Typhoon disaster risk assessment method and device for power distribution system and electronic equipment

Abstract

The application relates to the technical field of power systems, and provides a power distribution system disaster risk assessment method and device under typhoon disasters and electronic equipment. The method comprises the following steps: inputting geographic information data and typhoon trajectory parameters of a power distribution system; initializing the operation state of each feeder of the power distribution system; calculating the disaster resistance of each feeder; constructing a dynamic typhoon wind field model based on the geographic information data and the typhoon trajectory parameters, and solving the surface wind speed at the position of the feeder; comparing the surface wind speed with the disaster resistance, updating the feeder fault state based on the comparison result; calculating the power outage evaluation index of the power distribution system based on the updated feeder fault state, and returning to the step of initializing the operation state of each feeder of the power distribution system until a preset simulation end condition is reached, and outputting the power outage evaluation index. Through the iteration mechanism of dynamic wind field modeling and disaster resistance comparison, the disaster simulation resolution is self-adaptive, the accuracy is high, more scientific decision support is provided, and the power distribution system flexibility is enhanced.

Description

Technical Field

[0001] This application relates to the field of power system technology, specifically to a method, device, and electronic equipment for assessing the disaster risk of a power distribution system under typhoon disasters. Background Technology

[0002] As a core infrastructure of society, modern power systems must be designed to ensure power supply reliability under both normal and abnormal operating conditions. However, in recent years, large-scale power outages caused by extreme weather events such as typhoons have become frequent. Statistics show that extreme weather has become one of the main causes of major power outages, and the frequency of catastrophic power outages is showing a significant upward trend. Due to the relatively weak disaster resistance of distribution system infrastructure, its risk of damage under extreme weather conditions is particularly prominent, necessitating precise analysis of spatiotemporal outage patterns to support fault location and resilient network design.

[0003] Existing research largely employs sequential Monte Carlo simulations to quantify disaster impacts. These methods calculate time-varying failure probabilities of components using vulnerability functions and randomly sample operational states. However, they suffer from the "curse of probability": high-frequency sampling leads to a cumulative overestimation of failure probabilities, and component resilience is mistakenly treated as a time-varying variable, ignoring its inherent properties. While scaling failure probabilities over time intervals can mitigate some bias, the sampling frequency is limited by the manually set simulation duration, leaving the problem unresolved. Furthermore, traditional methods lack sufficient spatiotemporal simulation accuracy, and due to a lack of verification through real-world power outages, these shortcomings have long remained largely unresolved.

[0004] Therefore, a more accurate method is needed to assess the disaster risk of power distribution systems. Summary of the Invention

[0005] In view of this, the embodiments of this application provide a method, device and electronic equipment for assessing the disaster risk of a power distribution system under typhoon disasters, which can accurately assess the disaster risk of a power distribution system under the influence of typhoon disasters, provide more scientific decision support, and thus enhance the resilience of the power distribution system.

[0006] The first aspect of this application provides a method for assessing the disaster risk of a power distribution system under typhoon disasters, including:

[0007] Input the geographic information data of the power distribution system and the typhoon trajectory parameters;

[0008] Initialize the operating status of each feeder in the power distribution system;

[0009] Calculate the disaster resistance capability of each feeder;

[0010] Based on the geographic information data and the typhoon trajectory parameters, a dynamic typhoon wind field model is constructed to solve for the surface wind speed at the location of the feeder.

[0011] The surface wind speed is compared with the disaster resistance capability, and the feeder fault status is updated based on the comparison result;

[0012] The power outage evaluation index of the power distribution system is calculated based on the updated feeder fault status, and the process of initializing the operating status of each feeder of the power distribution system is returned until the preset simulation end condition is met, and the power outage evaluation index is output.

[0013] A second aspect of this application provides a disaster risk assessment device for a power distribution system under typhoon disasters, comprising:

[0014] The initialization module is used to input geographic information data of the power distribution system and typhoon trajectory parameters;

[0015] Initialize the operating status of each feeder in the power distribution system;

[0016] The disaster resilience calculation module is used to calculate the disaster resilience of each feeder.

[0017] The wind field module is used to construct a dynamic typhoon wind field model based on the geographic information data and the typhoon trajectory parameters, and to solve for the surface wind speed at the location of the feeder.

[0018] The feeder fault status update module is used to compare the surface wind speed with the disaster resistance capability and update the feeder fault status based on the comparison result.

[0019] The power outage evaluation index calculation module is used to calculate the power outage evaluation index of the power distribution system based on the updated feeder fault status, and return to the steps of initializing the operating status of each feeder of the power distribution system until the preset simulation end condition is reached, and output the power outage evaluation index.

[0020] A third aspect of this application provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device enables the electronic device to implement the typhoon disaster risk assessment method for power distribution systems provided in the first aspect of this application.

[0021] A fourth aspect of this application provides a computer program product including a computer program that, when run, causes the method described in the first aspect of this application to be performed.

[0022] The first aspect of this application provides a method for assessing the disaster risk of a power distribution system under typhoon disasters, including: inputting geographic information data of the power distribution system and typhoon trajectory parameters; initializing the operating status of each feeder of the power distribution system; calculating the disaster resistance capability corresponding to each feeder; constructing a dynamic typhoon wind field model based on the geographic information data and the typhoon trajectory parameters, and solving for the surface wind speed at the feeder location; comparing the surface wind speed with the disaster resistance capability, and updating the feeder fault status based on the comparison result; calculating the power outage evaluation index of the power distribution system based on the updated feeder fault status, and returning to execute the step of initializing the operating status of each feeder of the power distribution system until a preset simulation termination condition is reached, and outputting the power outage evaluation index. By establishing a dynamic typhoon wind field model, the inherent disaster resistance threshold of power distribution feeders under extreme weather conditions can be accurately quantified, significantly improving the spatiotemporal resolution of power outage simulation. Compared to the traditional sequential Monte Carlo method, which treats disaster resilience as a time variable, leading to the "curse of failure probability," overestimating the risk of failure and being limited by the artificially set sampling frequency, this invention breaks through the limitations of traditional static assessment methods through an iterative mechanism of dynamic wind field modeling and disaster resilience comparison. It achieves adaptive resolution in disaster simulation, overcomes the accuracy bottleneck of traditional methods, provides more scientific decision support, and thus enhances the resilience of the power distribution system.

[0023] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating a method for assessing the disaster risk of a power distribution system under typhoon disasters, provided in one embodiment of this application.

[0026] Figure 2 This is a flowchart illustrating a method for assessing the disaster risk of a power distribution system under typhoon disasters, provided in another embodiment of this application.

[0027] Figure 3 This is a flowchart illustrating a method for assessing the disaster risk of a power distribution system under typhoon disasters, provided in another embodiment of this application.

[0028] Figure 4 This is a schematic diagram of the structure of the power distribution system disaster risk assessment device provided in the embodiments of this application under typhoon disasters;

[0029] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0030] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0031] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0032] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0033] like Figure 1 As shown in the embodiments of this application, the method for assessing the disaster risk of a power distribution system under typhoon disasters includes the following steps S101 to S106:

[0034] Step S101: Input the geographic information data of the power distribution system and the typhoon trajectory parameters;

[0035] In applications, inputting geographic information data of the power distribution system and typhoon trajectory parameters refers to inputting a set of simulation iterations in the MATLAB simulation software. High-resolution geographic information data of power distribution systems and feeder sets Simulation time period And typhoon trajectory data.

[0036] Step S102: Initialize the operating status of each feeder in the power distribution system;

[0037] In application, initializing the operating status of each feeder in the power distribution system means setting the initial state of the feeders to normal, i.e. The initial values ​​for the power outage load loss and power outage evaluation index of the power distribution system are set to 0, i.e. and .

[0038] Step S103: Calculate the disaster resistance capability corresponding to each feeder;

[0039] Step S104: Construct a dynamic typhoon wind field model based on geographic information data and typhoon trajectory parameters, and solve for the surface wind speed at the location of the feeder.

[0040] Step S105: Compare surface wind speed with disaster resistance capability, and update feeder fault status based on comparison results;

[0041] Step S106: Calculate the power outage evaluation index of the power distribution system based on the updated feeder fault status, and return to the step of initializing the operating status of each feeder of the power distribution system until the preset simulation end condition is reached, and output the power outage evaluation index.

[0042] This application's embodiments, by establishing a dynamic typhoon wind field model, can accurately quantify the inherent disaster resistance threshold of distribution feeders under extreme weather conditions, significantly improving the spatiotemporal resolution of power outage simulations. Compared to the traditional sequential Monte Carlo method, which treats disaster resistance capability as a time variable, leading to the "curse of probability" problem, overestimating failure risk, and being limited by artificially set sampling frequencies, this invention overcomes the limitations of traditional static assessment methods through an iterative mechanism of dynamic wind field modeling and disaster resistance capability comparison. It achieves adaptive disaster simulation resolution, breaks through the accuracy bottleneck of traditional methods, provides more scientific decision support, and thus enhances the resilience of the distribution system. By introducing a disaster resistance capability-driven dynamic risk analysis framework, multi-scenario spatiotemporal power outage simulation results can be quickly generated, supporting real-time decision-making during disasters. Compared to traditional static vulnerability models, this invention integrates high-resolution meteorological and physical models with power grid geographic information data, dynamically analyzing the coupling mechanism between typhoon spatiotemporal evolution and equipment failure, providing minute-level prediction capabilities for fault location and emergency dispatch. This invention constructs a cross-domain collaborative assessment system for typhoon disasters, equipment status, and distribution system operation, achieving for the first time a multi-dimensional coupled analysis of meteorological parameters, equipment physical status, and power supply reliability. Traditional methods consider disaster intensity or equipment failure probability in isolation. This invention, through a disaster resilience threshold mapping mechanism, simultaneously assesses the cascading impact of extreme weather on power grid topology, load loss, and system stability, forming a full-chain decision support system from pre-disaster early warning to disaster response to post-disaster recovery.

[0043] In one embodiment, calculating the disaster resilience of each feeder includes:

[0044] Obtain uniformly distributed sampled values ​​for each feeder;

[0045] According to the formula Calculate disaster resilience;

[0046] in, The disaster resistance capability of the i-th feeder; for inverse transform; for The cumulative distribution function; These are uniformly distributed sampled values ​​for the i-th feeder. This is the inverse cumulative distribution function of the standard normal distribution; The logarithm of the scale parameter; This is the set of feeder numbers for the power distribution system.

[0047] In applications, obtaining uniformly distributed sampled values ​​for each feeder includes: for each feeder The generation follows a uniform distribution random numbers .

[0048] In application, according to the formula Calculating disaster resilience includes:

[0049] The vulnerability function of distribution feeders based on the log-normal cumulative distribution function is established according to the following formula:

[0050] ;

[0051] A mathematical model of the time-invariant feeder disaster resistance capability is established based on the following formula, and the solution is obtained. :

[0052] ;

[0053] .

[0054] in, Let be the failure probability of the i-th feeder; This is a Boolean variable representing the fault status of the i-th feeder, with 1 indicating a fault and 0 indicating no fault. Let be the surface wind speed of the typhoon at the location of the i-th feeder; The logarithm of the scale parameter, For the logarithm of the position parameter, The cumulative distribution function represents the standard normal distribution. (Formula) This represents the surface wind speed of the typhoon at the location of the i-th feeder. Under the influence of the typhoon, the cumulative distribution function of the disaster resistance capability of the i-th feeder is equal to the typhoon surface wind speed at the location of the i-th feeder. Failure probability under the influence; The disaster resistance capability of the i-th feeder; for The cumulative distribution function; for inverse transform; These are uniformly distributed sampled values ​​for the i-th feeder. It is the inverse cumulative distribution function of the standard normal distribution.

[0055] In one embodiment, the line power flow relaxation constraint is further established using the following formula:

[0056] ;

[0057] The voltage relationship can be established using the following formula:

[0058] ;

[0059] Power flow constraints are established using the following formula:

[0060] ;

[0061] ;

[0062] in, and These represent the active and reactive power of branch ij during time period t, respectively. and Let be the current in branch ij during time period t and the voltage in node i during time period t, respectively. Let be the resistance of branch ij; The reactance of branch ij; Let be the electrical conductance at node j; Let be the susceptance of node j.

[0063] In one embodiment, step S104 includes steps S401 to S403:

[0064] Step S401: Construct wind field models for the outer and inner regions of the typhoon based on geographic information data and typhoon trajectory parameters;

[0065] Step S402: Fuse the wind field model of the outer region of the typhoon and the wind field model of the inner region of the typhoon to obtain a dynamic typhoon wind field model;

[0066] Step S403: Solve the dynamic typhoon wind field model to obtain the surface wind speed at the location of the feeder.

[0067] In one embodiment, step S401 specifically includes:

[0068] The following formula is used to construct the wind field model outside the typhoon area:

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] in, Let t be the absolute angular momentum of the region outside the typhoon. Let t be the distance from the feeder to the center of the typhoon; These are environmental parameters; Let t be the azimuth wind speed at the feeder. Where is the outer radius of the typhoon; f is the Coriolis parameter; It is the surface resistance coefficient; is the magnitude of the radiative subsidence rate in the free troposphere; u is the radial velocity; h is the boundary layer depth; z is the near-surface height.

[0074] The following formula is used to construct the wind field model of the inner region of the typhoon:

[0075] ;

[0076] ;

[0077] in, Let t be the absolute angular momentum of the typhoon's inner region; The maximum wind radius; The angular momentum at the maximum wind radius; This is the ratio of the heat-momentum exchange coefficient, typically taken as 0.4–1.0.

[0078] In one embodiment, in step S402, the wind field model of the outer region of the typhoon and the wind field model of the inner region of the typhoon are fused to obtain the dynamic typhoon wind field model as follows:

[0079] ;

[0080] ;

[0081] ;

[0082] in, The angular momentum at the maximum wind radius; Let be the absolute angular momentum of the region outside the typhoon at the point of fusion at time t; The distance from the fusion point (i.e., the location of the feeder) to the center of the typhoon at time t; The maximum wind radius; The outer radius of the typhoon; Let t be the azimuth wind speed at the fusion point; For environmental parameters; f is the Coriolis parameter; Let be the surface wind speed of the typhoon at the location of the i-th feeder at time t; This is the boundary layer correction factor; This is the set of feeder numbers for the power distribution system.

[0083] In one embodiment, in step S403, the dynamic typhoon wind field model is solved to obtain the expression for the surface wind speed at the location of the feeder, as follows:

[0084] ;

[0085] in, This refers to the set of feeder numbers in the power distribution system. This is a set of simulation time period numbers; Let be the surface wind speed of the typhoon at the location of the i-th feeder at time t; This is the boundary layer correction factor, typically taken as 0.85–0.9.

[0086] In one embodiment, updating the feeder fault state based on the comparison result includes:

[0087] If the comparison result is or Then update the feeder fault status to ;

[0088] Conversely, update the feeder fault status to... ;

[0089] in, Let be the surface wind speed of the typhoon at the location of the i-th feeder at time t; The disaster resistance capability of the i-th feeder; Let i be a Boolean variable representing the fault state of the i-th feeder at time t. This is a Boolean variable representing the fault state of the i-th feeder at time t-1.

[0090] In application, the feeder fault status is updated as follows: This means updating the feeder fault status to "Fault". This means updating the feeder fault status to "normal operation".

[0091] In one embodiment, calculating the power outage evaluation index of the power distribution system based on the updated feeder fault status includes:

[0092] According to the formula Calculate the power outage load loss of the power distribution system at time t. ;

[0093] According to the formula Calculate the power outage evaluation index of the power distribution system at time t in the j-th simulation. ;

[0094] in, Let i be a Boolean variable representing the fault state of the i-th feeder at time t. Let be the load power of the i-th feeder at time t; This is a set of simulation time period numbers; This is a set of simulation number indices; This is the set of feeder numbers for the power distribution system.

[0095] In one embodiment, the method further includes calculating the branch power flow over-limit risk index and returning to the step of performing the initialization of the operating status of each feeder of the power distribution system until the preset simulation end condition is met, and outputting the branch power flow over-limit risk index.

[0096] The risk index for branch power flow exceeding the limit is calculated using the following formula:

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] in, For the risk indicator of power flow exceeding the limit in branch lines; This represents the actual transmission power of the branch. The probability of branch power flow exceeding the limit; The time when the branch line experiences a risk of tidal current exceeding the limit; To observe the duration of normal operation of the branch; For branch line power flow exceeding the limit; The severity of the tidal current exceeding the limit on the branch line; This represents the upper limit of the transmission power of the branch.

[0102] In one embodiment, the method further includes calculating the bus voltage over-limit risk index and returning to the step of performing the initialization of the operating status of each feeder of the power distribution system until the preset simulation end condition is met, and outputting the bus voltage over-limit risk index.

[0103] The bus voltage over-limit risk index is calculated using the following formula:

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] in, This is an indicator of the risk of bus voltage exceeding limits. This serves as a comprehensive voltage over-limit risk indicator for the power system. This represents the probability of bus voltage exceeding the limit. The duration of the risk of voltage exceeding the limit in a branch; To observe the duration of normal operation of the branch; The severity of the bus voltage exceeding the limit; This represents the total number of buses in the system. This is the per-unit value of the bus voltage; This refers to the bus voltage exceeding the limit.

[0110] In one embodiment, the preset simulation termination condition can be a preset number of simulations.

[0111] In the application, if the preset number of simulations is 10, then when the number of simulations is less than 10, the process returns to step S102 to continuously update the calculated corresponding indicators (which can be any one or more combinations of power outage evaluation indicators, branch power flow over-limit risk indicators, and bus voltage over-limit risk indicators) until the 10th simulation is reached, at which point the corresponding indicators calculated in the 10th simulation (i.e., the last simulation) are output. The specific indicators to be calculated can be configured according to actual needs, and this application does not impose any restrictions.

[0112] In one embodiment, such as Figure 3 As shown, after each simulation calculates the corresponding index, it first checks whether the simulation time has reached the set time threshold. If not, it returns to step S104, which involves constructing a dynamic typhoon wind field model based on geographic information data and typhoon trajectory parameters to calculate the surface wind speed at the feeder location. If yes, it further checks whether the number of simulations has reached the preset number of simulations. If the preset number of simulations has not been reached (i.e., the current simulation is not the last simulation), it returns to step S102, which initializes the operating status of each feeder in the power distribution system. If the preset number of simulations has been reached (i.e., the current simulation is the last simulation), it outputs the corresponding index calculated in the last simulation. For example, when the index is... When, output In the formula for The matrix form represents a t-dimensional column vector. Other indicators are also output in matrix form.

[0113] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0114] This application also provides a device for assessing the disaster risk of a power distribution system under typhoon disasters, used to perform the steps in the above-described embodiments of the method for assessing the disaster risk of a power distribution system under typhoon disasters. The device for assessing the disaster risk of a power distribution system under typhoon disasters can be a virtual appliance in an electronic device, run by the processor of the electronic device, or it can be the electronic device itself.

[0115] like Figure 4 As shown in the embodiment of this application, the power distribution system disaster risk assessment device 100 under typhoon disasters includes:

[0116] Input module 101 is used to input geographic information data of the power distribution system and typhoon trajectory parameters;

[0117] Initialization module 102 is used to initialize the operating status of each feeder in the power distribution system;

[0118] Disaster resistance calculation module 103 is used to calculate the disaster resistance capacity of each feeder;

[0119] Wind field module 104 is used to build a dynamic typhoon wind field model based on geographic information data and typhoon trajectory parameters, and to solve for the surface wind speed at the location of the feeder.

[0120] The feeder fault status update module 105 is used to compare surface wind speed with disaster resistance capability and update the feeder fault status based on the comparison result.

[0121] The power outage evaluation index calculation module 106 is used to calculate the power outage evaluation index of the power distribution system based on the updated feeder fault status, and return to the steps of executing the initialization of the operating status of each feeder of the power distribution system until the preset simulation end condition is reached, and output the power outage evaluation index.

[0122] In applications, the modules in the power distribution system disaster risk assessment device under typhoon disasters can be software program modules, or they can be implemented by different logic circuits integrated in the processor, or they can be implemented by multiple distributed processors.

[0123] like Figure 5 As shown, this application embodiment also provides an electronic device 200, including: at least one processor 201 ( Figure 5The diagram shows only one processor, memory 202, and computer program 203 stored in memory 202 and executable on at least one processor 201. When processor 201 executes computer program 203, it implements the steps in the various method embodiments described above.

[0124] In applications, electronic devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that... Figure 5 This is merely an example of an electronic device and does not constitute a limitation on the electronic device. It may include more or fewer components than shown, or a combination of certain components, or different components.

[0125] In applications, the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0126] In applications, memory can be an internal storage unit of an electronic device in some embodiments, such as a hard drive or RAM. In other embodiments, memory can be an external storage device of the electronic device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, memory can include both internal and external storage units of the electronic device. Memory is used to store operating systems, applications, bootloaders, data, and other programs, such as program code for computer programs. Memory can also be used to temporarily store data that has been output or will be output.

[0127] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0128] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0129] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the various method embodiments above.

[0130] This application provides a computer program product, including a computer program, which, when run on an electronic device, enables the electronic device to perform the steps described in the various method embodiments above.

[0131] 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 computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0132] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0133] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0134] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for assessing the disaster risk of a power distribution system under typhoon disasters, characterized in that, include: Input the geographic information data of the power distribution system and the typhoon trajectory parameters; Initialize the operating status of each feeder in the power distribution system; Calculate the disaster resistance capability of each feeder; Based on the geographic information data and the typhoon trajectory parameters, a dynamic typhoon wind field model is constructed to solve for the surface wind speed at the location of the feeder. The surface wind speed is compared with the disaster resistance capability, and the feeder fault status is updated based on the comparison result; The power outage evaluation index of the power distribution system is calculated based on the updated feeder fault status, and the steps of initializing the operating status of each feeder of the power distribution system are returned until the preset simulation end condition is reached, and the power outage evaluation index is output. The calculation of the disaster resistance capability corresponding to each feeder includes: Obtain uniformly distributed sampled values ​​for each feeder; According to the formula Calculate disaster resilience; in, The disaster resistance capability of the i-th feeder; for inverse transform; for The cumulative distribution function; r i Φ represents the uniformly distributed sampled values ​​of the i-th feeder; -1 (r i Ω is the inverse cumulative distribution function of the standard normal distribution; F P is the set of feeder numbers in the power distribution system. i (s i =0|w i ) represents the failure probability of the i-th feeder; s i w is a Boolean variable representing the fault state of the i-th feeder. i Let be the typhoon surface wind speed at the location of the i-th feeder; β is the logarithm of the scale parameter, λ is the logarithm of the location parameter, and Φ represents the cumulative distribution function of the standard normal distribution.

2. The method for assessing the disaster risk of power distribution systems under typhoon disasters as described in claim 1, characterized in that, The step of constructing a dynamic typhoon wind field model based on the geographic information data and the typhoon trajectory parameters, and solving for the surface wind speed at the location of the feeder, includes: Based on the geographic information data and the typhoon trajectory parameters, construct wind field models for the outer and inner regions of the typhoon. By fusing the wind field model of the outer region of the typhoon and the wind field model of the inner region of the typhoon, a dynamic typhoon wind field model is obtained. The surface wind speed at the location of the feeder is obtained by solving the dynamic typhoon wind field model.

3. The method for assessing the disaster risk of power distribution systems under typhoon disasters as described in claim 1, characterized in that, The dynamic typhoon wind field model is as follows: The surface wind speed at the location of the feeder is Among them, M m The angular momentum at the maximum wind radius; Let r be the absolute angular momentum of the region outside the typhoon at the point of convergence at time t; a,t r is the distance from the fusion point to the typhoon center at time t; m r0 is the maximum wind radius; r0 is the outer radius of the typhoon; V a,t denoted as azimuth wind speed at the fusion point at time t; χ represents environmental parameters; f represents Coriolis parameters; w represents... i (t) represents the typhoon surface wind speed at the location of the i-th feeder at time t; η is the boundary layer correction factor; Ω F This is the set of feeder numbers for the power distribution system.

4. The method for assessing the disaster risk of power distribution systems under typhoon disasters as described in claim 1, characterized in that, The process of updating the feeder fault status based on the comparison results includes: If the comparison result is or s i If (t-1) = 0, then the feeder fault state is updated to s. i (t) = 0; Conversely, update the feeder fault status to s. i (t) = 1; Among them, w i (t) represents the surface wind speed of the typhoon at the location of the i-th feeder at time t; The disaster resistance capability of the i-th feeder; s i (t) is a Boolean variable representing the fault state of the i-th feeder at time t; s i (t-1) is a Boolean variable representing the fault state of the i-th feeder at time t-1.

5. The method for assessing the disaster risk of power distribution systems under typhoon disasters as described in claim 1, characterized in that, The power outage evaluation index for the power distribution system calculated based on the updated feeder fault status includes: According to the formula Calculate the power outage load loss L of the power distribution system at time t. sys (t); According to the formula Calculate the power outage evaluation index of the power distribution system at time t in the j-th simulation. Among them, s i (t) is a Boolean variable representing the fault state of the i-th feeder at time t; L i (t) represents the load power of the i-th feeder at time t; Ω T Ω is the set of simulation time period indices. S Ω is the set of simulation number sequences; F This is the set of feeder numbers for the power distribution system.

6. The method for assessing the disaster risk of power distribution systems under typhoon disasters as described in claim 1, characterized in that, It also includes the steps of calculating the branch power flow over-limit risk index and returning to execute the operation status of each feeder of the initial power distribution system until the preset simulation end condition is reached, and outputting the branch power flow over-limit risk index. The branch power flow over-limit risk index is calculated using the following formula: Among them, R b (s) is the risk indicator for branch power flow exceeding the limit; s b P represents the actual transmission power of the branch; r (s b ) represents the probability of branch power flow exceeding the limit; τ is the time at which the branch line experiences the risk of tidal current exceeding the limit. b To observe the duration of normal operation of the branch; w sb For branch power flow exceeding the limit; s0 represents the severity of power flow exceeding the limit in the branch; s0 represents the upper limit of the transmission power of the branch.

7. The method for assessing the disaster risk of power distribution systems under typhoon disasters as described in claim 1, characterized in that, It also includes the steps of calculating the bus voltage over-limit risk index and returning to execute the initialization of the operating status of each feeder of the power distribution system until the preset simulation end condition is reached, and outputting the bus voltage over-limit risk index. The bus voltage over-limit risk index is calculated using the following formula: Among them, R d (u) is the bus voltage over-limit risk index; R(u) is the comprehensive voltage over-limit risk index of the power system; P r (u d ) represents the probability of bus voltage exceeding the limit; τ is the duration of the risk of voltage over-limit in a branch. d To observe the duration of normal operation of the branch; The severity of voltage exceedance by the busbar; D is the total number of buses in the system; U d This is the per-unit value of the bus voltage; w d This refers to the bus voltage exceeding the limit.

8. A device for assessing the disaster risk of a power distribution system under typhoon disasters, characterized in that, The apparatus for implementing the method as described in any one of claims 1 to 7, the apparatus comprising: The input module is used to input geographic information data of the power distribution system and typhoon trajectory parameters; The initialization module is used to initialize the operating status of each feeder in the power distribution system. The disaster resilience calculation module is used to calculate the disaster resilience of each feeder. The wind field module is used to construct a dynamic typhoon wind field model based on the geographic information data and the typhoon trajectory parameters, and to solve for the surface wind speed at the location of the feeder. The feeder fault status update module is used to compare the surface wind speed with the disaster resistance capability and update the feeder fault status based on the comparison result. The power outage evaluation index calculation module is used to calculate the power outage evaluation index of the power distribution system based on the updated feeder fault status, and return to the steps of initializing the operating status of each feeder of the power distribution system until the preset simulation end condition is reached, and output the power outage evaluation index.

9. An electronic device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, the electronic device performs the method as described in any one of claims 1-7.

Images