A power distribution network resilience evaluation method considering typhoon disaster and secondary faults thereof

Abstract

The application provides a power distribution network resilience evaluation method considering typhoon disasters and secondary faults, which comprises the following steps: using an extreme weather model to perform load calculation on a to-be-processed line to obtain a line load of the to-be-processed line; analyzing the line load and a preset maximum load capacity value of the to-be-processed line to obtain a line fault rate of the to-be-processed line; using a fault probability resilience index algorithm to obtain a line fault resilience index; using a preset PageRank algorithm to update preset importance values of each power supply node corresponding to the to-be-processed line to obtain current importance values of the power supply nodes and determine load resilience indexes of the corresponding power supply nodes; and determining a comprehensive resilience index of the to-be-processed line according to the line fault resilience index and the corresponding load resilience indexes. The method can adapt to the demand of extreme coupling natural disasters, so as to more accurately evaluate and improve the resistance and recovery level of the power distribution network to extreme disasters.

Description

Technical Field

[0001] This application relates to the field of power distribution network technology, and more specifically, to a method for assessing the resilience of power distribution networks that takes into account typhoon disasters and their secondary faults. Background Technology

[0002] As one of the most important infrastructures in modern society, the power system supports commerce, economy, social development, and people's basic lives, serving as a crucial link in the lifeline of cities. Therefore, a large-scale power system failure will cause power outages with significant impacts on infrastructure such as healthcare, industry, communications, transportation, and production. In recent years, climate change has led to an increase in the frequency and intensity of extreme natural disasters, resulting in a rise in large-scale power outages. Therefore, research on post-disaster recovery of distribution networks is of great significance for enhancing their disaster resilience and reducing economic losses.

[0003] Many extreme weather events do not occur in isolation, but are accompanied by causal relationships and interdependent characteristics. In existing resilience assessment systems, fault analysis methods and fault scenario generation often only consider single natural disasters, which cannot fully reflect the impact of multiple natural disasters on distribution network facilities, thus failing to consider the problem of increased failure rates caused by cascading disasters on the distribution network. Summary of the Invention

[0004] The purpose of this application is to provide a method for assessing the resilience of a power distribution network that takes into account typhoon disasters and their secondary faults, thereby solving the aforementioned problems in the prior art and comprehensively reflecting the impact of multiple natural disasters on power distribution network facilities.

[0005] Firstly, a method for assessing the resilience of distribution networks considering typhoon disasters and their secondary faults is provided. This method may include:

[0006] An extreme weather model is used to calculate the load on the line to be treated, thereby obtaining the line load of the line to be treated; the line load represents the load capacity of the line to be treated to extreme weather; the extreme weather model includes a typhoon disaster model and a rainstorm disaster model, the typhoon disaster model is used to calculate the load borne by the line to be treated under typhoon disaster, and the rainstorm disaster model is used to calculate the load borne by the line to be treated under rainstorm disaster;

[0007] The line load and the preset maximum load capacity value of the line to be processed are analyzed to obtain the line failure rate of the line to be processed.

[0008] The fault probability resilience index algorithm is used to process the characteristic fault scenarios obtained through the line fault rate to obtain the line fault resilience index.

[0009] The preset importance value of each power supply node corresponding to the line to be processed is updated using the preset PageRank algorithm to obtain the current importance value of the power supply node.

[0010] Based on the current importance value, determine the load resilience index of the corresponding power supply node;

[0011] Based on the line fault resilience index and the corresponding load resilience index, the comprehensive resilience index of the line to be treated is determined.

[0012] In one possible implementation, a preset PageRank algorithm is used to update the preset importance values ​​of each power supply node corresponding to the line to be processed, thereby obtaining the current importance value of the power supply node, including:

[0013] Obtain the preset importance coefficient of the power supply node;

[0014] The PageRank algorithm is used to process the preset importance coefficient to obtain the initial importance value of the node;

[0015] The initial importance value is updated using a preset PageRank algorithm to obtain the current importance value of the power supply node.

[0016] In one possible implementation, the preset PageRank algorithm is:

[0017]

[0018] PR i 0 Let be the initial importance value of power supply node i, α be the probability of jumping from one power supply node to the next, and M be the initial importance value of power supply node i. i H is the set of all power supply nodes connected to power supply node i; j It is the set of all power supply nodes connected to power supply node j, PR j 0 The initial importance value of power supply node j, PR i Let N be the current importance value of power supply node i, and N be the total number of power supply nodes.

[0019] In one possible implementation, based on the current importance value, the load resilience index of the corresponding power supply node is determined, including:

[0020] A preset importance index algorithm is used to process the current importance value to determine the importance index of the power supply node;

[0021] The importance index is processed using a preset node load resilience index algorithm to obtain the load resilience index of the power supply node.

[0022] In one possible implementation, the algorithm for the preset importance index is as follows:

[0023]

[0024] in, For the importance index of power supply node i, PR i The current importance value of power supply node i, ω i is the importance coefficient of power supply node i.

[0025] In one possible implementation, the algorithm for the preset node load resilience index is as follows:

[0026]

[0027] Where R1 is the load resilience index of power supply node i, Φ is the set of all power supply nodes in the distribution network, and Φ L For the set of faulty nodes, For the importance index of power supply node i, For the importance index of power supply node j, P i P j , respectively, represent the active power of the loads carried by nodes i and j.

[0028] In one possible implementation, a fault probability resilience index algorithm is used to process the occurrence probability of characteristic fault scenarios obtained through the line fault rate to obtain a line fault resilience index, including:

[0029] Monte Carlo simulation technology is used to process the line failure rate to obtain characteristic fault scenarios; the fault scenarios are various types of line faults.

[0030] The probability of occurrence of the characteristic fault scenarios is processed using a fault probability resilience index algorithm to obtain the line fault resilience index.

[0031] In one possible implementation, the algorithm for the failure probability resilience index is as follows:

[0032]

[0033] Where, p k Ψ represents the probability of failure scenario k occurring; F For a set of characteristic fault scenarios; Ω k Let Φ be the set of power-out nodes under fault scenario k; Φ be the set of all power supply nodes in the distribution network; and Pi and Pj be the active power of the loads carried by nodes i and j, respectively.

[0034] Secondly, a distribution network resilience assessment device considering typhoon disasters and their secondary faults is provided, the device including:

[0035] The processing unit is used to perform load calculations on the line to be processed using extreme weather models to obtain the line load of the line to be processed; the line load represents the load capacity value of the line to be processed to extreme weather; the extreme weather models include typhoon disaster models and rainstorm disaster models, the typhoon disaster model is used to calculate the load borne by the line to be processed under typhoon disasters; the rainstorm disaster model is used to calculate the load borne by the line to be processed under rainstorm disasters;

[0036] The analysis unit is used to analyze the line load and the preset load capacity value and maximum load capacity value of the line to be processed to obtain the line failure rate of the line to be processed.

[0037] The processing unit is also used to process the characteristic fault scenarios obtained through the line fault rate using a fault probability resilience index algorithm to obtain a line fault resilience index.

[0038] The processing unit is further configured to use a preset PageRank algorithm to update the preset importance value of each power supply node corresponding to the line to be processed, so as to obtain the current importance value of the power supply node.

[0039] The determining unit is used to determine the load resilience index of the corresponding power supply node based on the current importance value;

[0040] The determining unit is further configured to determine the comprehensive resilience index of the line to be processed based on the line fault resilience index and the corresponding load resilience index.

[0041] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0042] Memory, used to store computer programs;

[0043] When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0044] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0045] This application provides a method for assessing the resilience of a distribution network considering typhoon disasters and their secondary faults. The method includes: using an extreme weather model to calculate the load on the line to be treated, obtaining the line load; analyzing the line load and the preset load capacity and maximum load capacity of the line to be treated, obtaining the line fault rate; using a fault probability resilience index algorithm to process the characteristic fault scenarios obtained through the line fault rate, obtaining a line fault resilience index; using a preset PageRank algorithm to update the preset importance values ​​of each power supply node corresponding to the line to be treated, obtaining the current importance value of the power supply node; determining the load resilience index of the corresponding power supply node based on the current importance value; and determining the comprehensive resilience index of the line to be treated based on the line fault resilience index and the corresponding load resilience index. This method can adapt to the needs of extreme coupled natural disasters, thereby more accurately assessing and improving the distribution network's resistance and recovery level to extreme disasters. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A system architecture diagram for a distribution network resilience assessment method considering typhoon disasters and their secondary faults is provided in this application embodiment;

[0048] Figure 2 A flowchart illustrating a distribution network resilience assessment method considering typhoon disasters and their secondary faults, provided for an embodiment of this application;

[0049] Figure 3 A schematic diagram of the catenary and coordinate system provided in the embodiments of this application;

[0050] Figure 4 A schematic diagram of conductor stress analysis provided in an embodiment of this application;

[0051] Figure 5 A schematic diagram illustrating the vertical wind pressure generated by a slanted wind on a conductor according to an embodiment of this application;

[0052] Figure 6 A schematic diagram of the horizontal wind load decomposition of vertical wind pressure provided in the embodiments of this application;

[0053] Figure 7 This is a schematic diagram of the force exerted on the utility pole according to an embodiment of this application;

[0054] Figure 8 A schematic diagram of the conductor load on the pole provided in an embodiment of this application;

[0055] Figure 9 A schematic diagram illustrating the relationship between the strength and load effect of power distribution network facilities provided in the embodiments of this application;

[0056] Figure 10 A schematic diagram of a 62-node distribution network topology in a certain region provided in an embodiment of this application;

[0057] Figure 11 A schematic diagram showing the wind speed changes of power supply node 2 at different times after the typhoon makes landfall, as provided in the embodiments of this application.

[0058] Figure 12 A schematic diagram illustrating the relationship between the failure rate and wind speed of power distribution network facilities provided in this application embodiment;

[0059] Figure 13 A schematic diagram illustrating the relationship between different numbers of utility poles and the power line failure rate at different times after a typhoon makes landfall, provided for embodiments of this application;

[0060] Figure 14 A schematic diagram showing the change in the power line failure rate of line 50 provided in this application embodiment under two scenarios at different times after the typhoon makes landfall.

[0061] Figure 15 A schematic diagram illustrating the initial importance value of a power supply node provided in an embodiment of this application;

[0062] Figure 16 A schematic diagram illustrating the initial importance index of the power supply node provided in the embodiments of this application;

[0063] Figure 17 A schematic diagram of the structure of a power distribution network resilience assessment device considering typhoon disasters and their secondary faults, provided for an embodiment of this application;

[0064] Figure 18 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0065] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0066] For ease of understanding, the terms used in the embodiments of this application are explained below:

[0067] PageRank is a classic network retrieval ranking algorithm, often used to sort the importance of nodes in directed networks. It is based on the assumption of the number of nodes: the more nodes a node is connected to, the more outgoing nodes it has, and the more important the connected nodes are, the more important the node is.

[0068] Distribution network facilities may include poles and conductors.

[0069] The distribution network resilience assessment method considering typhoon disasters and their secondary faults provided in this application embodiment can be applied to... Figure 1 In the system architecture shown, such as Figure 1 As shown, the system may include a server and a terminal. The server can be a physical server, a server cluster consisting of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal may be a user equipment (UE) such as a mobile phone, smartphone, laptop, digital radio receiver, personal digital assistant (PDA), tablet computer (PAD), handheld device, in-vehicle device, wearable device, computing device, or other processing device connected to a wireless modem, mobile station (MS), mobile terminal, etc. The terminal and server can be directly or indirectly connected via wired or wireless communication methods; this application does not limit the connection.

[0070] The terminal is used to receive the parameters required by the user for each model and algorithm, and to send the parameters required by each model and algorithm to the terminal.

[0071] The server is used to receive the parameters required by each model and algorithm, and to execute the power distribution network resilience assessment method considering typhoon disasters and their secondary faults provided in this application.

[0072] Due to the large number of distribution network facilities, and the fact that most of these facilities are relatively fragile, extreme natural disasters can lead to widespread multiple failures. Furthermore, there are causal relationships between natural disasters; a large-scale, long-lasting disaster may trigger a series of secondary disasters at a certain point in its course, forming a time series of natural disasters that can increase the failure rate of distribution network facilities or cause secondary failures.

[0073] In existing resilience assessment systems, fault analysis methods and fault scenario generation often only consider a single natural disaster, which cannot fully reflect the impact of multiple natural disasters on distribution network facilities, thus failing to consider the problem of increased failure rate caused by cascading disasters in the distribution network.

[0074] Meanwhile, most studies have adopted graph theory when formulating resilience indicators, but have not considered the positional relationships of different loads in the network. Furthermore, each study is conducted under a single weather scenario, failing to consider the complexities between extreme weather events, and the selected resilience analysis scenarios are not comprehensive enough. Therefore, this application proposes a distribution network resilience assessment method that considers typhoon disasters and their secondary faults, adapting it to the needs of extreme coupled natural disasters, thereby more accurately assessing and improving the distribution network's resilience and recovery level against extreme disasters.

[0075] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0076] Figure 2 This is a flowchart illustrating a distribution network resilience assessment method considering typhoon disasters and their secondary faults, provided as an embodiment of this application. Figure 2 As shown, the method may include:

[0077] Step S210: Using an extreme weather model, calculate the load on the line to be processed to obtain the line load of the line to be processed.

[0078] Among them, line load represents the load capacity of the line to be treated to extreme weather, and the line to be treated is the overhead line to be treated in actual application.

[0079] Extreme weather models include typhoon disaster models and rainstorm disaster models; the typhoon disaster model is used to calculate the load on the lines to be treated under typhoon disasters; the rainstorm disaster model is used to calculate the load on the lines to be treated under rainstorm disasters.

[0080] Specifically, A) a typhoon disaster model is used to calculate the wind load on the conductors and poles in the line to be treated.

[0081] Typhoon disaster models can generally simulate typhoon wind fields and study the impact range of typhoon disasters and the wind speed and direction at a certain location.

[0082] In some embodiments, the method for calculating wind speed using a typhoon disaster model is as follows:

[0083]

[0084]

[0085]

[0086]

[0087] ΔP(t)=ΔP0-0.675(1+sinξ)t

[0088] Where V is the horizontal wind speed at point r (m / s), and its direction is the counterclockwise tangent direction on the simulated wind circle; r is the distance (km) of the power distribution network line from the typhoon center; R max V is the distance between the typhoon center and the strongest wind belt, i.e., the radius of maximum wind speed (km). Rmax ΔP is the wind speed (m / s) at the location corresponding to the radius of maximum wind speed. ΔP is the pressure difference (hPa) between the outer periphery and center of the typhoon cyclone, which is related to the time of landfall of the typhoon; the outer pressure is taken as 1010 hPa. f is the Coriolis force coefficient of Earth's rotation. θ is an empirical coefficient, taken as 6.52 (m / (s·mm1 / 2)). V gx V is the air velocity (m / s) caused by the pressure gradient force. T Let be the overall speed of the typhoon (m / s). ΔP(t) is the central pressure difference (hPa) at time t after the typhoon makes landfall; ΔP0 is the pressure difference (hPa) between the outer periphery and center of the cyclone at landfall; ξ is the angle between the local horizontal direction and the typhoon's direction of movement at landfall; t is the time of landfall (h); and e is the exponent.

[0089] The line to be processed includes conductors and poles; the specific calculation method for the wind load on the conductors and poles is as follows:

[0090] (1) The calculation process for the wind load borne by the conductor may include:

[0091] Generally, overhead conductors made of steel-cored aluminum stranded wire have a large distance between their suspension points, and the rigidity of the conductor material has a negligible effect on the shape of the conductor suspended in the air. Therefore, the suspended conductor can be assumed to be a flexible catenary in a hinged state. Based on this assumption, it can be concluded that the conductor can only withstand axial tension, and the bending moment at any point is zero. Secondly, it is assumed that the loads acting on the conductor all point in the same direction and are uniformly distributed along the conductor's length.

[0092] Whether the conductor is broken and its axial stress σ l The definition of stress is tension T. l The ratio of the conductor's cross-sectional area S to the conductor's cross-sectional area S:

[0093]

[0094] like Figure 3 As shown, a wire is suspended between points A and B. Then, at each suspension point A and B, there is an axial stress σ. A σ B When there is no wind, according to the force equilibrium condition of the conductor, the horizontal components of the stress at all points on the conductor should be equal, all equal to the horizontal stress σ0 at the lowest point O of the conductor. The vertical gravity load Tg of the conductor should be equal to the stress σ at the suspension point. A and σ B The perpendicular components are equal, such as Figure 4 As shown.

[0095] When the conductor is subjected to crosswinds and heavy rain, the combined load T on the conductor is... l The vector sum of the horizontal and vertical components: the tension T0 at the lowest point of the conductor suspension and the horizontal wind load N1 caused by wind pressure and rain erosion, and the vertical gravity load N2 caused by its own mass and external forces such as icing and rain.

[0096] Horizontal wind load N w Only the horizontal component of the wind pressure perpendicular to the conductor's axial direction needs to be considered; its force analysis is as follows: Figure 5 and Figure 6 As shown.

[0097] The wind pressure load per unit acting on the conductor is called the wind pressure ratio ι, which can be calculated using the following formula:

[0098]

[0099] in, is the wind speed non-uniformity coefficient, which is related to wind speed; C is the wind load shape coefficient; d is the outer diameter of the conductor. When the outer diameter of the conductor d is less than 17mm, C = 1.2; when the outer diameter of the conductor d is greater than 17mm, C = 1.1; and is the horizontal span of the overhead conductor.

[0100] Therefore, the horizontal wind load N on the conductor w for:

[0101]

[0102] (2) Wind load N borne by the pole pw The calculation process may include:

[0103]

[0104] Where β is the wind vibration coefficient; μ z The wind pressure height variation coefficient; μ s denoted as the wind load shape coefficient; A is the projected area of ​​the windward side of the tower structure component; V is the horizontal wind speed.

[0105] B. Using a rainstorm disaster model, calculate the rain load borne by the conductors and poles in the line to be treated.

[0106] Rainstorm disaster models are generally represented by the distribution of raindrop numbers, which is represented by the Marshall-Palmer exponent distribution.

[0107] n rain =n0exp(-Λd rain )

[0108] Λ=1.4I -0.21

[0109] Where: n rain The number of raindrops per unit volume; n0 = 8 × 10 3 (m) 3 ·mm); d rain Λ is the raindrop diameter; I is the slope factor; and I is the rainfall intensity (mm / h).

[0110] In practical applications, the rainfall speed v rain Affected by forces in both horizontal and vertical directions, let's take the vertical direction as the free-falling velocity v of a raindrop in the absence of wind. m Take the average wind speed v in the downwind direction s In the vertical direction, as the falling speed of raindrops increases, the air resistance they experience also increases. When air resistance and gravity reach equilibrium, the raindrops can be considered to fall at a constant speed, and the speed is related to the diameter of the raindrops.

[0111]

[0112]

[0113] (1) The calculation process for the rain load borne by the conductor may include:

[0114] Since the impact of heavy rainfall on power distribution network facilities can be divided into vertical and downwind directions, the magnitude of the force in each direction is related to the rate at which raindrops hit the power distribution network facilities in the corresponding direction.

[0115]

[0116] Among them, F r S represents the force exerted by a raindrop on a unit length of power distribution network infrastructure. rain The rain-facing area of ​​the power distribution network facilities; v rain This represents the final velocity of a raindrop before it collides with the power distribution network.

[0117] The impact of rainwater on power distribution network facilities can be divided into the downwind direction and the vertical direction. Therefore, the horizontal impact force F r1and the vertical impact force F r2 The following formulas can be used to calculate:

[0118]

[0119]

[0120] (2) The calculation process for the rain load borne by the pole may include:

[0121]

[0122] Furthermore, the combined load on the conductor is: Based on the above force analysis, the horizontal rain load N1 and the vertical rain load N2 acting on the conductor can be expressed as:

[0123] N1 = T0 + N w +F r1

[0124] N2=m0gl+F r2

[0125] Where m0 is the weight per unit length of the conductor; g is the conductor's self-weight load.

[0126] The magnitude of the tension on the conductor is related to the conductor's length; generally, the longer the conductor, the greater the tension. Therefore, the point where the conductor is most likely to break is the suspension point, where the axial load is:

[0127] Furthermore, the combined load of the pole is: N p =N pw +N pr

[0128] Furthermore, the line load of the line to be processed is obtained as follows:

[0129] It should be noted that while utility poles are inherently rigid, whether or not they break depends on the magnitude of the bending moment they bear. The largest portion of the bending moment typically occurs at the pole's base, which is the vector sum of two parts: one is the wind and rain load N acting on the pole. p The first bending moment is M1, which is oriented in the same direction as the wind speed. The second bending moment is caused by conductor tension. Since the horizontal tension components on both sides are usually equal, only the bending moment M2 indirectly caused to the pole by the horizontal wind load on the conductor is calculated. This bending moment is perpendicular to the line direction. A schematic diagram of the pole load effect is shown below. Figure 7 and Figure 8 As shown. The calculation methods for these two bending moments are as follows:

[0130] M1 = N p Z

[0131]

[0132] Where Z is the center height of the pole; h k is the vertical height of the k-th conductor; n is the number of conductors suspended on the pole.

[0133] Step S220: Process the preset importance values ​​of the line load and each power supply node to determine the line fault resilience index and the corresponding load resilience index.

[0134] Specifically, A. Processing line loads to determine line fault resilience indicators may include:

[0135] (1) Analyze the line load and the preset maximum load capacity value of the line to be treated to obtain the line failure rate of the line to be treated.

[0136] Specifically, all power distribution network facilities have certain errors during production. Even those with identical parameters and manufacturers will not have completely identical strengths, but rather fall within a certain range. Therefore, the probability of damage to the same type of power distribution network facility under a certain load is not a fixed value, but rather follows a certain probability distribution. The tensile strength of overhead conductors made of steel-cored aluminum stranded wire and the bending strength of annular concrete poles can both be simulated using a normal distribution.

[0137]

[0138]

[0139] Where, μ l δ l These are the mean and standard deviation of the tensile strength of the conductor, respectively; μ p δ p These are the mean and standard values ​​of the bending strength of the pole, respectively, μ. l δ l μ p and δ p All of these can be obtained through practical operational experience.

[0140] When the load on a power distribution network exceeds its structural strength, the network will be damaged. For ease of representation, the function of the power distribution network facility is defined as follows:

[0141] G = RS

[0142] Where R represents the strength of the distribution network facilities, and S represents the load effect corresponding to the distribution network facilities. The state of the distribution network facilities can be determined by the value of the function: G>0 indicates a reliable state, G=0 indicates a limiting state, and G<0 indicates a damaged state.

[0143] Because the strength of power distribution network facilities is not a constant, the reliable operation of these facilities is also an uncertain quantity, satisfying a certain probability. The probability P that the power distribution network facilities can operate reliably without damage is... r Represented as:

[0144] P r =P{G>0}

[0145] When the load on the distribution network facilities is predictable, the load is considered to be a certain value, and its probability density function f R The relationship between (r) and the load effect s is as follows: Figure 9 As shown.

[0146] Therefore, the failure rate P of the conductor fl The failure rate P of the utility pole fp They are respectively:

[0147]

[0148]

[0149] Assuming that the failures of different distribution network facilities along a power distribution line are independent events, and that the normal operation of overhead conductors is conditional upon the absence of faults in both the conductors and poles, the formula for calculating the line failure rate is:

[0150]

[0151] Among them, P f,ij is the failure rate of line ij; m is the number of poles between lines ij; n is the number of overhead conductors between lines ij.

[0152] (2) The failure probability resilience index algorithm is used to process the occurrence probability of characteristic failure scenarios obtained through line failure rate to obtain the line failure resilience index.

[0153] Specifically, Monte Carlo simulation technology is used to process the line failure rate and obtain characteristic failure scenarios. These scenarios represent various fault types on the lines. A failure scenario can be understood as the main impact of extreme weather on the distribution network, causing a significant increase in the failure rate of distribution network facilities, resulting in large-scale multiple faults. Given the large number of distribution network facilities and the exceptionally large number of multiple failure scenarios resulting from combinations of different faulty facilities, a scenario reduction method is needed to select typical characteristic failure scenarios to reduce the number of failure scenarios and decrease the computational load. Specifically, Monte Carlo simulation technology is used, with the failure probability of each line as input, to randomly select line failure scenarios to simulate the failure process of line towers in the distribution network system during a typhoon. s (s = 1, 2, 3…N) represents N generated fault scenarios, each with a probability p.s (p s >0), and ∑p s =1. Let DT = 1. s s' represents the fault scenario ζ s and failure scenarios ζ s’ The distance between them is defined as the norm of the two fault scenario vectors. Set S i This represents the reduced fault scenarios, where i represents the number of iterations required to reach the desired exit point during fault scenario selection. i = 0 represents the fault scenarios before reduction. Initially, S... i Let DS be the set of all scenarios, representing the reduced feature fault scenarios. Initially, DS is an empty set. Scenario reduction methods may include:

[0154] a. Calculate the distance DT between each fault scenario. s,s’ =DT(ζ s ,ζ s’ ), s, s'∈S0.

[0155] b. For each scenario k, find the fault scenario r with the shortest distance to it, i.e., DT. k,i (r)=minDT k,i,s’ k i s i '∈S i k i ≠s i '.

[0156] c. Calculate PD k,i+1 (r)=p k,i *DT k,i (r), k i ∈S i Find the fault scenario d that makes PD d,i =minPD k,i , k∈S.

[0157] d. Let S i+1 =S i -{d},Ds i+1 =DS i +{d},p r,i+1 =p r,i +p d,i .

[0158] e. Repeat steps (2)-(4) until a preset number of fault scenarios are selected.

[0159] The failure probability resilience index algorithm is used to process the occurrence probability of characteristic failure scenarios and obtain the line failure resilience index.

[0160] The algorithm for the failure probability resilience index is as follows:

[0161]

[0162] p k Ψ represents the probability of failure scenario k occurring; F For a set of characteristic fault scenarios; Ω k Let Φ be the set of power-out nodes under fault scenario k; Φ be the set of all power supply nodes in the distribution network; and P be the set of power supply nodes. i ,P j denoted as active power of the loads carried by nodes i and j, respectively.

[0163] R2 is a line fault resilience index, which can be understood as the sum of the products of the probability of occurrence of all fault scenarios and the corresponding load loss in each scenario.

[0164] B. Using the preset PageRank algorithm, update the preset importance value of each power supply node corresponding to the line to be processed to obtain the current importance value of the power supply node.

[0165] Specifically, (1) obtaining the preset importance coefficient of the power supply node may include: specifying the preset importance coefficient ω of each power supply node according to the load classification, with the preset importance coefficients of level I, II and III being 100:10:1.

[0166] (2) The PageRank algorithm is used to process the preset importance coefficient to obtain the initial importance value of the power supply node; specifically, the initial importance value of each power supply node i is calculated using the PageRank algorithm as follows:

[0167] Where max(ω) is the maximum ω value of all power supply nodes; N is the total number of power supply nodes.

[0168] (3) The initial importance value is updated using a preset PageRank algorithm to obtain the current importance value of the power supply node. Specifically, the preset PageRank algorithm can be:

[0169]

[0170] Among them, PR i 0 The initial importance value of power supply node i, PR j 0 Let be the initial importance value of power supply node j, α be the probability of jumping from one power supply node to the next, and M be the initial importance value of power supply node j. i H is the set of all power supply nodes connected to power supply node i; j It is the set of all power supply nodes connected to power supply node j, PR iGiven the current importance value of power supply node i, repeat this step continuously until the initial importance values ​​of all power supply nodes converge, thus obtaining the current importance value of each power supply node.

[0171] (4) Determine the load resilience index of the corresponding power supply node based on the current importance value;

[0172] Specifically, a preset importance index algorithm is used to process the current importance value and determine the importance index of the power supply node. The preset importance index algorithm is as follows:

[0173]

[0174] in, For the importance index of power supply node i, PR i The current importance value of power supply node i, ω i is the importance coefficient of power supply node i.

[0175] A preset node load resilience index algorithm is used to process the importance index, resulting in the load resilience index of the power supply node. The preset node load resilience index algorithm is as follows:

[0176]

[0177] Where R1 is the power supply load resilience index of node i, Φ is the set of all power supply nodes in the distribution network, and Φ L For the set of faulty nodes, For the importance index of power supply node i, For the importance index of power supply node j, P i ,P j denoted as active power of the loads carried by nodes i and j, respectively.

[0178] Step S230: Based on the line fault resilience index and the corresponding load resilience index, an efficient assessment is achieved regarding the ability of the distribution network to withstand all possible faults under extreme weather conditions and the recovery capability of the power grid under a specific fault scenario.

[0179] Compared to the load resilience index, the line fault resilience index takes into account all possible scenarios under a given extreme event, describing the load loss of the distribution network under anticipated extreme events. A line fault resilience index closer to 1 indicates a higher load survivability at each power supply node in the system, and a stronger resilience of the distribution network to disasters under extreme events. Therefore, the process of determining the comprehensive resilience index of the line to be treated by combining the line fault resilience index and the corresponding load resilience index provides a more accurate assessment of the resilience of the distribution network during the recovery phase.

[0180] In some embodiments, a case study of a 62-node distribution network in a certain region is analyzed. This includes three 10kV feeders, 62 nodes, and 65 lines to be processed. The 62 nodes include 59 power supply nodes and 6 tie switches. It is assumed that all nodes are at the same altitude, the average span of the overhead conductors is 50m, the conductor type is JKLYJ-101×50, the calculated cross-sectional area is 49.48mm², the outer diameter is 16.1mm, the calculated breaking force is 7011N, the calculated weight is 616kg / km, and the wind speed non-uniformity coefficient α = 0.85. The rainfall intensity is I = 100mm / h. A coordinate system is established with the location of feeder 1 as the origin. It is assumed that the initial landfall location of the typhoon is (-80km, -80km), and the typhoon center passes through the distribution network at a speed of 20km / h along the trajectory y = x in the coordinate system. The initial central pressure of the typhoon is 925hpa. In this example, 5 primary power supply nodes, 8 secondary power supply nodes, and the remaining power supply nodes are tertiary. The initial importance coefficient ratio of each level of power supply node is 100:10:1. To verify the distribution network resilience assessment method considering typhoon disasters and their secondary faults proposed in this application, a gas turbine was added at power supply node 33, a diesel generator at node 40, energy storage devices at nodes 22, 25, and 53, and wind turbines at nodes 38, 48, and 52. Figure 10 As shown. In addition, all primary power supply nodes are equipped with emergency power supplies with a capacity of 1.1 times and a backup time of 2 hours, and all secondary power supply nodes are equipped with emergency power supplies with a capacity of 1.1 times and a backup time of 1 hour.

[0181] According to typhoon disaster models, the wind speed is highest at the radius of the typhoon's maximum wind circle and lowest at the center. Over time, the radius of the typhoon's maximum wind speed gradually expands, while the maximum wind speed decreases. Based on the given meteorological conditions, the initial maximum wind speed radius at landfall is 31.5 km. In this example, the distribution network area is far smaller than the typhoon's maximum wind field influence range. Therefore, the distances between the various power supply nodes in the distribution network and the typhoon center are not significantly different, and their wind speeds at the same time are also not significantly different.

[0182] Taking power supply node 2 as an example, the wind speed at this power supply node at different times after the typhoon makes landfall is as follows: Figure 11 As shown, because the typhoon's maximum wind circle passes through the power distribution network twice, the wind speed change curve shows a trend of first rising and then falling sharply, then rising sharply again and falling sharply, with two peaks. The maximum wind speed is 42.44 m / s. The wind speed is minimum when the typhoon center is closest to this power supply node.

[0183] Since the speed difference between the two ends of the same line in the power distribution network is not large, the average wind speed at the two ends of the line is taken as the wind speed that the overhead conductors and poles on the line are subjected to. Figure 12This illustrates the relationship between the probability of faults in overhead power lines and poles and wind speed. Figure 12 It is known that typhoons have a minimal impact on the failure rate of overhead power lines. This is because overhead power lines have a high protection level. Even if the wind speed reaches 70 m / s, the wind load borne by the overhead power line is only 1.15 kN, which is far less than the current maximum breaking force of 7.011 kN for overhead power lines. The impact of rainstorms on overhead lines is also far less than that of wind. Therefore, the failure rate of the line only needs to consider the failure rate of the power poles.

[0184] Whether a line between two power supply nodes in a distribution network fails is also related to the number of poles between the two nodes. Assuming that the failure of each pole is an independent event, the more poles on a line, the lower the probability that the line will maintain normal operation. Taking lines 2, 27, and 31 as examples, their corresponding pole numbers are 2, 7, and 17 respectively. The relationship between the line failure rate and time for these three lines is as follows: Figure 13 As shown, the failure rate of lines with a large number of poles is significantly higher than that of lines with a small number of poles.

[0185] Considering whether secondary disasters occur during the passage of a typhoon through the power distribution network, it is necessary to consider the impact of the typhoon on the power distribution network and the effects of the typhoon and heavy rain on the power distribution network. Taking line 50 as an example, its failure rate in the two scenarios is as follows: Figure 14 As shown in the figure, secondary disasters occurring during a typhoon can lead to an increase in line failure rates. The trend of the increased line failure rate is consistent with the trend of the line failure rate considering only the impact of the typhoon, proving that wind force is the decisive factor causing line failures. However, the line failures that may be caused by secondary disasters should not be ignored.

[0186] Based on the preset PageRank algorithm, the current importance value and importance index of each power supply node are calculated. The calculation uses the initial topology of the distribution network, with all tie switches open. After 16 iterations, the initial importance values ​​converge, and the calculation results are as follows: Figure 15 As shown, its importance index is as follows: Figure 16As shown in the table, the importance values ​​of all power supply nodes are less than 1. Compared to power supply nodes with higher importance values, power supply nodes with more connected edges have higher importance values. For example, power supply nodes 5 and 25 were originally only level 3 power supply nodes, but they occupy a more important position in the topology. Therefore, their current importance values ​​are much higher than those of most other power supply nodes. Considering that the current importance values ​​have decreased compared to the previous importance values, the rule of level 1 > level 2 > level 3 has not changed, and the importance of loads at the same level has been differentiated. For example, the importance indices of level 1 power supply nodes 12 and 57 are 15.72 and 34.15, respectively. Power supply node 57 has far more connected power supply nodes and outgoing chains than 12, and the impact of its power outage is also greater than that of 12. Therefore, the importance index of power supply node 57 is higher than that of node 12. Therefore, the optimization objective is to minimize the product of the power supply node load loss and the improved power supply node importance. The system load loss under typical fault scenarios is solved, and the results are shown in Table 1.

[0187] Table 1 shows the results of the recovery strategy using importance indicators as the optimization target.

[0188]

[0189] Calculate the fault scenario set Ψ respectively F Based on the probability of occurrence and load recovery of all fault scenarios, the resilience index R² is 0.4237. This means that under the typhoon disaster scenario set in this example, facing all possible large-scale fault scenarios, the recovery strategy formulated based on the multi-source collaborative approach can ensure that an average of approximately 42.37% of the load continues to supply power during the fault time.

[0190] This application provides a method for assessing the resilience of a distribution network considering typhoon disasters and their secondary faults. The method includes: using an extreme weather model to calculate the load on the line to be treated, obtaining the line load of the line; analyzing the line load and the preset maximum load capacity value of the line to be treated to obtain the line fault rate of the line to be treated; using a fault probability resilience index algorithm to process the characteristic fault scenarios obtained through the line fault rate to obtain a line fault resilience index; using a preset PageRank algorithm to update the preset importance values ​​of each power supply node corresponding to the line to be treated, obtaining the current importance value of the power supply node; determining the load resilience index of the corresponding power supply node based on the current importance value; and determining the comprehensive resilience index of the line to be treated based on the line fault resilience index and the corresponding load resilience index. This method can adapt to the needs of extreme coupled natural disasters, thereby more accurately assessing and improving the distribution network's resistance and recovery level to extreme disasters.

[0191] Corresponding to the above method, this application also provides a power distribution network resilience assessment device that considers typhoon disasters and their secondary faults, such as... Figure 17 As shown, the device includes:

[0192] Processing unit 1710 is used to perform load calculations on the line to be processed using an extreme weather model to obtain the line load of the line to be processed; the line load represents the load capacity value of the line to be processed to extreme weather; the extreme weather model includes a typhoon disaster model and a rainstorm disaster model, the typhoon disaster model is used to calculate the load borne by the line to be processed under a typhoon disaster; the rainstorm disaster model is used to calculate the load borne by the line to be processed under a rainstorm disaster;

[0193] Analysis unit 1720 is used to analyze the line load and the preset maximum load capacity value of the line to be processed to obtain the line failure rate of the line to be processed.

[0194] The processing unit 1710 is also used to process the characteristic fault scenarios obtained through the line fault rate using a fault probability resilience index algorithm to obtain a line fault resilience index.

[0195] The processing unit 1710 is also used to update the preset importance value of each power supply node corresponding to the line to be processed by using a preset PageRank algorithm, so as to obtain the current importance value of the power supply node.

[0196] The determining unit 1730 is used to determine the load resilience index of the corresponding power supply node based on the current importance value;

[0197] The determining unit 1730 is further configured to determine the comprehensive resilience index of the line to be processed based on the line fault resilience index and the corresponding load resilience index.

[0198] The functions of each unit in the distribution network resilience assessment device considering typhoon disasters and their secondary faults provided in the above embodiments of this application can be implemented through the above-described methods and steps. Therefore, the specific working process and beneficial effects of each unit in the distribution network resilience assessment device considering typhoon disasters and their secondary faults provided in the embodiments of this application will not be repeated here.

[0199] This application also provides an electronic device, such as... Figure 18 As shown, it includes a processor 1810, a communication interface 1820, a memory 1830, and a communication bus 1840, wherein the processor 1810, the communication interface 1820, and the memory 1830 communicate with each other through the communication bus 1840.

[0200] Memory 1830 is used to store computer programs;

[0201] When processor 1810 executes a program stored in memory 1830, it performs the following steps:

[0202] An extreme weather model is used to calculate the load on the line to be treated, thereby obtaining the line load of the line to be treated; the line load represents the load capacity of the line to be treated to extreme weather; the extreme weather model includes a typhoon disaster model and a rainstorm disaster model, the typhoon disaster model is used to calculate the load borne by the line to be treated under typhoon disaster, and the rainstorm disaster model is used to calculate the load borne by the line to be treated under rainstorm disaster;

[0203] The line load and the preset maximum load capacity value of the line to be processed are analyzed to obtain the line failure rate of the line to be processed.

[0204] The fault probability resilience index algorithm is used to process the characteristic fault scenarios obtained through the line fault rate to obtain the line fault resilience index.

[0205] The preset importance value of each power supply node corresponding to the line to be processed is updated using the preset PageRank algorithm to obtain the current importance value of the power supply node.

[0206] Based on the current importance value, determine the load resilience index of the corresponding power supply node;

[0207] Based on the line fault resilience index and the corresponding load resilience index, the comprehensive resilience index of the line to be treated is determined.

[0208] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0209] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0210] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0211] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be 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, or discrete hardware components.

[0212] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 2 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0213] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform a distribution network resilience assessment method considering typhoon disasters and their secondary faults as described in any of the above embodiments.

[0214] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute a distribution network resilience assessment method considering typhoon disasters and their secondary faults as described in any of the above embodiments.

[0215] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0216] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0217] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0218] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0219] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0220] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for assessing the resilience of a distribution network considering typhoon disasters and their secondary faults, characterized in that, The method includes: An extreme weather model is used to calculate the load on the line to be treated, thereby obtaining the line load of the line to be treated; the line load represents the load capacity of the line to be treated to extreme weather; the extreme weather model includes a typhoon disaster model and a rainstorm disaster model, the typhoon disaster model is used to calculate the load borne by the line to be treated under typhoon disaster, and the rainstorm disaster model is used to calculate the load borne by the line to be treated under rainstorm disaster; The line load and the preset maximum load capacity value of the line to be processed are analyzed to obtain the line failure rate of the line to be processed. The fault probability resilience index algorithm is used to process the characteristic fault scenarios obtained through the line fault rate to obtain the line fault resilience index. The preset importance value of each power supply node corresponding to the line to be processed is updated using the preset PageRank algorithm to obtain the current importance value of the power supply node. Based on the current importance value, determine the load resilience index of the corresponding power supply node, including: A preset importance index algorithm is used to process the current importance value to determine the importance index of the power supply node; the preset importance index algorithm is as follows: in, For the importance index of power supply node i, PR i The current importance value of power supply node i, The importance coefficient of power supply node i; A preset node load resilience index algorithm is used to process the importance index to obtain the load resilience index of the power supply node; the preset node load resilience index algorithm is as follows: in, For the load resilience index of power supply node i, Φ For the set of all power supply nodes in the distribution network, Φ L For the set of faulty nodes, For the importance index of power supply node i, For the importance index of power supply node j, , respectively, represent the active power of the loads carried by nodes i and j; Based on the line fault resilience index and the corresponding load resilience index, the comprehensive resilience index of the line to be treated is determined.

2. The method as described in claim 1, characterized in that, Using a preset PageRank algorithm, the preset importance values ​​of each power supply node corresponding to the line to be processed are updated to obtain the current importance value of the power supply node, including: Obtain the preset importance coefficient of the power supply node; The PageRank algorithm is used to process the preset importance coefficient to obtain the initial importance value of the node; The initial importance value is updated using a preset PageRank algorithm to obtain the current importance value of the power supply node.

3. The method as described in claim 2, characterized in that, The preset PageRank algorithm is as follows: PR i 0 Let be the initial importance value of power supply node i, α be the probability of jumping from one power supply node to the next, and M be the initial importance value of power supply node i. i The set of all power supply nodes connected to power supply node i; H j It is the set of all power supply nodes connected to power supply node j, PR j 0 The initial importance value of power supply node j, PR i Let N be the current importance value of power supply node i, and N be the total number of power supply nodes.

4. The method as described in claim 1, characterized in that, A fault resilience index algorithm is used to process the occurrence probability of characteristic fault scenarios obtained from the line fault rate, resulting in a line fault resilience index, including: Monte Carlo simulation technology is used to process the line failure rate to obtain characteristic fault scenarios; the fault scenarios are various types of line faults. The probability of occurrence of the characteristic fault scenarios is processed using a fault probability resilience index algorithm to obtain the line fault resilience index.

5. The method as described in claim 4, characterized in that, The algorithm for the failure probability resilience index is as follows: Where, p k Ψ represents the probability of failure scenario k occurring; F For a set of characteristic fault scenarios; Ω k Let k be the set of power-loss nodes under fault scenario k; Φ For the set of all power supply nodes in the distribution network, denoted as active power of the loads carried by nodes i and j, respectively.

6. A power distribution network resilience assessment device considering typhoon disasters and their secondary faults, characterized in that, The device includes: The processing unit is used to perform load calculations on the line to be processed using extreme weather models to obtain the line load of the line to be processed; the line load represents the load capacity value of the line to be processed to extreme weather; the extreme weather models include typhoon disaster models and rainstorm disaster models, the typhoon disaster model is used to calculate the load borne by the line to be processed under typhoon disasters; the rainstorm disaster model is used to calculate the load borne by the line to be processed under rainstorm disasters; The analysis unit is used to analyze the line load and the preset load capacity value and maximum load capacity value of the line to be processed to obtain the line failure rate of the line to be processed. The processing unit is also used to process the characteristic fault scenarios obtained through the line fault rate using a fault probability resilience index algorithm to obtain a line fault resilience index. The processing unit is further configured to use a preset PageRank algorithm to update the preset importance value of each power supply node corresponding to the line to be processed, so as to obtain the current importance value of the power supply node. The determining unit is used to determine the load resilience index of the corresponding power supply node based on the current importance value, including: A preset importance index algorithm is used to process the current importance value to determine the importance index of the power supply node; the preset importance index algorithm is as follows: in, For the importance index of power supply node i, PR i The current importance value of power supply node i, The importance coefficient of power supply node i; A preset node load resilience index algorithm is used to process the importance index to obtain the load resilience index of the power supply node; the preset node load resilience index algorithm is as follows: in, For the load resilience index of power supply node i, Φ For the set of all power supply nodes in the distribution network, Φ L For the set of faulty nodes, For the importance index of power supply node i, For the importance index of power supply node j, , respectively, represent the active power of the loads carried by nodes i and j; The determining unit is further configured to determine the comprehensive resilience index of the line to be processed based on the line fault resilience index and the corresponding load resilience index.

7. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method steps of any one of claims 1-5.

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