Quantitative method, system and device for line fault of power distribution network under typhoon weather and medium

Abstract

This invention relates to the field of power system fault quantification technology, and discloses a method, system, equipment, and medium for quantifying distribution network line faults under typhoon weather. It calculates the wind-induced fault rate of distribution network lines by constructing a wind field correction model that incorporates micro-topography correction factors and wind direction-line angle correction factors. With the goal of minimizing power outage losses in the distribution network, a coupled influence model of transmission network faults on the distribution network line fault rate is constructed and solved to obtain the optimal load shedding amount for each load node in the distribution network and its corresponding operational constraint boundary. Under this boundary, the random process of load shedding allocation and load transfer of faulty lines within the distribution network is simulated using a double Monte Carlo sampling method, and the probability of line cascading faults caused by transmission-distribution coupling is calculated based on the simulation results. The combined time-varying fault rate and type of distribution network lines are determined by integrating the two fault rates, achieving accurate quantification of distribution network line faults under the influence of transmission-distribution coupling.

Description

Technical Field

[0001] This invention relates to the field of power system fault quantification technology, and in particular to a method, system, equipment and medium for quantifying faults in distribution network lines during typhoon weather. Background Technology

[0002] Typhoons, as extreme natural disasters, pose a serious threat to the safe operation of power systems, especially to the relatively fragile distribution networks. During typhoons, strong winds and rainfall can cause faults such as power line breaks, tower collapses, and insulator flashovers, leading to widespread power outages. To improve the power grid's ability to cope with typhoon disasters, accurately predicting the time-varying failure rate of distribution network lines during typhoons has become one of the key technologies for disaster prevention, mitigation, and resilience enhancement of power systems. Currently, existing research has proposed methods for predicting the failure rate of distribution network lines under typhoon disasters. These methods include constructing typhoon wind field models (such as the Holland model and Rankine model) based on historical typhoon data to simulate the spatiotemporal evolution of wind speed, and establishing failure rate models for components such as towers and conductors using structural reliability theory. Wind speed thresholds are set, and piecewise or exponential functions are used to describe the relationship between the failure rate and wind speed, thus obtaining the line failure rate of the distribution network. However, these methods often use static or simplified typhoon parameter models, which can lead to deviations in the estimation of the actual wind speed experienced by the lines during the typhoon's intensity decay after landfall, resulting in inaccurate calculated line failure rates.

[0003] Therefore, how to solve the problem of insufficient accuracy in predicting the line failure rate of the power distribution network under typhoon disasters in existing technologies has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] This invention provides a method, system, equipment, and medium for quantifying line faults in power distribution networks during typhoon weather, solving the problem that existing technologies do not accurately predict the line fault rate of power distribution networks under typhoon disasters.

[0005] To address the aforementioned technical problems, the first aspect of this invention provides a method for quantifying faults in power distribution network lines during typhoon weather, comprising: Geographic information data, power grid topology data, and meteorological data under typhoon weather of the power system are acquired to construct a wind field correction model that incorporates micro-topography correction factors and wind direction-line angle correction factors, and the line failure rate due to wind is calculated based on the wind field correction model. With the goal of minimizing power outage losses in the distribution network within the power system, a coupled impact model of transmission network faults on the failure rate of distribution network lines is constructed and solved to obtain the optimal load shedding amount of each load node in the distribution network and its corresponding operational constraint boundary. Based on the optimal load shedding amount and its corresponding operational constraint boundary, the random process of load shedding allocation and load transfer of faulty lines within the distribution network is simulated by the double Monte Carlo sampling method, and the probability of line cascading faults caused by transmission and distribution coupling is calculated based on the simulation results. The comprehensive time-varying failure rate and failure type of the distribution network are determined based on the line failure rate due to wind and the line cascading failure probability.

[0006] A second aspect of the present invention provides a fault quantification system for power distribution lines during typhoon weather, comprising: The wind-induced fault calculation module is used to acquire geographic information data, power grid topology data and meteorological data of the power system under typhoon weather, so as to construct a wind field correction model that incorporates micro-topography correction factors and wind direction line angle correction factors, and calculate the line fault rate due to wind based on the wind field correction model. The coupled model solving module is used to construct and solve a coupled impact model of transmission network faults on the fault rate of distribution network lines with the goal of minimizing power outage losses in the distribution network within the power system, so as to obtain the optimal load shedding amount of each load node in the distribution network and its corresponding operating constraint boundary. The cascading failure calculation module is used to simulate the random process of load shedding and load transfer of faulty lines within the distribution network using the double Monte Carlo sampling method based on the optimal load shedding amount and its corresponding operational constraint boundary, and to calculate the probability of cascading failures caused by transmission and distribution coupling based on the simulation results. The comprehensive fault determination module is used to determine the comprehensive time-varying fault rate and fault type of the distribution network based on the line wind-induced fault rate and the line cascading fault probability.

[0007] A third aspect of the present invention provides an electronic device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the method for quantifying faults in power distribution lines during typhoon weather as described above.

[0008] A fourth aspect of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the method for quantifying faults in power distribution lines during typhoon weather as described above.

[0009] Compared with the prior art, the beneficial effects of the embodiments of the present invention are as follows: By introducing micro-topography correction factors and wind direction-line angle correction factors into the wind field correction model, the intensity change process of typhoons after landfall can be dynamically reflected. Furthermore, through fine-tuning of local micro-topography and wind direction angles, the estimation accuracy of the actual wind speed that the lines can withstand is significantly improved, thus making the calculation of wind-induced failure rates more consistent with the actual physical process. A transmission-distribution coupling impact model was constructed, transforming the load shedding command issued by the transmission network into the optimal load shedding allocation scheme and defining the physical feasible boundary for subsequent simulations. Based on this, a pioneering two-step method of "optimization delimitation-stochastic simulation" was developed: within the defined feasible boundary, double Monte Carlo sampling (load shedding allocation sampling + load transfer sampling) simulates the randomness in actual scheduling, achieving probabilistic coverage of a large number of possible operating scenarios. This eliminates many infeasible scenarios from pure random sampling and overcomes the limitations of purely deterministic analysis, achieving accurate quantification and scientific assessment of the cascading failure risk under the influence of transmission-distribution coupling, providing support for the systematic improvement of power grid resilience under extreme weather conditions. Attached Figure Description

[0010] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of a method for quantifying faults in power distribution lines during typhoon weather, provided in a certain embodiment of the present invention; Figure 2 This is a structural diagram of a power distribution network line fault quantification system under typhoon weather provided in a certain embodiment of the present invention; Figure 3 This is a structural diagram of an electronic device provided in a certain embodiment of the present invention; Figure label: Among them, 10 is the wind-induced fault calculation module; 20 is the coupled model solution module; 30 is the cascading fault calculation module; 40 is the comprehensive fault determination module; 5000 is the electronic equipment; 5001 is the processor; 5002 is the bus; 5003 is the memory; and 5004 is the transceiver. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings and examples. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0013] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art will be able to understand the specific meaning of the above terms in this application according to the specific circumstances.

[0014] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is merely for describing specific embodiments and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0015] In one embodiment, such as Figure 1 As shown, the first aspect of the present invention provides a method for quantifying faults in power distribution lines during typhoon weather, comprising: S1. Acquire geographic information data, power grid topology data, and meteorological data under typhoon weather for the power system to construct a wind field correction model incorporating micro-topography correction factors and wind direction-line angle correction factors. Calculate the line failure rate due to wind based on the wind field correction model. Specifically, this invention acquires geographic information data of the distribution network lines from the geographic information system (GIS) of the area where the power system is located, including the line's latitude and longitude coordinates, altitude, terrain type along the line (e.g., ridge, slope, flatland, valley), span and suspension point height of the line towers, a 30m resolution digital elevation model of the target area, land cover data, vegetation height data, and building outline data. It also acquires power grid topology data from the power grid dispatch automation system, including the connection relationships between nodes (busbars, load points) and branches (lines, transformers), line type and electrical parameters, rated capacity and load type of each load node, line parameters (impedance, transmission limit, wind resistance speed, etc.), and distributed power generation and energy storage parameters. Simultaneously, it accesses real-time typhoon forecasts and measured data from meteorological departments to obtain historical and forecast meteorological data such as typhoon path, central pressure, maximum wind speed radius, movement speed, and hourly wind speed and direction at various meteorological observation points. Additionally, it collects meteorological data and line fault records from historical typhoon periods for model training and validation.

[0016] In one embodiment, constructing a wind field correction model that incorporates micro-topography correction factors and wind direction line angle correction factors includes: A basic model of typhoon wind speed is constructed based on the meteorological data, and the theoretical wind speed is calculated based on the basic model of typhoon wind speed. The wind direction line angle is determined based on the power grid topology data and the meteorological data, and the wind direction line angle correction factor is calculated based on the wind direction line angle. The geographic information data, the power grid topology data, and the meteorological data are input into a pre-constructed micro-topography acceleration ratio prediction model for processing, and the micro-topography correction factor is output. The wind field correction model is constructed based on the theoretical wind speed, the micro-topography correction factor, and the wind direction line angle correction factor.

[0017] Specifically, this invention constructs a basic model of typhoon wind speed based on the data collected above, which is expressed by the following formula: In the formula, For the line The wind speed it can withstand; The distance from the typhoon center is calculated using the latitude and longitude of the route and the latitude and longitude of the typhoon center. This represents the maximum average wind speed of the typhoon. The radius is the radius of maximum wind speed.

[0018] For four core typhoon parameters—maximum wind speed radius, maximum average typhoon wind speed, central pressure difference, and maximum gradient wind speed—time-varying evolution formulas are constructed to simulate the physical characteristics of typhoon intensity decay and parameter dynamic changes after landfall, addressing the discrepancy between static parameters and actual typhoon processes. The evolution process of the core parameters is expressed by the following formula: In the formula, denoted as the central pressure difference attenuation; x is the empirical fitting index, used to correct the nonlinear effect of the pressure difference on the radius of maximum wind speed. It is obtained by fitting historical typhoon data, and the commonly used value range is 0.8-1.2, while the commonly used value in engineering is 1.0. This represents the initial central pressure difference. t represents the angle of landfall of the typhoon (angle of incidence); t represents the duration of landfall of the typhoon. This refers to the typhoon's movement speed; This represents the maximum gradient wind speed of the typhoon. is the gradient wind empirical coefficient (wind field intensity coefficient), with a commonly used value range of 6.5-7.5, and a commonly used value of 7.0 in engineering, used to correct the deviation between theoretical calculations and actual observations; f is the Coriolis parameter.

[0019] The evolution of these parameters is substituted into the basic typhoon wind speed model to obtain a time-varying wind field model. Alternatively, the basic typhoon wind speed model can also be represented by the existing mature Holland axisymmetric wind field model. The core typhoon evolution formula is supplemented by the general time-varying attenuation empirical formula in typhoon meteorology, thus obtaining a time-varying wind field model. Subsequently, relevant meteorological data collected in real time are substituted into this time-varying model to calculate the theoretical wind speed at the location of each line in the power distribution network.

[0020] When calculating the actual wind speed at the location of the railway line, not only the theoretical wind speed output by the typhoon model should be considered, but also factors such as micro-topography, local meteorological conditions, land cover type (such as vegetation and buildings), and the deflection effect of topography on wind direction should be comprehensively considered. Therefore, this invention combines the route location and wind direction and speed data to determine the wind direction-line angle (obtained from meteorological data and route topology, using a combination of geometric positioning and dynamic correction methods or rigid bar methods, etc., which will not be elaborated here), and calculates the wind direction-line angle correction factor based on the wind direction-line angle. This process is expressed by the following formula: In the formula, This is a correction factor for the wind direction line angle; The angle between the wind direction and the line.

[0021] Simultaneously, a micro-topography speedup prediction model is introduced to dynamically correct wind speed. This prediction model uses a fully connected neural network or a convolutional neural network for regression prediction. Its input layer consists of topographic and meteorological feature vectors; hidden layers consist of 2-3 layers, with the number of neurons in each layer adjusted according to the feature dimension, and the activation function is recommended to use a modified linear unit; the output layer consists of 1 neuron, which outputs the predicted speedup, and the activation function is linear (if the speedup range is between 0 and 2); the loss function is mean squared error; and the optimizer is adaptive moment estimation. High-precision digital elevation model data (resolution ≤30m), land cover data, vegetation height data, and building outline data for the target area are collected. Historical meteorological observation data (wind speed, wind direction, air pressure, temperature, etc.) during typhoons are also collected. Computational fluid dynamics simulations or field measurements are used to obtain sample data on wind speed acceleration ratios under different terrains and wind directions. These are compiled into a sample set. The sample set is used for model training, and the validation set is used for hyperparameter tuning. The test set evaluates model performance, with a target mean absolute percentage error ≤10%. During training, input features include terrain features (slope, aspect, roughness, elevation coefficient of variation), land surface type, vegetation cover, building density, and wind direction angle. The output label is the wind speed acceleration ratio at the corresponding location. Alternatively, existing micro-topography wind speed correction lookup tables can be used to directly look up the acceleration ratio corresponding to the terrain.

[0022] The relevant data from geographic information data, power grid topology data, and meteorological data are input into a pre-constructed micro-topography acceleration ratio prediction model for prediction, and the micro-topography correction factor is output. Finally, a wind field correction model is constructed based on theoretical wind speed, micro-topography correction factor, and wind direction line angle correction factor. This model is expressed by the following formula: In the formula, The actual wind speed of line b in the distribution network at time t is the corrected wind speed. Let be the theoretical wind speed of line b in the distribution network at time t; The acceleration ratio for micro-terrain.

[0023] This invention incorporates the local amplification or attenuation effect of terrain on wind speed, as well as the influence of the relative angle between the line direction and the instantaneous wind direction on the effective wind load, into the wind field model by introducing "micro-topography correction factor" and "wind direction-line angle correction factor". This can reflect the equivalent wind speed actually acting on the conductor and significantly improve the physical accuracy of calculating the line failure rate due to wind.

[0024] Finally, a model for calculating the wind-induced failure rate of power lines is constructed based on the wind field correction model. This model is used to calculate the time-by-time direct wind-induced failure rate of each power line and the set of power lines with exceeded wind speed limits. The calculation model is expressed by the following formula: In the formula, For lines in the distribution network exist The failure rate due to wind at any given time; For the line The wind resistance speed is determined by using the extreme value Gumbel distribution or P-III type distribution model to perform probability statistics on the maximum wind speed samples over the years, and calculate the maximum design wind speed corresponding to the return period (such as 30 years, 50 years, 100 years).

[0025] S2. With the goal of minimizing the power outage losses of the distribution network in the power system, construct and solve a coupled influence model of transmission network faults on the fault rate of distribution network lines to obtain the optimal load shedding amount of each load node in the distribution network and its corresponding operating constraint boundary. Typhoons not only affect distribution lines but also significantly impact the power structure of the transmission network, especially renewable energy sources such as offshore wind power connected to the main grid. During a typhoon, some offshore wind farms may proactively shut down due to excessive wind speeds or operational safety considerations, leading to localized power loss and power flow redistribution in the transmission network, thus affecting its power supply capacity to the distribution network and its load shedding strategies. Under typhoon disasters, the transmission network may implement emergency control measures such as load shedding to maintain system stability. These measures, through the topology-electrical coupling relationship between the transmission and distribution networks, affect the power distribution of the distribution network and may trigger cascading failures. To quantify this impact, this invention constructs a coupled impact model of transmission network faults on the distribution network line failure rate, aiming to minimize power outage losses in the distribution network within the power system. This model transforms the fault / emergency load shedding commands of the transmission network under typhoons into the optimal load reduction scheme for the distribution network that meets engineering operational constraints, thus defining the physical feasibility boundary for subsequent stochastic simulations. The coupled impact is expressed by the following equation: In the formula, It is the set of all load nodes in the distribution network; For load nodes Unit active power outage losses; For load nodes Unit reactive power outage losses; The estimated duration of the power outage; For load nodes The amount of active power that was cut off; For load nodes The amount of reactive load that has been removed.

[0026] The constraints of the coupled influence model include power balance constraints, upper and lower limits of load shedding, power adjustable range constraints, load supply guarantee constraints, node voltage constraints, power factor constraints, coupling constraints between the adjustable range of distribution network nodes and load shedding, and offshore wind power output constraints. Among these, the power balance constraint states that the total load shedding is equal to the total load shedding command issued by the transmission network, expressed by the following formula: In the formula, The total load that the distribution network is required to disconnect for the transmission network; The total reactive load that the distribution network is required to cut off for the transmission network.

[0027] Upper and lower limits of cut-off amount constraints: The amount of load cut off at each node cannot exceed its actual load, and must be non-negative, as expressed by the following formula: In the formula, For load nodes The original active load; For load nodes The original reactive load.

[0028] Power Adjustable Range Constraint: Considering the real-time active power regulation capabilities of adjustable resources such as distributed generation (DG) and energy storage (ESS) in the distribution network, and considering the reactive power regulation capabilities of distributed generation (DG), energy storage (ESS), and capacitor banks (CB), the actual load that can be cut off at a node is affected by its net load, which is expressed by the following formula: In the formula, For load nodes Real-time output of distributed power sources; Power for charging (positive) or discharging (negative) energy storage; , , They are respectively load nodes The reactive power output of distributed power sources, energy storage, and capacitor banks.

[0029] Load supply constraints: For critical load nodes such as hospitals and emergency command centers The allowable cut ratio is limited by the node importance level, which is expressed by the following formula: In the formula, For the allowable resection ratio ( The smaller the value, the higher the priority of ensuring supply.

[0030] Node voltage constraint: After load shedding, the node voltage should be within the allowable range to prevent voltage exceedance. This is expressed by the following formula: In the formula, For load nodes The voltage amplitude can be approximated by power flow calculations or linearized models; , For load nodes The minimum and maximum voltage amplitude.

[0031] Power factor constraint: To maintain system voltage stability, the node power factor must be kept within a reasonable range when disconnecting loads. This is expressed by the following formula: In the formula, For load nodes The reactive load that is removed; This is the maximum permissible power factor angle.

[0032] The coupling constraint between the adjustable range of distribution network nodes and the load shedding amount: The net injected power of a node after load shedding should be within the adjustable range of distributed resources, which is expressed by the following formula: In the formula, and They are respectively load nodes Minimum and maximum adjustable power after taking into account resources such as DG and ESS.

[0033] Offshore wind power output constraints: Offshore wind farms w At any moment Contributing to the cause Depending on the wind speed at its location The power characteristic curve of the wind turbine and the shutdown strategy are determined, and it is expressed by the following formula: In the formula, To cut in wind speed; Rated wind speed; To cut off the wind speed; This is the rated output. During a typhoon, if the wind speed exceeds... The wind farm will automatically shut down, with zero power output.

[0034] The above coupling effect model is a linear programming problem, which can be solved using the simplex method or the interior point method. The output results are for each load node. Optimal active power load shedding and reactive load shedding The output of step S2 includes quantified values ​​of operational constraints such as node voltage, line power flow, adjustable resource output, and critical load shedding ratio. This represents the optimal load shedding amount and its corresponding operational constraints. The result reflects the internal load reduction scheme of the distribution network in response to transmission network load shedding commands, satisfying multiple operational constraints. This load shedding scheme will serve as the basis for evaluating cascading failures in subsequent steps. It is important to emphasize that the output of step S2 is not a simple numerical result, but rather provides deterministic boundaries with physical and operational constraints for the stochastic simulation in step S3. This optimized solution is the optimal load shedding scheme obtained under multiple strict constraints, including node voltage, power factor, and critical load supply. It ensures that any load shedding operation is feasible from an engineering perspective. However, in actual scheduling, due to differences in load unit characteristics and the randomness of the operation sequence, the final load shedding allocation may fluctuate around the optimal solution. Therefore, this optimization result will serve as the constraint condition for the double Monte Carlo sampling in step S3.

[0035] The constraints of the coupled model constructed in this invention cover the basic physical constraints of traditional power grid operation, such as power balance, equipment capacity, node voltage, and power factor. At the same time, it introduces operational boundaries that reflect the flexibility of dispatching decisions and the reliability of power supply, such as upper and lower limits of cut-off amount, load supply guarantee, and power adjustable range. This enables the model to accurately reflect the real state of the distribution network under the dual constraints of physical limits and operational strategies during typhoon disasters, avoiding the problem that the solution results are not feasible in practice due to the lack of constraints.

[0036] S3. Based on the optimal load shedding amount and its corresponding operational constraint boundary, the random process of load shedding allocation within the distribution network and the random process of load transfer on faulty lines are simulated using the double Monte Carlo sampling method. The probability of cascading failures caused by transmission-distribution coupling is calculated based on the simulation results. Specifically, in the typhoon disaster scenario, to accurately simulate the random redistribution process of load in the power grid after a fault occurs and to assess the risk of cascading failures, a Monte Carlo sampling method is used for multi-scenario simulation. This method mainly includes two sampling stages: load shedding allocation sampling within the distribution network and load transfer sampling on faulty lines. Finally, power flow calculation is used to determine whether a cascading failure has occurred and to calculate the failure probability. The core logic of this step is to introduce randomness within the optimization boundary determined in step S2 to simulate the uncertainty of actual scheduling. Step S2 obtains the optimal total load shedding allocation scheme that satisfies multiple operational constraints through the optimization model. However, in actual execution, load shedding operations are often completed by a combination of multiple smallest units (such as different transformers or different feeder branches), and their shedding order and specific allocation have a certain degree of randomness. Therefore, based on the optimization results of S2, this invention further employs double Monte Carlo sampling to simulate various real-world scheduling scenarios that may occur near the optimal solution.

[0037] In one embodiment, step S3 includes: Based on the optimal load shedding amount, several minimum load shedding units are obtained. Load level and fairness are introduced to calculate the comprehensive weight of each load node. Based on each comprehensive weight, a multi-round random sampling process is carried out on each minimum load shedding unit within the operation constraint boundary to generate multiple load shedding scenarios. A transfer scaling factor is introduced to perform a random sampling process for load transfer after a line fault, generating multiple load transfer scenarios. Based on the aforementioned load shedding scenarios and load transfer scenarios, power flow is solved using the forward-backward substitution method, and the probability of line cascading failures is determined based on the solution results.

[0038] For sampling of load shedding distribution within the distribution network, this invention first uses the optimized total load shedding amount for each node. and It is considered to be composed of multiple "minimum load shedding units". ,satisfy: In the formula, The average power factor angle of the distribution network; The active component of the minimum load shedding unit; This represents the reactive component of the minimum load shedding unit.

[0039] The sampling process simulates the randomness and priority of load shedding in actual scheduling. This invention performs a multi-round random allocation process based on the comprehensive weights of load nodes calculated using load levels and fairness. In one embodiment, the calculation of the comprehensive weights of each load node using load levels and fairness includes: Calculate the voltage deviation and electrical distance of each load node, and determine the allocation weight of each load node based on the voltage deviation and electrical distance. The load nodes are classified according to their importance, and their priority weights are determined based on these priority coefficients. Obtain the cumulative number of times each load node is selected during the historical sampling process, and determine the historical removal fairness weight of each load node based on the cumulative number of times selected. The overall weight of each load node is determined based on the shared weight, the priority weight, and the historical cut-off fairness weight.

[0040] Each node apportionment weight Taking into account electrical distance, node importance level, and adjustable resource distribution, this invention calculates the load node allocation weight based on the actual voltage of the load node and the voltage deviation between its rated voltage and the actual voltage, as well as the electrical distance of the load node, and in conjunction with the adjustable resource margin of the load node. This process is expressed by the following formula: In the formula, , These are the active and reactive power weighting coefficients, respectively. , This is the weighting adjustment coefficient; For nodes Electrical distance (calculated using topological impedance distance); The maximum electrical distance among all nodes; The minimum electrical distance among all nodes; | |For load nodes Voltage deviates from its rated value The amount; The node importance coefficient (important load nodes are assigned higher values, ranging from [0.2, 1.0], with Class I loads assigned 1.0, Class II loads assigned 0.6, and Class III loads assigned 0.2). For load nodes jThe adjustable resource output coefficient (the more adjustable resources, the lower the weight; the value ranges from [0, 1], where nodes with no adjustable resources have a value of 0, nodes with a few adjustable resources have a value of 0.1~0.5, nodes with strong adjustability have a value of 0.6~0.9, and nodes that are completely self-sufficient have a value of 1). It should be noted that closer electrical distances mean faster fault propagation and faster dispatch response, which has a greater impact on system stability. Therefore, the closer the distance, the higher the weight, and priority is given to ensuring power supply. Large voltage deviations indicate that the node's voltage is fragile; to prevent voltage collapse, the weight of such nodes must be increased, and their power supply must be prioritized.

[0041] The load nodes in the distribution network are divided into three levels according to their importance: Level I loads: critical loads (such as hospitals, emergency command centers, communication hubs, etc.), with the highest priority for power supply; Level II loads: relatively important loads (such as commercial areas, schools, transportation hubs, etc.); Level III loads: general loads (such as residential areas, ordinary industrial areas, etc.), with the lowest priority for power supply. The level coefficient of each load node is obtained, and its priority weight is determined accordingly. This process is expressed by the following formula: In the formula, Let be the priority weight of load node j; , where is the load level coefficient for load node j; This is the weighting coefficient for the grade (usually a large value, such as 10), so that the lower grade loads dominate the weighting.

[0042] To balance the number of times each node has been shelved historically and avoid some nodes being repeatedly shelved, this invention determines the corresponding historical shelving fairness weight based on the cumulative number of times each load node has been selected during the historical sampling process. This process is expressed by the following formula: In the formula, Assign a fair weight to the historical cutoff of load node j; For load nodes The cumulative number of times selected in historical sampling; This is a fairness adjustment factor (usually taken as 2-3); This represents the maximum number of times a node has been selected in history.

[0043] Finally, the corresponding comprehensive weight is calculated based on the allocation weight, priority weight, and historical clearing fairness weight of each load node. This process is expressed by the following formula: In the formula, The comprehensive weight of load node j.

[0044] This invention constructs a comprehensive weighting system to implement a tiered load shedding strategy: "first shedding level 3 loads, then supplementing level 2 if insufficient, and finally adjusting level 1 if still insufficient," while striving to balance the historical shedding counts of each node. During the sampling process, level 3 loads are preferentially shedding: only level L... j Sampling is performed on nodes with a load capacity of 3 until the total load that can be switched out reaches the demand or can no longer be switched out; if the total load capacity of Level III is insufficient, the insufficient portion is recorded. If insufficient, supplement with Level II load: continue sampling from the remaining Level II load nodes, with the amount removed not exceeding [amount not specified]. If still insufficient, record the new shortfall. If still insufficient, consider Level I load: under strict limits (e.g.) Minimal (smallest) shelving of Class I loads, prioritizing nodes with fewer historical shelving occurrences, longer electrical distances, and more adjustable resources. Update node selection count: In each sampling round, update the historical selection count of the node. It is expressed by the following formula: In the formula, For the first nodes in round sampling Whether it is selected (1 or 0).

[0045] This invention incorporates the electrical operating status of the power grid into the random sampling basis for load shedding allocation. Voltage deviation reflects the current voltage stability margin of a node, while electrical distance reflects the electrical proximity of the node to the power source or root node. The combination of these two factors makes load shedding allocation more inclined to prioritize implementation in electrically remote and voltage-deficient areas, avoiding situations where load shedding decisions contradict the physical characteristics of the power grid. Classifying loads by importance coefficients and determining priority weights quantifies the power supply guarantee requirements of different loads into sampling weights, significantly reducing the probability of high-level loads being selected in the random load shedding process, making the simulation results more aligned with the value orientation of actual power grid dispatch. By recording the cumulative number of times each load node was selected in the historical sampling process and using this to determine the historical shedding fairness weight, the problem of "centralized shedding" that may occur in multi-round random sampling is effectively solved. Nodes that were selected multiple times in the early stages automatically have their weights reduced in subsequent samplings, ensuring the relative fairness of load shedding allocation among loads of the same level and avoiding simulation distortion caused by excessive concentration of randomness in individual nodes. Finally, the apportionment weight (electrical dimension), priority weight (social value dimension), and historical fairness weight (statistical equilibrium dimension) are integrated to form a unified comprehensive weight. This weighting takes into account both the physical operation of the power grid and the social attributes of the load, while also balancing the multiple sampling processes. This ensures that the final generated load shedding scenario is coordinated in terms of electrical rationality, value orientation, and statistical fairness, significantly improving the authenticity and reliability of subsequent cascading failure probability calculations.

[0046] Subsequently, several rounds of sampling are conducted. In each round, a node is randomly selected according to the comprehensive weights obtained above to be allocated the minimum load shedding unit, that is, an active power unit is allocated simultaneously. and a reactive power unit So the first The actual active and reactive load shedding amount ultimately allocated to each node , for: In the formula, For load nodes The number of times it was selected in the sample; , The active and reactive power of the minimum load shedding unit of the unit wheel.

[0047] In addition, the following constraints need to be verified in real time within the operational constraint boundaries during the sampling process: Single node load shedding limit: Constraints on ensuring the supply of critical loads: Power factor range constraints: In the formula, The minimum allowable power factor angle; This is the maximum permissible power factor angle.

[0048] Consistency of total load shedding: If the sampling results do not meet the constraints, sampling is repeated until they are met. After sampling is complete, the active and reactive loads of each load node j are updated as follows: , : Repeat the above process until the total load shedding reaches the transmission network command value, generating multiple load shedding scenarios that meet the conditions. Each scenario corresponds to a set of { }and{ } and calculate respectively and .

[0049] In one embodiment, the process of introducing a transfer scaling factor to perform random sampling of load transfer after a line fault generates multiple load transfer scenarios, including: Take any faulty line in the set of faulty lines as the target line, and use the proportion of load transferred from the target line to the adjacent line as the transfer ratio factor. The load transfer amount of the target line is calculated based on the transfer ratio factor and the power data of the target line. The target line is updated, and the load transfer calculation process is repeated based on the updated target line to obtain the load transfer amount corresponding to all faulty lines. The total transfer power of each load node is determined based on the load transfer amount corresponding to all faulty lines. Multiple load transfer scenarios are constructed based on the transfer ratio factor and its corresponding load transfer amount and total transfer power.

[0050] For load transfer sampling of faulted lines, when a line trips due to a fault, its original load will be randomly transferred to an adjacent line. This simulates the process of load randomly transferring to an adjacent line after the initial faulted line trips in step S1, generating multiple load transfer scenarios. To simulate this process, this invention introduces a transfer ratio factor for random sampling.

[0051] Randomly select one faulty line from the set of faulty lines as the target line. Let the target line be... The active power flowing before the trip was The set of its left and right adjacent lines (that is, finding the faulty line through topological relationships). The set of directly connected adjacent lines is Transfer ratio factor Indicates the target route Medium load to adjacent lines The proportion of transfer satisfies: When the target line After a fault trip, its original active power and reactive power The user will be randomly transferred to an adjacent line. Transfer ratio factor. Applicable to both active and reactive power, the active and reactive load transfer amounts of the target line can be calculated by combining the power data of the target line with its corresponding transfer ratio factor. , : In the formula, For indicator functions, when the line With faulty lines If adjacent, take 1; otherwise, take 0.

[0052] The target lines are then updated, and the load transfer calculation process is repeated based on the updated target lines to obtain the load transfer amounts corresponding to all faulty lines. Load nodes... It may receive transferred power from multiple faulty lines. Let the set of faulty lines be... Then the load node Total active and reactive power received and transferred , for: Repeat the above process to generate multiple load transfer scenarios, each scenario corresponding to a set of { } and its corresponding { , } and calculate respectively and .

[0053] This invention uses the transfer ratio factor as a random variable to independently sample the proportion of load transferred from each faulty line to adjacent lines, effectively characterizing the combined impact of various uncertainties in the actual power grid. By adopting an iterative update method line by line, the load transfer amount is calculated sequentially for each line in the faulty line set, and the increased load after transfer is superimposed on the adjacent lines. The updated adjacent lines are then used as new "target lines" to participate in subsequent iterations. This accurately reflects the cascading transfer effect of load when multiple lines fail successively, avoiding the neglect of the fault propagation process in a single static transfer calculation.

[0054] In one embodiment, the step of solving the power flow problem using the forward-backward substitution method based on each of the load shedding scenarios and each of the load transfer scenarios, and determining the probability of the line cascading failure based on the solution results, includes: The net injected power of each load node is determined based on each of the load shedding scenarios and each of the load transfer scenarios. Based on the net injected power, the power of each line is calculated forward from the end node to the root node of the distribution network to obtain the end power of the line. Based on the power at the end of the line, voltage back-substitution calculation is performed from the root node to the end node to obtain the voltage amplitude of each load node; The power forward calculation process and the voltage backward calculation process are repeated until the voltage amplitude of all load nodes reaches the preset convergence threshold. The actual power of each line is obtained and combined with the transmission limit of each line to make an over-limit judgment. Based on the over-limit judgment result, the probability of the line cascading failure is determined.

[0055] Specifically, after completing the sampling of load shedding and distribution within the distribution network and the sampling of load transfer on faulted lines, the net injected active power of each node in each sampling scenario is... and reactive power All have been updated. This is to calculate the actual power of each line. and Power flow calculations are required. This invention employs a forward-backward substitution method suitable for radial distribution networks to solve the power flow problem. Based on load shedding and load transfer scenarios obtained through a sampling algorithm, the net active and reactive power injections at each load node are calculated. , : The line power is then calculated. Initialization is performed first; except for the slack node, the voltages of all other nodes are set to their rated voltages. .

[0056] Power forward calculation is performed based on the calculated net injected power of each load node, that is, calculating the power loss and power flow of each line from the end node to the root node. For the line (Connecting load nodes) and , As the parent node, (for child nodes), power at the end of the line , for: In the formula, , For nodes Net injection power; , For downstream lines Active and reactive power losses; , For the line Active and reactive power losses; , For the linel Resistance and reactance.

[0057] Voltage back-substitution calculation is performed based on the calculated line-end power, that is, updating the node voltage from the root node to the end node. For the line The line impedance is Then the load node voltage for: In the formula, For load nodes The voltage amplitude.

[0058] Repeat the power forward calculation and voltage backward calculation processes until the voltage amplitude at all load nodes reaches the preset convergence threshold to obtain the actual active and reactive power of each line. P l , Q l : There is another special case: for a faulty line, its power is 0.

[0059] The limit is determined by combining the transmission limits of each line. If the line... l If the power exceeds its transmission limit (i.e., exceeds the line's active power transmission limit or exceeds the line's reactive power transmission limit), it is determined to be a cascading fault; finally, the probability of a line cascading fault is determined based on the number of times the line exceeds the limit in the sampling. : In the formula, For the circuit in the sampling Number of times power exceeded the limit; This represents the total number of samples.

[0060] This invention, through a two-step method of "first optimizing to determine the boundary, then randomly simulating fluctuations," achieves a scientific quantification of the probability of cascading failures in distribution networks under the influence of transmission-distribution coupling. Specifically, the optimization model in step S2 ensures the engineering feasibility of the load shedding scheme, avoiding violations of rigid constraints such as voltage, power factor, and critical load supply, thus providing a deterministic boundary for the stochastic simulation. The dual Monte Carlo simulation in step S3, within the aforementioned boundary, fully considers the uncertainties in actual scheduling and the randomness of load transfer through two stochastic processes: "load shedding allocation sampling" and "load transfer sampling," achieving probabilistic coverage of a large number of possible operating scenarios. This method avoids the problems of numerous infeasible scenarios and wasted computational resources that pure random sampling might lead to, and overcomes the limitations of pure deterministic analysis, which cannot quantify uncertainty and produces overly simplistic predictions. Through the deep integration of optimization and stochastic methods, this invention can more accurately capture the cascading failure risks of distribution networks under typhoon disasters, providing a solid and reliable data foundation for subsequent fault type differentiation and differentiated recovery strategy formulation.

[0061] S4. Determine the comprehensive time-varying failure rate and fault type of the distribution network lines based on the wind-induced failure rate and the cascading failure probability of the lines; wherein, the comprehensive time-varying failure rate of the lines is expressed by the following formula: In the formula, is the comprehensive time-varying fault rate of the line to which load node i belongs in the distribution network; m is the number of nodes of the distribution network line on the power source side.

[0062] This invention not only outputs fault probability values, but also further classifies predicted faults into two categories based on their physical causes: Type I Fault (Permanent Physical Fault): Primarily caused by direct damage to equipment structure due to typhoon effects (such as tower collapse, conductor breakage, and insulator breakdown). After such a fault occurs, the equipment loses its normal function and cannot be restored via remote control; manual on-site repair or equipment replacement is necessary. The probability of this fault mainly stems from the direct wind-related fault rate in step S1. When wind speed exceeds the equipment's wind resistance threshold, the probability of structural damage increases significantly.

[0063] Type II faults (transient functional faults): These are mainly caused by the activation of protection devices (such as overcurrent tripping) due to electrical quantity exceeding limits (overload, voltage exceeding limits), without physical damage to the equipment itself. After the cause of the fault is eliminated, power supply can be quickly restored through automatic reclosing or remote control. The probability of this type of fault mainly comes from the cascading fault rate in step S3, reflecting the risk of line power exceeding limits after power flow redistribution.

[0064] For each line and at each time point, the fault type is determined based on a dual dimension of fault rate percentage and physical cause: When the line's wind-induced fault rate is not less than a preset multiple (preferably 0.7) of the line's overall time-varying fault rate, the fault type is classified as Type I; when the line's cascading fault probability is not less than a preset multiple (preferably 0.7) of the line's overall time-varying fault rate, the fault type is classified as Type II; if the difference between the line's wind-induced fault rate and the line's cascading fault probability is less than a preset multiple (preferably 0.3) of the line's overall time-varying fault rate, it is marked as a mixed fault and classified according to the dominant cause. Additionally, auxiliary verification can be performed for both fault types: Type I fault auxiliary verification: actual wind speed ≥ critical wind speed; Type II fault auxiliary verification: line power exceeding limits or node voltage exceeding limits. Furthermore, a direct fault cause tracing method can be used to determine the fault type: wind speed exceeding limits trigger → Type I fault; power flow / voltage exceeding limits trigger → Type II fault.

[0065] In the actual evolution of typhoons, Type I and Type II failures are not isolated events, but rather constitute a dynamically evolving coupled chain, and human control strategies can effectively intervene in the transmission and deterioration of this chain: The "triggering" effect of Type I faults: In the early stages of a typhoon, strong winds directly impact power distribution network equipment, potentially causing Type I faults (permanent physical damage, such as tower collapses or line breaks) on some lines. These Type I faults alter the power grid topology, forcing loads originally carried by the faulty lines to shift to adjacent healthy lines, triggering a redistribution of power flow. This is the initial driving force behind subsequent cascading faults.

[0066] The propagation of Type I faults to Type II faults: After power flow redistribution, some healthy lines may become overloaded due to the transfer of loads, triggering protection devices to trip, thus generating a Type II fault (transient functional fault). At this point, the Type II fault is a direct consequence of the Type I fault, manifesting as a lateral propagation of the fault from "structural damage" to "protective action." It is worth noting that if timely intervention (such as proactive load shedding or adjusting network topology) is implemented at this stage, the occurrence of Type II faults can be completely prevented.

[0067] Type II faults can exacerbate and worsen Type I faults: If Type II faults are not eliminated in time or occur repeatedly, leading to a continuous deterioration of the power grid's operating condition, a reverse aggravating effect will occur. For example, after multiple lines trip due to overcurrent, the load on the remaining lines increases further, potentially causing more severe overloads, even exceeding the thermal stability limits of the equipment, ultimately leading to new Type I faults such as conductor melting and insulation damage. Furthermore, the increased risk of voltage collapse may also trigger grid disconnection, causing more widespread permanent damage. In this stage, Type II faults become the trigger for new Type I faults, forming a vicious cycle of "Type I → Type II → Type I," with the fault range expanding exponentially.

[0068] The prediction results of this invention can directly empower active control strategies to intervene in fault chains: Blocking transmission: When it is predicted that a Type II fault may occur on a certain line due to power flow shift, dispatchers can take preventive measures such as shedding loads, starting backup power, or adjusting tie switches in advance to reduce the load rate of the line, thereby blocking the transmission of Type I faults to Type II faults.

[0069] Reversing the deterioration: For a Type II fault that has already occurred, power can be restored by fast reclosing or remote control, which can cut off the path of the Type II fault to a new Type I fault and prevent the fault range from expanding.

[0070] Dynamic Fault Rate Update: After the implementation of the above control measures, the power flow distribution of the power grid changes. This method can update the fault probability and type label of each line in real time, realizing a closed-loop iteration of prediction and control. This means that human intervention can reverse the prediction results—the originally predicted type II fault probability may decrease or even return to zero, while the risk of inducing type I faults is also reduced.

[0071] The above analysis reveals a complex "trigger-transmission-feedback" coupling relationship between Type I and Type II faults. This invention, through refined modeling and stochastic simulation, reveals this dynamic evolutionary pattern for the first time in the prediction stage, providing a quantitative basis for human intervention and achieving a fundamental leap from "passive prediction" to "active defense."

[0072] This invention innovatively proposes a "Type I / II Fault" classification and prediction mechanism based on the physical mechanism of faults, filling the technological gap from "prediction" to "recovery". Existing technologies typically use a single fault probability value when outputting fault prediction results, reflecting only the likelihood of equipment failure and failing to reveal whether the fault is caused by structural strength exceeding limits (permanent) or by protection actions (transient), resulting in an "information gap" between prediction results and post-disaster recovery decisions. This invention constructs a technical chain of "causal decomposition - type labeling - strategy mapping," decomposing the overall line fault rate into the direct wind-induced fault rate calculated in step S1 (reflecting the risk of equipment structural strength damage) and the cascading fault rate calculated in step S3 (reflecting the risk of protection actions). Based on the dominant cause, the fault point is labeled as Type I fault (permanent physical fault, requiring manual repair) and Type II fault (transient functional fault, which can be automatically recovered by reclosing). More importantly, this invention reveals the dynamic evolution of Type I and Type II faults: Type I faults (such as tower collapse and line breakage) first change the grid topology, triggering power flow shifts, which in turn induce Type II faults (protection tripping). If Type II faults are not eliminated in time, persistent overload or voltage problems will further lead to new Type I faults, forming a vicious cycle of "I→II→I". This invention quantifies this transmission mechanism for the first time through load transfer sampling and power flow calculation in step S3. At the same time, the prediction results can directly empower active control—when a Type II fault is predicted to occur on a certain line, dispatchers take preventative measures such as load shedding to change the injected power at nodes, thereby correcting the fault probability in reverse, realizing a closed-loop interaction of "prediction → control → re-prediction". This makes fault quantification no longer a passive risk assessment, but the core engine of active defense and precise recovery, providing support for the systematic improvement of grid resilience under extreme weather conditions.

[0073] This application addresses the problem of insufficient accuracy in predicting line failure rates in distribution networks under typhoon disasters in existing technologies. It designs a method for quantifying line failures in distribution networks during typhoon weather. This method introduces micro-topography correction factors and wind direction-line angle correction factors into the wind field correction model. This not only dynamically reflects the intensity changes of the typhoon after landfall but also significantly improves the accuracy of estimating the actual wind speed the lines are subjected to through fine-tuning of local micro-topography and wind direction angles. Therefore, the calculation of wind-induced failure rates more closely reflects the actual physical process. Furthermore, a transmission and distribution coupling impact model is constructed to convert the load shedding commands issued by the transmission network into... The optimal load shedding scheme is transformed into a physical feasible boundary for subsequent simulations. Based on this, a two-step method of "optimization and delineation-stochastic simulation" is pioneered: within the determined feasible boundary, the randomness in actual scheduling is simulated through double Monte Carlo sampling (load shedding sampling + load transfer sampling), which achieves probabilistic coverage of a large number of possible operating scenarios. This not only eliminates a large number of infeasible scenarios from pure random sampling, but also overcomes the singleness of pure deterministic analysis. It achieves accurate quantification and scientific assessment of the risk of cascading failures under the influence of transmission-distribution coupling, and provides support for the systematic improvement of power grid resilience under extreme weather conditions.

[0074] It should be noted that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders.

[0075] In another embodiment, such as Figure 2 As shown, a second aspect of the present invention provides a power distribution network line fault quantification system under typhoon weather, comprising: The wind-induced fault calculation module 10 is used to acquire geographic information data, power grid topology data and meteorological data of the power system under typhoon weather, so as to construct a wind field correction model that incorporates micro-topography correction factor and wind direction line angle correction factor, and calculate the line fault rate due to wind based on the wind field correction model. The coupled model solving module 20 is used to construct and solve a coupled influence model of transmission network faults on the fault rate of distribution network lines with the goal of minimizing the power outage loss of the distribution network in the power system, so as to obtain the optimal load shedding amount of each load node in the distribution network and its corresponding operating constraint boundary. The cascading fault calculation module 30 is used to simulate the random process of load shedding and load transfer of faulty lines within the distribution network using the double Monte Carlo sampling method based on the optimal load shedding amount and its corresponding operating constraint boundary, and to calculate the probability of cascading faults caused by transmission and distribution coupling based on the simulation results. The comprehensive fault determination module 40 is used to determine the comprehensive time-varying fault rate and fault type of the distribution network based on the line wind-induced fault rate and the line cascading fault probability.

[0076] It should be noted that each module in the aforementioned typhoon weather distribution network line fault quantification system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module. For specific limitations regarding the typhoon weather distribution network line fault quantification system, please refer to the limitations of the typhoon weather distribution network line fault quantification method described above; both have the same function and role, and will not be repeated here.

[0077] A third aspect of the present invention provides an electronic device comprising: Processor, memory, and bus; The bus is used to connect the processor and the memory; The memory is used to store operation instructions; The processor is configured to execute the operation instructions by calling the operation instructions, thereby causing the processor to perform the operation corresponding to the method for quantifying faults in power distribution lines during typhoon weather as shown in the first aspect of this application.

[0078] In one alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The illustrated electronic device 5000 includes a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, for example, via a bus 5002. Optionally, the electronic device 5000 may also include a transceiver 5004. It should be noted that in practical applications, the transceiver 5004 is not limited to one type, and the structure of this electronic device 5000 does not constitute a limitation on the embodiments of this application.

[0079] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 5001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0080] Bus 5002 may include a path for transmitting information between the aforementioned components. Bus 5002 may be a PCI bus or an EISA bus, etc. Bus 5002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0081] The memory 5003 may be a ROM or other type of static storage device capable of storing static information and instructions, RAM or other type of dynamic storage device capable of storing information and instructions, or it may be an EEPROM, CD-ROM or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0082] The memory 5003 is used to store application code that executes the scheme of this application, and its execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the foregoing method embodiments.

[0083] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers.

[0084] The fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for quantifying faults in power distribution lines during typhoon weather as described in the first aspect of this application.

[0085] Another embodiment of this application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0086] Furthermore, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0087] In summary, this invention relates to the field of power system fault quantification technology, and discloses a method, system, equipment, and medium for quantifying distribution network line faults under typhoon weather. It calculates the wind-induced fault rate of distribution network lines by constructing a wind field correction model that incorporates micro-topography correction factors and wind direction-line angle correction factors. With the goal of minimizing power outage losses in the distribution network, a coupled influence model of transmission network faults on the distribution network line fault rate is constructed and solved to obtain the optimal load shedding amount for each load node in the distribution network and its corresponding operational constraint boundary. Under this boundary, the random process of load shedding allocation and the random process of load transfer on faulty lines within the distribution network are simulated using a double Monte Carlo sampling method, and the probability of line cascading faults caused by transmission-distribution coupling is calculated based on the simulation results. The combined time-varying fault rate and type of distribution network lines are determined by integrating the two fault rates, achieving accurate quantification of distribution network line faults under the influence of transmission-distribution coupling.

[0088] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0089] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A method for quantifying faults in power distribution lines during typhoon weather, characterized in that, include: Geographic information data, power grid topology data, and meteorological data under typhoon weather of the power system are acquired to construct a wind field correction model that incorporates micro-topography correction factors and wind direction-line angle correction factors, and the line failure rate due to wind is calculated based on the wind field correction model. With the goal of minimizing power outage losses in the distribution network within the power system, a coupled impact model of transmission network faults on the failure rate of distribution network lines is constructed and solved to obtain the optimal load shedding amount of each load node in the distribution network and its corresponding operational constraint boundary. Based on the optimal load shedding amount and its corresponding operational constraint boundary, the random process of load shedding allocation and load transfer of faulty lines within the distribution network is simulated by the double Monte Carlo sampling method, and the probability of line cascading faults caused by transmission and distribution coupling is calculated based on the simulation results. The comprehensive time-varying failure rate and failure type of the distribution network are determined based on the line failure rate due to wind and the line cascading failure probability.

2. The method for quantifying faults in power distribution lines during typhoon weather according to claim 1, characterized in that, The construction of the wind field correction model, which incorporates micro-topography correction factors and wind direction line angle correction factors, includes: A basic model of typhoon wind speed is constructed based on the meteorological data, and the theoretical wind speed is calculated based on the basic model of typhoon wind speed. The wind direction line angle is determined based on the power grid topology data and the meteorological data, and the wind direction line angle correction factor is calculated based on the wind direction line angle. The geographic information data, the power grid topology data, and the meteorological data are input into a pre-constructed micro-topography acceleration ratio prediction model for processing, and the micro-topography correction factor is output. The wind field correction model is constructed based on the theoretical wind speed, the micro-topography correction factor, and the wind direction line angle correction factor.

3. The method for quantifying faults in power distribution lines during typhoon weather according to claim 1, characterized in that, The constraints of the coupled influence model include power balance constraints, upper and lower limits of load shedding, power adjustable range constraints, load supply constraints, node voltage constraints, power factor constraints, coupling constraints between the adjustable range of distribution network nodes and load shedding, and offshore wind power output constraints.

4. The method for quantifying faults in power distribution lines during typhoon weather according to claim 1, characterized in that, Based on the optimal load shedding amount and its corresponding operational constraint boundaries, the random process of load shedding allocation and load transfer on faulty lines within the distribution network is simulated using a double Monte Carlo sampling method. The probability of line cascading faults caused by transmission-distribution coupling is calculated based on the simulation results, including: Based on the optimal load shedding amount, several minimum load shedding units are obtained. Load level and fairness are introduced to calculate the comprehensive weight of each load node. Based on each comprehensive weight, a multi-round random sampling process is carried out on each minimum load shedding unit within the operation constraint boundary to generate multiple load shedding scenarios. A transfer scaling factor is introduced to perform a random sampling process for load transfer after a line fault, generating multiple load transfer scenarios. Based on the aforementioned load shedding scenarios and load transfer scenarios, power flow is solved using the forward-backward substitution method, and the probability of line cascading failures is determined based on the solution results.

5. The method for quantifying faults in power distribution lines during typhoon weather according to claim 4, characterized in that, The calculation of the comprehensive weight of each load node in the introduction of load level and fairness includes: Calculate the voltage deviation and electrical distance of each load node, and determine the allocation weight of each load node based on the voltage deviation and electrical distance. The load nodes are classified according to their importance, and their priority weights are determined based on these priority coefficients. Obtain the cumulative number of times each load node is selected during the historical sampling process, and determine the historical removal fairness weight of each load node based on the cumulative number of times selected. The overall weight of each load node is determined based on the shared weight, the priority weight, and the historical cut-off fairness weight.

6. The method for quantifying faults in power distribution lines during typhoon weather according to claim 4, characterized in that, The process of introducing a transfer scaling factor to randomly sample load transfers after a line fault generates multiple load transfer scenarios, including: Take any faulty line in the set of faulty lines as the target line, and use the proportion of load transferred from the target line to the adjacent line as the transfer ratio factor. The load transfer amount of the target line is calculated based on the transfer ratio factor and the power data of the target line. The target line is updated, and the load transfer calculation process is repeated based on the updated target line to obtain the load transfer amount corresponding to all faulty lines. The total transfer power of each load node is determined based on the load transfer amount corresponding to all faulty lines. Multiple load transfer scenarios are constructed based on the transfer ratio factor and its corresponding load transfer amount and total transfer power.

7. The method for quantifying faults in power distribution lines during typhoon weather according to claim 4, characterized in that, The process of solving power flow problems using the forward-backward substitution method based on the load shedding and load transfer scenarios, and determining the probability of line cascading failures based on the solution results, includes: The net injected power of each load node is determined based on each of the load shedding scenarios and each of the load transfer scenarios. Based on the net injected power, the power of each line is calculated forward from the end node to the root node of the distribution network to obtain the end power of the line. Based on the power at the end of the line, voltage back-substitution calculation is performed from the root node to the end node to obtain the voltage amplitude of each load node; The power forward calculation process and the voltage backward calculation process are repeated until the voltage amplitude of all load nodes reaches the preset convergence threshold. The actual power of each line is obtained and combined with the transmission limit of each line to make an over-limit judgment. Based on the over-limit judgment result, the probability of the line cascading failure is determined.

8. A fault quantification system for power distribution lines during typhoon weather, characterized in that, include: The wind-induced fault calculation module is used to acquire geographic information data, power grid topology data and meteorological data of the power system under typhoon weather, so as to construct a wind field correction model that incorporates micro-topography correction factors and wind direction line angle correction factors, and calculate the line fault rate due to wind based on the wind field correction model. The coupled model solving module is used to construct and solve a coupled impact model of transmission network faults on the fault rate of distribution network lines with the goal of minimizing power outage losses in the distribution network within the power system, so as to obtain the optimal load shedding amount of each load node in the distribution network and its corresponding operating constraint boundary. The cascading failure calculation module is used to simulate the random process of load shedding and load transfer of faulty lines within the distribution network using the double Monte Carlo sampling method based on the optimal load shedding amount and its corresponding operational constraint boundary, and to calculate the probability of cascading failures caused by transmission and distribution coupling based on the simulation results. The comprehensive fault determination module is used to determine the comprehensive time-varying fault rate and fault type of the distribution network based on the line wind-induced fault rate and the line cascading fault probability.

9. An electronic device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the method for quantifying faults in power distribution lines during typhoon weather as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the method for quantifying faults in power distribution lines during typhoon weather as described in any one of claims 1 to 7.

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