Resilience assessment method for transmission system under extreme wildfire disasters

Abstract

The present invention proposes a method for evaluating the resilience of power transmission systems under extreme wildfire disasters, and establishes a method for evaluating the resilience of power transmission networks that considers the entire disaster process. First, the impact of extreme wildfire disasters on the failure rate of transmission lines is quantified, a mathematical relationship between the location of the fire point and the line failure rate is established, and the system information entropy is used to obtain typical failure scenarios to describe the scale of failures that may be caused by wildfire disasters; secondly, combined with factors such as the geographical location of the faulty components, the scheduling of maintenance personnel, and the repair time, a power transmission system recovery model is constructed with the goal of minimizing the load reduction under wildfire disasters, and a method for evaluating the resilience of power transmission systems that considers the entire disaster process is proposed. The present invention can be used to evaluate the impact of various measures on system resilience under wildfire disasters and to formulate optimal resilience improvement measures, and can provide a quantifiable reference basis for the operation and planning of power transmission systems.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission system resilience assessment, and in particular to a method for assessing the resilience of a transmission system under extreme wildfire disasters. Background Art

[0002] Currently, global climate change has led to frequent wildfire disasters, significantly increasing the risk of large-scale power outages in the transmission system. Therefore, in order to establish an accurate resilience assessment method to quantify the ability of the transmission system to cope with wildfire disasters, it is urgent to deepen the research on transmission system resilience assessment. Based on the problem that existing resilience assessment research does not fully consider wildfire disasters and their entire process, considering the characteristics of wildfire disasters and the entire process of disaster response and post-disaster recovery in the transmission system assessment is of great significance for accurately perceiving the ability of the transmission system to cope with wildfire disasters. Existing resilience assessment research has defects and deficiencies in the study of wildfire disaster characteristics and fault recovery processes, and it is necessary to improve the characteristics assessment method of the transmission system under wildfire disasters. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method for evaluating the resilience of the power transmission system under extreme wildfire disasters, to construct a disaster response and post-disaster recovery model for the power transmission system under wildfire disasters, and to use the missing area of the load curve to represent the system resilience, which can simultaneously consider the time required for the system to return to normal and the losses during the disaster.

[0004] The present invention establishes a transmission network resilience assessment method that considers the entire disaster process. First, the impact of extreme wildfire disasters on the failure rate of transmission lines is quantified, a mathematical relationship between the location of the fire point and the line failure rate is established, and the system information entropy is used to obtain typical failure scenarios to describe the scale of failures that may be caused by wildfire disasters; secondly, combined with factors such as the geographical location of the faulty components, maintenance personnel scheduling and repair time, a transmission system recovery model is constructed with the goal of minimizing the load reduction under wildfire disasters, and a transmission system resilience assessment method that considers the entire disaster process is proposed; finally, taking the IEEERTS-79 node transmission system as an example, the effectiveness of the proposed model and resilience assessment method is verified, and the effects of three typical resilience improvement measures are quantitatively analyzed. The present invention can be used to evaluate the impact of various measures on system resilience under wildfire disasters and to formulate optimal resilience improvement measures, and can provide a quantifiable reference basis for the operation and planning of transmission systems.

[0005] The present invention specifically adopts the following technical solutions:

[0006] A method for evaluating the resilience of a power transmission system under extreme wildfire disasters, comprising the following steps:

[0007] Step S1: Establish a transmission line failure rate model through a wildfire disaster spread model;

[0008] Step S2: Select typical fault scenarios based on the transmission line failure rate model and system information entropy to determine the possible fault scenarios and fault scales caused by wildfire disasters;

[0009] Step S3: generating the optimal load reduction of the system based on the transmission system disaster response model;

[0010] Step S4: Generate the optimal fault line repair path and repair time in combination with the transmission system post-disaster recovery model, and evaluate the transmission system resilience index value based on the system performance.

[0011] Furthermore, step S1 specifically includes the following steps:

[0012] Step S11: Use the improved Anderson ellipse model to describe the wildfire spread range:

[0013] In a windless state, the fire is assumed to spread at the same speed in all directions, and the fire spreads outward in a circular shape from the center of the fire point. In a windy state, the fire is assumed to spread outward in an elliptical shape with the wind direction as the major axis and the fire point as the focus. Based on this, the wildfire spread range equation is as follows:

[0014]

[0015] Where v is the wind speed; R0 is the initial speed of wildfire spread, which is related to the initial wind speed; Δt is the diffusion time; is the shape coefficient of the elliptical fire scene; x and y are the horizontal and vertical coordinates of the ellipse centered on the fire point respectively;

[0016] Assume that the angle between the line connecting the alarm tower and the fire point and the direction of maximum spread speed is γ, then the wildfire spread speed in this direction is R γ for:

[0017]

[0018] Step S12: Establish a line failure rate model under extreme wildfire disasters as follows:

[0019] P=D R ·D B ·D F ·P V (3)

[0020] Where: P is the comprehensive probability of line failure under wildfire disaster; D R is the wildfire tripping precipitation factor, which takes the value of 1 only when the statistical precipitation in the past 3 hours is greater than 2 mm and the predicted precipitation in the next hour is greater than 1 mm, otherwise it takes the value of 0; B is the surface vegetation factor, which takes the value of 1 when the area between the fire point and the transmission line is covered with vegetation, otherwise it takes the value of 0; D Fis the wildfire spread factor, which is related to the wildfire spread range; P V is the probability of a flashover failure occurring on the transmission line;

[0021] Step S13: Calculate the wildfire spread factor, which is characterized as follows:

[0022]

[0023] Where: L f is the distance between the fire point and the alarm tower, which is calculated by the latitude and longitude coordinates of the fire point identified by satellite and the coordinates of the alarm tower;

[0024] Step S14: Calculate the transmission line flashover failure probability, the calculation formula is as follows:

[0025]

[0026] Where, U is the phase voltage of the transmission line; U 99 99% withstand breakdown voltage of transmission lines, U 99 =0.756U jc , U jc It is the breakdown voltage of the transmission line to ground under wildfire conditions.

[0027] Furthermore, step S2 specifically includes the following steps:

[0028] Step S21: According to the concept of information entropy in Shannon's information theory, the uncertainty event of whether each transmission line fails in the fault scenario is represented by its information entropy as follows:

[0029]

[0030] Where, represents the set of transmission network lines; p l,t is the failure rate of transmission line l during period t; z l,t Indicates whether line l has a fault during period t. If a fault occurs, the value is 1; otherwise, the value is 0.

[0031] Step S22: Use the total information entropy of the system as the uncertainty budget for generating fault scenarios, and select typical fault scenarios according to the following formula:

[0032]

[0033] Furthermore, step S3 specifically includes the following steps:

[0034] Step S31: Establishing the objective function for optimal load reduction of the transmission system under wildfire disaster:

[0035]

[0036] Where, is the load demand and load reduction of node j in period t;

[0037] Step S32: Establish the following constraints for the power transmission system response under wildfire disasters:

[0038]

[0039]

[0040]

[0041]

[0042] -f l,max ·(1-z l,t )≤F l ≤f l,max ·(1-z l,t ) (13)

[0043] Where: is the active power output of the i-th generator set during period t; and are the load demand and load reduction of node j in period t, respectively; is the upper limit of active output of the i-th generator set; x l 、 and are the reactance of line l and the voltage phase angles at the beginning and end nodes of line l respectively; F l and f l,max are the active power flow and active power flow limit of line l respectively; N G and N D are the number of generators and load nodes, respectively.

[0044] Furthermore, step S4 specifically includes the following steps:

[0045] Step S41: The index of the maintenance team and its set are recorded as c and C respectively, and the number of maintenance teams is n. crew , suppose the maintenance team goes to the fault line along a certain path, extinguishes the fire around the line and repairs the fault in the shortest time, and then returns to the maintenance center; there are F fault lines, and the set of the maintenance center and the fault lines is set to Where 0 represents the maintenance center, 1, 2, ..., F represents the number of the fault line; let α r is the completion time of the faulty component r. The objective of the transmission system restoration model is to minimize the total repair time of all faulty lines. The objective function of the transmission system restoration model is established as follows:

[0046]

[0047] Step S42: Assume that the maintenance personnel scheduling path decision variable is x r,s,c , if the maintenance team c arrives at point s from point r, its value is 1, otherwise it is 0; the maintenance team c must start from the maintenance center and return to the maintenance center after completing all maintenance tasks; after the maintenance team c completes the repair of a line, it will go to the next faulty line. The constraints are expressed as follows:

[0048]

[0049]

[0050] Step S43: Set binary y r,c A binary variable indicating whether the maintenance team c passes through the fault line r. If so, the value is 1, otherwise it is zero. All fault lines only need to be repaired once during the maintenance process, which satisfies:

[0051]

[0052]

[0053] Step S44: Assume that the time t required for the maintenance team to repair the faulty component r r,c ; The time it takes for maintenance team c to travel from point r to point s The time it takes for the maintenance team c to arrive at the fault line r is T r,c , then the time relationships in the maintenance path of the maintenance team satisfy:

[0054]

[0055]

[0056] Step S45: Introduce binary variable h r,t Indicates the time period when the faulty component r is repaired; if the faulty component r is repaired within time period t, then h r,t The value is 1, otherwise it is 0; introduce the binary variable q r,t Indicates the repair status of the faulty component; if the faulty component r has been repaired in time period t, then q r,t The value is 1, otherwise it is 0; the line status constraints are as follows:

[0057]

[0058]

[0059] Step S46: Using the missing area of the system load curve under extreme wildfire disasters to reflect the resilience of the transmission system:

[0060]

[0061] Where: E(·) represents the expected value; P k is the probability of scenario k occurring, K is the total number of scenarios; L(t) is the actual load curve of the system when it is hit by a wildfire disaster; TL(t) is the normal load curve of the system when there is no fault; AR represents the resilience of the transmission system when it is hit by a wildfire disaster. The larger the AR, the stronger the system's ability to cope with wildfire disasters.

[0062] Compared with existing technologies, the present invention and its preferred solution combine the transmission system's response during a disaster and the post-disaster maintenance personnel dispatch process to accurately assess system resilience during wildfires. The proposed resilience assessment method quantitatively analyzes the effectiveness of various resilience-enhancing measures, providing a reference for finding the optimal resilience-enhancing measure. Furthermore, the proposed resilience assessment method establishes a line failure probability model based on the wildfire evolution process, which can be applied to power system early warning systems. This is of great significance in rationally assessing the transmission network's ability to respond to wildfire disasters and reducing failure losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0064] Figure 1 Flowchart of an embodiment of the present invention.

[0065] Figure 2 This is a geographical location diagram of a power transmission system according to an embodiment of the present invention.

[0066] Figure 3 1 is a load curve diagram of the system under various conditions of an embodiment of the present invention.

[0067] Figure 4 This is a schematic diagram of the probability of tripping due to a wildfire fault on each line under a wildfire disaster according to an embodiment of the present invention.

[0068] Figure 5 Schematic diagram of the entropy distribution of the system according to an embodiment of the present invention.

[0069] Figure 6 A schematic diagram of a typical extreme wildfire disaster failure scenario set of IEEE RTS-79 system failure scenarios is selected for the embodiment of the present invention. DETAILED DESCRIPTION

[0070] Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand the present application and implement the present application. Without violating the principles of the present application, the features of different embodiments may be combined to obtain new implementations, or certain features of certain embodiments may be substituted to obtain other preferred implementations.

[0071] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0072] To make the features and advantages of this patent more clearly understood, the following embodiments are specifically described in detail as follows:

[0073] like Figure 1 As shown, this embodiment provides a method for evaluating the resilience of a power transmission system under extreme wildfire disasters, including the following steps:

[0074] Step S1: Establish a transmission line failure rate model through a wildfire disaster spread model;

[0075] Step S2: Select typical fault scenarios based on the transmission line failure rate model and system information entropy to determine the possible fault scenarios and fault scales caused by wildfire disasters;

[0076] Step S3: generating the optimal load reduction of the system based on the transmission system disaster response model;

[0077] Step S4: Generate the optimal fault line repair path and repair time in combination with the transmission system post-disaster recovery model, and evaluate the transmission system resilience index value based on the system performance.

[0078] In this embodiment, step S1 specifically includes the following steps:

[0079] Step S11: The improved Anderson ellipse model is used to describe the wildfire spread range. In a windless state, the fire is assumed to spread at a uniform speed in all directions, and the fire spreads outward in a circular shape from the center of the fire point. In a windy state, the fire is assumed to spread outward in an elliptical shape with the wind direction as the major axis and the fire point as the focus. Based on this, the wildfire spread range equation is as follows:

[0080]

[0081] Where v is the wind speed, m / s; R0 is the initial speed of wildfire spread, m / s, which is related to the initial wind speed; Δt is the diffusion time, s; is the shape coefficient of the elliptical fire scene; x and y are the horizontal and vertical coordinates of the ellipse centered on the fire point.

[0082] Assume that the angle between the line connecting the alarm tower and the fire point and the direction of maximum spread speed is γ, then the wildfire spread speed in this direction is R γ (m / s) is:

[0083]

[0084] Step S12: Establish a line failure rate model under extreme wildfire disasters as follows:

[0085] P=D R ·D B ·D F ·P V (3)

[0086] Where: P is the comprehensive probability of line failure under wildfire disaster; D R is the wildfire tripping precipitation factor, which takes the value of 1 only when the statistical precipitation in the past 3 hours is greater than 2 mm and the predicted precipitation in the next hour is greater than 1 mm, otherwise it takes the value of 0; B It is the surface vegetation factor, which takes the value of 1 when the area between the fire point and the transmission line is covered with vegetation, such as grassland or jungle, otherwise it takes the value of 0, such as desert, river or highway. F is the wildfire spread factor, which is related to the wildfire spread range; P V is the probability of a flashover failure occurring in the transmission line.

[0087] Step S13: Calculate the wildfire spread factor, which is characterized as follows:

[0088]

[0089] Where: L f is the distance between the fire point and the alarm tower, m, which can be calculated through the latitude and longitude coordinates of the fire point identified by satellite and the coordinates of the alarm tower.

[0090] Step S14: Calculate the transmission line flashover failure probability, the calculation formula is as follows:

[0091]

[0092] Where, U is the phase voltage of the transmission line; U 99 99% withstand breakdown voltage of transmission lines, U 99 =0.756U jc , U jcIt is the breakdown voltage of the transmission line to ground under wildfire conditions.

[0093] In this embodiment, step S2 specifically includes the following steps:

[0094] Step S21: According to the concept of information entropy in Shannon's information theory, the uncertainty event of whether each transmission line fails in the fault scenario is represented by its information entropy as follows:

[0095]

[0096] Where, represents the set of transmission network lines; p l,t is the failure rate of transmission line l during period t; z l,t Indicates whether line l has a fault during period t. If a fault occurs, the value is 1; otherwise, the value is 0.

[0097] Step S22: The total information entropy value of the system reflects the degree of uncertainty about whether the system line has failed, which is an inherent property of the system itself. The total information entropy of the system is used as the uncertainty budget for generating fault scenarios, that is, typical fault scenarios are screened according to the following formula:

[0098]

[0099] In this embodiment, step S3 specifically includes the following steps:

[0100] Step S31: Establishing the objective function for optimal load reduction of the transmission system under wildfire disaster:

[0101]

[0102] Where, is the load demand and load reduction of node j in period t;

[0103] Step S32: Establish the following constraints for the power transmission system response under wildfire disasters:

[0104]

[0105]

[0106]

[0107]

[0108] -f l,max ·(1-z l,t )≤F l ≤f l,max ·(1-z l,t )(13)

[0109] Where: is the active power output of the i-th generator set during period t; and are the load demand and load reduction of node j in period t, respectively; is the upper limit of active output of the i-th generator set; x l 、 and are the reactance of line l and the voltage phase angles at the beginning and end nodes of line l respectively; F l and f l,max are the active power flow and active power flow limit of line l respectively; N G and N D are the number of generators and load nodes, respectively.

[0110] In this embodiment, step S4 specifically includes the following steps:

[0111] Step S41: The index of the maintenance team and its set are recorded as c and C respectively, and the number of maintenance teams is n. crew , the maintenance team will go to the fault line along a certain path, extinguish the fire around the line and repair the fault in the shortest time, and then return to the maintenance center. There are F fault lines, and the set of maintenance centers and fault lines is set as Where 0 represents the maintenance center, 1, 2, ..., F represents the number of the fault line. Let α r is the completion time of the faulty component r. The objective of the transmission system restoration model is to minimize the total repair time of all faulty lines. The objective function of the transmission system restoration model is established as follows:

[0112]

[0113] Step S42: Assume that the maintenance personnel scheduling path decision variable is x r,s,c , if the maintenance team c reaches point s from point r, its value is 1, otherwise it is 0. The maintenance team c must start from the maintenance center and return to the maintenance center after completing all maintenance tasks; after the maintenance team c completes the repair of a line, it will go to the next faulty line. This constraint can be expressed as follows:

[0114]

[0115]

[0116] Step S43: Set binary y r,c A binary variable indicating whether the maintenance team c passes through the fault line r. If so, the value is 1, otherwise it is zero. All fault lines only need to be repaired once during the maintenance process, which satisfies:

[0117]

[0118]

[0119] Step S44: Assume that the time t required for the maintenance team to repair the faulty component r r,c ; The time it takes for maintenance team c to travel from point r to point s The time it takes for the maintenance team c to arrive at the fault line r is T r,c , then the time relationships in the maintenance path of the maintenance team satisfy:

[0120]

[0121]

[0122]

[0123] Step S45: Introduce binary variable h r,t Indicates the time period when the faulty component r is repaired. If the faulty component r is repaired within time period t, then h r,t The value is 1, otherwise it is 0; introduce the binary variable q r,t Indicates the repair status of the faulty component. If the faulty component r has been repaired in time period t, then q r,t The value is 1, otherwise it is 0. The line state constraints are as follows:

[0124]

[0125]

[0126] Step S46: Using the missing area of the system load curve under extreme wildfire disasters to reflect the resilience of the transmission system:

[0127]

[0128] Where: E(·) represents the expected value; P k is the probability of scenario k occurring, K is the total number of scenarios; L(t) is the actual load curve of the system when it is hit by a wildfire disaster; TL(t) is the normal load curve of the system when there is no fault; AR represents the resilience of the transmission system when it is hit by a wildfire disaster. The larger the AR, the stronger the system's ability to cope with wildfire disasters.

[0129] Preferably, in this embodiment, a pre-disaster line fault model, a disaster transmission system response model, and a post-disaster transmission system recovery model are established in a distributed manner, that is, the entire process of the impact of wildfire disasters on the transmission system is taken into account.

[0130] Preferably, this embodiment provides a method for evaluating the resilience of a power transmission system under extreme wildfire disasters, which can accurately evaluate the system resilience under wildfire disasters and quantitatively analyze the improvement effect of various resilience improvement measures.

[0131] Preferably, this embodiment performs test case simulation in MATLAB environment and uses CPLEX solver to solve the model. The modeling and solving process is shown in Figure 1 .

[0132] According to a specific example of this embodiment, the elasticity evaluation method is applied to the IEEE RTS-79 system for verification, wherein the longitude and latitude of each node are set as shown in Table A1 and Figure 2 As shown, nodes 1, 2, 7, 13, 14, 15, 16, 18, 21, and 22 are connected to the generator sets.

[0133] Table A1 Node longitude and latitude

[0134]

[0135]

[0136] In this embodiment, the probability of tripping due to a wildfire fault on each line can be calculated from step 1 as follows: Figure 4 shown.

[0137] analyze Figure 4 The line failure probability obtained in Figure 1 shows that in areas with sufficient rainfall or when there are terrain obstacles such as rivers or roads between the fire point and the transmission line, the line will not be affected by wildfires and trip, and the transmission line failure probability is zero. Wildfire intensity decays over time, so the closer the line is to the fire point, the higher the failure probability. Among them, L18 has the highest failure probability, at 84.30%, followed by L10, L11, and L19, at 83.40%, 74.30%, and 72.20%, respectively. To further reduce the impact of wildfires on transmission lines, reinforcement can be implemented on transmission lines with high failure rates during disasters.

[0138] Generate fault scenarios based on line fault probability. The probability distribution of each fault scenario is the corresponding system entropy distribution. The system entropy distribution is as follows: Figure 5 shown.

[0139] Depend on Figure 5 It can be seen that the entropy value between 11 and 15 has a high probability, and the corresponding scenario can be considered a typical failure scenario. min and W max Take 11 and 15 respectively to select a typical extreme wildfire disaster fault scenario set, such as Figure 6 shown.

[0140] In this embodiment, the following four scenarios are designed based on different elasticity improvement strategies:

[0141] Case 1: The system is configured according to the parameter settings in Table 1, the number of maintenance teams is 1, and the time required to repair the faulty line is set to 2 hours.

[0142] Case 2: Based on Case 1, the four fault lines L10, L11, L18 and L19 with higher fault lines under wildfire disasters are reinforced.

[0143] Case 3: Based on Case 1, the original 1 maintenance team is expanded to 3 maintenance teams.

[0144] Case 4: Based on Case 1, the repair time of each line is halved.

[0145] Assuming a wildfire disaster occurs at 01:00, maintenance personnel have 0.5 hours to prepare, and a time step of 1 hour, the CPLEX solver is used on the MATLAB platform for calculation. The elasticity indicators for each case are shown in Table 1:

[0146] Table 1 Transmission system resilience assessment results

[0147] Condition Case 1 Case 2 Case 3 Case 4 AR 0.8311 0.8583 0.8915 0.8941

[0148] Analysis of Table 1 shows that the resilience indicators for Cases 2, 3, and 4 are 3.27%, 7.27%, and 7.58% higher than those for Case 1, respectively. Therefore, measures such as line reinforcement, increased maintenance teams, and improved repair efficiency can effectively improve system resilience. Furthermore, the proposed method can accurately quantitatively evaluate the performance of different resilience-enhancing measures.

[0149] Figure 3 The load curves for the normal operation of the transmission system and the load curves for four situations after a wildfire disaster. Figure 3The load curve for Case 1 shows that at 01:00, the wildfire began to affect the transmission system, causing a large-scale line fault and tripping. The system's load shedding reached a maximum of 356.87 MW, accounting for 28.61% of the total system load. After 5 hours, maintenance personnel gradually repaired the faulty line, and the system load gradually recovered, returning to its initial state after 23 hours. The load curve for Case 2 shows that by reinforcing the four lines with high failure rates (L10, L11, L18, and L19), these lines no longer tripped due to wildfires in the selected fault scenarios, enhancing the system's ability to withstand external interference. Specifically, the system's maximum load shedding reached 328.81 MW, accounting for 26.36% of the total system load. Compared to Case 1, the system's load recovery increased by 28.06 MW. Furthermore, in Case 2, the lines with a higher failure rate were reinforced, resulting in a smaller number of faulty lines compared to Case 1. Maintenance personnel therefore had to repair fewer lines, resulting in a shorter system recovery time than in Case 1, with the system returning to its initial state after 22 hours. The maximum load shedding in Cases 3 and 4 was the same as in Case 1, as the number of faulty lines did not decrease. However, in Case 3, the number of maintenance teams increased from one to two, resulting in a shorter overall repair time than in Case 1, with the system returning to its initial state after 16 hours. In Case 4, however, the system load began to gradually recover after 3 hours and returned to normal after 16 hours. This is because the fault repair time in Case 4 was halved, improving maintenance efficiency and enabling a faster system recovery.

[0150] The main processes implemented in this embodiment include calculating line failure probabilities under wildfire conditions, selecting typical failure scenarios using system information entropy, and analyzing system load changes and resilience indicators for different resilience enhancement measures. The proposed resilience assessment method quantitatively analyzes the effectiveness of each resilience enhancement measure and provides a reference for finding the optimal resilience enhancement measure.

[0151] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

[0152] This patent is not limited to the above-mentioned optimal implementation method. Anyone can derive other forms of resilience assessment methods for power transmission systems under extreme wildfire disasters based on the inspiration of this patent. All equivalent changes and modifications made within the scope of the patent application of this invention should be covered by this patent.

Claims

1. A method for evaluating the resilience of a power transmission system under extreme wildfire disasters, characterized in that: The following steps are involved: Step S1: Establish a transmission line failure rate model through a wildfire disaster spread model; Step S2: Select typical fault scenarios based on the transmission line failure rate model and system information entropy to determine the possible fault scenarios and fault scales caused by wildfire disasters; Step S3: generating the optimal load reduction of the system based on the transmission system disaster response model; Step S4: Generate the optimal fault line repair path and repair time based on the transmission system post-disaster recovery model, and evaluate the transmission system resilience index value based on the system performance, including: Step S41: The index of the maintenance team and its set are recorded as c and C respectively, and the number of maintenance teams is n. crew , suppose the maintenance team goes to the fault line along a certain path, extinguishes the fire around the line and repairs the fault in the shortest time, and then returns to the maintenance center; there are F fault lines, and the set of the maintenance center and the fault lines is set to Where 0 represents the maintenance center, 1, 2, ..., F represents the number of the fault line; let α r is the completion time of the faulty component r. The goal of the transmission system post-disaster recovery model is to minimize the total repair time of all faulty lines. The objective function of the transmission system post-disaster recovery model is as follows: Step S42: Assume that the maintenance personnel scheduling path decision variable is x r,s,c , if the maintenance team c arrives at point s from point r, its value is 1, otherwise it is 0; the maintenance team c must start from the maintenance center and return to the maintenance center after completing all maintenance tasks; after the maintenance team c completes the repair of a line, it will go to the next faulty line. The constraints are expressed as follows: Step S43: Set binary y r,c A binary variable indicating whether the maintenance team c passes through the fault line r. If so, the value is 1, otherwise it is zero. All fault lines only need to be repaired once during the maintenance process, which satisfies: Step S44: Assume that the time t required for the maintenance team to repair the faulty component r r,c ; The time it takes for maintenance team c to travel from point r to point s The time it takes for the maintenance team c to arrive at the fault line r is T r,c , then the time relationships in the maintenance path of the maintenance team satisfy: Step S45: Introduce binary variable h r,t Indicates the time period during which the repair of the faulty component r is completed; If the faulty component r is repaired within time t, then h r,t The value is 1, otherwise it is 0; introduce the binary variable q r,t Indicates the repair status of the faulty component; if the faulty component r has been repaired in time period t, then q r,t The value is 1, otherwise it is 0; the line status constraints are as follows:

2. The method for evaluating the resilience of a power transmission system under extreme wildfire disasters according to claim 1, characterized in that: Step S1 specifically includes the following steps: Step S11: Use the improved Anderson ellipse model to describe the wildfire spread range: In a windless state, the fire is assumed to spread at the same speed in all directions, and the fire spreads outward in a circular shape from the center of the fire point. In a windy state, the fire is assumed to spread outward in an elliptical shape with the wind direction as the major axis and the fire point as the focus. Based on this, the wildfire spread range equation is as follows: Where v is the wind speed; R0 is the initial speed of wildfire spread, which is related to the initial wind speed; Δt is the diffusion time; is the shape coefficient of the elliptical fire scene; x and y are the horizontal and vertical coordinates of the ellipse centered on the fire point respectively; Assume that the angle between the line connecting the alarm tower and the fire point and the direction of maximum spread speed is γ, then the wildfire spread speed in this direction is R γ for: Step S12: Establish a line failure rate model under extreme wildfire disasters as follows: P=D R ·D B ·D F ·P V (3) Where: P is the comprehensive probability of line failure under wildfire disaster; D R is the wildfire tripping precipitation factor, which takes the value of 1 only when the statistical precipitation in the past 3 hours is greater than 2 mm and the predicted precipitation in the next hour is greater than 1 mm, otherwise it takes the value of 0; B is the surface vegetation factor, which takes the value of 1 when the area between the fire point and the transmission line is covered with vegetation, otherwise it takes the value of 0; D F is the wildfire spread factor, which is related to the wildfire spread range; P V is the probability of a flashover failure occurring on the transmission line; Step S13: Calculate the wildfire spread factor, which is characterized as follows: Where: L f is the distance between the fire point and the alarm tower, which is calculated by the latitude and longitude coordinates of the fire point identified by satellite and the coordinates of the alarm tower; Step S14: Calculate the transmission line flashover failure probability, the calculation formula is as follows: Where, U is the phase voltage of the transmission line; U 99 99% withstand breakdown voltage of transmission lines, U 99 =0.756U jc , U jc It is the breakdown voltage of the transmission line to ground under wildfire conditions.

3. The method for evaluating the resilience of a power transmission system under extreme wildfire disasters according to claim 1, characterized in that: Step S2 specifically includes the following steps: Step S21: According to the concept of information entropy in Shannon's information theory, the uncertainty event of whether each transmission line fails in the fault scenario is represented by its information entropy as follows: Where, represents the set of transmission network lines; p l,t is the failure rate of transmission line l during period t; z l,t Indicates whether line l has a fault during period t. If a fault occurs, the value is 1; otherwise, the value is 0. Step S22: Use the total information entropy of the system as the uncertainty budget for generating fault scenarios, and select typical fault scenarios according to the following formula:

4. The method for evaluating the resilience of a power transmission system under extreme wildfire disasters according to claim 1, characterized in that: Step S3 specifically includes the following steps: Step S31: Establishing the objective function for optimal load reduction of the transmission system under wildfire disaster: Where, is the load demand and load reduction of node j in period t; Step S32: Establish the following constraints for the power transmission system response under wildfire disasters: -f l,max ·(1-z l,t )≤F l ≤f l,max ·(1-z l,t ) (13)Where: is the active power output of the i-th generator set during period t; and are the load demand and load reduction of node j in period t, respectively; is the upper limit of active output of the i-th generator set; x l 、 and are the reactance of line l and the voltage phase angles at the beginning and end nodes of line l respectively; F l and f l,max are the active power flow and active power flow limit of line l respectively; N G and N D are the number of generators and load nodes, respectively.

5. The method for evaluating the resilience of a power transmission system under extreme wildfire disasters according to claim 1, characterized in that: Step S4 further includes the following steps: Step S46: Using the missing area of the system load curve under extreme wildfire disasters to reflect the resilience of the transmission system: Where: E[·] represents the expected value; P k is the probability of scenario k occurring, K is the total number of scenarios; L(t) is the actual load curve of the system when it is hit by a wildfire disaster; TL(t) is the normal load curve of the system when there is no fault; AR represents the resilience of the transmission system when it is hit by a wildfire disaster. The larger the AR, the stronger the system's ability to cope with wildfire disasters.

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