Power distribution network disaster risk early warning method and device and nonvolatile storage medium

Abstract

The invention discloses a power distribution network disaster risk early warning method and device and a nonvolatile storage medium. The method comprises the following steps: constructing fault probability models corresponding to elements in the power distribution network under various disaster types; based on the fault probability models corresponding to the multiple disaster types, fault rate models corresponding to multiple elements in the power distribution network are determined; setting risk evaluation indexes based on the fault rate models corresponding to the plurality of elements; an objective function and constraint conditions are set, and objectives of the objective function include minimizing influences of disasters on elements in the power distribution network and maximizing the overall line power transmission amount in the power distribution network; and determining an early warning result based on the risk evaluation index, the objective function and the constraint condition. The invention solves the technical problems that the structure of the power distribution network is complex, a large number of elements exist, the elements are easy to break down under the destructive influence of severe weather, and the operation of the power distribution network is abnormal.

Description

Technical Field

[0001] This invention relates to the field of power distribution network risk early warning technology, and more specifically, to a method, device, and non-volatile storage medium for early warning of power distribution network disaster risks. Background Technology

[0002] With the continuous optimization of my country's power system grid structure, the distribution network, as a crucial component of the power system, is increasingly demonstrating its irreplaceable role. The safe and stable operation of the distribution network is vital to the overall safe and stable operation of the power system. Severe weather events, such as typhoons, rainstorms, and hail, are having an increasingly significant impact on the distribution network. The main reasons include: First, the distribution network has a complex structure with numerous lines, switches, transformers, and other components. These components are prone to failure under the destructive effects of severe weather (such as typhoons and rainstorms), leading to short circuits, line breaks, and tripping. Second, the distribution network's layout is often unreasonable, with some lines located in disaster-prone areas. Furthermore, the distribution network has a low level of automation and poor disaster resilience, making timely restoration after faults difficult. Third, there are deficiencies in the operation and maintenance of the distribution network. Under extreme weather conditions, it is difficult to promptly troubleshoot, repair, and restore power, resulting in low power supply reliability and causing power outages for many users.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, device, and non-volatile storage medium for early warning of disaster risks in power distribution networks, at least addressing the technical problem that power distribution networks have complex structures with a large number of components, which are prone to failure under the destructive effects of severe weather, leading to abnormal operation of the power distribution network.

[0005] According to one aspect of the present invention, a method for early warning of disaster risks in a distribution network is provided, comprising: constructing fault probability models corresponding to components in the distribution network under various disaster types; determining fault rate models corresponding to multiple components in the distribution network based on the fault probability models corresponding to the various disaster types; setting risk assessment indicators based on the fault rate models corresponding to the multiple components, wherein the risk assessment indicators include line load rate, line weighted power flow risk rate, and line power flow transfer risk rate; setting objective function and constraints, wherein the objective function aims to minimize the impact of disasters on components in the distribution network and maximize the overall line power transmission in the distribution network; and determining early warning results based on the risk assessment indicators, objective function, and constraints.

[0006] Optionally, a fault probability model is constructed for each component in the power distribution network under various disaster types, including typhoons. This includes: setting a typhoon wind speed prediction model based on the intensity and duration of historical typhoons; predicting the maximum wind speed radius and typhoon movement speed based on the typhoon wind speed prediction model; predicting the duration of the typhoon based on its movement speed; and determining the fault probability model corresponding to the typhoon based on its duration and maximum wind speed radius.

[0007] Optionally, construct fault probability models for components in the distribution network under various disaster types, including rainstorms. This involves: acquiring historical rainfall data for the area where the distribution network is located, including historical rainfall amount, historical rainfall duration, and historical wind speed during rainfall; predicting future rainfall based on historical rainfall data; determining the landslide intensity coefficient based on the landslide disaster mechanism model according to the future rainfall data; and determining the fault probability model corresponding to the rainfall based on the landslide intensity coefficient and the future rainfall data.

[0008] Optionally, based on the fault probability models corresponding to each of the various disaster types, the fault rate models corresponding to each of the multiple components in the distribution network are determined. In the case where the various disaster types include typhoons, this includes: determining the outage probability of each of the multiple components at multiple wind speeds based on the fault probability model corresponding to the typhoon; determining the total stress on the overhead components among the multiple components based on the fault probability model corresponding to the typhoon; and determining the fault rate models corresponding to each of the multiple components under the typhoon based on the outage probability of each of the multiple components at multiple wind speeds and the total stress on the overhead components among the multiple components.

[0009] Optionally, risk assessment indicators are set based on the failure rate models corresponding to multiple components, including: constructing a line load rate model in the distribution network; determining a line weighted failure risk rate model in the distribution network based on the failure rate models corresponding to multiple components; determining the power flow transfer impact of lines in the distribution network during a disaster; determining the power flow transfer risk rate based on the power flow transfer impact; and determining risk assessment indicators based on the line load rate model, the line weighted failure risk rate model, and the power flow transfer risk rate.

[0010] Optionally, an objective function and constraints are set, including: determining the objective function based on risk assessment indicators and the power transmission capacity of lines in the distribution network; and determining the constraints based on the maximum load of lines in the distribution network.

[0011] According to another aspect of the present invention, an early warning device for disaster risk in a distribution network is also provided, comprising: a construction module for constructing fault probability models of components in the distribution network under various disaster types;

[0012] The first determination module is used to determine the failure rate model corresponding to each of the multiple components in the distribution network based on the failure probability models corresponding to each of the various disaster types. The first setting module is used to set risk assessment indicators based on the failure rate models corresponding to each of the multiple components. The risk assessment indicators include line load rate, line weighted power flow risk rate, and line power flow transfer risk rate. The second setting module is used to set the objective function and constraints. The objective function aims to minimize the impact of disasters on the components in the distribution network and maximize the overall line power transmission in the distribution network. The second determination module is used to determine the early warning result based on the risk assessment indicators, objective function, and constraints.

[0013] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-described methods for early warning of power distribution network disaster risks.

[0014] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program, when running, executes any of the above-described methods for early warning of power distribution network disaster risks.

[0015] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described methods for early warning of power distribution network disaster risks.

[0016] In this embodiment of the invention, a method for early warning of disaster risks in distribution networks is adopted. This involves constructing fault probability models for components in the distribution network under various disaster types; determining fault rate models for multiple components based on these models; setting risk assessment indicators, including line load rate, line weighted power flow risk rate, and line power flow transfer risk rate; setting objective functions and constraints, where the objective function aims to minimize the impact of disasters on components in the distribution network and maximize the overall line power transmission in the distribution network; and determining early warning results based on the risk assessment indicators, objective function, and constraints. This achieves the goal of providing early warning of the impact of disasters, thereby reducing the probability of component failures in the distribution network. This addresses the technical problem of distribution networks having complex structures and numerous components, which are prone to failure under the destructive influence of severe weather, leading to abnormal operation of the distribution network. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 A hardware block diagram of a computer terminal for implementing an early warning method for disaster risks in a power distribution network is shown.

[0019] Figure 2 This is a flowchart illustrating the early warning method for power distribution network disaster risks provided in an embodiment of the present invention;

[0020] Figure 3 This is a structural block diagram of an early warning device for power distribution network disaster risks provided in an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] According to an embodiment of the present invention, a method embodiment for early warning of disaster risks in a power distribution network is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0024] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1A hardware block diagram of a computer terminal for implementing an early warning method for disaster risks in power distribution networks is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0025] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element in the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0026] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the early warning method for power grid disaster risks in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned application method for early warning of power grid disaster risks. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0027] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0028] Figure 2This is a flowchart illustrating the early warning method for power distribution network disaster risks provided by an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0029] Step S202: Construct the fault probability model for each component in the distribution network under various disaster types.

[0030] In this step, constructing fault probability models for distribution network components under various disaster types is a crucial step in improving the power system's resilience to natural disasters. The following is the basic framework for constructing fault probability models for distribution network components under different disaster types (such as typhoons, ice storms, rainstorms, and their derivative disasters):

[0031] For example, a failure probability model under typhoon disaster can be composed of the following models:

[0032] 1. Typhoon wind speed model: Using the Batts wind field model to superimpose gradient wind speed and moving wind speed, the wind speed at any point within the typhoon wind field is calculated based on the typhoon radius, intensity, and moving path.

[0033] 2. Tower outage probability model: A model is established that considers the comparison between the design wind speed and the actual wind speed. When the wind speed reaches a certain threshold, the tower outage probability begins to rise until the wind speed exceeds its tolerance range, at which point the outage probability reaches 1.

[0034] 3. Overhead conductor failure rate model: Based on the stress analysis of the conductor, including wind load, self-weight load and maximum conductor bearing capacity, the failure rate of the conductor under typhoon is calculated.

[0035] Failure probability model under ice storm:

[0036] 1. Ice and wind load models: Ice and wind loads at different locations are estimated using the Chaine model and other meteorological models.

[0037] 2. Icing Growth Rate Model: A model for icing growth rate is established by combining parameters such as ice density, rain density, and air water content.

[0038] 3. Icing model for towers and conductors: Calculate the icing thickness of towers and conductors based on the icing growth rate and time.

[0039] 4. Failure rate model based on icing amount: When the icing amount exceeds a certain threshold, the failure rate of the tower and conductor begins to increase.

[0040] Failure probability model under rainstorms and related disasters:

[0041] 1. Landslide disaster model: Analyze topographic slope, soil stability and precipitation intensity to calculate landslide risk coefficient.

[0042] 2. Flash flood and debris flow disaster model: The intensity of flash floods and debris flows is estimated by analyzing factors such as topography, channel density, and congestion coefficient.

[0043] 3. Pole / Tower Failure Rate Model: Based on the above disaster intensity and the geographical location of the poles / towers, a failure rate model for poles / towers under landslides, flash floods, and debris flows is established.

[0044] Specifically, historical disaster data, physical parameters of power distribution network components, and operational data can be collected. Based on the collected data, parameters for each model are determined, such as wind speed, rainfall, ice load, and terrain parameters. Historical fault data is used to verify the accuracy and effectiveness of the models, and model parameters are adjusted to improve prediction accuracy.

[0045] These models enable distribution network managers to predict and assess the vulnerability of line components under various disaster conditions, thereby developing effective prevention and emergency response strategies to reduce the damage of disasters to the power grid and ensure the stability and reliability of power supply.

[0046] Step S204: Based on the fault probability models corresponding to each of the various disaster types, determine the fault rate models corresponding to each of the multiple components in the distribution network.

[0047] In this step, determining the failure rate models for multiple components in the distribution network based on the failure probability models corresponding to various disaster types is a complex but crucial task. Its aim is to assess the impact of different disasters on distribution network components, thereby providing a scientific basis for developing proactive defense strategies. The following are the specific steps for constructing failure rate models for distribution network components for different disaster types, such as typhoons, ice storms, rainstorms, and their derivative disasters:

[0048] Failure rate model under typhoon disaster:

[0049] 1. Wind Speed ​​Prediction Model: First, based on the predicted path and intensity of the typhoon, the wind speed at any point within the typhoon's influence area is calculated using the Batts wind field model or a similar method. This provides the necessary wind speed input for the subsequent failure rate model.

[0050] 2. Tower Failure Rate Model: This model studies the wind resistance standards and design wind speeds of towers. When the actual wind speed exceeds the design standard, a tower outage probability model is established. Typically, the outage probability increases with wind speed, until it reaches twice the maximum design wind speed, at which point the outage probability is 1.

[0051] 3. Overhead Conductor Failure Rate Model: By analyzing the stress on the conductor under wind load and self-weight load, the total stress on the conductor is calculated and compared with the tensile strength of the conductor to construct a failure rate model. For example, when the wind speed reaches a certain threshold, the failure rate of the conductor begins to increase.

[0052] Failure rate model under ice storm:

[0053] 1. Icing Growth Model: Using the Chaine model and other meteorological models, air humidity, temperature and precipitation are predicted to estimate icing thickness and growth rate.

[0054] 2. Conductor Icing Failure Rate Model: Based on ice thickness and considering the physical characteristics of the conductor, such as diameter and material strength, an ice accretion failure rate model for conductors under ice storms is established. The failure rate of the conductor increases accordingly with increasing ice thickness.

[0055] 3. Tower Icing Failure Rate Model: Similarly, based on the ice thickness and the structural characteristics of the tower, an icing failure rate model is constructed. Icing may cause the tower to bear additional weight and force, increasing its failure risk.

[0056] Failure rate model under rainstorms and related disasters:

[0057] 1. Landslide, flash flood, and debris flow hazard models: Utilizing geological, topographical, and meteorological data, these models predict the probability of landslides, flash floods, and debris flows. These hazards can directly damage power distribution network components or indirectly impose additional loads.

[0058] 2. Line tower failure rate model: Based on landslide intensity, flash flood intensity, and debris flow intensity, and combined with the geographical location information of towers and lines, a failure rate model of line towers under these derivative disasters is constructed.

[0059] Comprehensive model construction:

[0060] 1. Component failure rate calculation: Convert the failure probability model of each disaster type into a failure rate model, that is, the number of times or probability of component failure per unit time.

[0061] 2. Line Risk Assessment Indicators: Based on the failure rate model of line components, establish unified line risk assessment indicators. These may include line load rate, weighted failure risk rate, and line power flow transfer risk rate, etc.

[0062] 3. Objective Function and Constraints: An objective function is set to minimize the line risk assessment indicators and maximize the overall power transmission capacity of the system's lines. Simultaneously, corresponding constraints are set according to the disaster type and distribution network structure, such as distribution line load constraints and node power balance constraints.

[0063] Through the above steps, a comprehensive failure rate model for distribution network components can be constructed. These models can provide a scientific basis for disaster early warning and proactive defense strategies for distribution networks, helping the power system to make more accurate and timely responses when disasters occur, so as to protect the stability of the power grid structure and power supply.

[0064] Step S206: Based on the failure rate models corresponding to each of the multiple components, set risk assessment indicators, including line load rate, line weighted power flow risk rate, and line power flow transfer risk rate.

[0065] In this step, when setting risk assessment indicators based on multiple component failure rate models, distribution network managers need to comprehensively consider the robustness and safety of distribution network lines under different disaster conditions.

[0066] Line load factor is the most direct indicator of line operating status; it is the ratio of the actual transmitted power to the line's maximum rated transmitted power. The line-weighted power flow risk rate reflects the impact of a line fault on the entire distribution system, especially when a line fault leads to a redistribution of power flow, potentially placing additional load pressure on other lines and increasing the risk of further faults. When a line is taken out of service due to a disaster, the power flow it was transmitting needs to be transferred to other normally operating lines, which will place additional loads on those lines. The line power flow transfer risk rate is used to assess the impact of this transfer on the entire system, particularly the potential threat to the operational safety and stability of other lines.

[0067] To comprehensively assess the risks of the route under extreme conditions, it is necessary to integrate the three risk assessment indicators mentioned above. Considering the significant contribution of all indicators in risk assessment, a weighted summation method can be used to establish a comprehensive risk assessment indicator.

[0068] By setting these risk assessment indicators, distribution network managers can more effectively identify vulnerable lines in the power grid, enabling them to take preventative measures before disasters occur, such as adjusting loads in advance, optimizing power dispatch, and strengthening line maintenance, thereby reducing the negative impact of disasters on distribution network operation. At the same time, these indicators also provide quantitative data for rapid post-disaster recovery and optimization of distribution network operation.

[0069] Step S208: Set the objective function and constraints, wherein the objective function aims to minimize the impact of disasters on components in the distribution network and maximize the overall line power transmission in the distribution network.

[0070] In this step, objective functions and constraints are set to optimize the operation of the distribution network under disaster conditions, aiming to minimize the impact of disasters on components in the distribution network and maximize the overall line power transmission capacity. In the disaster mitigation strategy for the distribution network, the objective function may include the following two main objectives:

[0071] Minimizing disaster impact: This can be achieved by minimizing the failure probability or risk assessment index of distribution network components. The objective function can be designed as a weighted sum of the risk assessment indices of all important lines.

[0072] Maximizing overall line power transmission: Another part of the objective function is to maximize the power transmission capacity of the distribution network under disaster conditions. This is typically expressed as maximizing the sum of the ratios of the actual transmission power of all lines to the maximum rated power of each line.

[0073] Combining the two objectives mentioned above, the final objective function may be a bi-objective optimization problem, which can be expressed through appropriate linear combinations or priority settings. For example, a weighted objective function can be defined.

[0074] Constraints are used to ensure that the operation of the distribution network during the optimization process meets physical and operational limitations. These constraints may include: distribution line load constraints, ensuring that the load on the lines does not exceed their design limits to prevent overload-induced faults; node power balance constraints, ensuring that the power input and output of each node are balanced to maintain system stability; line fault repair constraints, ensuring that the lines most affected by disasters are repaired first; distributed generation and energy storage device power constraints, limiting the power output of distributed generation and energy storage devices to ensure safe operation and system stability; and line power flow constraints, ensuring that the power flow of the lines does not exceed their thermal limits to avoid line faults due to overload.

[0075] These objective functions and constraints together constitute an optimization model, which can be solved using linear programming, mixed integer programming, genetic algorithms, or other optimization algorithms to find the operating strategy that minimizes the impact of disasters and maximizes power delivery while satisfying all operational constraints.

[0076] Step S210: Determine the early warning result based on risk assessment indicators, objective function, and constraints.

[0077] In this step, determining the early warning results based on risk assessment indicators, objective functions, and constraints is the core step in the distribution network disaster early warning and proactive defense strategy. This process aims to identify high-risk lines and components in the distribution network so that measures can be taken in advance to reduce the potential impact of disasters. Based on the solution of the optimization model, it can be determined which lines and components are high-risk and require early warning. Early warning results may include the failure probability of a specific line, risk indicator values, and suggested defensive measures (e.g., early line disconnection, adjusting power output, etc.). Early warning information should be promptly communicated to relevant distribution network managers and maintenance personnel so that they can take appropriate actions based on the early warning results. Based on the early warning results, distribution network operators can initiate defensive measures, such as adjusting the distribution of power sources and loads, optimizing line switching strategies, and strengthening line structures to cope with impending disasters. Disaster prediction and the operating status of the distribution network are dynamic; therefore, early warning results and defense strategies should also be updated regularly.

[0078] Through this process, the power distribution network can identify and respond to high-risk areas in advance based on disaster prediction, thereby reducing the actual damage of disasters to the power system and improving the system's resilience and reliability. The key to this approach lies in accurate risk assessment, reasonable target setting, and effective resource scheduling to optimize disaster early warning and proactive defense.

[0079] Through the above steps, the goal of early warning of the impact of disasters can be achieved, thereby reducing the probability of component failure in the distribution network. This solves the technical problem that the distribution network has a complex structure with a large number of components, which are prone to failure under the destructive influence of severe weather, leading to abnormal operation of the distribution network.

[0080] As an optional implementation, a fault probability model is constructed for each component in the power distribution network under various disaster types, including typhoons. This includes: setting a typhoon wind speed prediction model based on the intensity and duration of historical typhoons; predicting the maximum wind speed radius and typhoon movement speed of the typhoon based on the typhoon wind speed prediction model; predicting the duration of the typhoon based on the typhoon movement speed; and determining the fault probability model corresponding to the typhoon based on the duration and the maximum wind speed radius.

[0081] Optionally, when calculating the failure rate of power distribution network components during typhoon disasters, the intensity and duration of the typhoon are mainly considered, including the force relationships, wind speed, and typhoon radius within the typhoon wind field. A semi-numerical, semi-empirical Yan Meng model can be used to characterize the force relationships in the wind field after considering frictional correction. Secondly, the Batts wind field model is used, superimposing gradient wind speed and moving wind speed, and extrapolating outwards from the typhoon center to obtain the wind speed at any point within the typhoon wind field. Since typhoons are mobile disasters, they will continuously affect power distribution network components at different locations for a period of time during their movement after entering the country. Based on the typhoon path and the moving speed of the typhoon center, the duration of the typhoon's impact can be obtained.

[0082]

[0083] In the formula, T t L represents the time it takes for the typhoon center to move; t v represents the distance the typhoon center has moved; t This represents the speed at which the typhoon's center moves.

[0084] Based on the typhoon disaster model established above, it can be seen that as time changes, the maximum wind speed of a typhoon first increases and then decreases, and the radius of the maximum wind speed first decreases and then increases, eventually gradually becoming a normal tropical cyclone.

[0085] As an optional implementation, a fault probability model is constructed for each component in the power distribution network under various disaster types, including rainstorms. This involves: acquiring historical rainfall data for the area where the power distribution network is located, including historical rainfall amount, historical rainfall duration, and historical wind speed during the rainfall; predicting future rainfall data based on the historical rainfall data; determining the landslide intensity coefficient based on the landslide disaster mechanism model according to the future rainfall data; and determining the fault probability model corresponding to the rainfall based on the landslide intensity coefficient and the future rainfall data.

[0086] Optionally, when constructing the failure probability corresponding to rainstorms, historical rainfall data can be obtained first, and predictions can be made based on this data. Since rainfall-related disasters can also easily trigger landslides, the impact of landslides must also be considered. Therefore, the landslide intensity coefficient can be determined based on the landslide disaster mechanism model, and thus the failure probability model corresponding to rainfall can be established.

[0087] Specifically, power distribution networks may generate various derivative disaster scenarios under diffuse disasters, requiring separate modeling for each derivative scenario. For typical rainstorm disasters such as landslides, flash floods, and debris flows, a rainstorm disaster model can be established by reducing the affected scenarios using clustering algorithms.

[0088] First, we analyze the mechanism of landslide disasters caused by rainstorms, mainly considering the landslide strength coefficient and the tower vulnerability coefficient:

[0089]

[0090] In the formula, E s λ is the landslide strength coefficient. t R is the vulnerability coefficient of the tower; e Effective rainfall; S1 is the topographic slope coefficient; S2 is the topographic height coefficient; S3 is the slope shape coefficient; α g For geological coefficients; α f The line wear factor; α h For hydraulic conditions; α b This represents the relative disaster location coefficient of the line.

[0091] Under extreme weather-related disasters such as flash floods and debris flows, the corresponding disaster intensity coefficients can be obtained using a similar method:

[0092] E m =α c α k α h S1S2R e

[0093]

[0094] In the formula, Em E represents the intensity coefficient of flash flood disaster. d α is the intensity coefficient of debris flow disaster; c α is the channel density coefficient; k K represents the congestion coefficient of the ditch. d This represents the soil stability coefficient.

[0095] When a power distribution network experiences extreme ice storms, ice and wind loads are the main causes of component failures. The impact of the disaster over a time scale can be characterized using freezing rain rainfall and wind speed. The degree of disaster impact varies at different locations within the disaster area depending on the distance from the freezing rain center. During an ice storm, wind speeds differ at different distances from the meteorological center; the Chaine model is used to reflect the influence of air humidity and temperature on icing. Furthermore, the rainfall rates at different locations within the power distribution network area are obtained:

[0096]

[0097] In the formula, x c (t), y c (t) represents the equivalent horizontal and vertical coordinates of the distribution network area affected by the ice storm, with the meteorological center as the origin; V b (x,y) represents the rainfall rate of the power distribution network at coordinates (x,y); v bmax is the maximum rainfall rate; k2 is the attenuation coefficient.

[0098] The rate of ice accumulation can be obtained from the rainfall rate and wind speed, and then the failure probability model corresponding to the rainfall can be determined.

[0099] As an optional embodiment, based on the fault probability models corresponding to each of the various disaster types, the fault rate models corresponding to each of the multiple components in the distribution network are determined. In the case where the various disaster types include typhoons, the following steps are taken: based on the fault probability model corresponding to the typhoon, the outage probability of each of the multiple components at multiple wind speeds is determined; based on the fault probability model corresponding to the typhoon, the total stress of the overhead components among the multiple components is determined; based on the outage probability of each of the multiple components at multiple wind speeds and the total stress of the overhead components among the multiple components, the fault rate models corresponding to each of the multiple components under the typhoon are determined.

[0100] Optionally, when a mobile disaster occurs, the scope of its impact will change over time. Taking a typhoon as an example, flashover of insulators in power poles can lead to tripping and transformer damage. The outage probability model for power poles during a typhoon is as follows: when the typhoon wind speed v is less than the design wind speed V of the substation, the outage probability is 0; if it is greater than twice the design wind speed, the outage probability is 1; and when the typhoon wind speed is otherwise, the outage probability follows an exponential function.

[0101]

[0102] In the formula, p f This represents the probability of faults in the power distribution network's transmission towers, i.e., the probability of outages.

[0103] For overhead transmission lines, the main components include conductors, insulators, and towers, most of which are exposed to the outside. According to the aforementioned power distribution network design specifications, the vertical distance between 110kV and below overhead lines and the ground should not be less than 7m. When wind loads and self-weight loads on overhead lines exceed the bearing capacity of the conductors and towers, the overhead lines will be damaged. Based on stress analysis, the total stress on the overhead conductors can be determined.

[0104] Among them, the tension is greatest at the highest suspension point of the overhead conductor, making it most prone to failure. Therefore, the conductor stress at this point can be analyzed. According to the structure of the overhead conductor, when its bearing capacity is less than the load it receives, the components have a certain probability of failure. Since the tensile strength of the conductor and the bending strength of the tower both follow a normal distribution, the failure rate of overhead conductor components in the typhoon wind field can be obtained from the wind speed and stress calculated above.

[0105] When distributed disasters occur, the climatic impact varies depending on the location's distance from the meteorological center. Taking ice storms as an example, the amount of ice accumulation on overhead conductors varies at different times and locations at different distances from the disaster center, resulting in different failure rates for distribution network components. Therefore, using the distributed time-varying ice storm model established in the previous section, we analyze the amount of ice accumulation on overhead conductors and towers at different times and spaces, determine the component failure rate using their total load, and determine the overall failure probability of the line by combining the series reliability principle. The ice thickness of the overhead conductors can be obtained from the aforementioned ice accumulation growth rate. Then, based on the overhead line tower parameters and the overhead line ice thickness, the amount of ice accumulation on the towers and conductors is calculated. And from their load, the failure rate of overhead line components under ice storms can be obtained.

[0106] When a widespread disaster occurs, the secondary disasters caused by extreme weather vary, and their impact on the power distribution network lines also differs. Taking rainstorm disasters as an example, rainstorms can lead to different secondary disasters such as mudslides, flash floods, and landslides. The intensity of these secondary disasters varies, and their impact on the probability of tower failure also differs. Therefore, based on the modeling of different secondary disaster intensities in the previous section, we can further obtain the tower failure rate under landslide disasters:

[0107]

[0108] In the formula, p ij,s,tower To determine the failure rate of power line towers under landslide disasters; ω i For disaster membership degree; f l (E s λ τ) represents the failure rate corresponding to different disaster intensities and pole damage rates. Similarly, the pole failure rate under debris flow and flash flood-related disasters can also be obtained: p ij,m,tower p ij,d,tower .

[0109] Under extreme rainstorm disasters, the failure rate of transmission line towers is mainly considered, and the aggregate failure rate of distribution network line ij under rainstorm disasters is finally obtained: p ij,tower,R =1-(1-p) ij,s,tower (1-p) ij,m,tower (1-p) ij,d,tower ).

[0110] As an optional embodiment, risk assessment indicators are set based on the failure rate models corresponding to multiple components, including: constructing a line load rate model in the distribution network; determining a line weighted failure risk rate model in the distribution network based on the failure rate models corresponding to multiple components; determining the power flow transfer impact of lines in the distribution network during a disaster; determining the power flow transfer risk rate based on the power flow transfer impact; and determining risk assessment indicators based on the line load rate model, the line weighted failure risk rate model, and the power flow transfer risk rate.

[0111] Optionally, there are many factors that affect the faults of distribution network lines, and the changes in their critical values ​​are generally not affected by micro factors. Therefore, three macro-characteristic indicators that take into account the interruption of distribution network lines are selected: line load rate, line weighted power flow risk rate, and line power flow transfer risk rate. These are normalized to obtain a new type of disaster line risk assessment index for distribution networks.

[0112] 1) Line load factor. The load factor of a distribution network line is an indicator of the steady-state operation of the line. It is the ratio of the actual transmitted power to the maximum rated power of the line. The calculation formula is as follows:

[0113]

[0114] In the formula, W ij,Load L represents the load factor of line ij. ij,max L represents the maximum rated power of the distribution line ij; g,ij This represents the actual power transmitted by the distribution line ij.

[0115] 2) Line-weighted fault risk rate. The line-weighted fault risk rate characterizes the impact of a line fault on the power distribution system. Higher fault risk and active power load may increase the scope and severity of the line fault's impact. It characterizes the line's impact on the system from a disaster dynamics perspective. Its calculation method is as follows:

[0116] The active power load rate of line ij is:

[0117]

[0118] In the formula, w ij,P,t P represents the active power load factor of line ij. fij,max P represents the rated active power capacity of line ij; fij,t The active power flow of line ij.

[0119] The line fault rate ij under different extreme disaster z conditions is calculated from the line conductor fault rate and tower fault rate in the previous section:

[0120] p ij,z,t =p lij,z,t +p tij,z,t

[0121]

[0122] In the formula, p lij,z,t The failure rate of line ij under extreme weather disaster z during the disaster period t, where T represents typhoon disaster, B represents snow and ice disaster, and R represents rainstorm disaster; p tij,z,t The failure rate of the towers on line ij during the period t when the extreme weather disaster z occurs.

[0123] Therefore, the line weighted power flow risk rate is: H ij,flow,t =w ij,P,t p ij,z,t lnp ij,z,t .

[0124] 3) Line power flow transfer risk rate. When a line in a distribution network system is taken out of service under extreme disaster conditions, the power flow on that line needs to be transferred, thus impacting other normally operating lines. The disaster-induced line power flow transfer risk rate reflects the impact of this power flow shock. First, the disaster-induced power flow transfer impact is calculated:

[0125] ΔE pq,ij,t =|P pq,ij,t -P pq,0 |

[0126] In the formula, ΔE pq,ij,t P represents the power flow transfer caused by changes in line ij during time period t, where pq is the power flow transfer amount caused by these changes. pq,ij,t P represents the power flow pq of the line after the change of line ij due to a disaster fault; pq,0 The pq power flow of the line before the change of the line ij disaster fault.

[0127] Further, the power flow impact rate after the line ij disaster fault was obtained:

[0128]

[0129] In the formula, η ij,tΔE represents the power flow transfer impact rate during fault period t of line ij. ij,t denoted as , where is the change in power flow impact on the system during time period t after a fault in line ij; and is the number of lines affected by the fault in line ij.

[0130] The power flow transfer risk rate of disaster line ij in time period t: H ij,trans,t =w ij,P,t η ij,t lnη ij,t .

[0131] In summary, establishing new disaster-prone line risk assessment indicators for distribution networks can reflect the failure rate of line ij under extreme disasters and its critical impact. All three indicators can reflect the different impacts of line failures on other operational lines in the overall distribution network system.

[0132] As an optional embodiment, setting the objective function and constraints includes: determining the objective function based on risk assessment indicators and the power transmission capacity of lines in the distribution network; and determining the constraints based on the maximum load of lines in the distribution network.

[0133] Optionally, the distribution network fault prevention model sets two disaster fault prevention objectives to form an objective function. In extreme disasters, the state of distribution network lines changes over time, and lines may lose power supply capacity due to faults. It is necessary to reinforce weak line links based on the real-time system status and future predicted status; therefore, it is necessary to minimize the disaster line risk assessment index value. Meanwhile, the loads are supplied by each closed-loop design line. Maximizing the overall system line power transmission capacity will ensure sufficient power supply to the loads. The objective function can be set to reflect both the magnitude of the power supply to the loads and the importance of the corresponding loads, maximizing the value of load power supply.

[0134] Traditional active defense strategies for large power grids mainly consider two approaches: rescheduling of thermal power units and load shedding. However, new fault prevention models for distribution network systems need to consider distributed power dispatch and the charging and discharging methods of energy storage devices, thus resulting in the following constraints.

[0135] 1) Distribution line load constraints. When no fault occurs or when power flow needs to be transferred in advance after a fault occurs, the risk of line overload needs to be considered to achieve uniform power flow and improve the safety margin of the line operation. Since a single distribution line can operate at 1.1 times its maximum power for 15 minutes to 6 hours without thermal stability risks.

[0136] 2) Node power balance constraints. Constraints are applied to the power balance of the same node:

[0137]

[0138] In the formula, P DG,i,tP represents the distributed power supply at node i; S,i,t P represents the power of the energy storage device at node i; PG,i,t Let be the power output of the thermal power plant at node i; Let i be the power output of the thermal power unit at node i. For distributed power generation tripping power; P Load,i,t Let i be the total load power at node i; The load shearing power at node i; Ω V This refers to the set of nodes within the disaster-affected area of ​​the power distribution network.

[0139] 3) Line fault repair constraints. Disconnect high-risk lines in advance and reinforce lines with potential risks:

[0140]

[0141] In the formula, γ represents the switching state of the line starting at node i and ending at node j during time period t. A value of 0 indicates that the faulty line is open, and a value of 1 indicates that the faulty line is connected; M represents the maximum number of faulty lines that can be repaired simultaneously during a time period t; i,j The value ij indicates the importance of the line. The value is 1 for a line that transmits electrical energy to a primary load, 0.5 for a line that transmits electrical energy to a secondary load, and 0.1 for a line that transmits electrical energy to a primary load.

[0142] 4) Distributed power source constraints. Traditional proactive defense strategies involve rescheduling thermal power units, but for new power systems, the rescheduling of distributed power sources needs to be considered.

[0143]

[0144] In the formula, P wind,i,t P pv,i,t These represent the active and reactive power outputs of wind and solar power sources at node i within time period t. These represent the maximum and minimum active power outputs of wind and solar power at node i within time period t; This refers to the power switching capacity of distributed power sources.

[0145] 5) Energy storage system (ESS) constraints: For power dispatching in new power systems, power dispatching of energy storage devices must be considered.

[0146]

[0147] In the formula, μ s,i,t This indicates the charging and discharging status of the energy storage power station at node i in time period t; P represents the maximum active and reactive power of the ESS charging and discharging at node i, respectively; S,i,t Q S,i,t These represent the active and reactive power of the energy storage power station at node i in time period t, respectively. Let A be the operating power loss of the energy storage power station at node i during time period t; S,i E represents the operating coefficient of the energy storage power station at node i. S,i,t Let i be the electrical energy storage capacity of the energy storage station at node i in time period t. and These represent the maximum and minimum energy capacity of the energy storage power station at node i during time period t, respectively.

[0148] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0149] Through the above description of the embodiments, those skilled in the art can clearly understand that the early warning method for power distribution network disaster risks according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0150] According to embodiments of the present invention, an early warning device for power distribution network disaster risk is also provided for implementing the above-described early warning method for power distribution network disaster risk. Figure 3 This is a structural block diagram of an early warning device for power distribution network disaster risks provided according to an embodiment of the present invention, such as... Figure 3 As shown, the early warning device for disaster risk in the power distribution network includes: a construction module 302, a first determination module 304, a first setting module 306, a second setting module 308, and a second determination module 310. The early warning device for disaster risk in the power distribution network will be described below.

[0151] Module 302 is used to construct fault probability models for components in the distribution network under various disaster types.

[0152] The first determining module 304, connected to the construction module 302, is used to determine the fault rate model corresponding to each of the multiple components in the power distribution network based on the fault probability model corresponding to each of the multiple disaster types.

[0153] The first setting module 306, connected to the first determining module 304, is used to set risk assessment indicators based on the failure rate models corresponding to the multiple components, wherein the risk assessment indicators include line load rate, line weighted power flow risk rate and line power flow transfer risk rate.

[0154] The second setting module 308, connected to the first setting module 306, is used to set the objective function and constraints, wherein the objective function aims to minimize the impact of disasters on the components in the distribution network and maximize the overall line power transmission in the distribution network.

[0155] The second determining module 310, connected to the second setting module 308, is used to determine the early warning result based on the risk assessment index, the objective function, and the constraints.

[0156] It should be noted that the aforementioned construction module 302, first determining module 304, first setting module 306, second setting module 308, and second determining module 310 correspond to steps S202 to S210 in the embodiments. Multiple modules implement the same instances and application scenarios as their corresponding steps, but are not limited to the content disclosed in the above embodiments. It should also be noted that the aforementioned modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0157] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0158] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the early warning method and device for power distribution network disaster risks in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned early warning method for power distribution network disaster risks. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0159] The processor can access information and applications stored in memory via a transmission device to perform the following steps: constructing fault probability models for components in the distribution network under various disaster types; determining fault rate models for multiple components in the distribution network based on the fault probability models for each disaster type; setting risk assessment indicators based on the fault rate models for multiple components, including line load rate, line weighted power flow risk rate, and line power flow transfer risk rate; setting objective functions and constraints, where the objective function aims to minimize the impact of disasters on components in the distribution network and maximize the overall line power transmission in the distribution network; and determining early warning results based on the risk assessment indicators, objective functions, and constraints.

[0160] Optionally, the processor may also execute program code for the following steps: constructing fault probability models for components in the power distribution network under various disaster types, including typhoons, including: setting a typhoon wind speed prediction model based on the intensity and duration of historical typhoons; predicting the maximum wind speed radius and typhoon movement speed of the typhoon based on the typhoon wind speed prediction model; predicting the duration of the typhoon based on the typhoon movement speed; and determining the fault probability model corresponding to the typhoon based on the duration and the maximum wind speed radius.

[0161] Optionally, the processor may also execute program code for the following steps: constructing fault probability models for components in the distribution network under various disaster types, including rainstorms, including: acquiring historical rainfall data for the area where the distribution network is located, including historical rainfall amount, historical rainfall duration, and historical wind speed during rainfall; predicting future rainfall data based on historical rainfall data; determining landslide intensity coefficient based on the future rainfall data and the model corresponding to the landslide disaster mechanism; and determining the fault probability model corresponding to the rainfall based on the landslide intensity coefficient and the future rainfall data.

[0162] Optionally, the processor may also execute program code for the following steps: determining the failure rate model for each of multiple components in the power distribution network based on the failure probability model corresponding to each of the multiple disaster types; in the case where typhoons are included among the multiple disaster types, this includes: determining the outage probability of each of the multiple components at multiple wind speeds based on the failure probability model corresponding to the typhoon; determining the total stress on the overhead components among the multiple components based on the failure probability model corresponding to the typhoon; and determining the failure rate model for each of the multiple components under a typhoon based on the outage probability of each of the multiple components at multiple wind speeds and the total stress on the overhead components among the multiple components.

[0163] Optionally, the processor may also execute program code for the following steps: setting risk assessment indicators based on the failure rate models corresponding to multiple components, including: constructing a line load rate model in the distribution network; determining a line weighted failure risk rate model in the distribution network based on the failure rate models corresponding to multiple components; determining the power flow transfer impact amount of the lines in the distribution network during a disaster; determining the power flow transfer risk rate based on the power flow transfer impact amount; and determining risk assessment indicators based on the line load rate model, the line weighted failure risk rate model, and the power flow transfer risk rate.

[0164] Optionally, the processor may also execute program code that performs the following steps: setting objective functions and constraints, including: determining the objective function based on risk assessment indicators and the power transmission capacity of lines in the distribution network; and determining constraints based on the maximum load of lines in the distribution network.

[0165] This invention provides a method for early warning of disaster risks in distribution networks. It involves constructing fault probability models for components in the distribution network under various disaster types; determining fault rate models for multiple components based on these models; setting risk assessment indicators, including line load rate, line weighted power flow risk rate, and line power flow transfer risk rate; setting objective functions and constraints, where the objective function aims to minimize the impact of disasters on components in the distribution network and maximize the overall line power transmission capacity; and determining early warning results based on the risk assessment indicators, objective function, and constraints. This method achieves the goal of providing early warning of the impact of disasters, thereby reducing the probability of component failures in the distribution network. It also addresses the technical problem of complex distribution network structures with numerous components, which are prone to failure under the destructive effects of severe weather, leading to abnormal operation of the distribution network.

[0166] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0167] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the early warning method for power distribution network disaster risks provided in the above embodiments.

[0168] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0169] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: constructing fault probability models for components in the distribution network under various disaster types; determining fault rate models for multiple components in the distribution network based on the fault probability models for each of the various disaster types; setting risk assessment indicators based on the fault rate models for each of the multiple components, wherein the risk assessment indicators include line load rate, line weighted power flow risk rate, and line power flow transfer risk rate; setting objective function and constraints, wherein the objective function aims to minimize the impact of disasters on components in the distribution network and maximize the overall line power transmission in the distribution network; and determining early warning results based on the risk assessment indicators, objective function, and constraints.

[0170] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: constructing fault probability models for components in the power distribution network under various disaster types, wherein the various disaster types include typhoons, including: setting a typhoon wind speed prediction model based on the intensity and duration of historical typhoons; predicting the maximum wind speed radius and typhoon movement speed of the typhoon based on the typhoon wind speed prediction model; predicting the duration of the typhoon based on the typhoon movement speed; and determining the fault probability model corresponding to the typhoon based on the duration and the maximum wind speed radius.

[0171] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: constructing fault probability models for components in the distribution network under various disaster types, wherein the various disaster types include rainstorms, including: acquiring historical rainfall data of the area where the distribution network is located, wherein the historical rainfall data includes historical rainfall amount, historical rainfall duration and historical wind speed during the rainfall; predicting future rainfall data based on the historical rainfall data; determining the landslide intensity coefficient based on the future rainfall data and the model corresponding to the landslide disaster mechanism; and determining the fault probability model corresponding to the rainfall based on the landslide intensity coefficient and the future rainfall data.

[0172] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the failure rate model corresponding to each of multiple components in the distribution network based on the failure probability model corresponding to each of the multiple disaster types; in the case where the multiple disaster types include typhoons, the steps include: determining the outage probability of each of the multiple components at multiple wind speeds based on the failure probability model corresponding to the typhoon; determining the total stress corresponding to the overhead components among the multiple components according to the failure probability model corresponding to the typhoon; and determining the failure rate model corresponding to each of the multiple components under the typhoon based on the outage probability of each of the multiple components at multiple wind speeds and the total stress corresponding to the overhead components among the multiple components.

[0173] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: setting risk assessment indicators based on the failure rate models corresponding to multiple components, including: constructing a line load rate model in the distribution network; determining a line weighted failure risk rate model in the distribution network based on the failure rate models corresponding to multiple components; determining the power flow transfer impact amount of the lines in the distribution network during a disaster; determining the power flow transfer risk rate based on the power flow transfer impact amount; and determining the risk assessment indicators based on the line load rate model, the line weighted failure risk rate model, and the power flow transfer risk rate.

[0174] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: setting objective function and constraints, including: determining the objective function based on risk assessment indicators and the power transmission capacity of lines in the distribution network; and determining constraints based on the maximum load of lines in the distribution network.

[0175] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: construct fault probability models corresponding to components in a distribution network under various disaster types; determine fault rate models corresponding to multiple components in the distribution network based on the fault probability models corresponding to the various disaster types; set risk assessment indicators based on the fault rate models corresponding to the multiple components, wherein the risk assessment indicators include line load rate, line weighted power flow risk rate, and line power flow transfer risk rate; set objective function and constraints, wherein the objective function aims to minimize the impact of disasters on components in the distribution network and maximize the overall line power transmission in the distribution network; and determine early warning results based on the risk assessment indicators, objective function, and constraints.

[0176] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0177] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0178] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0179] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0180] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0181] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0182] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for early warning of disaster risks in a power distribution network, characterized in that, include: Construct fault probability models for components in the power distribution network under various disaster types; Based on the fault probability models corresponding to each of the various disaster types, the fault rate models corresponding to each of the multiple components in the distribution network are determined. Based on the failure rate models corresponding to each of the multiple components, risk assessment indicators are set, including line load rate, line weighted power flow risk rate, and line power flow transfer risk rate. Set an objective function and constraints, wherein the objective function aims to minimize the impact of disasters on components in the distribution network and maximize the overall line power transmission in the distribution network; Based on the risk assessment indicators, the objective function, and the constraints, the early warning result is determined.

2. The method according to claim 1, characterized in that, The method involves constructing fault probability models for components in the power distribution network under various disaster types, including typhoons. A typhoon wind speed prediction model was set up based on the intensity and duration of historical typhoons. Based on the typhoon wind speed prediction model, the maximum wind speed radius and typhoon movement speed of the typhoon are predicted. Based on the typhoon's movement speed, the duration of the typhoon is predicted; Based on the duration and the maximum wind speed radius, a failure probability model corresponding to the typhoon is determined.

3. The method according to claim 1, characterized in that, The method involves constructing fault probability models for components in the power distribution network under various disaster types, including rainstorms. Obtain historical rainfall data for the area where the power distribution network is located, wherein the historical rainfall data includes historical rainfall amount, historical rainfall duration, and wind speed during historical rainfall; Based on the historical rainfall data, predict future rainfall data; Based on the aforementioned future rainfall data, the landslide intensity coefficient is determined according to the model corresponding to the landslide disaster mechanism. Based on the landslide intensity coefficient and future rainfall data, a failure probability model corresponding to the rainfall is determined.

4. The method according to claim 1, characterized in that, The method of determining the failure rate model for each of the multiple disaster types based on their respective failure probability models, including the case where typhoons are among the multiple disaster types, includes: Based on the failure probability model corresponding to the typhoon, the outage probability of each of the multiple components under multiple wind speeds is determined. Based on the failure probability model corresponding to the typhoon, determine the total force on the overhead components among the multiple components; Based on the outage probability of each of the multiple components under multiple wind speeds and the total force of the overhead components among the multiple components, the failure rate model of each of the multiple components under typhoons is determined.

5. The method according to claim 1, characterized in that, The risk assessment indicators are set based on the failure rate models corresponding to each of the multiple components, including: Construct a line load rate model for the aforementioned distribution network; Based on the failure rate models corresponding to each of the multiple components, determine the line weighted failure risk rate model in the distribution network; Determine the power flow transfer impact of the lines in the power distribution network during the disaster; The current transfer risk rate is determined based on the current transfer impact amount. The risk assessment index is determined based on the line load rate model, the line weighted fault risk rate model, and the power flow transfer risk rate.

6. The method according to claim 1, characterized in that, The setting of the objective function and constraints includes: The objective function is determined based on the risk assessment indicators and the power transmission capacity of the lines in the distribution network. The constraint conditions are determined based on the maximum load of the lines in the distribution network.

7. An early warning device for disaster risks in a power distribution network, characterized in that, include: The module is used to build fault probability models for components in the power distribution network under various disaster types. The first determining module is used to determine the failure rate model corresponding to each of the multiple components in the distribution network based on the failure probability model corresponding to each of the multiple disaster types. The first setting module is used to set risk assessment indicators based on the failure rate models corresponding to the multiple components, wherein the risk assessment indicators include line load rate, line weighted power flow risk rate and line power flow transfer risk rate. The second setting module is used to set the objective function and constraints, wherein the objective function aims to minimize the impact of disasters on the components in the distribution network and maximize the overall line power transmission in the distribution network; The second determining module is used to determine the early warning result based on the risk assessment index, the objective function, and the constraints.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the early warning method for power distribution network disaster risk as described in any one of claims 1 to 6.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the early warning method for power distribution network disaster risk according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the early warning method for power distribution network disaster risk as described in any one of claims 1 to 6.

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