Power emergency dispatching method and device in typhoon scene, terminal equipment and storage medium

Abstract

The invention provides an electric power emergency dispatching method and device in a typhoon scene, terminal equipment and a storage medium, and belongs to the technical field of electric power. According to typhoon weather information, a topological structure, equipment parameters and power flow data, a first power failure probability of each key guarantee user is predicted; generating an initial emergency scheduling strategy and a second power failure probability by taking minimization of the first power failure probability as a target, predicting predicted response time of power supply recovery of each key guarantee user after power failure according to the initial emergency scheduling strategy, and performing power supply recovery of each key guarantee user by taking minimization of the predicted response time and the second power failure probability as a target. And performing iterative optimization on the initial emergency scheduling strategy according to the typhoon weather information, the topological structure and the power flow data, generating a target emergency scheduling strategy, and performing power scheduling on the power distribution network according to the target emergency scheduling strategy. Therefore, the power utilization of specific users can be pertinently guaranteed in typhoon meteorological disasters.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a power emergency dispatching method, device, terminal equipment and storage medium in typhoon scenarios. Background Technology

[0002] In typhoon meteorological disasters, modern power dispatching has transformed from traditional post-fault response to proactive, full-process, digitalized emergency management. Relying on intelligent systems, it achieves efficient command across the entire chain, from disaster relief and supply assurance to rapid recovery, serving as the "most powerful brain" for enhancing power grid resilience.

[0003] However, current mainstream dispatch strategies, while aiming to protect the majority of users from disasters, fail to adequately differentiate between user types and their varying needs. In reality, the capacity of residential, commercial, and industrial users to withstand power outages and the potential extent of damage differ significantly. Power outages in some critical infrastructure could even trigger severe socio-economic consequences. Therefore, how to specifically guarantee power supply to specific users during typhoon disasters has become a crucial issue that urgently needs to be addressed. Summary of the Invention

[0004] This invention provides a method, device, terminal equipment, and storage medium for emergency power dispatching in typhoon scenarios. The method can specifically guarantee the power supply of specific users during typhoon meteorological disasters.

[0005] An embodiment of the present invention provides a power emergency dispatching method in a typhoon scenario, comprising: Obtain typhoon meteorological information, information on several key users requiring protection, the topology of the distribution network, equipment parameters, and power flow data; Based on typhoon meteorological information, topology, equipment parameters, and power flow data, predict the probability of the first power outage for each of the key users to be protected. With the goal of minimizing the first power outage probability, an initial emergency dispatch strategy and an optimized second power outage probability for each of the key protection users are generated based on typhoon meteorological information, topology, equipment parameters and power flow data. Based on the initial emergency dispatch strategy, predict the response time for each key user to have their power restored after a power outage; With the goal of minimizing the predicted response time and the second power outage probability, the initial emergency dispatch strategy is iteratively optimized based on typhoon meteorological information, topology, equipment parameters and power flow data to generate a target emergency dispatch strategy. Power dispatch is implemented on the distribution network according to the target emergency dispatch strategy.

[0006] Furthermore, the prediction of the first power outage probability for the critical users based on typhoon meteorological information, topology, equipment parameters, and power flow data includes: The typhoon meteorological information, the topology, the equipment parameters, and the power flow data are input into a preset power outage probability prediction model, so that the power outage probability prediction model can predict the equipment failure probability in the distribution network based on the typhoon meteorological information and the equipment parameters, and predict the first power outage probability of each of the key users based on the equipment failure probability, the topology, and the power flow data.

[0007] Furthermore, the construction of the power outage probability prediction model includes: Acquire historical typhoon weather information, equipment failure records corresponding to each typhoon weather information, and power outage reports; Historical typhoon meteorological information is aggregated to construct a typhoon fault dataset for different typhoon intensities; wherein, the typhoon fault dataset includes: historical typhoon meteorological information and equipment fault records; For each typhoon intensity, statistical regression analysis is performed to construct a device vulnerability sub-model for evaluating the failure probability of various types of equipment under typhoons of different intensities. Based on the topology, the equipment fault records, and the power outage reports, a fault propagation sub-model is constructed to assess the probability of power outages caused by fault propagation of various equipment failures. The first data missing degree and the first prediction bias of the equipment vulnerability sub-model, and the second data missing degree and the second prediction bias of the fault propagation sub-model are evaluated respectively. An uncertainty sub-model is constructed based on the first data missing degree, the first prediction bias, the second data missing degree, and the second prediction bias. The equipment vulnerability sub-model, the fault propagation sub-model, and the uncertainty sub-model are coupled to generate the power outage probability prediction model.

[0008] Furthermore, the step of generating an initial emergency dispatch strategy and an optimized second power outage probability for each of the key users, with the goal of minimizing the first power outage probability, based on typhoon meteorological information, topology, equipment parameters, and power flow data, includes: Based on the typhoon meteorological information, the regional user density of key protection users, and the historical disaster loss rate, the target weight of each key protection user is determined. The sum of the products of the target weight and the first power outage probability is used as the comprehensive power outage probability, and an objective function is constructed with the goal of minimizing the comprehensive power outage probability. Based on the topology and power flow data, the initial power supply path for each of the key protected users is determined; Based on the objective function, the power flow data, the topology, and the device parameters, several initial power supply paths are iteratively optimized. When the objective function value is determined to converge, the latest optimized initial power supply path is taken as the target power supply path, and an initial emergency dispatch strategy and a second power outage probability are generated based on the target power supply path.

[0009] An embodiment of the present invention also provides a power emergency dispatching device for typhoon scenarios, comprising: The data acquisition module is used to acquire typhoon meteorological information, information on several key users, the topology of the distribution network, equipment parameters, and power flow data. The power outage prediction module is used to predict the first power outage probability for each of the key users based on typhoon meteorological information, topology, equipment parameters and power flow data. The strategy generation module is used to generate an initial emergency dispatch strategy and an optimized second power outage probability for each of the key protection users, with the goal of minimizing the first power outage probability, based on typhoon meteorological information, topology, equipment parameters and power flow data. The response prediction module is used to predict the predicted response time for each key user to regain power after a power outage, based on the initial emergency dispatch strategy. The strategy optimization module is used to iteratively optimize the initial emergency dispatch strategy based on typhoon meteorological information, topology, equipment parameters and power flow data, with the goal of minimizing the predicted response time and the second power outage probability, and generate a target emergency dispatch strategy. An emergency dispatch module is used to implement power dispatch on the distribution network according to the target emergency dispatch strategy.

[0010] Furthermore, the power outage prediction module, based on typhoon meteorological information, topology, equipment parameters, and power flow data, predicts the first power outage probability for the critical users, including: The typhoon meteorological information, the topology, the equipment parameters, and the power flow data are input into a preset power outage probability prediction model, so that the power outage probability prediction model can predict the equipment failure probability in the distribution network based on the typhoon meteorological information and the equipment parameters, and predict the first power outage probability of each of the key users based on the equipment failure probability, the topology, and the power flow data.

[0011] Furthermore, the power emergency dispatching device for typhoon scenarios also includes: a model building module; The model building module is used to acquire historical typhoon meteorological information, equipment failure records corresponding to each typhoon meteorological information, and power outage reports; Historical typhoon meteorological information is aggregated to construct a typhoon fault dataset for different typhoon intensities; wherein, the typhoon fault dataset includes: historical typhoon meteorological information and equipment fault records; For each typhoon intensity, statistical regression analysis is performed to construct a device vulnerability sub-model for evaluating the failure probability of various types of equipment under typhoons of different intensities. Based on the topology, the equipment fault records, and the power outage reports, a fault propagation sub-model is constructed to assess the probability of power outages caused by fault propagation of various equipment failures. The first data missing degree and the first prediction bias of the equipment vulnerability sub-model, and the second data missing degree and the second prediction bias of the fault propagation sub-model are evaluated respectively. An uncertainty sub-model is constructed based on the first data missing degree, the first prediction bias, the second data missing degree, and the second prediction bias. The equipment vulnerability sub-model, the fault propagation sub-model, and the uncertainty sub-model are coupled to generate the power outage probability prediction model.

[0012] Furthermore, the strategy generation module, aiming to minimize the first power outage probability, generates an initial emergency dispatch strategy and an optimized second power outage probability for each of the key protection users based on typhoon meteorological information, topology, equipment parameters, and power flow data, including: Based on the typhoon meteorological information, the regional user density of key protection users, and the historical disaster loss rate, the target weight of each key protection user is determined. The sum of the products of the target weight and the first power outage probability is used as the comprehensive power outage probability, and an objective function is constructed with the goal of minimizing the comprehensive power outage probability. Based on the topology and power flow data, the initial power supply path for each of the key protected users is determined; Based on the objective function, the power flow data, the topology, and the device parameters, several initial power supply paths are iteratively optimized. When the objective function value is determined to converge, the latest optimized initial power supply path is taken as the target power supply path, and an initial emergency dispatch strategy and a second power outage probability are generated based on the target power supply path.

[0013] This application also provides a terminal device, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a power emergency dispatching method in a typhoon scenario as described in the above embodiments of the invention.

[0014] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a power emergency dispatching method in a typhoon scenario as described in the above embodiments.

[0015] The following benefits can be obtained by implementing the present invention: This invention provides a power emergency dispatching method, apparatus, terminal equipment, and storage medium for typhoon scenarios. The method predicts the initial power outage probability for each critical user based on typhoon meteorological information, topology, and power flow data. Then, aiming to minimize the initial power outage probability, it generates an initial emergency dispatching strategy and an optimized second power outage probability for each critical user based on the same data, further reducing the likelihood of power outages. Further, based on the initial emergency dispatching strategy, it predicts the recovery time for each critical user after a power outage, minimizing the predicted response time and the second power outage probability. Based on the typhoon meteorological information, topology, and power flow data, iteratively optimizes the initial emergency dispatching strategy to generate a target emergency dispatching strategy. This ensures efficient power recovery for critical users affected by typhoons and reduces outage time. Finally, power dispatching is implemented on the distribution network according to the target emergency dispatching strategy. Therefore, this invention can prioritize power supply to critical users during disaster emergencies. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a power emergency dispatching method in a typhoon scenario according to a certain embodiment of this application; Figure 2 This is a schematic diagram of the structure of a power emergency dispatching device in a typhoon scenario provided in a certain embodiment of this application; Figure 3 This is a schematic diagram of the structure of a terminal device provided in a certain embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0020] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0023] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0024] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0025] See Figure 1 To address the problems in the prior art, an embodiment of the present invention provides a power emergency dispatching method under typhoon conditions, comprising: S1. Obtain typhoon meteorological information, information on several key users, the topology of the distribution network, equipment parameters, and power flow data; In a preferred embodiment of the present invention, typhoon meteorological information includes meteorological data such as wind speed, rainfall, and path. Power grid topology and equipment parameters refer to relevant information describing the physical structure of the power grid and the characteristics of its components, including the location, connection method, and rated parameters of equipment such as transmission lines, substations, and towers. For example, in an optional implementation, a topology map of a regional power grid is drawn, marking the length, material, and current carrying capacity of each transmission line, as well as the capacity and protection configuration of each substation. Power flow data refers to the amount of electricity transmitted on each transmission line and at each node.

[0026] Furthermore, key protection targets are typically users or areas that are of great importance to social stability, economic development, or people's livelihood, such as hospitals, government agencies, and large enterprises. Once a pre-selected list of important users is obtained, key protection targets will be determined based on actual disaster prevention needs. For example, users on the list located along the typhoon's path will be designated as key protection users for this emergency response.

[0027] A critical user list refers to a list of key entities that have high demand for electricity supply. The normal operation of these entities during a disaster is crucial to the overall social order and safety, such as hospitals, communication base stations, and transportation hubs. For example, in an optional implementation, the critical user list may include all tertiary hospitals in the city, core base stations of major telecommunications operators, and control centers of major urban transportation routes.

[0028] S2. Based on typhoon meteorological information, topology, equipment parameters, and power flow data, predict the probability of the first power outage for each of the key users to be protected. Preferably, predicting the probability of the first power outage for the critical users based on typhoon meteorological information, topology, equipment parameters, and power flow data includes: The typhoon meteorological information, the topology, the equipment parameters, and the power flow data are input into a preset power outage probability prediction model, so that the power outage probability prediction model can predict the equipment failure probability in the distribution network based on the typhoon meteorological information and the equipment parameters, and predict the first power outage probability of each of the key users based on the equipment failure probability, the topology, and the power flow data.

[0029] In a preferred embodiment of the present invention, the impact of a typhoon on equipment is predicted by a power outage probability prediction model, and then the power outage probability of users on the power supply line where the affected equipment is located is predicted, and finally the first power outage probability of each of the key users is accurately predicted.

[0030] Preferably, the construction of the power outage probability prediction model includes: The system acquires historical typhoon meteorological information, corresponding equipment failure records, and power outage reports. It aggregates the historical typhoon meteorological information to construct typhoon failure datasets for different typhoon intensities. These datasets include historical typhoon meteorological information and equipment failure records. For each typhoon failure dataset of different intensities, statistical regression analysis is performed to construct an equipment vulnerability sub-model to assess the failure probability of various equipment under typhoons of different intensities. Based on the topology, equipment failure records, and power outage reports, a fault propagation sub-model is constructed to assess the probability of power outages caused by fault propagation of various equipment failures. The system evaluates the first data missing degree and first prediction bias of the equipment vulnerability sub-model, and the second data missing degree and second prediction bias of the fault propagation sub-model. Based on these factors, an uncertainty sub-model is constructed. Finally, the system couples the equipment vulnerability sub-model, the fault propagation sub-model, and the uncertainty sub-model to generate the power outage probability prediction model.

[0031] In a preferred embodiment of the present invention, to predict the probability of power outages affecting users during typhoon scenarios, data resources such as meteorological observation data, power grid fault records, and user power outage reports from historical typhoon events can be utilized. First, multiple typical typhoon intensity levels are determined. Statistical regression analysis is performed at each intensity level to establish the correspondence between typhoon wind speed and tower collapse rate, using this as the basic data. Based on the power grid topology and equipment parameters, key parameters in a pre-defined power grid vulnerability model are identified, resulting in an equipment vulnerability sub-model. This sub-model can simulate the relationship between wind speed, rainfall, and equipment failure probability. A fault propagation sub-model is constructed to simulate the relationship between equipment failure, power supply path interruption, and user power outage probability. An uncertainty sub-model is also constructed to simulate the impact of missing data and model bias on the final power outage probability prediction result. Finally, the equipment vulnerability sub-model, fault propagation sub-model, and uncertainty sub-model are coupled to obtain the power outage probability prediction model.

[0032] It should be noted that building a power outage probability prediction model refers to establishing a comprehensive model system that can simulate the impact of typhoon intensity on power grid equipment failure and user power outages by integrating historical meteorological observation data, power grid fault records, and user power outage reports, combined with statistical analysis and modeling techniques. This system is used to predict the operating status of the power grid and the reliability of user power supply under different meteorological conditions.

[0033] For example, in one optional implementation, meteorological data such as wind speed and rainfall from multiple typhoon events in a certain region over the past ten years are obtained. Combined with records of power grid tower collapse and user outage feedback in the region, regression analysis is used to determine the probability distribution of tower collapse under different wind speeds. Furthermore, the relationship between wind speed and equipment failure is quantified, ultimately forming a coupled model that comprehensively reflects meteorological factors, power grid vulnerability, and the propagation of user outages.

[0034] It should be noted that the meteorological observation data, power grid fault records, and user power outage reports in historical typhoon events refer to the collection of relevant meteorological variables such as wind speed, rainfall, and air pressure data for typhoon events occurring in specific areas. At the same time, the actual performance of power grid equipment in these events is recorded, such as the location, type, and time of faults, as well as the power outage situation of users, such as the scope, duration, and feedback information of power outages, to provide basic data support for subsequent modeling.

[0035] For example, in one optional implementation, typhoon event data for a coastal city over the past five years is collected, including hourly updated meteorological station observations, substation tripping and line disconnection events recorded by the power company, and power outage complaints submitted by users through hotlines or apps. By organizing and classifying this data, a complete dataset is formed for model training and validation.

[0036] It should be noted that typical typhoon intensity level points refer to several representative wind speed ranges divided according to statistical data of historical typhoon events. Each range corresponds to an intensity level, which is used to characterize the degree of impact of typhoons of different intensities on power grid equipment and user power supply.

[0037] For example, in one optional implementation, typhoon wind speeds are divided into levels for every 10 m / s increase. For instance, 30 m / s to 40 m / s is defined as a medium intensity level, and 40 m / s to 50 m / s is defined as a high intensity level. The tower collapse rate and equipment failure probability are then calculated at each level to clarify the power grid response characteristics under different intensities.

[0038] It should be noted that statistical regression analysis refers to the process of using mathematical methods to fit historical data, find the quantitative relationship between input variables such as wind speed and output variables such as tower collapse rate, and build a predictive model based on this relationship.

[0039] For example, in one optional implementation, a linear regression method is used to analyze the relationship between wind speed and tower collapse ratio in historical typhoon events in a certain region. It is found that when the wind speed reaches 35 m / s, the tower collapse rate is 10%, while when the wind speed increases to 45 m / s, the collapse rate rises to 30%. This establishes a functional relationship between wind speed and collapse rate for subsequent prediction.

[0040] It should be noted that power grid topology and equipment parameters refer to relevant information describing the physical structure of the power grid and the characteristics of its components, including the location, connection method, and rated parameters of equipment such as transmission lines, substations, and towers.

[0041] For example, in one optional implementation, a topology map of a regional power grid is drawn, labeling the length, material, and current carrying capacity of each transmission line, as well as the capacity and protection configuration of each substation. These parameters are used to identify critical nodes susceptible to typhoon impact, thereby providing a basis for power grid vulnerability modeling. It should be noted that critical parameters refer to variables that play a decisive role in the probability of equipment failure in the power grid vulnerability model, such as equipment design standards, aging levels, and environmental conditions.

[0042] For example, in one optional implementation, for a certain type of tower, its wind resistance design standard, service life and installation location are selected as key parameters. The influence of these parameters on the failure probability of the tower under different wind speeds is analyzed through experiments or simulations, thereby optimizing the accuracy of the model.

[0043] It should be noted that the fault propagation sub-model refers to the calculation module that simulates the changes in the range and probability of power outages for users caused by power supply path interruptions after the failure of power grid equipment.

[0044] For example, in one optional implementation, assuming that a main line is damaged by a typhoon, the model can dynamically extrapolate the probability of power outages for multiple users downstream of the line, and predict the time window for power restoration by combining the backup power switching time and repair progress.

[0045] It should be noted that the uncertainty sub-model refers to a module that considers the possible deviations in the final prediction results caused by factors such as data quality, model assumptions, and external disturbances, and quantifies this uncertainty through probability distribution or other forms.

[0046] For example, in an alternative implementation, taking into account the lack of real-time monitoring data for some towers, the model introduces a random disturbance factor to simulate the potential error range of these data and assess its impact on the power outage probability prediction results, thereby improving the robustness of the prediction.

[0047] It should be noted that coupling refers to the organic combination of the functions and output results of multiple sub-models to form a whole model, which can more comprehensively reflect the behavioral characteristics of complex systems.

[0048] For example, in one optional implementation, the equipment failure probability generated by the equipment vulnerability sub-model is input into the fault propagation sub-model, and then combined with the adjusted result of the uncertainty sub-model to obtain the final user power outage probability distribution, thereby realizing the full-process simulation from meteorological input to user output.

[0049] It should be noted that constructing a power outage probability prediction model under the influence of typhoons can provide a comprehensive and accurate foundational framework for subsequent forecasting work. Based on this power outage probability prediction model, the interaction between typhoon meteorological parameters, power grid equipment status, and user characteristics can be analyzed more scientifically, thereby more accurately predicting the probability of power outages affecting users.

[0050] Specifically, the expression for the equipment vulnerability sub-model is: ; In this invention, the following settings are provided: For the first The failure probability of individual power grid equipment; selected in this invention This indicates the current wind speed of the typhoon in the area, expressed in meters per second; in this invention, the selected... The rainfall intensity is expressed in millimeters per hour; in this invention, it is set as follows: For the first The equipment vulnerability parameters for each sub-region are determined by factors such as equipment type, service life, and terrain shielding effect; in this invention, the following parameters are selected. The regression function is trained using historical typhoon event data and is used to map the nonlinear relationship between meteorological input and equipment failure probability.

[0051] Subsequently, the expression for the fault propagation sub-model is: ; In this invention, the following settings are provided: For users The probability of power outages under the influence of typhoons; selected in this invention This indicates all direct impacts on users. A collection of power grid equipment, including feeders, switches, poles, or substations; this invention includes... For equipment For users The power supply dependence intensity, whose value is between 0 and 1, is uniquely determined by the power grid topology and power supply path; in this invention, it is selected... For equipment The failure probability is output by the aforementioned power grid vulnerability sub-model.

[0052] To further enhance the reliability of the prediction results, a prediction uncertainty sub-model is introduced to quantify data gaps and model biases during the prediction process. The expression for the uncertainty sub-model is: ; In this invention, the following settings are provided: The total uncertainty index of the current prediction results is selected in this invention. This indicates the degree of missing input data, such as the sparsity of meteorological observation stations or the delay in power grid status updates; in this invention, it is selected... The systematic bias of the model itself can be obtained through historical prediction error statistics; in this invention, a setting is provided. and This is a weighting coefficient used to balance the impact of data quality and model accuracy on uncertainty. Its value can be determined based on actual operating experience or cross-validation.

[0053] S3. With the goal of minimizing the first power outage probability, an initial emergency dispatch strategy and the optimized second power outage probability for each of the key protection users are generated based on typhoon meteorological information, topology, equipment parameters and power flow data. The goal of minimizing the first power outage probability involves generating an initial emergency dispatch strategy and an optimized second power outage probability for each key user, based on typhoon meteorological information, topology, equipment parameters, and power flow data. This includes: Based on the typhoon meteorological information, the regional user density of key users, and historical disaster loss rates, target weights for each key user are determined. The sum of the products of the target weights and the first power outage probability is used as the comprehensive power outage probability, and an objective function is constructed to minimize the comprehensive power outage probability. Based on the topology and power flow data, initial power supply paths for each key user are determined. Based on the objective function, the power flow data, the topology, and the equipment parameters, several initial power supply paths are iteratively optimized. When the objective function value converges, the latest optimized initial power supply path is taken as the target power supply path, and an initial emergency dispatch strategy and a second power outage probability are generated based on the target power supply path.

[0054] In a preferred embodiment of the present invention, the target weight refers to the basic parameter value set according to the sensitivity of different regions to meteorological factors, population density, and historical disaster loss rate when predicting power risks. It is used to reflect the potential risk level of power outage in the region under different meteorological conditions. For example, in an optional implementation, the target weight of risk areas that are close to the coastline and are susceptible to typhoons will be set relatively high to reflect the high-risk characteristics of the region under severe weather.

[0055] Furthermore, based on the topology and power flow data, the initial power supply paths for each critical user are analyzed. Based on the equipment along these initial paths, optimization is performed to switch the power supply paths for each critical user to paths less affected by typhoons. The optimized outage probability is then predicted using a power outage probability prediction model, and the objective function value is calculated. Specifically, by identifying all intermediate nodes from the substation to the distribution node where the critical user is located, and the connection paths between these intermediate nodes, several candidate power supply paths are constructed. A particle swarm optimization algorithm is used, with each candidate power supply path as an initial particle and the objective function as the fitness function. Each candidate power supply path is optimized by switching the connection paths within it. The outage probability prediction model then predicts the user outage probability for each candidate power supply path after each optimization, and the fitness function value is calculated based on the user outage probability. When the fitness function value converges, the target power supply path for each critical user is generated.

[0056] By comparing each initial power supply path with the corresponding target power supply path, the target switching device that needs to be switched on / off is determined, and an initial emergency dispatch strategy is generated based on the target power supply path, the target switching device, and the corresponding target on / off state.

[0057] S4. Based on the initial emergency dispatch strategy, predict the expected response time for each key user to have their power restored after a power outage; In a preferred embodiment of the present invention, by using a simulation model, the failure of equipment on the target power supply path of each key user is sequentially set, and the power restoration time required for each key user after a series of power restoration operations such as power outage, autonomous fault isolation, autonomous fault repair or switching of power supply lines is determined. The average power restoration time of each equipment failure is used as the predicted response time.

[0058] S5. With the goal of minimizing the predicted response time and the second power outage probability, the initial emergency dispatch strategy is iteratively optimized based on typhoon meteorological information, topology, equipment parameters and power flow data to generate a target emergency dispatch strategy. In a preferred embodiment of the present invention, an objective function is constructed based on a weighted sum of the predicted response time and the second power outage probability, with the weights being pre-selected values ​​set by the user according to their needs. Following the same method as in step S3, an initial particle swarm is constructed based on several target power supply paths. The objective function is used as the fitness function, and a particle swarm optimization algorithm is employed to optimize each target power supply path by updating the connection paths within the target power supply paths. A power outage probability prediction model is used to predict the user's power outage probability for each target power supply path after each optimization. The fitness function value is calculated based on the user's power outage probability. When the fitness function value converges, the optimized target power supply paths for each key user are generated. Each initial power supply path is compared with the optimized target power supply path to determine the target switching device whose on / off state needs to be switched. A target emergency dispatch strategy is generated based on the optimized target power supply path, the target switching device, and the corresponding target on / off state.

[0059] S6. Implement power dispatching on the distribution network according to the target emergency dispatching strategy.

[0060] In a preferred embodiment of the present invention, the target switching device is switched to the target open state according to the target switching device and the corresponding target open state in the target emergency dispatch strategy, the network structure in the distribution network is adjusted, and the initial power supply path of each key user is switched to the optimized target power supply path.

[0061] See Figure 2 This invention provides a power emergency dispatching device for typhoon scenarios, comprising: The data acquisition module is used to acquire typhoon meteorological information, information on several key users, the topology of the distribution network, equipment parameters, and power flow data. The power outage prediction module is used to predict the first power outage probability for each of the key users based on typhoon meteorological information, topology, equipment parameters and power flow data. The strategy generation module is used to generate an initial emergency dispatch strategy and an optimized second power outage probability for each of the key protection users, with the goal of minimizing the first power outage probability, based on typhoon meteorological information, topology, equipment parameters and power flow data. The response prediction module is used to predict the predicted response time for each key user to regain power after a power outage, based on the initial emergency dispatch strategy. The strategy optimization module is used to iteratively optimize the initial emergency dispatch strategy based on typhoon meteorological information, topology, equipment parameters and power flow data, with the goal of minimizing the predicted response time and the second power outage probability, and generate a target emergency dispatch strategy. An emergency dispatch module is used to implement power dispatch on the distribution network according to the target emergency dispatch strategy.

[0062] Furthermore, the power outage prediction module, based on typhoon meteorological information, topology, equipment parameters, and power flow data, predicts the first power outage probability for the critical users, including: The typhoon meteorological information, the topology, the equipment parameters, and the power flow data are input into a preset power outage probability prediction model, so that the power outage probability prediction model can predict the equipment failure probability in the distribution network based on the typhoon meteorological information and the equipment parameters, and predict the first power outage probability of each of the key users based on the equipment failure probability, the topology, and the power flow data.

[0063] Furthermore, the power emergency dispatching device for typhoon scenarios also includes: a model building module; The model building module is used to acquire historical typhoon meteorological information, equipment failure records corresponding to each typhoon meteorological information, and power outage reports; Historical typhoon meteorological information is aggregated to construct a typhoon fault dataset for different typhoon intensities; wherein, the typhoon fault dataset includes: historical typhoon meteorological information and equipment fault records; For each typhoon intensity, statistical regression analysis is performed to construct a device vulnerability sub-model for evaluating the failure probability of various types of equipment under typhoons of different intensities. Based on the topology, the equipment fault records, and the power outage reports, a fault propagation sub-model is constructed to assess the probability of power outages caused by fault propagation of various equipment failures. The first data missing degree and the first prediction bias of the equipment vulnerability sub-model, and the second data missing degree and the second prediction bias of the fault propagation sub-model are evaluated respectively. An uncertainty sub-model is constructed based on the first data missing degree, the first prediction bias, the second data missing degree, and the second prediction bias. The equipment vulnerability sub-model, the fault propagation sub-model, and the uncertainty sub-model are coupled to generate the power outage probability prediction model.

[0064] Furthermore, the strategy generation module, aiming to minimize the first power outage probability, generates an initial emergency dispatch strategy and an optimized second power outage probability for each of the key protection users based on typhoon meteorological information, topology, equipment parameters, and power flow data, including: Based on the typhoon meteorological information, the regional user density of key protection users, and the historical disaster loss rate, the target weight of each key protection user is determined. The sum of the products of the target weight and the first power outage probability is used as the comprehensive power outage probability, and an objective function is constructed with the goal of minimizing the comprehensive power outage probability. Based on the topology and power flow data, the initial power supply path for each of the key protected users is determined; Based on the objective function, the power flow data, the topology, and the device parameters, several initial power supply paths are iteratively optimized. When the objective function value is determined to converge, the latest optimized initial power supply path is taken as the target power supply path, and an initial emergency dispatch strategy and a second power outage probability are generated based on the target power supply path.

[0065] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the power emergency dispatching method in a typhoon scenario provided by any of the above-described method embodiments of the present invention.

[0066] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0067] See Figure 3 One embodiment of this application also provides a terminal device, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a power emergency dispatching method in a typhoon scenario as described above.

[0068] The processor controls the overall operation of the terminal device to complete all or part of the steps of the power emergency dispatching method in a typhoon scenario described above. The memory stores various types of data to support the operation of the terminal device. This data may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The memory can be implemented using 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.

[0069] In an exemplary embodiment, the terminal device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute a power emergency dispatching method in a typhoon scenario as described in any of the above embodiments, and achieve the same technical effect as the above method.

[0070] In another exemplary embodiment, a computer-readable storage medium including a computer program is also provided. When executed by a processor, the computer program implements the steps of a power emergency dispatching method in a typhoon scenario as described in any of the foregoing embodiments. For example, the computer-readable storage medium may be the aforementioned memory including the computer program, which may be executed by a processor of a terminal device to complete the power emergency dispatching method in a typhoon scenario as described in any of the foregoing embodiments and achieve the same technical effects as the aforementioned method.

[0071] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power emergency dispatching method in a typhoon scenario, characterized in that, The application relates to a method for generating an emergency dispatch strategy for a power distribution network, comprising: acquiring typhoon meteorological information, a plurality of key guarantee users, a topological structure of a power distribution network, equipment parameters and power flow data; predicting first power failure probabilities of the key guarantee users according to the typhoon meteorological information, the topological structure, the equipment parameters and the power flow data; generating an initial emergency dispatch strategy and second power failure probabilities of the key guarantee users after optimization according to the typhoon meteorological information, the topological structure, the equipment parameters and the power flow data, with the objective of minimizing the first power failure probabilities; predicting predicted response times of the key guarantee users for resuming power supply after power failure according to the initial emergency dispatch strategy; iteratively optimizing the initial emergency dispatch strategy according to the typhoon meteorological information, the topological structure, the equipment parameters and the power flow data, with the objective of minimizing the predicted response times and the second power failure probabilities, to generate a target emergency dispatch strategy; implementing power dispatch for the power distribution network according to the target emergency dispatch strategy.

2. The power emergency dispatch method in a typhoon scenario of claim 1, wherein, The method comprises the following steps: inputting the typhoon meteorological information, the topological structure, the equipment parameters and the power flow data into a preset power failure probability prediction model, so that the power failure probability prediction model predicts device failure probabilities in the power distribution network according to the typhoon meteorological information and the equipment parameters, and predicts the first power failure probabilities of the key guarantee users according to the device failure probabilities, the topological structure and the power flow data.

3. The power emergency dispatch method in a typhoon scenario of claim 2, wherein, The method comprises the following steps: acquiring historical typhoon meteorological information, device failure records corresponding to each typhoon meteorological information and power failure reports; aggregating each historical typhoon meteorological information to construct typhoon failure data sets under different typhoon intensities; wherein the typhoon failure data sets comprise historical typhoon meteorological information and device failure records; performing statistical regression analysis on the typhoon failure data set of each typhoon intensity to construct a device vulnerability sub-model for evaluating failure probabilities of various types of devices under different intensities of typhoons; constructing a fault propagation sub-model for evaluating power failure probabilities caused by power interruption due to failure propagation of various types of devices according to the topological structure, the device failure records and the power failure reports; respectively evaluating a first data missing degree and a first prediction deviation of the device vulnerability sub-model, a second data missing degree and a second prediction deviation of the fault propagation sub-model; constructing an uncertainty sub-model according to the first data missing degree, the first prediction deviation, the second data missing degree and the second prediction deviation; coupling the device vulnerability sub-model, the fault propagation sub-model and the uncertainty sub-model to generate the power failure probability prediction model.

4. The power emergency dispatch method in a typhoon scenario of claim 3, wherein, The method comprises the following steps: According to the typhoon meteorological information, the regional user density of the key support user, and the historical disaster loss rate, a target weight of each key support user is determined; A target function is constructed with a minimum comprehensive power failure probability as a target, and a sum of products of the target weight and the first power failure probability is taken as the comprehensive power failure probability; According to the topological structure and the power flow data, an initial power supply path of each key support user is determined; According to the target function, the power flow data, the topological structure, and the device parameters, a plurality of initial power supply paths are iteratively optimized, and when the target function value converges, the initial power supply path generated by the latest optimization is taken as a target power supply path, and an initial emergency dispatching strategy and a second power failure probability are generated according to the target power supply path.

5. A power emergency dispatching device in a typhoon scenario, characterized in that, Comprise: A data acquisition module is configured to acquire typhoon meteorological information, a plurality of key support users, a topological structure of a power distribution network, device parameters, and power flow data; A power failure prediction module is configured to predict a first power failure probability of each key support user according to the typhoon meteorological information, the topological structure, the device parameters, and the power flow data; A strategy generation module is configured to generate an initial emergency dispatching strategy and a second power failure probability of each key support user after optimization, with a minimum first power failure probability as a target, according to the typhoon meteorological information, the topological structure, the device parameters, and the power flow data; A response prediction module is configured to predict a predicted response time of each key support user for restoring power supply after power failure according to the initial emergency dispatching strategy; A strategy optimization module is configured to generate a target emergency dispatching strategy by iteratively optimizing the initial emergency dispatching strategy, with a minimum predicted response time and a second power failure probability as a target, according to the typhoon meteorological information, the topological structure, the device parameters, and the power flow data; An emergency dispatching module is configured to implement power dispatching on the power distribution network according to the target emergency dispatching strategy.

6. The power emergency dispatching device in a typhoon scenario according to claim 5, wherein, The power failure prediction module is configured to predict a first power failure probability of each key support user according to the typhoon meteorological information, the topological structure, the device parameters, and the power flow data, and comprises: The typhoon meteorological information, the topological structure, the device parameters, and the power flow data are input into a preset power failure probability prediction model, so that the power failure probability prediction model predicts a device failure probability in the power distribution network according to the typhoon meteorological information and the device parameters, and predicts a first power failure probability of each key support user according to the device failure probability, the topological structure, and the power flow data.

7. The power emergency dispatching device in a typhoon scenario according to claim 6, wherein, Further comprising a model construction module; The model construction module is configured to acquire historical typhoon meteorological information, device failure records corresponding to each typhoon meteorological information, and power failure reports; Each historical typhoon meteorological information is aggregated to construct a typhoon failure data set under different typhoon intensities; wherein the typhoon failure data set comprises historical typhoon meteorological information and device failure records; For each typhoon failure data set of a typhoon intensity, statistical regression analysis is performed to construct a device vulnerability sub-model for evaluating the failure probability of each type of device under different intensities of typhoons; According to the topology, the device failure record, and the power failure report, a fault propagation sub-model for evaluating a power failure probability of a power failure caused by fault propagation of each type of device failure is constructed; A first data missing degree of the device vulnerability sub-model, a first prediction bias, a second data missing degree of the fault propagation sub-model, and a second prediction bias are respectively evaluated; According to the first data missing degree, the first prediction bias, the second data missing degree, and the second prediction bias, an uncertainty sub-model is constructed; The device vulnerability sub-model, the fault propagation sub-model, and the uncertainty sub-model are coupled to generate the power failure probability prediction model.

8. The power emergency dispatching device in a typhoon scenario according to claim 7, wherein, The strategy generation module generates an initial emergency dispatching strategy and a second power failure probability of each key support user after optimization according to typhoon meteorological information, topology, device parameters, and power flow data, with the objective of minimizing a first power failure probability, including: According to the typhoon meteorological information, the regional user density of the key support user, and the historical disaster damage rate, a target weight of each key support user is determined; The sum of the product of the target weight and the first power failure probability is taken as a comprehensive power failure probability, and a target function with the objective of minimizing the comprehensive power failure probability is constructed; According to the topology and the power flow data, an initial power supply path of each key support user is determined; According to the target function, the power flow data, the topology, and the device parameters, a plurality of initial power supply paths are iteratively optimized, and when the value of the target function converges, the initial power supply path generated by the latest optimization is taken as a target power supply path, and an initial emergency dispatching strategy and a second power failure probability are generated according to the target power supply path.

9. A terminal device, comprising: Comprise: One or more processors; Memory coupled to the processor for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the power emergency dispatching method under a typhoon scenario as claimed in any one of claims 1-4.

10. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the power emergency dispatching method under a typhoon scenario as claimed in any one of claims 1-4.

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