Power distribution system risk assessment method for extreme weather disasters

Abstract

The invention provides a power distribution system risk assessment method for extreme weather disasters, and the method comprises the following steps: A, estimating the short-time rainfall intensity according to the time-space distribution of the occurrence frequency of extreme rainstorm weather disasters; step B, respectively constructing fault probability models of the power distribution equipment in extreme weather such as rainstorm, flood and debris flow, and obtaining a comprehensive fault probability model; and step C, simulating node states of the power distribution network at different moments by using a state sampling method, constructing a power distribution system risk assessment index, and giving a power distribution system operation risk index from two aspects of elements and systems by establishing a comprehensive time-varying fault probability model of the power distribution system, thereby improving the risk prevention capability of the power distribution system for extreme weather disasters, and improving the reliability of the power distribution system. And the capability of accurately identifying fragile links and the emergency response capability of the power distribution system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution system risk assessment, and in particular to a method for risk assessment of a power distribution system under extreme weather disasters. Background Art

[0002] In recent years, natural disasters such as heavy rain, floods, and mudslides have occurred frequently, causing numerous large-scale power outages in distribution networks and severely threatening the safety and stability of system operations. Distribution systems are typically located close to the load end, and their facilities are vulnerable and easily affected by natural disasters, leading to failures. In severe cases, this can cause power outages to critical loads. In the face of extreme weather disasters, the focus of disaster relief and repair work is generally on the pre-disaster prevention stage, taking into account the time and cost of work before, during, and after a disaster. Therefore, to reduce the impact of severe weather on distribution systems, improve the distribution network's ability to withstand extreme disasters, and enhance its post-disaster resource scheduling and repair capabilities, it is urgent to develop a method that can accurately assess the operational risks of distribution systems facing multiple extreme disasters such as heavy rain, floods, and mudslides.

[0003] While significant progress has been made in research on the damage mechanisms, risk assessment, and corresponding defense strategies for distribution systems caused by extreme weather disasters, research on the impact of rainstorms on the system, fault generation, and defense control methods is lacking. Furthermore, in practice, extreme rainstorms often trigger a series of secondary disasters, such as floods and mudslides, forming a chain of disasters that seriously endanger power grid security. Therefore, comprehensive risk assessments of distribution systems that consider multiple extreme weather disasters are necessary. In addition to directly flooding power equipment and causing failures, severe rainstorms can also indirectly damage line insulation by causing changes in ambient environmental factors such as humidity and temperature, increasing the probability of power equipment failure. Therefore, comprehensive consideration of the direct and indirect impacts of rainstorms and their secondary hazards on power equipment is necessary. Therefore, improvements to existing technologies are necessary to address these issues. Summary of the Invention

[0004] The purpose of the present invention is to disclose a method for constructing a power equipment failure probability model under various disaster conditions such as heavy rain, floods, and mudslides in the distribution network, and to establish a comprehensive time-varying failure probability model of the distribution system, thereby providing distribution system operation risk indicators from both component and system aspects.

[0005] To achieve the above objectives, the present invention provides a method for risk assessment of power distribution systems under extreme weather disasters, comprising the following steps:

[0006] Step A: Calculate the short-term rainfall intensity based on the spatiotemporal distribution of the frequency of extreme weather disasters obtained from existing meteorological data;

[0007] Step B: constructing the failure probability model of the distribution equipment under extreme weather conditions according to different extreme weather conditions, and combining the failure probability models under different weather conditions to obtain a comprehensive time-varying failure probability model;

[0008] Step C: Based on the comprehensive time-varying fault probability and using the state sampling method to simulate and establish the constraints of the node flow in the distribution system, construct the distribution system risk assessment index, and evaluate the risk of the distribution system based on the index value.

[0009] As a further improvement of the present invention, the short-term rainfall intensity in step A is specifically:

[0010]

[0011] Where t is the duration of the rainstorm, which is counted from the beginning of rainfall t=0 to time t; i R (t) is the rainstorm intensity at time t; P is the return period of rainstorm intensity; α refers to the rainfall in 1 minute within the unit return period; β refers to the rainfall duration correction parameter; γ refers to the rain force variation parameter; τ refers to the rainstorm attenuation index.

[0012] As a further improvement of the present invention, the different extreme weather conditions in step B include: heavy rain, floods, and mudslides, wherein the failure probability model of the power distribution equipment components under heavy rain weather is specifically obtained by the following steps:

[0013] Step B1-1: Based on the DEM data of the power distribution network area, grid processing is performed on it. Assuming that the water accumulation height in the same grid is the same, iteratively calculate the water accumulation height h of the grid Z where the power equipment is located at time t+Δt Z (t+Δt):

[0014]

[0015] The subscript * refers to the four directions of grid Z, namely east, south, west and north. Z For the west of grid Z, E Z For the west of grid Z, N Z is the north of grid Z, S Z is the south of grid Z, Q * is the water flow in the direction of *, when Q * >0, indicating water inflow, when Q * <0, indicating that the accumulated water flows out; θ is the coverage of the buildings in the grid Z; V * Indicates the flow velocity of accumulated water along the * direction; q Z (t) represents the drainage volume of grid Z in time period t; c represents the number of drainage wells in the grid; μ is the drainage coefficient, 0≤μ≤1; A P is the cross-sectional area of ​​the drainage well; I Zis the width of the grid z; Δt is the calculation period; i * (Δt) is the intensity of heavy rainfall in the calculation period in the * direction of grid Z; g is the acceleration of gravity; d is the water depth; t is the duration of heavy rainfall;

[0016] Step B1-2: The direct failure probability P of power equipment, i.e. node i, at time t due to extreme rainstorm disaster i M (t) Specifically:

[0017]

[0018]

[0019] in, is the failure rate of power equipment i at time t, where power equipment i is defined as a node; h i (t) represents the water depth of the grid where the device is located at time t; D i D is the designed flood-proof height of the power distribution room; Bi is the height of the high-voltage switchgear cable connector to the ground; ζ is the attenuation coefficient; γ is the damping coefficient; T is the evaluation time;

[0020] Step B1-3: The indirect failure probability P of power equipment, i.e. node i, due to extreme rainstorm at time t i N (t) Specifically:

[0021]

[0022] in, is the time-varying failure rate of power equipment i due to its own factors; i0 (t) represents the equipment benchmark time-varying failure rate; exp(a·X) is the covariate function, X is the covariate reflecting the equipment status, and a is the covariate parameter; X W1 Indicates the humidity inside the switch cabinet, X W2 Indicates cable connector humidity, X W3 represents the insulation defect of the cabinet, a1, a2, and a3 are the covariate parameters of the corresponding covariates, where a1 is X W1 The influence coefficient of humidity in the switch cabinet, a2 is X W2 The influence coefficient of humidity on cable joints, a3 is X W3 Influence coefficient of cabinet insulation defects.

[0023] As a further improvement of the present invention, the failure probability model of the power distribution equipment components under flood weather is specifically obtained by the following steps:

[0024] Step B2-1: The probability of failure of power equipment due to flood disaster at time t is P iH (t) Specifically:

[0025]

[0026] Where t is the duration of rainstorm, C0 is the fitting coefficient, ξ is the maintenance level of power equipment; i R (t) is the rainfall intensity at time t.

[0027] As a further improvement of the present invention, the failure probability model of the power distribution equipment components under debris flow weather is specifically obtained by the following steps:

[0028] Step B2-2: Calculate the failure probability P of power equipment due to debris flow disaster at time t i S (t):

[0029]

[0030] in, is the average failure rate, τ is the integral variable, the range of τ is (0, t), E is the debris flow intensity coefficient, η is the debris flow vulnerability coefficient, α c is the channel distribution coefficient, α k is the channel clogging coefficient, α h is the hydrological coefficient, S1 is the terrain slope coefficient, S2 is the terrain height coefficient, K is the power equipment stability coefficient, α f is the fatigue coefficient of power equipment, α p is the power equipment location coefficient, α b is the safety factor of electrical equipment.

[0031] As a further improvement of the present invention, the comprehensive time-varying fault probability of the distribution network node is specifically obtained by the following steps:

[0032] Step B2-3: Calculate the comprehensive time-varying failure probability P of the distribution network node i (t):

[0033] P i (t) = 1-[1-P i N (t)][1-P i M (t)][1-P i S (t)][1-P i H (t)] (7);

[0034] Among them, P i N (t) is the indirect failure probability of power equipment, i.e. node i, due to extreme rainstorm at time t, Pi M (t) is the direct failure probability of power equipment, i.e. node i, at time t due to extreme rainstorm disaster, P i H (t) is the failure probability of power equipment due to flood disaster at time t, P i S (t) is the failure probability of power equipment due to debris flow disaster at time t.

[0035] As a further improvement of the present invention, the risk assessment index of the power distribution system in step C includes a component risk index and a system risk index, and the system risk index includes a system reliability index R RL , system safety index R SL and system economic index R EL , are obtained by the following steps:

[0036] Step C-1: Combine the time-varying fault probability P of the distribution network nodes i (t) as a component risk indicator;

[0037] Step C-2: During extreme weather events, the distribution network may experience Nk faults with time-series correlation, leading to node failures and line power flow shifts. Therefore, the operating status of nodes and branches needs to be considered. Constraints for node power flows in the distribution system are established, specifically:

[0038]

[0039] in, represents the injection power of node i, represents the load of node i, S ij represents the power of line ij connected to node i, P ij represents the active power of line ij connected to node i, Q ij represents the reactive power of the line ij connected to node i; Ω is the set of nodes connected to node i, are the upper and lower limits of the power allowed to pass through line ij, are the upper and lower limits of the allowed node voltage, U i is the actual voltage value of node i; is the running status of node i;

[0040] when When the system operates normally; when When the system fails;

[0041] Step C-3: Calculate the load loss of the distribution network, i.e. the system reliability index R RL :

[0042]

[0043] Among them, M u is the number of Monte Carlo sampling of the node status at each moment in the distribution network; P y,i is the load loss of node i at the yth sampling time; n is the total number of nodes;

[0044] Step C-4: Calculate the voltage stability of power equipment, i.e. the system safety index R SL :

[0045]

[0046] Among them, B i is the weight coefficient of node i; is the voltage fluctuation of node i at the yth sampling time; U N is the rated voltage;

[0047] Step C-5: Calculate the economic losses caused by extreme weather, i.e. the system economic index R EL :

[0048]

[0049] Among them, c0 is the economic loss of power outage per unit load; c R is the unit cost of equipment maintenance; T p,i Repair duration, is the average node repair time; t RE End time for extreme weather disasters; is the failure time of node i; N K,y is the number of equipment losses in the y-th sampling.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. Based on the spatiotemporal distribution of the frequency of extreme rainstorm weather disasters, the team estimated the intensity of short-term rainfall and constructed power equipment failure probability models for distribution networks under various disaster conditions, including rainstorms, floods, and mudslides. Furthermore, they obtained a comprehensive time-varying failure probability model for the distribution system, improving the distribution system's risk prevention capabilities in the face of extreme weather disasters.

[0052] 2. The comprehensive time-varying failure probability of the distribution system, as a risk indicator for distribution network components, helps improve the ability to accurately identify vulnerable links in the distribution system and is used in applications such as distribution network emergency planning and resource deployment.

[0053] 3. Based on the load loss of the distribution network, the voltage stability of power equipment and the economic losses caused by extreme weather, the system reliability, safety and economy risk indicators are evaluated, and then a comprehensive assessment of the system risk indicators is achieved to provide auxiliary decision support for power grid operation and dispatching personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of a method for risk assessment of a power distribution system under extreme weather disasters according to the present invention. DETAILED DESCRIPTION

[0055] The present invention is described in detail below with reference to the various embodiments shown in the accompanying drawings, but it should be noted that these embodiments are not limitations of the present invention, and any equivalent transformations or substitutions in functions, methods, or structures made by ordinary technicians in this field based on these embodiments are all within the scope of protection of the present invention.

[0056] like Figure 1 The present invention shows a method for risk assessment of a power distribution system under extreme weather disasters, comprising the following steps:

[0057] Step A: Calculate the short-term rainfall intensity based on the spatiotemporal distribution of the frequency of extreme weather disasters; the short-term rainfall intensity is specifically:

[0058]

[0059] Where t is the duration of the rainstorm; i R (t) is the rainstorm intensity during time period t; P is the rainstorm intensity return period; α is the rainfall per minute within the unit return period; β is the rainfall duration correction parameter; γ is the rain force variation parameter; and τ is the rainstorm attenuation index. Rainfall intensity here refers to the cumulative rainfall of a short-term rainstorm, and t is the duration of the rainstorm, measured from the start of rainfall at t = 0 to time t.

[0060] Step B: Construct a failure probability model for distribution equipment under extreme weather conditions based on different extreme weather conditions, and combine the failure probability models under different weather conditions to obtain a comprehensive time-varying failure probability model. Extreme weather conditions include heavy rain, floods, and mudslides. The failure probability model for distribution equipment components under extreme heavy rain is specifically obtained through the following steps:

[0061] Step B1-1: Based on the DEM data of the power distribution network area, grid processing is performed on it. Assuming that the water accumulation height in the same grid is the same, iteratively calculate the water accumulation height h of the grid Z where the power equipment is located at time t+Δt Z (t+Δt):

[0062]

[0063] The subscript * refers to the four directions of grid Z, namely east, south, west and north. Z For the west of grid Z, E Z For the west of grid Z, N Zis the north of grid Z, S Z is the south of grid Z, Q * is the water flow in the direction of *, when Q * >0, indicating water inflow, when Q * <0, indicating that the accumulated water flows out; θ is the coverage of the buildings in the grid Z; V * Indicates the flow velocity of accumulated water along the * direction; q Z (t) represents the drainage volume of grid Z in time period t; c represents the number of drainage wells in the grid; μ is the drainage coefficient, 0≤μ≤1; A P is the cross-sectional area of ​​the drainage well; I Z is the width of the grid z; Δt is the calculation period; i * (Δt) is the intensity of heavy rainfall in the calculation period in the * direction of grid Z; g is the acceleration of gravity; d is the water depth; t is the duration of heavy rainfall;

[0064] Step B1-2: The direct failure probability P of power equipment, i.e. node i, at time t due to extreme rainstorm disaster i M (t) Specifically:

[0065]

[0066] in, is the time-varying failure rate of power equipment i due to its own factors; power equipment i is defined as a node; h i (t) represents the water depth of the grid where the device is located at time t; D i D is the designed flood-proof height of the power distribution room; Bi is the height of the high-voltage switchgear cable connector to the ground; ζ is the attenuation coefficient; γ is the damping coefficient; T is the evaluation time;

[0067] Step B1-3: The indirect failure probability P of power equipment, i.e. node i, due to extreme rainstorm at time t i N (t) Specifically:

[0068]

[0069] in, is the time-varying failure rate of power equipment i due to its own factors; i0 (t) represents the equipment benchmark time-varying failure rate; exp(a·X) is the covariate function, X is the covariate reflecting the equipment status, and a is the covariate parameter; X W1 Indicates the humidity inside the switch cabinet, X W2 Indicates cable connector humidity, X W3 represents the insulation defect of the cabinet, a1, a2, and a3 are the covariate parameters of the corresponding covariates, where a1 is X W1The influence coefficient of humidity in the switch cabinet, a2 is X W2 The influence coefficient of humidity on cable joints, a3 is X W3 Influence coefficient of cabinet insulation defects.

[0070] Step B2-1: The failure probability P of power equipment due to extreme flooding at time t i H (t) Specifically:

[0071]

[0072] Where C0 is the fitting coefficient, ξ is the maintenance level of power equipment; i R (t) is the rainfall intensity at time t;

[0073] Step B2-2: Calculate the failure probability P of power equipment due to extreme debris flow disaster at time t i S (t):

[0074]

[0075] in, is the average failure rate; E is the debris flow intensity coefficient, η is the debris flow vulnerability coefficient; α c is the channel distribution coefficient, α k is the channel clogging coefficient, α h is the hydrological coefficient; S1 is the terrain slope coefficient, S2 is the terrain height coefficient; K is the power equipment stability coefficient; α f is the fatigue coefficient of power equipment, α p is the power equipment location coefficient, α b is the safety factor of power equipment;

[0076] Step B2-3: Calculate the comprehensive time-varying failure probability P of the distribution network node i (t):

[0077] P i (t) = 1-[1-P i N (t)][1-P i M (t)][1-P i S (t)][1-P i H (t)] (7).

[0078] Step C: Use the state sampling method to simulate the node status of the distribution network at different times, construct the distribution system risk assessment index, and evaluate the risk of the distribution system based on the index value.

[0079] The risk assessment indicators of the power distribution system include component risk indicators and system risk indicators. The system risk indicators include system reliability indicators R RL , system safety index R SL and system economic index R EL , the three indicator values ​​are proportional to the risk level and can be obtained by the following steps:

[0080] Step C-1: The comprehensive time-varying fault probability P of the distribution network node in step B2-3 is i (t) as a component risk indicator;

[0081] Step C-2: During extreme weather events, the distribution network may experience Nk faults with time-series correlation, resulting in node failures and line power flow shifts. Therefore, it is necessary to consider the operating status of nodes and branches and establish the constraints on node power flows in the distribution system. Specifically,

[0082]

[0083] in, represents the injection power of node i, represents the load of node i, S ij represents the power of line ij connected to node i, P ij represents the active power of line ij connected to node i, Q ij represents the reactive power of the line ij connected to node i; Ω is the set of nodes connected to node i, are the upper and lower limits of the power allowed to pass through line ij, are the upper and lower limits of the allowed node voltage, U i is the actual voltage value of node i; is the operating status of node i. When the system is operating normally, When the system fails,

[0084] Step C-3: Calculate the load loss of the distribution network, i.e. the system reliability index R RL :

[0085]

[0086] Among them, M u is the number of Monte Carlo sampling of the node status at each moment in the distribution network; y,i is the load loss of node i at the yth sampling time; n is the total number of nodes;

[0087] Step C-4: Calculate the voltage stability of power equipment, i.e. the system safety index R SL :

[0088]

[0089] Among them, B i is the weight coefficient of node i; is the voltage fluctuation of node i at the yth sampling time; U N is the rated voltage;

[0090] Step C-5: Calculate the economic losses caused by extreme weather, i.e. the system economic index R EL :

[0091]

[0092] Among them, c0 is the economic loss of power outage per unit load; c R is the unit cost of equipment maintenance; T p,i Repair duration, is the average node repair time; t RE End time for extreme weather disasters; is the failure time of node i; N K,y is the number of equipment losses in the y-th sampling.

[0093] By establishing a comprehensive time-varying fault probability model for the distribution system, the distribution system operation risk indicators are given from both the component and system aspects, which improves the distribution system's risk prevention capabilities in the face of extreme weather disasters, and improves the distribution system's ability to accurately identify vulnerable links and emergency response capabilities.

[0094] Based on the spatiotemporal distribution of the frequency of extreme rainstorm weather disasters, the team estimated the intensity of short-term rainfall and constructed probability models for power equipment failure in distribution networks under various disaster conditions, including rainstorms, floods, and mudslides. Furthermore, they obtained a comprehensive time-varying failure probability model for the distribution system, improving the distribution system's risk prevention capabilities in the face of extreme weather disasters.

[0095] The comprehensive time-varying failure probability of the distribution system is used as a risk indicator to measure distribution network components. It helps improve the ability to accurately identify vulnerable links in the distribution system and is used in applications such as distribution network emergency planning and resource deployment.

[0096] Based on the load loss of the distribution network, the voltage stability of power equipment and the economic losses caused by extreme weather, an assessment of the three risk indicators of system reliability, safety and economy is achieved, and then a comprehensive assessment of system risk indicators is achieved to provide auxiliary decision support for power grid operation and dispatching personnel.

[0097] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for risk assessment of power distribution systems under extreme weather disasters, characterized in that: The steps include: Step A: Calculate the short-term rainfall intensity based on the spatiotemporal distribution of the frequency of extreme weather disasters obtained from existing meteorological data; Step B: constructing the failure probability model of the distribution equipment under extreme weather conditions according to different extreme weather conditions, and combining the failure probability models under different weather conditions to obtain a comprehensive time-varying failure probability model; Step C: Based on the comprehensive time-varying fault probability and using the state sampling method to simulate and establish the constraints of the node flow in the distribution system, construct the distribution system risk assessment index, and evaluate the risk of the distribution system based on the index value.

2. A method for risk assessment of power distribution system under extreme weather disasters according to claim 1, characterized in that: The short-term rainfall intensity in step A is specifically: Where t is the duration of the rainstorm, which is counted from the beginning of rainfall t=0 to time t; i R (t) is the rainstorm intensity at time t; P is the return period of rainstorm intensity; α refers to the rainfall in 1 minute within the unit return period; β refers to the rainfall duration correction parameter; γ refers to the rain force variation parameter; τ refers to the rainstorm attenuation index.

3. The method for risk assessment of power distribution system under extreme weather disasters according to claim 1, characterized in that: The different extreme weather conditions in step B include: heavy rain, floods, and mudslides. The failure probability model of the power distribution equipment components under heavy rain is specifically obtained by the following steps: Step B1-1: Based on the DEM data of the power distribution network area, grid processing is performed on it. Assuming that the water accumulation height in the same grid is the same, iteratively calculate the water accumulation height h of the grid Z where the power equipment is located at time t+Δt Z (t+Δt): The subscript * refers to the four directions of grid Z, namely east, south, west and north. Z For the west of grid Z, E Z For the west of grid Z, N Z is the north of grid Z, S Z is the south of grid Z, Q * is the water flow in the direction of *, when Q * >0, indicating water inflow, when Q * <0, indicating that the accumulated water flows out; θ is the coverage of the buildings in the grid Z; V * Indicates the flow velocity of accumulated water along the * direction; q Z (t) represents the drainage volume of grid Z in time period t; c represents the number of drainage wells in the grid; μ is the drainage coefficient, 0≤μ≤1; A P is the cross-sectional area of ​​the drainage well; I Z is the width of the grid z; Δt is the calculation period; i * (Δt) is the intensity of heavy rainfall in the calculation period in the * direction of grid Z; g is the acceleration of gravity; d is the water depth; t is the duration of heavy rainfall; Step B1-2: The direct failure probability P of power equipment, i.e. node i, at time t due to extreme rainstorm disaster i M (t) Specifically: in, is the failure rate of power equipment i at time t, where power equipment i is defined as a node; h i (t) represents the water depth of the grid where the device is located at time t; D i D is the designed flood-proof height of the power distribution room; Bi is the height of the high-voltage switchgear cable connector to the ground; ζ is the attenuation coefficient; γ is the damping coefficient; T is the evaluation time; Step B1-3: The indirect failure probability P of power equipment, i.e. node i, due to extreme rainstorm at time t i N (t) Specifically: in, is the time-varying failure rate of power equipment i due to its own factors; i0 (t) represents the equipment benchmark time-varying failure rate; exp(a·X) is the covariate function, X is the covariate reflecting the equipment status, and a is the covariate parameter; X W1 Indicates the humidity inside the switch cabinet, X W2 Indicates cable connector humidity, X W3 represents the insulation defect of the cabinet, a1, a2, and a3 are the covariate parameters of the corresponding covariates, where a1 is X W1 The influence coefficient of humidity in the switch cabinet, a2 is X W2 The influence coefficient of humidity on cable joints, a3 is X W3 Influence coefficient of cabinet insulation defects.

4. The method for risk assessment of power distribution system under extreme weather disasters according to claim 1, characterized in that: The failure probability model of power distribution equipment components under flood weather is obtained through the following steps: Step B2-1: The probability of failure of power equipment due to flood disaster at time t is P i H (t) Specifically: Where t is the duration of rainstorm, C0 is the fitting coefficient, ξ is the maintenance level of power equipment; i R (t) is the rainfall intensity at time t.

5. The method for risk assessment of power distribution system under extreme weather disasters according to claim 1, characterized in that: The failure probability model of power distribution equipment components under debris flow weather is obtained through the following steps: Step B2-2: Calculate the failure probability P of power equipment due to debris flow disaster at time t i S (t): in, is the average failure rate, τ is the integral variable, the range of τ is (0, t), E is the debris flow intensity coefficient, η is the debris flow vulnerability coefficient, α c is the channel distribution coefficient, α k is the channel clogging coefficient, α h is the hydrological coefficient, S1 is the terrain slope coefficient, S2 is the terrain height coefficient, K is the power equipment stability coefficient, α f is the fatigue coefficient of power equipment, α p is the power equipment location coefficient, α b is the safety factor of electrical equipment.

6. The method for risk assessment of power distribution system under extreme weather disasters according to claim 1, characterized in that: The comprehensive time-varying fault probability of the distribution network node is obtained by the following steps: Step B2-3: Calculate the comprehensive time-varying failure probability P of the distribution network node i (t): P i (t)=1-[1-P i N (t)][1-P i M (t)][1-P i S (t)][1-P i H (t)] (7); Among them, P i N (t) is the indirect failure probability of power equipment, i.e. node i, due to extreme rainstorm at time t, P i M (t) is the direct failure probability of power equipment, i.e. node i, at time t due to extreme rainstorm disaster, P i H (t) is the failure probability of power equipment due to flood disaster at time t, P i S (t) is the failure probability of power equipment due to debris flow disaster at time t.

7. The method for risk assessment of power distribution system under extreme weather disasters according to claim 1, characterized in that: The risk assessment index of the power distribution system in step C includes component risk index and system risk index, and the system risk index includes system reliability index R RL , system safety index R SL and system economic index R EL , are obtained by the following steps: Step C-1: Combine the time-varying fault probability P of the distribution network nodes i (t) as a component risk indicator; Step C-2: During extreme weather events, the distribution network may experience Nk faults with time-series correlation, resulting in node failures and line power flow shifts. Therefore, it is necessary to consider the operating status of nodes and branches and establish the constraints on node power flows in the distribution system. Specifically, in, represents the injected power of node i, represents the load of node i, S ij represents the power of line ij connected to node i, P ij represents the active power of line ij connected to node i, Q ij represents the reactive power of the line ij connected to node i; Ω is the set of nodes connected to node i, are the upper and lower limits of the power allowed to pass through line ij, are the upper and lower limits of the allowed node voltage, U i is the actual voltage value of node i; is the running status of node i; when When the system operates normally; when When the system fails; Step C-3: Calculate the load loss of the distribution network, i.e. the system reliability index R RL : Among them, M u is the number of Monte Carlo sampling of the node status at each moment in the distribution network; P y,i is the load loss of node i at the yth sampling time; n is the total number of nodes; Step C-4: Calculate the voltage stability of power equipment, i.e. the system safety index R SL : Among them, B i is the weight coefficient of node i; is the voltage fluctuation of node i at the yth sampling time; U N is the rated voltage; Step C-5: Calculate the economic losses caused by extreme weather, i.e. the system economic index R EL : Among them, c0 is the economic loss of power outage per unit load; c R is the unit cost of equipment maintenance; T p,i Repair duration, is the average node repair time; t RE End time for extreme weather disasters; is the failure time of node i; N K,y is the number of equipment losses in the y-th sampling.

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