The acquisition module 31 is used to acquire the distribution of damaged locations of the power distribution network under the freezing disaster, the initial location of each repair team, the reserve quantity of each material warehouse, the meteorological data corresponding to the distribution of damaged locations, and real-time traffic data.

[0206] The determination module 32 is used to determine the optimal travel route and repair task order of each repair team in a multi-dimensional constraint space based on the meteorological and traffic coupled scheduling model, according to the distribution of damaged locations, the initial location of each repair team, the reserve of each material warehouse, meteorological data and real-time traffic data. The meteorological and traffic coupled scheduling model is used to dynamically correct the road passage time based on real-time meteorological data and real-time traffic data, with the shortest total repair time as the optimization objective.

[0207] Evaluation module 33 is used to evaluate the power outage areas corresponding to the distribution of damaged locations based on the differentiated demand load classification evaluation system, and obtain the power restoration priority ranking of each power outage area.

[0208] The control module 34 is used to dispatch the emergency repair team to perform physical emergency repairs on the main power grid according to the optimal driving route and the order of emergency repair tasks; and to control the restoration of power to the target power outage area according to the power restoration priority before the physical emergency repairs on the main power grid are completed.

[0209] In one possible implementation, the multiple attributes include topology attributes, reliability attributes, and economic attributes. The evaluation module 33 is specifically used to: obtain a differentiated demand weight vector, which reflects the differentiated demands of different regions for topology attributes, reliability attributes, and economic attributes, and is used for evaluating all power outage areas; for each power outage area corresponding to the distribution of damaged locations, determine the topology attribute value of the power outage area based on a pre-built topology evaluation model; determine the reliability attribute value of the power outage area based on a pre-built reliability evaluation model; determine the economic attribute value of the power outage area based on a pre-built economic evaluation model; determine the correlation degree between the differentiated demand weight vector and the attribute feature vector of each power outage area, where the attribute feature vector includes topology attribute values, reliability attribute values, and economic attribute values; and determine the power restoration priority ranking of each power outage area in descending order of correlation degree.

[0210] In one possible implementation, the differentiated demand weight vector is obtained by: using fuzzy hierarchical analysis to obtain the initial subjective weights of multiple experts on topological, reliability, and economic attributes; and clustering and weighting the initial subjective weights of the multiple experts to obtain the differentiated demand weight vector.

[0211] In one possible implementation, the control module 34 is specifically used to: select at least one outage area ranked high according to the power restoration priority as the target outage area; obtain the main grid repair progress corresponding to the target outage area to determine the expected main grid repair time for the target outage area; when the expected main grid repair time is greater than the user's tolerance time threshold, assess whether the target outage area meets the islanding operation conditions based on the load demand of the target outage area and the available emergency resources; if the target outage area meets the islanding operation conditions, schedule emergency resources to establish a temporary power supply island to supply power to the target outage area.

[0212] In one possible implementation, emergency resources include distributed power sources and / or mobile energy storage vehicles. The control module 34 is further configured to: use the total output of the distributed power sources and / or the power supply capacity of the mobile energy storage vehicles available in the target power outage area as the numerator and the load demand of the target power outage area as the denominator to obtain the emergency power restoration support index of the target power outage area. The emergency power restoration support index characterizes the degree to which emergency resources meet the demand for high-priority loads. When the emergency power restoration support index is greater than or equal to the emergency power restoration support index threshold, it is determined that the target power outage area meets the islanding operation conditions.

[0213] In one possible implementation, the control module 34 further includes: after the physical repair work of the main grid in the target power outage area is completed, using quasi-synchronous grid connection technology, smoothly switching the temporary power supply island to the main grid power supply.

[0214] In one possible implementation, the determining module 32 is specifically used to: dynamically adjust the expected travel time of each road segment based on meteorological data and real-time traffic data; construct an optimization model based on the distribution of damaged locations, the initial locations of each repair team, the reserve quantity of each material warehouse, and the expected travel time of each road segment; and use an intelligent optimization algorithm to iteratively solve the optimization model to obtain the optimal travel route and repair task order of each repair team. The optimization model takes the shortest total repair time as its objective function, and is constrained by the total material demand of each repair team at the damaged location being less than or equal to the reserve quantity of the corresponding material warehouse, and the priority of repairing main lines over branch lines. The total repair time includes the travel time of each repair team and the repair time of each damaged location.

[0215] In one possible implementation, the acquisition module 31 is specifically used to: acquire monitoring data of insulator strings of each straight tower, including axial tension, tilt angle, and wind deflection angle; determine the vertical load ratio of the conductor under icing conditions based on the overhead line state equation and tower mechanical equilibrium equation using a successive approximation method; correct the vertical load ratio based on historical monitoring data during the same meteorological condition window period when there is no icing, wherein the same meteorological condition window period represents the ambient temperature, wind speed, and humidity matching the current icing monitoring period; determine the equivalent icing thickness based on the corrected vertical load ratio; determine the icing-damaged sections of the line based on the equivalent icing thickness; and use the icing-damaged sections of the line as the distribution of damaged locations in the distribution network under freezing disasters.

[0216] The emergency repair and power restoration device for power distribution networks under freezing disasters provided in this embodiment can execute the method provided in the above-mentioned method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0217] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 11 provided in this embodiment includes at least one processor 111 and a memory 112. Optionally, the electronic device 11 further includes a communication component 113. The processor 111, the memory 112, and the communication component 113 are connected via a bus 114.

[0218] In a specific implementation, at least one processor 111 executes computer execution instructions stored in memory 112, causing at least one processor 111 to perform the above-described method.

[0219] The specific implementation process of processor 111 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0220] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0221] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0222] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0223] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0224] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0225] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0226] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0227] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0228] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0229] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0230] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0231] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0232] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for emergency power restoration of a power distribution network under freezing disasters, characterized in that, include: The system acquires the distribution of damaged locations in the power distribution network under freezing disasters, the initial locations of each repair team, the reserve quantities of each material warehouse, meteorological data corresponding to the distribution of damaged locations, and real-time traffic data. Based on the meteorological and traffic coupled scheduling model, the optimal travel route and repair task order of each repair team are iteratively determined in a multi-dimensional constrained space according to the distribution of the damaged locations, the initial locations of each repair team, the reserve quantity of each material warehouse, the meteorological data, and the real-time traffic data. The meteorological and traffic coupled scheduling model is used to dynamically correct the road passage time according to the real-time meteorological data and real-time traffic data, with the shortest total repair time as the optimization objective. Based on the differentiated demand load classification evaluation system, the power outage areas corresponding to the distribution of the damaged locations are evaluated with multiple attributes to obtain the power restoration priority ranking for each power outage area. Based on the optimal driving route and the order of emergency repair tasks, the emergency repair team is dispatched to perform physical emergency repairs on the main power grid; and before the physical emergency repairs on the main power grid are completed, the power restoration of the target power outage area is controlled according to the power restoration priority.

2. The method for emergency power restoration of a power distribution network under freezing disasters according to claim 1, characterized in that, The multiple attributes include topology attributes, reliability attributes, and economic attributes. The differentiated demand load grading evaluation system performs multi-attribute evaluations on the power outage areas corresponding to the distribution of damaged locations, obtaining a power restoration priority ranking for each power outage area, including: Obtain a differentiated demand weight vector, which reflects the differentiated demands of different regions for the topology attribute, the reliability attribute, and the economic attribute, and is used for the evaluation of all power outage areas; For each power outage area corresponding to the distribution of the damaged locations, the topological attribute value of the power outage area is determined based on a pre-built topological evaluation model; the reliability attribute value of the power outage area is determined based on a pre-built reliability evaluation model; and the economic attribute value of the power outage area is determined based on a pre-built economic evaluation model. The correlation between the differentiated demand weight vector and the attribute feature vector of each of the power outage areas is determined respectively. The attribute feature vector includes the topology attribute value, the reliability attribute value and the economic attribute value. The power restoration priority of each power outage area is determined according to the degree of correlation from largest to smallest.

3. The method for emergency power restoration of a power distribution network under freezing disasters according to claim 2, characterized in that, The differentiated demand weight vector is obtained in the following way: The fuzzy hierarchical analysis method is used to obtain the initial subjective weights of multiple experts on the topological attribute, the reliability attribute, and the economic attribute; The initial subjective weights of the multiple experts are clustered and weighted to obtain a differentiated demand weight vector.

4. The method for emergency power restoration of a power distribution network under freezing disasters according to any one of claims 1 to 3, characterized in that, The step of controlling the restoration of power to the target power outage area according to the power restoration priority order includes: Based on the power restoration priority ranking, at least one of the top-ranked power outage areas is selected as the target power outage area. Obtain the main grid repair progress corresponding to the target power outage area to determine the estimated repair time of the main grid in the target power outage area; When the estimated repair time of the main grid is greater than the user's tolerance time threshold, the target power outage area is assessed to determine whether it meets the islanding operation conditions based on the load demand and available emergency resources of the target power outage area. If the target power outage area meets the islanding operation conditions, the emergency resources will be dispatched to establish a temporary power supply island to supply power to the target power outage area.

5. The method for emergency power restoration of a power distribution network under freezing disasters according to claim 4, characterized in that, The emergency resources include distributed power sources and / or mobile energy storage vehicles. The assessment of whether the target power outage area meets the islanding operation conditions based on the load demand and available emergency resources of the target power outage area includes: Using the total output of the available distributed power sources and / or the power supply capacity of mobile energy storage vehicles within the target power outage area as the numerator and the load demand of the target power outage area as the denominator, the emergency power restoration support index of the target power outage area is obtained. The emergency power restoration support index represents the degree to which emergency resources meet the high-priority load demand. When the emergency power restoration support index is greater than or equal to the emergency power restoration support index threshold, the target power outage area is determined to meet the islanding operation conditions.

6. The method for emergency power restoration of a power distribution network under freezing disasters according to claim 4, characterized in that, Also includes: After the physical repair work on the main grid in the target power outage area is completed, the temporary power supply island is smoothly switched to main grid power supply using quasi-synchronous grid connection technology.

7. The method for emergency power restoration of a power distribution network under freezing disasters according to any one of claims 1 to 3, characterized in that, Based on the distribution of damaged locations, the initial locations of each repair team, the reserves of each material depot, the meteorological data, and the real-time traffic data, the optimal travel routes and repair task sequences for each repair team are iteratively determined within a multi-dimensional constrained space, including: Based on the meteorological data and the real-time traffic data, the expected travel time for each road segment is dynamically adjusted. An optimization model is constructed based on the distribution of damaged locations, the initial locations of each repair team, the reserves of each material depot, and the expected travel time for each road segment. The optimization model is iteratively solved using an intelligent optimization algorithm to obtain the optimal driving route and repair task order for each of the emergency repair teams. The optimization model takes the shortest total repair time as the objective function and is constrained by the total material demand of each repair team at the damaged location being less than or equal to the reserve of the corresponding material warehouse, and the priority of repairing the backbone line over the branch line. The total repair time includes the travel time of each repair team and the repair time of each damaged location.

8. The method for emergency power restoration of a power distribution network under freezing disasters according to any one of claims 1 to 3, characterized in that, Obtain the location distribution of the power distribution network damaged by the freezing disaster, including: Acquire monitoring data for each insulator string of the straight tower, including axial tension, tilt angle, and wind deflection angle; Using the successive approximation method, based on the overhead line state equation and the tower mechanical equilibrium equation, the vertical specific load of the conductor under icing conditions is determined according to the monitoring data. Based on historical monitoring data during the same meteorological condition window period without icing, the vertical load ratio is corrected. The same meteorological condition window period indicates that the ambient temperature, wind speed and humidity match the current icing monitoring period. Determine the equivalent icing thickness based on the corrected vertical load ratio; Based on the equivalent icing thickness, the icing-damaged sections of the line are determined; The ice-damaged sections of the aforementioned lines are used as the distribution locations of damage to the power distribution network under freezing disasters.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory, causing the processor to perform the emergency power restoration method for the power distribution network under freezing disaster as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the emergency repair and power restoration method for a power distribution network under freezing disasters as described in any one of claims 1 to 8.

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