Power distribution network post-disaster risk assessment method considering natural disaster time-space evolution model

Abstract

The invention discloses a power distribution network post-disaster risk assessment method considering a natural disaster time-space evolution model, and the method comprises the steps: 1, carrying out the natural disaster time-space correlation modeling through considering the causes and influence factors of the time-space evolution of a plurality of natural disasters; 2, providing a method for calculating the equipment fault probability of the power distribution network based on a natural disaster time-space correlation model; 3, proposing a power distribution network disaster risk assessment method, sampling the equipment state by using a non-sequential Monte Carlo method, and simulating the operation condition of the power distribution network equipment under the influence of various natural disasters, and 4, establishing a power distribution network disaster risk assessment system from the aspects of reliability and safety, and calculating each risk index value. According to the method, the post-disaster risk of the power distribution network under the influence of various natural disasters can be accurately evaluated, so that the post-disaster recovery of the power distribution network can be accelerated.

Description

Technical Field

[0001] The present invention relates to the field of distribution network post-disaster risk assessment, and in particular to a distribution network post-disaster risk assessment method taking into account a natural disaster spatiotemporal evolution model. Background Art

[0002] As global climate change continues to intensify, the frequency and intensity of natural disasters are increasing, posing a serious threat to the safe operation of power systems. In particular, distribution networks, as the terminal part of the power system, often face more complex post-disaster recovery problems. The impact of natural disasters on distribution networks involves many aspects, including equipment damage, network disruption, power supply interruption, etc. After a disaster occurs, how to timely and accurately assess the post-disaster risk of distribution networks and provide a scientific basis for recovery decisions is the key to ensuring the stability and reliability of power systems. Existing distribution network post-disaster risk assessment methods are mostly based on a single disaster scenario, ignoring the complexity of the temporal and spatial evolution of disasters, and cannot fully reflect the comprehensive impact of the evolution of multiple natural disasters on distribution networks. Traditional post-disaster risk assessment methods often fail to consider the spatial and temporal changes of disasters in the process of simulating the spread and impact of disasters, resulting in inaccurate assessment results. In addition, existing distribution network recovery models often focus on island problems or equipment failure simulations, but lack a comprehensive assessment of the operating status of distribution networks under the combined influence of multiple natural disasters. Therefore, how to conduct post-disaster risk assessment of distribution networks in a multi-hazard, full-time and full-space environment based on the spatiotemporal evolution model of natural disasters is an important problem in the current research on post-disaster recovery of distribution networks. Summary of the invention

[0003] In order to overcome the deficiencies in the above-mentioned prior art, the present invention proposes a distribution network post-disaster risk assessment method taking into account the spatiotemporal evolution model of natural disasters, in order to accurately assess the post-disaster risk of the distribution network under the influence of various natural disasters, especially under the influence of complex disaster chains, to provide more scientific decision-making support, thereby accelerating the post-disaster recovery process.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] The method for post-disaster risk assessment of a distribution network considering the spatiotemporal evolution model of natural disasters of the present invention is characterized in that it is carried out according to the following steps:

[0006] Step 1: Establish a natural disaster spatiotemporal evolution model, including typhoon disaster model, rainstorm disaster model and waterlogging disaster model;

[0007] Step 2: Establish the probability constraint of distribution network equipment failure under the spatiotemporal evolution model of natural disasters

[0008] Step 3: Use the non-sequential Monte Carlo sampling method to simulate the power outage scenario of the distribution network under the natural disaster spatiotemporal evolution model to obtain the operating status of all nodes and transmission lines in the distribution network at all times;

[0009] Step 4: According to the simulation results, determine whether there is an island in the distribution network in the disaster-affected power outage scenario. If there is an island, determine whether the island is active, and perform power flow calculation for the active island, record the voltage value and the transmission line power flow, and then execute step 5. If it is a passive island, record the load shedding amount of the nodes in the passive island, and execute step 5. If there is no island, perform power flow calculation for the active island, record the voltage value and the line power flow, and then execute step 5.

[0010] Step 5: Establish a distribution network risk assessment system under the natural disaster spatiotemporal evolution model to calculate the values ​​of various risk indicators.

[0011] The method for post-disaster risk assessment of a distribution network considering a spatiotemporal evolution model of natural disasters described in the present invention is also characterized in that the first step is performed as follows:

[0012] Step 1.1: Use equations (1) to (5) to establish a typhoon disaster model:

[0013] (1)

[0014] (2)

[0015] (3)

[0016] (4)

[0017] (5)

[0018] In formula (1) to formula (5): is the distribution network node serial number set; is a set of time sequence numbers; is the distance from the cyclone center to the strongest wind belt of the typhoon wind field at the tth moment; is the pressure difference between the periphery and the center of the tropical cyclone at the tth moment; is the air flow velocity caused by the pressure gradient force at the tth moment; is the Coriolis force coefficient of the Earth's rotation; is the empirical coefficient; is the average maximum wind speed at time t; is the overall speed of the typhoon at the tth moment; is the near-surface typhoon wind speed at the i-th node at the t-th moment; is the distance between the ith node and the cyclone center of the typhoon wind field at the tth moment; Parameters related to the radial intensity attenuation of the typhoon; is the central pressure difference at the tth moment after the typhoon landed; It is the pressure difference between the periphery and the center of the cyclone when the typhoon makes landfall; It is the angle between the typhoon's moving direction and the typhoon's landing direction on the coastline; is the direction angle from the center of the typhoon; is the Coriolis parameter;

[0019] Step 1.2: Use equations (6) to (10) to establish a rainstorm disaster model under typhoon conditions:

[0020] (6)

[0021] (7)

[0022] (8)

[0023] (9)

[0024] (10)

[0025] In formula (6) to formula (10): is the total rainfall at the i-th node at the t-th moment predicted by the rainstorm disaster model; is the symmetric rainfall at the i-th node at the t-th moment; is the asymmetric rainfall induced by vertical wind shear at the ith node at the tth moment; is the rainfall caused by the terrain at the i-th node at the t-th moment; r i,t is the distance from the typhoon center at the ith node at the tth moment; is the rainfall in the typhoon center at the tth moment; is the maximum rainfall on the radial section of typhoon rainfall at the tth moment; is the maximum rainfall radius at the tth moment; is the e-folding radius of the maximum rainfall at the tth moment; is the maximum wind speed of the typhoon at the tth moment; is the average total column water volume within a certain range from the typhoon center at the tth moment; , , , is the rainfall parameter The four regression coefficients of ; , , , is the rainfall parameter The four regression coefficients of ; , , , is the rainfall parameter The four regression coefficients of ; , , , is the rainfall parameter The four regression coefficients of ; and are the sine and cosine rainfall Fourier coefficients of wave number k in radial direction, respectively; is the angle of vertical wind shear; is the vertical velocity caused by the terrain at the ith node at the tth moment;

[0026] Step 1.3: Use equations (11) to (14) to establish a waterlogging disaster model under typhoon-rainstorm conditions:

[0027] (11)

[0028] (12)

[0029] (13)

[0030] (14)

[0031] In formula (11) to formula (14): is a set of coordinates of geographic locations; is the depth of accumulated water at the geographical location (x, y) at the tth moment; is the water depth at the geographical location (x, y) at time t+1; It is the surface runoff from the geographical location (x, y) to the geographical location (x, y+1) at the tth moment; It is the surface runoff from the geographical location (x, y) to the geographical location (x, y-1) at the tth moment; It is the surface runoff from the geographical location (x, y) to the geographical location (x+1, y) at the tth moment; It is the surface runoff from the geographical location (x, y) to the geographical location (x-1, y) at the tth moment; is the drainage capacity at the geographical location (x, y) at the tth moment; is the rainfall intensity at the geographical location (x, y) at the tth moment; The length of the unit scale of the horizontal coordinate of the geographic location; The length of the unit scale of the vertical coordinate of the geographic location; The duration of a moment; is the water volume of the drainage well at the geographical location (x, y); is the cross-sectional area of ​​the drainage well at the geographical location (x, y); is the drainage coefficient; is the acceleration due to gravity; is the connection ratio of two adjacent geographical locations; is the water surface elevation at the geographical location (x, y) at the tth moment; is the water surface elevation at the geographical location (x, y+1) at the tth moment; is the water surface elevation at the geographical location (x, y-1) at the tth moment; is the water surface elevation at the geographical location (x-1, y+1) at the tth moment; is the water surface elevation at the geographical location (x+1, y+1) at the tth moment; is the water surface elevation gradient from the geographical location (x, y) to the geographical location (x, y+1) at the tth moment; n is the Manning coefficient; k is the unit conversion coefficient.

[0032] Furthermore, the step 2 is performed as follows:

[0033] Step 2.1: Use equations (15) to (21) to establish the substation failure probability constraint under the natural disaster spatiotemporal evolution model:

[0034] (15)

[0035] (16)

[0036] (17)

[0037] (18)

[0038] (19)

[0039] (20)

[0040] (twenty one)

[0041] In formula (15) to formula (21): is the node number set where the substation is located; is the tth moment Critical rainfall intensity in case of insulator flashover in each substation; is the critical value of insulator flashover voltage; is the current ambient air pressure; is standard atmospheric pressure; , and are three empirical constants; is the density function of rainfall intensity, u is the rainfall intensity; is the tth moment The critical rainfall intensity of a substation in the event of a failure due to rainwater entering the transformer insulating oil; is the tth moment The moisture content of transformer insulating oil in each substation; and are 2 empirical constants; is the duration of rainfall; is the tth moment The critical rainfall intensity of a substation when rainwater enters the transformer and causes the transformer oil-impregnated paper to become damp and fail; is the tth moment The moisture content of transformer oil-impregnated paper in each substation; , , and are 4 empirical constants; For the The probability of flashover of a single transformer insulator in a substation; is the tth moment The probability of a substation failure due to water ingress into the insulating oil of a single transformer; is the tth moment The probability of a substation failure due to water ingress into the oil-immersed paper of a single transformer; is the tth moment The probability of a substation failure; For the The number of insulators in a substation; For the The number of transformers in a substation;

[0042] Step 2.2: Use equations (22) to (25) to establish the transmission line failure probability constraint under the natural disaster spatiotemporal evolution model:

[0043] (twenty two)

[0044] (twenty three)

[0045] (twenty four)

[0046] (25)

[0047] In formula (22)-formula (25): It is the serial number set of towers on the transmission line; It is a set of serial numbers of transmission lines in the distribution network; is the tth moment Wind loads on transmission lines; is the tth moment Wind load on each tower; is the wind pressure unevenness coefficient; is the wind pressure height variation coefficient; For the The resistance coefficient of the transmission line; For the The outer diameter of the transmission line; For the The length of the transmission line; is the tth moment The angle between the transmission line and the typhoon direction; is the tth moment The typhoon wind speed at the location of the transmission line; is the wind vibration coefficient; For the The wind load shape factor of each tower; For the The projected area of ​​each tower; is the tth moment The typhoon wind speed at the location of each tower; is the tth moment The bending moment borne by each tower; is the tth moment Each tower is subjected to the tensile load of the transmission line; For the The height of the tower; For the The distance from the pole base to the cross arm of a tower; For the Additional bending moment coefficient of each tower; For the The top diameter of the pole; For the The cross-sectional diameter of the pole root of each tower; is the tth moment The probability of a tower failure; For the The maximum bending moment that a tower can withstand; For the Standard deviation of the bending strength of each tower; For the The average bending strength of the towers.

[0048] Furthermore, in step 5, the distribution network risk assessment system under the natural disaster spatiotemporal evolution model is established using equations (26) to (29).

[0049] (26)

[0050] (27)

[0051] (28)

[0052] (29)

[0053] In formula (26) to formula (29): is the sequence number set of the number of non-sequential Monte Carlo state sampling; S is the total number of non-sequential Monte Carlo state sampling; is the load loss at the i-th node at the t-th moment under the s-th sampling; To use load shedding as a reliability risk indicator for distribution network under the spatiotemporal evolution model of natural disasters; is the voltage over-limit index at the ith node at the tth moment under the sth sampling; is the voltage at the i-th node at the t-th moment under the s-th sampling; is the voltage upper limit of the i-th node; is the rated voltage; is the voltage lower limit of the i-th node; is the overload degree of the lth transmission line at the tth moment under the sth sampling; is the actual transmission power on the lth transmission line at the tth moment under the sth sampling; is the rated capacity of the lth transmission line; It is the transmission line overload risk indicator.

[0054] An electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the distribution network post-disaster risk assessment method, and the processor is configured to execute the program stored in the memory.

[0055] The present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, which is characterized in that when the computer program is executed by a processor, the steps of the distribution network post-disaster risk assessment method are executed.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0057] 1. The present invention can comprehensively consider the spatiotemporal characteristics of different types of disasters and their multi-dimensional impacts on the distribution network by establishing a natural disaster spatiotemporal evolution model, thereby significantly improving the accuracy of post-disaster risk assessment. Traditional technologies usually ignore the spatiotemporal correlation of disasters, resulting in a lack of accuracy in post-disaster risk assessment results. The present invention provides more accurate risk prediction and assessment by comprehensively modeling the disaster evolution process.

[0058] 2. By introducing the non-sequential Monte Carlo method to sample equipment fault states, the present invention can quickly simulate the operating states of the distribution network in different post-disaster scenarios, significantly accelerating the post-disaster recovery process. Compared with the traditional fault analysis method based on static assumptions, the present invention can dynamically reflect the equipment failure and recovery process, and provide timely support for recovery decisions, thereby shortening the recovery time and reducing power outage losses.

[0059] 3. The present invention not only considers the physical impact of disasters on the distribution network, but also combines reliability and safety indicators to provide a comprehensive post-disaster risk assessment framework. Traditional methods often focus on a single factor, while the present invention conducts a comprehensive analysis from multiple dimensions, which can provide a more comprehensive and scientific decision-making basis for the post-disaster recovery of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart for non-sequential Monte Carlo state sampling;

[0061] Figure 2 It is a framework diagram of the method of the present invention. DETAILED DESCRIPTION

[0062] In the present embodiment, a distribution network post-disaster risk assessment method that considers a natural disaster spatiotemporal evolution model is used in the field of distribution network post-disaster risk assessment, including: considering the causes of the spatiotemporal evolution of various natural disasters and their influencing factors, and carrying out natural disaster spatiotemporal correlation modeling; based on the natural disaster spatiotemporal correlation model, a method for calculating the probability of distribution network equipment failure is proposed; a distribution network disaster risk assessment method is proposed, and a non-sequential Monte Carlo method is used to sample the equipment status, simulate the operation of the distribution network equipment under the influence of various natural disasters, and establish a distribution network disaster risk assessment system from the perspective of reliability and safety. The present invention provides a distribution network post-disaster risk assessment method based on a natural disaster spatiotemporal evolution model, which can accurately assess the post-disaster risk of the distribution network under the influence of various natural disasters, and obtain information including: surface wind speed of each node, rainfall of each node, depth of water accumulation at the location of each node, substation failure probability, transmission line failure probability, reliability index, and safety index. Specifically, if Figure 2 As shown, the method is performed in the following steps:

[0063] Step 1: Establish a natural disaster spatiotemporal evolution model, including typhoon disaster model, rainstorm disaster model and waterlogging disaster model:

[0064] Step 1.1: Use equations (1) to (5) to establish a typhoon disaster model:

[0065] (1)

[0066] (2)

[0067] (3)

[0068] (4)

[0069] (5)

[0070] Formula (1) is the calculation formula for calculating the distance from the cyclone center to the strongest wind belt in the wind field; Formula (2) is the calculation formula for calculating the air flow speed caused by the pressure gradient force; Formula (3) is the calculation formula for the distance from the cyclone center to the strongest wind belt in the typhoon wind field at the tth moment; Formula (4) is the calculation formula for the instantaneous wind speed at different positions in the wind field; Formula (5) is the calculation formula for the change of the pressure difference in the cyclone center over time after the typhoon lands; In Formulas (1) to (5): is the distribution network node serial number set; is a set of time sequence numbers; is the distance from the cyclone center to the strongest wind belt of the typhoon wind field at the tth moment; is the pressure difference between the periphery and the center of the tropical cyclone at the tth moment; is the air flow velocity caused by the pressure gradient force at the tth moment; is the Coriolis force coefficient of the Earth's rotation; is the empirical coefficient; is the average maximum wind speed at time t; is the overall speed of the typhoon at the tth moment; is the near-surface typhoon wind speed at the i-th node at the t-th moment; is the distance between the ith node and the cyclone center of the typhoon wind field at the tth moment; Parameters related to the radial intensity attenuation of the typhoon; is the central pressure difference at the tth moment after the typhoon landed; It is the pressure difference between the periphery and the center of the cyclone when the typhoon makes landfall; It is the angle between the typhoon's moving direction and the typhoon's landing direction on the coastline; is the direction angle from the center of the typhoon; is the Coriolis parameter.

[0071] Step 1.2: Use equations (6) to (10) to establish a rainstorm disaster model under typhoon conditions:

[0072] (6)

[0073] (7)

[0074] (8)

[0075] (9)

[0076] (10)

[0077] Formula (6) is the calculation formula for the total rainfall at the i-th node at the t-th moment predicted by the model; Formula (7) shows that the symmetric rainfall increases linearly with the radius until , and then decreases exponentially; Formula (8) is , , , The calculation formula of asymmetric rainfall is and vertical wind shear angle function; Formula (10) represents the terrain downslope effect ( <0) causes more rainfall than the upslope effect ( >0) is 5 times smaller; in formula (6)-formula (10): is the total rainfall at the i-th node at the t-th moment predicted by the rainstorm disaster model; is the symmetric rainfall at the i-th node at the t-th moment; is the asymmetric rainfall induced by vertical wind shear at the ith node at the tth moment; is the rainfall caused by the terrain at the i-th node at the t-th moment; r i,t is the distance from the typhoon center at the ith node at the tth moment; is the rainfall in the typhoon center at the tth moment; is the maximum rainfall on the radial section of typhoon rainfall at the tth moment; is the maximum rainfall radius at the tth moment; is the e-folding radius of the maximum rainfall at the tth moment; is the maximum wind speed of the typhoon at the tth moment; is the average total column water volume within a certain range from the typhoon center at the tth moment; , , , is the rainfall parameter The four regression coefficients of ; , , , is the rainfall parameter The four regression coefficients of ; , , , is the rainfall parameter The four regression coefficients of ; , , , is the rainfall parameter The four regression coefficients of ; and are the sine and cosine rainfall Fourier coefficients of wave number k in radial direction, respectively; is the angle of vertical wind shear; is the vertical velocity caused by the terrain at the ith node at the tth moment.

[0078] Step 1.3: Use equations (11) to (14) to establish a waterlogging disaster model under typhoon-rainstorm conditions:

[0079] (11)

[0080] (12)

[0081] (13)

[0082] (14)

[0083] Formula (11) is the calculation formula for the water depth at each location at each time; Formula (12) is the calculation formula for the drainage capacity at each location; Formula (13) is the calculation formula for the surface runoff from the geographical location (x, y) to the geographical location (x, y + 1). The same calculation method can be used to calculate , and ; Formula (14) is the calculation formula for the water surface elevation gradient from the geographical location (x, y) to the geographical location (x, y+1); In formula (11)-formula (14): is a set of coordinates of geographic locations; is the depth of accumulated water at the geographical location (x, y) at the tth moment; is the water depth at the geographical location (x, y) at time t+1; It is the surface runoff from the geographical location (x, y) to the geographical location (x, y+1) at the tth moment; It is the surface runoff from the geographical location (x, y) to the geographical location (x, y-1) at the tth moment; It is the surface runoff from the geographical location (x, y) to the geographical location (x+1, y) at the tth moment; It is the surface runoff from the geographical location (x, y) to the geographical location (x-1, y) at the tth moment; is the drainage capacity at the geographical location (x, y) at the tth moment; is the rainfall intensity at the geographical location (x, y) at the tth moment; The length of the unit scale of the horizontal coordinate of the geographic location; The length of the unit scale of the vertical coordinate of the geographic location; The duration of a moment; is the water volume of the drainage well at the geographical location (x, y); is the cross-sectional area of ​​the drainage well at the geographical location (x, y); is the drainage coefficient; is the acceleration due to gravity; is the connection ratio of two adjacent geographical locations; is the water surface elevation at the geographical location (x, y) at the tth moment; is the water surface elevation at the geographical location (x, y+1) at the tth moment; is the water surface elevation at the geographical location (x, y-1) at the tth moment; is the water surface elevation at the geographical location (x-1, y+1) at the tth moment; is the water surface elevation at the geographical location (x+1, y+1) at the tth moment; is the water surface elevation gradient from the geographical location (x, y) to the geographical location (x, y+1) at the tth moment; n is the Manning coefficient; k is the unit conversion coefficient.

[0084] Step 2: Establish the probability constraint of distribution network equipment failure under the natural disaster spatiotemporal evolution model:

[0085] Step 2.1: Use equations (15) to (21) to establish the substation failure probability constraint under the natural disaster spatiotemporal evolution model:

[0086] (15)

[0087] (16)

[0088] (17)

[0089] (18)

[0090] (19)

[0091] (20)

[0092] (twenty one)

[0093] Formula (15) is a calculation formula for calculating the critical rainfall intensity under the condition of insulator flashover; Formula (16) is a calculation formula for calculating the probability of flashover of a single insulator; Formula (17) is a calculation formula for calculating the critical rainfall intensity under the condition of rainwater entering the transformer insulating oil and causing a fault; Formula (18) is a calculation formula for calculating the critical rainfall intensity under the condition of transformer oil-impregnated paper getting wet and causing a fault; Formula (19) is a calculation formula for calculating the probability of a substation failure due to water entering the transformer insulating oil; Formula (20) is a calculation formula for calculating the probability of a substation failure due to water entering the transformer oil-impregnated paper; Formula (21) is a calculation formula for calculating the probability of a substation failure; In Formulas (15) to (21): is the node number set where the substation is located; is the tth moment Critical rainfall intensity in case of insulator flashover in each substation; is the critical value of insulator flashover voltage; is the current ambient air pressure; is standard atmospheric pressure; , and are three empirical constants; is the density function of rainfall intensity, u is the rainfall intensity; is the tth moment The critical rainfall intensity of a substation in the event of a failure due to rainwater entering the transformer insulating oil; is the tth moment The moisture content of transformer insulating oil in each substation; and are 2 empirical constants; is the duration of rainfall; is the tth moment The critical rainfall intensity of a substation when rainwater enters the transformer and causes the transformer oil-impregnated paper to become damp and fail; is the tth moment The moisture content of transformer oil-impregnated paper in each substation; , , and are 4 empirical constants; For the The probability of flashover of a single transformer insulator in a substation; is the tth moment The probability of a substation failure due to water ingress into the insulating oil of a single transformer; is the tth moment The probability of a substation failure due to water ingress into the oil-immersed paper of a single transformer; is the tth moment The probability of a substation failure; For the The number of insulators in a substation; For the The number of transformers in a substation.

[0094] Step 2.2: Use equations (22) to (25) to establish the transmission line failure probability constraint under the natural disaster spatiotemporal evolution model:

[0095] (twenty two)

[0096] (twenty three)

[0097] (twenty four)

[0098] (25)

[0099] Formula (22) is the calculation formula for the wind load on the transmission line; Formula (23) is the calculation formula for the wind load on the tower; Formula (24) is the calculation formula for the bending moment borne by the tower after considering the wind load on the transmission line and the tower; Formula (25) is the calculation formula for the failure probability of the transmission line; In Formula (22)-Formula (25): It is the serial number set of towers on the transmission line; It is a set of serial numbers of transmission lines in the distribution network; is the tth moment Wind loads on transmission lines; is the tth moment Wind load on each tower; is the wind pressure unevenness coefficient; is the wind pressure height variation coefficient; For the The resistance coefficient of the transmission line; For the The outer diameter of the transmission line; For the The length of the transmission line; is the tth moment The angle between the transmission line and the typhoon direction; is the tth moment The typhoon wind speed at the location of the transmission line; is the wind vibration coefficient; For the The wind load shape factor of each tower; For the The projected area of ​​each tower; is the tth moment The typhoon wind speed at the location of each tower; is the tth moment The bending moment borne by each tower; is the tth moment Each tower is subjected to the tensile load of the transmission line; For the The height of the tower; For the The distance from the pole base to the cross arm of a tower; For the Additional bending moment coefficient of each tower; For the The top diameter of the pole; For the The cross-sectional diameter of the pole root of each tower; is the tth moment The probability of a tower failure; For the The maximum bending moment that a tower can withstand; For the Standard deviation of the bending strength of each tower; For the The average bending strength of the towers.

[0100] Step 3. In order to construct a post-disaster risk assessment model for the distribution system, it is first necessary to simulate the operating status of the system during the disaster chain. According to the failure probability of each component obtained in step 2, the distribution system nodes are classified according to the equipment type, mainly nodes with transformers as the core. Then, the non-sequential Monte Carlo sampling method is used to simulate the status of nodes and lines in the disaster-affected power outage scenario of the distribution network under the spatiotemporal evolution model of natural disasters, and the operating status of all nodes and transmission lines in the distribution network at all times is obtained, thereby realizing the reconstruction of the distribution system status, such as Figure 1 As shown, the specific steps are as follows:

[0101] Step 3.1, set the total number of moments T and the duration of a moment T d and sampling times S, initialize the sampling round s=1 and sampling time t=t0, a=1, t0 is the initial sampling time, a is an iteration variable;

[0102] Step 3.2, calculate the failure probability of the node and the transmission line at the tth time;

[0103] Step 3.3: Select a node or transmission line for sampling and generate a random number that is uniformly distributed between (0,1) The node or transmission line failure probability calculated in step 2 is compared with it. If the node or transmission line failure probability is greater than , then the node or transmission line fails. If the probability of node or transmission line failure is not greater than , then the node or transmission line is normal;

[0104] Step 3.4: Determine whether the sampling of all nodes and transmission lines is completed. If not, proceed to step 3.3. If the sampling is completed, determine t0+aT d ≥T is true, if not, execute a=a+1, t=t0+aT d , and go to step 3.3. If it is true, determine whether s≥S is true. If not, execute s=s+1 and go to step 3.3. If it is true, the sampling is completed.

[0105] Step 4. Based on the simulation results, determine whether an island appears in the distribution network in the disaster-affected power outage scenario. If an island appears, determine whether the island is active, and perform power flow calculations for active islands. After recording the voltage value and transmission line power flow, execute step 5. If it is a passive island, record the load shedding of the nodes in the passive island and execute step 5. If no island appears, perform power flow calculations for active islands. After recording the voltage value and line power flow, execute step 5.

[0106] Step 5: Use equations (26) to (29) to establish a distribution network risk assessment system under the natural disaster spatiotemporal evolution model to calculate the values ​​of various risk indicators:

[0107] (26)

[0108] (27)

[0109] (28)

[0110] (29)

[0111] Formula (26) is a calculation formula for using load shedding as a measure of the reliability risk index of the distribution system under the temporal and spatial evolution of natural disasters; Formula (27) is a calculation formula for the voltage over-limit degree index at the i-th node at the t-th moment under the s-th sampling; Formula (28) is a calculation formula for the overload degree of the l-th transmission line at the t-th moment under the s-th sampling; Formula (29) is a calculation formula for the overload risk index of the transmission line caused by extreme disasters; In formulas (26) to (29): is the sequence number set of the number of non-sequential Monte Carlo state sampling; S is the total number of non-sequential Monte Carlo state sampling; is the load loss at the i-th node at the t-th moment under the s-th sampling; To use load shedding as a reliability risk indicator for distribution network under the spatiotemporal evolution model of natural disasters; is the voltage over-limit index at the ith node at the tth moment under the sth sampling; is the voltage at the i-th node at the t-th moment under the s-th sampling; is the voltage upper limit of the i-th node; is the rated voltage; is the voltage lower limit of the i-th node; is the overload degree of the lth transmission line at the tth moment under the sth sampling; is the actual transmission power on the lth transmission line at the tth moment under the sth sampling; is the rated capacity of the lth transmission line; It is the transmission line overload risk indicator.

[0112] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0113] In this embodiment, a computer-readable storage medium stores a computer program on the computer-readable storage medium, and the computer program executes the steps of the above method when executed by a processor.

Claims

1. A distribution network post-disaster risk assessment method considering the spatiotemporal evolution model of natural disasters, characterized in that: The steps are as follows: Step 1: Establish a natural disaster spatiotemporal evolution model, including typhoon disaster model, rainstorm disaster model and waterlogging disaster model; Step 2: Establish the probability constraint of distribution network equipment failure under the spatiotemporal evolution model of natural disasters Step 3: Use the non-sequential Monte Carlo sampling method to simulate the power outage scenario of the distribution network under the natural disaster spatiotemporal evolution model to obtain the operating status of all nodes and transmission lines in the distribution network at all times; Step 4: According to the simulation results, determine whether there is an island in the distribution network in the disaster-affected power outage scenario. If there is an island, determine whether the island is active, and perform power flow calculation for the active island, record the voltage value and the transmission line power flow, and then execute step 5. If it is a passive island, record the load shedding amount of the nodes in the passive island, and execute step 5. If there is no island, perform power flow calculation for the active island, record the voltage value and the line power flow, and then execute step 5. Step 5: Establish a distribution network risk assessment system under the natural disaster spatiotemporal evolution model to calculate the values ​​of various risk indicators.

2. A distribution network post-disaster risk assessment method considering the spatiotemporal evolution model of natural disasters according to claim 1, characterized in that: The first step is carried out as follows: Step 1.1: Use equations (1) to (5) to establish a typhoon disaster model: (1) (2) (3) (4) (5) In formula (1) to formula (5): is the distribution network node number set; is a set of time sequence numbers; is the distance from the cyclone center to the strongest wind belt of the typhoon wind field at the tth moment; is the pressure difference between the periphery and the center of the tropical cyclone at the tth moment; is the air flow velocity caused by the pressure gradient force at the tth moment; is the Coriolis force coefficient of the Earth's rotation; is the empirical coefficient; is the average maximum wind speed at time t; is the overall speed of the typhoon at the tth moment; is the near-surface typhoon wind speed at the i-th node at the t-th moment; is the distance between the ith node and the cyclone center of the typhoon wind field at the tth moment; Parameters related to the radial intensity attenuation of the typhoon; is the central pressure difference at the tth moment after the typhoon landed; It is the pressure difference between the periphery and the center of the cyclone when the typhoon makes landfall; It is the angle between the typhoon's moving direction and the typhoon's landing direction on the coastline; is the direction angle from the center of the typhoon; is the Coriolis parameter; Step 1.2: Use equations (6) to (10) to establish a rainstorm disaster model under typhoon conditions: (6) (7) (8) (9) (10) In formula (6) to formula (10): is the total rainfall at the i-th node at the t-th moment predicted by the rainstorm disaster model; is the symmetric rainfall at the i-th node at the t-th moment; is the asymmetric rainfall induced by vertical wind shear at the ith node at the tth moment; is the rainfall caused by the terrain at the i-th node at the t-th moment; r i,t is the distance from the typhoon center at the ith node at the tth moment; is the rainfall in the typhoon center at the tth moment; is the maximum rainfall on the radial section of typhoon rainfall at the tth moment; is the maximum rainfall radius at the tth moment; is the e-folding radius of the maximum rainfall at the tth moment; is the maximum wind speed of the typhoon at the tth moment; is the average total column water volume within a certain range from the typhoon center at the tth moment; , , , is the rainfall parameter The four regression coefficients of ; , , , is the rainfall parameter The four regression coefficients of ; , , , is the rainfall parameter The four regression coefficients of ; , , , is the rainfall parameter The four regression coefficients of ; and are the sine and cosine rainfall Fourier coefficients of wave number k in radial direction, respectively; is the angle of vertical wind shear; is the vertical velocity caused by the terrain at the ith node at the tth moment; Step 1.3: Use equations (11) to (14) to establish a waterlogging disaster model under typhoon-rainstorm conditions: (11) (12) (13) (14) In formula (11) to formula (14): is a set of coordinates of geographic locations; is the depth of accumulated water at the geographical location (x, y) at the tth moment; is the water depth at the geographical location (x, y) at time t+1; It is the surface runoff from the geographical location (x, y) to the geographical location (x, y+1) at the tth moment; It is the surface runoff from the geographical location (x, y) to the geographical location (x, y-1) at the tth moment; It is the surface runoff from the geographical location (x, y) to the geographical location (x+1, y) at the tth moment; It is the surface runoff from the geographical location (x, y) to the geographical location (x-1, y) at the tth moment; is the drainage capacity at the geographical location (x, y) at the tth moment; is the rainfall intensity at the geographical location (x, y) at the tth moment; The length of the unit scale of the horizontal coordinate of the geographic location; The length of the unit scale of the vertical coordinate of the geographic location; The duration of a moment; is the water volume of the drainage well at the geographical location (x, y); is the cross-sectional area of ​​the drainage well at the geographical location (x, y); is the drainage coefficient; is the acceleration due to gravity; is the connection ratio of two adjacent geographical locations; is the water surface elevation at the geographical location (x, y) at the tth moment; is the water surface elevation at the geographical location (x, y+1) at the tth moment; is the water surface elevation at the geographical location (x, y-1) at the tth moment; is the water surface elevation at the geographical location (x-1, y+1) at the tth moment; is the water surface elevation at the geographical location (x+1, y+1) at the tth moment; is the water surface elevation gradient from the geographical location (x, y) to the geographical location (x, y+1) at the tth moment; n is the Manning coefficient; k is the unit conversion coefficient.

3. A distribution network post-disaster risk assessment method considering the spatiotemporal evolution model of natural disasters according to claim 2, characterized in that: The step 2 is carried out as follows: Step 2.1: Use equations (15) to (21) to establish the substation failure probability constraint under the natural disaster spatiotemporal evolution model: (15) (16) (17) (18) (19) (20) (21) In formula (15) to formula (21): is the node number set where the substation is located; is the tth moment Critical rainfall intensity in case of insulator flashover in each substation; is the critical value of insulator flashover voltage; is the current ambient air pressure; is standard atmospheric pressure; , and are three empirical constants; is the density function of rainfall intensity, u is the rainfall intensity; is the tth moment The critical rainfall intensity of a substation in the event of a failure due to rainwater entering the transformer insulating oil; is the tth moment The moisture content of transformer insulating oil in each substation; and are 2 empirical constants; is the duration of rainfall; is the tth moment The critical rainfall intensity of a substation when rainwater enters the transformer and causes the transformer oil-impregnated paper to become damp and fail; is the tth moment The moisture content of transformer oil-impregnated paper in each substation; , , and are 4 empirical constants; For the The probability of flashover of a single transformer insulator in a substation; is the tth moment The probability of a substation failure due to water ingress into the insulating oil of a single transformer; is the tth moment The probability of a substation failure due to water ingress into the oil-immersed paper of a single transformer; is the tth moment The probability of a substation failure; For the The number of insulators in a substation; For the The number of transformers in a substation; Step 2.2: Use equations (22) to (25) to establish the transmission line failure probability constraint under the natural disaster spatiotemporal evolution model: (22) (23) (24) (25) In formula (22)-formula (25): It is the serial number set of towers on the transmission line; It is a set of serial numbers of transmission lines in the distribution network; is the tth moment Wind loads on transmission lines; is the tth moment Wind load on each tower; is the wind pressure unevenness coefficient; is the wind pressure height variation coefficient; For the The resistance coefficient of the transmission line; For the The outer diameter of the transmission line; For the The length of the transmission line; is the tth moment The angle between the transmission line and the typhoon direction; is the tth moment The typhoon wind speed at the location of the transmission line; is the wind vibration coefficient; For the The wind load shape factor of each tower; For the The projected area of ​​each tower; is the tth moment The typhoon wind speed at the location of each tower; is the tth moment The bending moment borne by each tower; is the tth moment Each tower is subjected to the tensile load of the transmission line; For the The height of the tower; For the The distance from the base of the pole to the cross arm of a tower; For the Additional bending moment coefficient of each tower; For the The top diameter of the pole; For the The cross-sectional diameter of the pole root of each tower; is the tth moment The probability of a tower failure; For the The maximum bending moment that a tower can withstand; For the Standard deviation of the bending strength of each tower; For the The average bending strength of the towers.

4. A distribution network post-disaster risk assessment method considering the spatiotemporal evolution model of natural disasters according to claim 3, characterized in that: In step 5, the risk assessment system of distribution network under the spatiotemporal evolution model of natural disasters is established by using equations (26) to (29). (26) (27) (28) (29) In formula (26) to formula (29): is the sequence number set of the number of non-sequential Monte Carlo state sampling times; S is the total number of non-sequential Monte Carlo state sampling; is the load loss at the i-th node at the t-th moment under the s-th sampling; To use load shedding as a reliability risk indicator for distribution network under the spatiotemporal evolution model of natural disasters; is the voltage over-limit index at the ith node at the tth moment under the sth sampling; is the voltage at the i-th node at the t-th moment under the s-th sampling; is the voltage upper limit of the i-th node; is the rated voltage; is the voltage lower limit of the i-th node; is the overload degree of the lth transmission line at the tth moment under the sth sampling; is the actual transmission power on the lth transmission line at the tth moment under the sth sampling; is the rated capacity of the lth transmission line; It is the transmission line overload risk indicator.

5. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the distribution network post-disaster risk assessment method as described in any one of claims 1-4, and the processor is configured to execute the program stored in the memory.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for post-disaster risk assessment of a distribution network described in any one of claims 1 to 4 are executed.

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