Dynamic analysis method of typhoon disaster chain in distribution network based on dynamic Bayesian network

Abstract

The present invention belongs to the technical field of distribution network fault analysis, and relates to a dynamic analysis method for a typhoon disaster chain of a distribution network based on a dynamic Bayesian network, comprising: sorting out typhoon disaster mechanisms according to historical disaster events to form a typhoon disaster chain; establishing a typhoon probability model; establishing a spatiotemporal distribution model of typhoon wind speed; establishing a spatiotemporal distribution model of typhoon rainfall; establishing a component failure rate model; and analyzing the dynamic evolution characteristics of the typhoon disaster chain using a dynamic Bayesian network; the present invention combines the impact of secondary disasters of heavy rains in the typhoon disaster chain on the failure of distribution network components, and broadens the application scope of the calculation of the failure rate of distribution network components under typhoon disasters in real scenarios; adopts empirical models and fuzzy models to replace mechanical calculations, so as to make calculations more convenient and improve application potential; and adopts a dynamic Bayesian network to replace a static Bayesian network, which is conducive to capturing the key elements of the dynamic evolution of the typhoon disaster chain in time sequence.

Description

Technical Field

[0001] The invention belongs to the technical field of distribution network fault analysis, and relates to a dynamic analysis method of a typhoon disaster chain in a distribution network based on a dynamic Bayesian network. Background Art

[0002] When analyzing distribution network failures caused by typhoon disasters, the existing technology generally determines the faulty line based on component failure information in historical typhoon disasters; or generates typhoon failure scenarios using the Monte Carlo method based on various typhoon wind field models and component vulnerability models; or calculates the line break failure rate by treating the wire as a hinge, obtains the failure probability through mechanical calculations on the wire, and then generates a disaster chain, and relies on the Bayesian network for posterior probability calculation.

[0003] However, most of the existing typhoon disaster analysis methods for distribution networks ignore secondary disasters such as floods and mudslides caused by rainfall in the typhoon disaster chain, resulting in inaccurate component failure rates. Some parameters in the mechanical calculations on the conductors require the conductors to be measured under laboratory conditions, which makes the process relatively cumbersome and not conducive to practical application. Even when using Bayesian networks, it is difficult to fully explain the dynamic evolution characteristics of the disaster chain in time series using only static Bayesian networks.

[0004] Therefore, an analysis method that can consider the impact of secondary disasters of heavy rain in the typhoon disaster chain on the failure of distribution network components and has more convenient calculations is needed to solve the above technical problems. Summary of the invention

[0005] The technical solution adopted by the present invention to solve the technical problem is: a dynamic analysis method of typhoon disaster chain in distribution network based on dynamic Bayesian network, comprising the following steps:

[0006] According to historical disaster events, the typhoon disaster mechanism is sorted out to form a typhoon disaster chain; the typhoon disaster chain is used as the basic structure of the subsequent dynamic Bayesian network, which directly affects the formation of the causal relationship between events in adjacent time slices in the dynamic Bayesian network;

[0007] Establish a typhoon probability model; the typhoon probability model is established based on statistical regression of historical typhoon data;

[0008] Establish a spatiotemporal distribution model of typhoon wind speed; The spatiotemporal distribution model of typhoon wind speed is established based on the Batts wind field model;

[0009] Establish a spatiotemporal distribution model of typhoon rainfall; The spatiotemporal distribution model of typhoon rainfall is established based on the joint distribution of typhoon wind speed and rainfall;

[0010] Establish component failure rate model; component failure rate model is established based on empirical model and fuzzy model;

[0011] The typhoon probability model, the spatiotemporal distribution model of typhoon wind speed, the spatiotemporal distribution model of typhoon rainfall, and the component failure rate model are combined to calculate the dynamic Bayesian network prior probability;

[0012] Based on the typhoon disaster mechanism and typhoon disaster chain, a dynamic Bayesian network of typhoon disaster chain is constructed. After calculating the prior probability according to the typhoon probability model, the spatiotemporal distribution model of typhoon wind speed, the spatiotemporal distribution model of typhoon rainfall, and the component failure rate model, the dynamic Bayesian network is used to calculate the conditional probability and analyze the dynamic evolution characteristics of the typhoon disaster chain.

[0013] Preferably, the typhoon disaster chain includes: typhoon causes strong winds and / or heavy rains, strong winds cause mechanical overloads, heavy rains cause floods and mud-rock flows, mechanical overloads cause line breaks and / or tower collapses, floods and mud-rock flows cause tower collapses, and line breaks or tower collapses cause line shutdowns.

[0014] Preferably, the elements of the typhoon probability model include: typhoon landing location , Typhoon center pressure difference , Typhoon movement direction and movement speed ; The probability distribution of the elements is:

[0015]

[0016]

[0017]

[0018]

[0019] in, Indicates the upper limit of the x-axis coordinate of the typhoon landing area, Indicates the lower limit of the x-axis coordinate of the typhoon landing area, Indicates the upper limit of the y-axis coordinate of the typhoon landing area, Indicates the lower limit of the y-axis coordinate of the typhoon landing area, and represents the standard deviation and mean of the lognormal distribution, and represents the standard deviation of the two normal distributions in a bimodal normal distribution, 1 and 2 represents the mean of two normal distributions, Represents the weights of the first normal distribution.

[0020] Preferably, the spatiotemporal distribution model of typhoon wind speed includes:

[0021]

[0022] in, represents the average wind speed, Indicates the maximum wind speed.

[0023] Preferably, in the spatiotemporal distribution model of typhoon rainfall, a copula function is used to establish a joint probability distribution model of wind speed and rainfall; first, independent probability distribution models of wind speed and rainfall are fitted, and then the Archimedean copula is used to establish a joint probability distribution model of wind speed and rainfall; after fitting, the optimal fitting function is selected according to the error formula;

[0024]

[0025] in, represents the predicted value, represents the actual value, and err represents the error of the fitting function;

[0026] After establishing the joint probability distribution model of wind speed and rainfall, the probability distribution of rainfall in the typhoon wind field is determined according to the spatiotemporal distribution of typhoon wind speed.

[0027] Preferably, in the component failure rate model, an empirical model and a fuzzy model are used to establish the component failure rate model:

[0028] The failure rate empirical model of mechanical overload is as follows:

[0029]

[0030]

[0031]

[0032]

[0033] in, and represents the failure rate of towers and lines, Indicates the typhoon wind speed, and Indicates the design wind speed of the tower and line. and represents the failure probability of towers and lines, Indicates the total number of divided time periods. Indicates the length of each divided time period, , are the parameters obtained after fitting;

[0034] In the failure rate fuzzy model, the landslide strength coefficient and tower vulnerability factor for:

[0035]

[0036]

[0037] The component failure rate in a compound disaster scenario is:

[0038]

[0039] in For natural disasters The component failure rate under represents the terrain slope coefficient, represents the terrain height coefficient, represents the slope shape coefficient, represents the effective rainfall, represents the hydrological condition coefficient, represents the geological coefficient; represents the fatigue coefficient, Represents the position coefficient of the tower relative to the disaster body, It represents the tower bedrock safety factor, Represents the component failure rate under compound disaster scenarios.

[0040] Preferably, the method of calculating conditional probability by using a dynamic Bayesian network and analyzing the dynamic evolution characteristics of a typhoon disaster chain specifically includes:

[0041] Based on the static Bayesian network, each environmental factor at each time point is represented by a corresponding random variable, and the changing environment is modeled in this way; the dynamic Bayesian network abstractly slices the time-continuous process into a series of snapshots, each snapshot is a time slice;

[0042] According to the concept of dynamic Bayesian network and the failure rate model, the dynamic Bayesian network of the typhoon disaster chain is obtained; based on the dynamic Bayesian network of the typhoon disaster chain, the conditional probabilities between disaster events are calculated, and the events that play a key role in the evolution of disasters during the evolution of the disaster chain are analyzed, so as to determine the direction of disaster prevention and mitigation.

[0043] The present invention also discloses a distribution network typhoon disaster chain dynamic analysis device based on a dynamic Bayesian network, the distribution network typhoon disaster chain dynamic analysis device based on a dynamic Bayesian network is used to implement the above-mentioned distribution network typhoon disaster chain dynamic analysis method, the device comprises:

[0044] Typhoon model module: The typhoon model module is used to establish a typhoon probability model, a typhoon wind speed spatiotemporal distribution model, a typhoon rainfall spatiotemporal distribution model, and a component failure rate model;

[0045] The analysis and evolution module is used to calculate the prior probability based on the typhoon probability model, the spatiotemporal distribution model of typhoon wind speed, the spatiotemporal distribution model of typhoon rainfall, and the component failure rate model, and then use the dynamic Bayesian network to calculate the conditional probability to analyze the dynamic evolution characteristics of the typhoon disaster chain.

[0046] The present invention also discloses a computer-readable storage medium, in which at least one instruction is stored. The instruction is loaded and executed by a processor to implement the operations performed by the above-mentioned distribution network typhoon disaster chain dynamic analysis method.

[0047] The beneficial effects of the present invention are:

[0048] The present invention combines the impact of the secondary disaster of heavy rain in the typhoon disaster chain on the failure of distribution network components, broadens the application scope of the failure rate calculation of distribution network components under typhoon disasters in real scenarios; adopts empirical models and fuzzy models to replace mechanical calculations, making calculations more convenient and improving application potential; adopts dynamic Bayesian networks to replace static Bayesian networks, which is conducive to capturing the key elements of the dynamic evolution of the typhoon disaster chain in time series. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a typhoon disaster chain diagram of the typhoon disaster chain dynamic analysis method of the distribution network based on the dynamic Bayesian network of the present invention;

[0050] Figure 2 It is a Batts wind field model diagram of the present invention;

[0051] Figure 3 is a schematic diagram of a dynamic Bayesian network of the present invention;

[0052] Figure 4 It is a schematic diagram of a dynamic Bayesian network of a typhoon disaster chain of the present invention;

[0053] Figure 5 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the relevant technologies in the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] refer to Figure 1-5 As shown, in this implementation, the dynamic analysis method of the typhoon disaster chain of the distribution network based on the dynamic Bayesian network includes the following steps:

[0056] Step 1: Based on historical disaster events, sort out the typhoon disaster mechanism and form a typhoon disaster chain. The impact mechanism of typhoons and their secondary derivative disasters on distribution network equipment is as follows:

[0057] 1) The main damage modes of strong wind are mechanical overload, wind deflection flashover, and foreign objects hanging on the line. The damage results are as follows: 1. The pole tower foundation is pulled off, the pole tower is bent and deformed, and the pole tower falls; 2. The line is broken, flashover and phase short circuit occur; 3. Insulator collision damage and insulator string breakage; 4. Hardware breakage and hardware collision.

[0058] 2) The main damage modes of heavy rain are erosion and corrosion and short circuit due to moisture. The damage results are as follows: 1. Short circuit due to moisture; 2. Heavy rain washes the foundation of the tower, causing the tower to overturn; 3. Insulator flashover.

[0059] 3) The main destructive modes of floods and debris flows are impact and burial. The destructive results are: 1. Pole tower bending and deformation, pole tower structure collapse, and pole tower foundation settlement; 2. Substation collapse, substation flooding, and substation burial.

[0060] The main disaster events that affect the suspension of line operation are selected to form a typhoon disaster chain. Figure 1 shown.

[0061] Step 2: Establish a typhoon probability model. A typhoon can be described by four elements: typhoon landing location , Typhoon center pressure difference , Typhoon movement direction and movement speed The probability distribution of the four factors is established based on historical data as follows:

[0062] (1)

[0063] (2)

[0064] (3)

[0065] (4)

[0066] Step 3: Establish a spatiotemporal distribution model of typhoon wind speed. Batts typhoon model is widely used in typhoon wind field simulation, such as Figure 2 As shown. The wind speed value at the research point is determined by an empirical formula. The Batts typhoon model does not require solving complex differential equations. Its mathematical equation form is simple and the calculation complexity is low, which is conducive to large-scale calculations in engineering.

[0067] Maximize the wind speed gradient After neglecting friction, it can be expressed as:

[0068] (5)

[0069] in, is the Coriolis force coefficient of the Earth's rotation, ; is the empirical coefficient, generally taken as 6.72; is the maximum wind speed radius, in km; Indicates the pressure difference in the center of the typhoon, in hPa.

[0070] Usually the average maximum wind speed of a typhoon generally occurs at the maximum wind speed radius The maximum wind speed radius Pressure difference with typhoon center The statistical fitting relationship between them is:

[0071] (6)

[0072] The average maximum wind speed for 10 minutes at a height of 10m can be expressed as the maximum gradient wind speed and center moving speed The superposition of:

[0073] (7)

[0074] The distribution of the typhoon's horizontal wind field varies in different regions. The distance from the typhoon center to the typhoon wind field is Maximum wind speed It can be expressed as:

[0075] (8)

[0076] in, It is a parameter related to the radial intensity attenuation of a typhoon, and its value ranges from 0.5 to 0.7. Indicates that at a height of 10m, the distance between the straight line OM and the typhoon center O is The 10-minute average wind speed at the time of the typhoon, and the straight line OM represents the clockwise direction of the typhoon's movement. The straight line of the angle (M is an arbitrary point on this straight line, which is only for the convenience of describing the straight line and does not affect the calculation of wind speed).

[0077] The angle between the straight line OM and The distance from the typhoon center is The average wind speed at the research point at a height of 10m for 10 minutes is:

[0078] (9)

[0079] After a typhoon lands on the coast, its central pressure difference will continue to decay, causing the typhoon's intensity to gradually weaken until it disappears. Batts believes that the typhoon's moving direction remains unchanged near the coast and after landing, and the central air pressure remains basically unchanged near the coast before the typhoon lands. The decay of the typhoon's central pressure difference is related to the time the typhoon has traveled after landing and the angle between the typhoon's moving direction and the coastline, that is, the central air pressure difference decays according to the following formula after the typhoon lands:

[0080] (10)

[0081] in, It indicates the travel time of the typhoon after landing, in h; After the typhoon landed The central air pressure difference at that moment; It is the central pressure difference at sea before the typhoon lands; It is the angle between the typhoon's moving direction and the coastline when it lands. .

[0082] Step 4: Establish a spatiotemporal distribution model of typhoon rainfall. Since typhoon rainfall is closely related to typhoon wind speed, the copula function is used to establish a joint probability distribution model of wind speed and rainfall.

[0083] First, the independent probability distribution models of wind speed and rainfall are fitted, and then the joint probability distribution model of wind speed and rainfall is established using Archimedean copula. After fitting, the optimal fitting function is selected according to the error formula.

[0084] (11)

[0085] in, is the predicted value, is the actual value, and err represents the error of the fitting function.

[0086] After establishing the joint probability distribution model of wind speed and rainfall, the probability distribution of rainfall in the typhoon wind field can be determined according to the spatiotemporal distribution of typhoon wind speed.

[0087] Step 5: Establish a component failure rate model. Since the failure rate model based on mechanical calculation is not conducive to practical application, the component failure rate model is established using empirical model and fuzzy model.

[0088] The failure rate empirical model of mechanical overload is as follows:

[0089] (12)

[0090] (13)

[0091] (14)

[0092] (15)

[0093] in, and is the failure rate of towers and lines, is the typhoon wind speed, and is the design wind speed of the tower and line, and are the parameters obtained after fitting, and is the failure probability of tower and line, is the total number of time periods divided, Indicates the length of each divided time period.

[0094] The fuzzy model of the failure rate of floods and debris flows is as follows:

[0095] Select relevant parameters of floods and debris flows: ① Effective rainfall ; ② Terrain slope coefficient , when the slope is between 20° and 40°, the value is 1.0; when the slope is greater than 40°, the value is 1.0 to 1.5; when the slope is less than 20°, the value is 0.5 to 1.0; ③ Terrain height coefficient , when the height is 50-100m, it is 1.0; when it is greater than 100m, it is 1.0-1.3; when it is less than 50m, it is 0.8-1.0; ④ Slope shape coefficient , taking the straight slope as the reference, the value is 1.0~1.5 for convex slope and 0.5~1.0 for concave slope; ⑤ Geological coefficient , based on the rock strata without faults and folds in the area where the tower is located, it is taken as 0.5 to 1.0 according to the deterioration of the fault and rock structure; ⑥ Hydrological condition coefficient , based on the situation that there is basically no surface runoff and the groundwater is buried very deep, it is taken as 1.0 to 2.0 according to the changes in surface runoff and groundwater burial depth conditions; ⑦ Fatigue coefficient ⑧ The position coefficient of the tower relative to the disaster body , outside the disaster impact area as the benchmark, otherwise it is taken as 1.0~2.0; ⑨ Tower bedrock safety factor , based on the bedrock being intact and non-weathered, and in other cases taking the value as 0.5 to 1.0.

[0096] The above factors are combined into two coefficients, namely the landslide intensity coefficient and tower vulnerability factor :

[0097] (16)

[0098] (17)

[0099] The failure rate is obtained by defuzzification using the min-max-centroid method of Mamdani reasoning.

[0100] The component failure rate in a compound disaster scenario is:

[0101] (18)

[0102] In the formula For natural disasters The component failure rate under represents the terrain slope coefficient, represents the terrain height coefficient, represents the slope shape coefficient, represents the effective rainfall, represents the hydrological condition coefficient, represents the geological coefficient; represents the fatigue coefficient, Represents the position coefficient of the tower relative to the disaster body, It represents the tower bedrock safety factor, Represents the component failure rate under compound disaster scenarios.

[0103] Step 6, use the dynamic Bayesian network to analyze the dynamic evolution characteristics of the typhoon disaster chain. The dynamic Bayesian network is developed from the concept of Bayesian network. On the basis of the static Bayesian network, each environmental factor at each time point is represented by a corresponding random variable, and the changing environment is modeled in this way. The dynamic Bayesian network abstractly slices the time continuity process into a series of snapshots, each snapshot is called a time slice. The definition of the dynamic Bayesian network is as follows: A dynamic Bayesian network can be defined as ( , ), is an initial static Bayesian network, For a Bayesian network containing two adjacent time slices, the conditional probability distribution between each variable node between the two time slices is defined, such as Figure 3 shown. In the example, the parent node of a node can exist in the same time slice or in the previous time slice. The conditional probability calculation method between a node and its parent node is the same as that of the static Bayesian network.

[0104] According to the concept of dynamic Bayesian network and the above failure rate model, the dynamic Bayesian network of typhoon disaster chain can be obtained as follows: Figure 4 As shown ( Figure 4 Because the rainfall is calculated based on the wind speed, the parent node of the rainstorm is the strong wind rather than the typhoon). Based on the network, the conditional probability between disaster events can be calculated, and the events that play a key role in the change of disasters during the evolution of the disaster chain can be analyzed, so as to determine the direction of disaster prevention and mitigation.

[0105] In summary, the present invention combines the impact of the secondary disaster of heavy rain in the typhoon disaster chain on the failure of distribution network components, broadens the application scope of the failure rate calculation of distribution network components under typhoon disasters in real scenarios; adopts empirical models and fuzzy models to replace mechanical calculations, making calculations more convenient and improving application potential; adopts dynamic Bayesian networks to replace static Bayesian networks, which is conducive to capturing the key elements of the dynamic evolution of the typhoon disaster chain in time series.

[0106] It should be emphasized that the above are only preferred embodiments of the present invention and do not limit the present invention in any form. Any simple modification made to the above embodiments based on the technical essence of the present invention also falls within the protection scope of the present invention. Other equivalent changes and modifications still fall within the scope of the technical solution of the present invention.

Claims

1. A dynamic analysis method for typhoon disaster chain in distribution network based on dynamic Bayesian network, characterized in that: The following steps are involved: Based on historical disaster events, the typhoon disaster mechanism is sorted out to form a typhoon disaster chain; Establish a probability model for typhoons; the probability model for typhoons is established based on statistical regression of historical typhoon data; establish a spatiotemporal distribution model for typhoon wind speed; the spatiotemporal distribution model for typhoon wind speed is established based on the Batts wind field model; establish a spatiotemporal distribution model for typhoon rainfall; the spatiotemporal distribution model for typhoon rainfall is established based on the joint distribution of typhoon wind speed and rainfall; establish a component failure rate model; the component failure rate model is established based on empirical models and fuzzy models; Based on the typhoon disaster mechanism and typhoon disaster chain, a dynamic Bayesian network of the typhoon disaster chain is constructed. After calculating the prior probability based on the typhoon probability model, the spatiotemporal distribution model of typhoon wind speed, the spatiotemporal distribution model of typhoon rainfall, and the component failure rate model, the dynamic Bayesian network is used to calculate the conditional probability and analyze the dynamic evolution characteristics of the typhoon disaster chain. In the component failure rate model, the empirical model and the fuzzy model are used to establish the component failure rate model: The failure rate empirical model of mechanical overload is as follows: in, and represents the failure rate of towers and lines, Indicates the typhoon wind speed, and Indicates the design wind speed of the tower and line. and represents the failure probability of towers and lines, Indicates the total number of divided time periods. Indicates the length of each divided time period, , are the parameters obtained after fitting; In the failure rate fuzzy model, the landslide strength coefficient and tower vulnerability factor for: The component failure rate under the compound disaster scenario is: in For natural disasters The component failure rate under represents the terrain slope coefficient, represents the terrain height coefficient, represents the slope shape coefficient, represents the effective rainfall, represents the hydrological condition coefficient, represents the geological coefficient; represents the fatigue coefficient, Represents the position coefficient of the tower relative to the disaster body, It represents the tower bedrock safety factor, Represents the component failure rate under compound disaster scenarios.

2. The method for dynamic analysis of typhoon disaster chain in distribution network based on dynamic Bayesian network according to claim 1 is characterized in that: The typhoon disaster chain includes: typhoons cause strong winds and / or heavy rains, strong winds cause mechanical overloads, heavy rains cause floods and mud-rock flows, mechanical overloads cause line breaks and / or tower collapses, floods and mud-rock flows cause tower collapses, and line breaks or tower collapses cause line suspensions.

3. The method for dynamic analysis of typhoon disaster chain in distribution network based on dynamic Bayesian network according to claim 1 is characterized in that: The elements of the typhoon probability model include: typhoon landing location , Typhoon center pressure difference , Typhoon movement direction and movement speed ; The probability distribution of the elements is: in, Indicates the upper limit of the x-axis coordinate of the typhoon landing area, Indicates the lower limit of the x-axis coordinate of the typhoon landing area, Indicates the upper limit of the y-axis coordinate of the typhoon landing area, Indicates the lower limit of the y-axis coordinate of the typhoon landing area, and represents the standard deviation and mean of the lognormal distribution, and represents the standard deviation of the two normal distributions in a bimodal normal distribution, 1 and 2 represents the mean of two normal distributions, Represents the weights of the first normal distribution.

4. The method for dynamic analysis of typhoon disaster chain in distribution network based on dynamic Bayesian network according to claim 1 is characterized in that: The spatiotemporal distribution model of the typhoon wind speed includes: in, represents the average wind speed, Indicates the maximum wind speed.

5. The method for dynamic analysis of typhoon disaster chain in distribution network based on dynamic Bayesian network according to claim 1 is characterized in that: In the spatiotemporal distribution model of typhoon rainfall, a joint probability distribution model of wind speed and rainfall is established; first, independent probability distribution models of wind speed and rainfall are fitted, and then a joint probability distribution model of wind speed and rainfall is established; after fitting, the optimal fitting function is selected according to the error formula; in, represents the predicted value, represents the actual value, and err represents the error of the fitting function; After establishing the joint probability distribution model of wind speed and rainfall, the probability distribution of rainfall in the typhoon wind field is determined according to the spatiotemporal distribution of typhoon wind speed.

6. The method for dynamic analysis of typhoon disaster chain in distribution network based on dynamic Bayesian network according to claim 1, characterized in that: The method of calculating conditional probability by using a dynamic Bayesian network and analyzing the dynamic evolution characteristics of the typhoon disaster chain specifically includes: Based on the static Bayesian network, each environmental factor at each time point is represented by a corresponding random variable, and the changing environment is modeled in this way; the dynamic Bayesian network abstractly slices the time-continuous process into a series of snapshots, each snapshot is a time slice; According to the concept of dynamic Bayesian network and the failure rate model, the dynamic Bayesian network of the typhoon disaster chain is obtained; based on the dynamic Bayesian network of the typhoon disaster chain, the conditional probabilities between disaster events are calculated, and the events that play a key role in the evolution of disasters during the evolution of the disaster chain are analyzed, so as to determine the direction of disaster prevention and mitigation.

7. A dynamic analysis device for a typhoon disaster chain in a distribution network based on a dynamic Bayesian network, wherein the dynamic analysis device for a typhoon disaster chain in a distribution network based on a dynamic Bayesian network is used to implement the dynamic analysis method for a typhoon disaster chain in a distribution network according to any one of claims 1 to 6, characterized in that: The device comprises: A typhoon model module, which is used to establish a typhoon probability model, a typhoon wind speed spatiotemporal distribution model, a typhoon rainfall spatiotemporal distribution model, and a component failure rate model; The analysis and evolution module is used to calculate the prior probability based on the typhoon probability model, the spatiotemporal distribution model of typhoon wind speed, the spatiotemporal distribution model of typhoon rainfall, and the component failure rate model, and then use the dynamic Bayesian network to calculate the conditional probability to analyze the dynamic evolution characteristics of the typhoon disaster chain.

8. A computer-readable storage medium, characterized in that: At least one instruction is stored in the storage medium, and the instruction is loaded and executed by the processor to implement the operation performed by the method for dynamic analysis of typhoon disaster chain in distribution network according to any one of claims 1 to 6.

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