Method for assessing resilience of distribution network under hail disaster

Abstract

This invention discloses a method for assessing the resilience of distribution networks under hail disasters, belonging to the technical field of AC distribution network resilience assessment. When calculating the component failure rate of a distribution network under hail disasters, this invention filters the importance of *d* physical characteristics affecting the distribution network, identifying the most influential physical characteristics. The component failure rate under hail disasters is then calculated using these selected important physical characteristics, solving the problems of instability and inaccurate calculation results caused by relying on experience to judge physical characteristics in existing technologies. This invention uses two dimensions for assessing distribution network resilience: the critical load loss rate and the mobile energy storage response rate, which more accurately reflect the actual situation. Furthermore, the probability of hail disaster occurrence and the component failure rate of the distribution network under hail disasters are introduced to correct the assessment results, making the assessment method of this invention more accurate.

Description

Technical Field

[0001] This invention specifically relates to a method for assessing the resilience of a power distribution network under hail disasters, belonging to the technical field of AC power distribution network resilience assessment. Background Technology

[0002] With the large-scale grid connection of new energy sources and the extensive use of power electronic devices, the power system structure has become increasingly complex and its scale has expanded accordingly. This makes the power system more vulnerable to extreme events such as hailstorms, and the risks to its safe operation have increased dramatically. In this context, the resilience index of the distribution network—which enables it to comprehensively, rapidly, and accurately perceive the grid's operational status, coordinate internal and external resources, proactively anticipate and prepare for various disturbances, actively defend against them, recover quickly, and continuously learn and improve—has become a crucial indicator for evaluating the distribution network's ability to withstand hailstorms. Therefore, assessing the distribution network's resilience under hailstorm conditions has become a key focus for ensuring the safe operation of the distribution network.

[0003] Current technologies for assessing the resilience of distribution networks under extreme disasters primarily rely on calculating resilience indicators based on the probability of fault occurrence and the level of load loss in each scenario. However, this approach has the following limitations:

[0004] 1. The parameters considered for the failure of the distribution network under disaster are relatively few. In extreme disasters, there are many parameters that can affect the failure of components in the distribution network. The existing technology mentioned above only selects parameters based on experience for calculation, resulting in insufficient accuracy of the calculation results, which in turn affects the assessed distribution network resilience index.

[0005] 2. The existing technologies mentioned above mainly consider the level of load loss when calculating the resilience index of the distribution network. However, the scope of consideration is insufficient, which leads to a deviation between the calculated resilience index of the distribution network and the actual situation. Summary of the Invention

[0006] The technical problem to be solved by this invention is: how to accurately assess the resilience index of power distribution networks under hail disasters.

[0007] To solve the above-mentioned technical problems, the technical solution proposed in this invention is: a method for assessing the resilience of a distribution network under hail disasters, comprising the following steps:

[0008] Step 1: Collect the number of hail disasters that occurred in the sixth and seventh months of the past year in the power distribution network as the historical hail disaster count A. Calculate the probability P(k) of the power distribution network experiencing k hail disasters in the past year using the following formula (1), where k is a natural number greater than or equal to 1.

[0009]

[0010] Step 2: Collect d physical characteristic data of the first hail disaster that occurred in the distribution network in the past year as the first historical hail disaster physical characteristic data B1, where d is a natural number greater than or equal to 3; collect the number of component failures in the distribution network under the first hail disaster as the first historical hail disaster component failure data Y1 corresponding to B1; repeat the above steps to sequentially collect the physical characteristics of the distribution network from the second hail disaster to the last hail disaster that occurred in the past year as the second historical hail disaster physical characteristic data B2 to the nth historical hail disaster physical characteristic data B1. n The number of component failures in the distribution network from the second hail disaster to the last hail disaster is collected sequentially as B2 to B. n The corresponding second historical hail disaster component failure data Y2 to the nth historical hail disaster component failure data Y n ;

[0011] The first historical hail disaster physical characteristic data B1 to the nth historical hail disaster physical characteristic data B n and the first historical hail disaster component failure data Y1 to the nth historical hail disaster component failure data Y n The collected data form a historical hail disaster dataset B, as shown in equation (2) below.

[0012]

[0013] In equation (2), ...arrive These are the first physical characteristic data to the dth physical characteristic data at the time of the first hail disaster that occurred in the power distribution network in the past year; ...arrive These are the first to the dth physical characteristic data of the second hail disaster that occurred in the power distribution network in the past year; ...arrive These are the first to the dth physical characteristic data points of the last hail disaster that occurred in the power distribution network in the past year;

[0014] Step 3: Collect the first physical characteristic data of the distribution network described in B from the first hail disaster to the last hail disaster in the past year to form the first physical characteristic data set. Repeat the above steps to obtain the second set of physical feature data. up to the d-th physical feature data set The arrive Collected to form a set of physical characteristics of historical hail disasters, B ξ As shown in equation (3) below,

[0015]

[0016] Using the first physical characteristic of hail disaster as the first variable and the number of component failures under hail disaster as the dependent variable; based on the above... and the Y1 to Y n The data in the dataset were fitted using the least squares method to fit the relationship between the first variable and the dependent variable.

[0017] If the optimal parameters can be found to successfully fit the relationship between the first variable and the dependent variable, it indicates that the first physical characteristic of the hail disaster represented by the first variable is an important physical characteristic and should be retained.

[0018] If the optimal parameters cannot be found to successfully fit the relationship between the first variable and the dependent variable, it indicates that the first physical characteristic of the hail disaster represented by the first variable is not an important physical characteristic and should be eliminated.

[0019] Repeat the above steps to determine the importance of the second to dth physical features of hail disasters in turn; filter the historical hail disaster dataset B using all the important physical features that are ultimately retained, retain the data in B that belong to important physical features, and remove the data in B that do not belong to important physical features, to form the historical hail disaster important feature dataset C, as shown in the following formula (4).

[0020]

[0021] In equation (4), C1 is the first historical hail disaster characteristic data B1 after filtering all important physical characteristics; b1′ 1 b1′ 2 b1′u represents the first important physical feature data belonging to the first important feature to the uth important physical feature data belonging to the last important feature in the first historical hail disaster feature data B1; C2 is the second historical hail disaster important feature data after filtering all important physical features in the second historical hail disaster feature data B2; b2′ 1 b2′ 2 ... to b2′u are respectively the first important physical feature data belonging to the first important feature to the uth important physical feature data belonging to the last important feature in the second historical hail disaster feature data B2; C n It is the nth historical hail disaster characteristic data B n Important feature data of historical hail disasters after filtering all important physical characteristics; b n ′1 b n ′ 2 ...to b n ′ u These are the nth historical hail disaster characteristic data B. n From the first important physical feature data belonging to the first important feature to the uth important physical feature data belonging to the last important feature;

[0022] Step 4: When the distribution network experiences a hail disaster, a detection period T is set, and real-time important physical characteristic data of the distribution network related to the hail disaster are collected in real time at time intervals of the detection period T throughout the entire hail disaster process. All the collected real-time important physical characteristic data are collected to form a real-time important physical characteristic data set D, as shown in the following formula (5).

[0023]

[0024] In equation (5), D1, D2, ... to D m These are the first real-time important physical feature data sequence to the m-th real-time important physical feature data sequence of the power distribution network during the entire hail disaster process, from the first detection period T to the last detection period T. ...arrive These are the first real-time important physical characteristic data of the power distribution network within the first detection period T of the entire hail disaster process, up to the u-th real-time important physical characteristic data. ...arrive These are the first real-time important physical characteristic data to the u-th real-time important physical characteristic data of the power distribution network during the second detection period T of the entire hail disaster process; ...arrive These are the first real-time important physical characteristic data to the u-th real-time important physical characteristic data within the last detection period T of the entire hail disaster process of the power distribution network;

[0025] Step 5: Substitute the data from C and D into the following formula (6) to calculate the first impact probability R1 of the first important physical characteristic of the hail disaster affecting the fault of the distribution network components during the g-th detection period T of the entire hail disaster process.

[0026]

[0027] In equation (6), This refers to the mean of all data for the first important physical feature in the historical hail disaster important feature dataset C; min{b1′ 1 b2′ 1 ... b n ′1} refers to the minimum value among all data of the first important physical feature in the historical hail disaster important feature dataset C;

[0028] The probability R2 of the second important physical characteristic of the hail disaster affecting the failure of the distribution network components during the g-th detection period T in the entire hail disaster process is calculated by the following formula (7).

[0029]

[0030] In equation (7), This refers to the mean of all data for the second most important physical feature in the historical hail disaster important feature dataset C; min{b1′ 2 b2′ 2 ... b n ′ 2} refers to the minimum value among all data of the second most important physical feature in the historical hail disaster important feature dataset C;

[0031] Repeat the above steps to calculate the third probability R3 to the uth probability R of the hail disaster affecting the power distribution network component failure during the g-th detection period T of the entire hail disaster process. u The probability of influence of the distribution network during the g-th detection period T of the entire hail disaster is calculated from the first influence probability R1 to the u-th influence probability R. u Substituting into equation (8), the component failure rate R of the distribution network in the g-th detection period T is calculated. g ,

[0032]

[0033] In equation (8), a1, a2, ... to a u These are the first proportional coefficients to the uth proportional coefficients of the first important physical characteristic to the uth important physical characteristic of the hail disaster, all of which are empirical values;

[0034] Step 6: Calculate the time from the start of the hail disaster t0 to the end of the g-th detection period T during the hail disaster using the following formula (9). The critical load loss rate X of the distribution network g ,

[0035]

[0036] In equation (9), P d This is the key load change curve of the power distribution network during a hail disaster;

[0037] The following formula (10) is used to calculate the time from the start of the hail disaster t0 to the end of the g-th detection period T during the hail disaster in the distribution network. Mobile energy storage response rate Y g ,

[0038]

[0039] In equation (10), L d This is the change curve of mobile energy storage when the power distribution network experiences a hail disaster;

[0040] The distribution network resilience assessment index Q during the g-th detection period T in the entire hail disaster process is calculated using the following formula (11). g To conduct a resilience assessment of the power distribution network under hail disaster.

[0041]

[0042] In equation (11), L i p is the mobile energy storage deployed for disaster recovery in the distribution network during the i-th detection period T of the entire hail disaster process; i,t It is the load of the power distribution network during the i-th detection period T throughout the entire hail disaster.

[0043] The beneficial effects of this invention are as follows: 1. The distribution network resilience assessment method under hail disasters in this invention, when calculating the component failure rate of the distribution network under hail disasters, filters the importance of d physical characteristics affected by hail disasters, selecting the most important physical characteristics with a greater impact. The component failure rate of the distribution network under hail disasters is then calculated using these selected important physical characteristics, solving the problems of instability and inaccurate calculation results caused by relying on experience to judge physical characteristics in existing technologies. 2. This invention uses two dimensions of indicators—the critical load loss rate of the distribution network and the response rate of mobile energy storage—for evaluating distribution network resilience. This considers more comprehensive factors and can more accurately reflect the actual situation. Furthermore, the probability of hail disasters P(k) and the component failure rate R of the distribution network under hail disasters are introduced when assessing distribution network resilience. g The evaluation results are then corrected to make the evaluation method in this invention more realistic and accurate. Attached Figure Description

[0044] Figure 1 This is a flowchart of a method for assessing the resilience of a power distribution network under hail disasters, as proposed in this invention. Detailed Implementation

[0045] The following description, in conjunction with specific embodiments and accompanying drawings, further illustrates the present invention's method for assessing the resilience of a distribution network under hail disasters.

[0046] Example

[0047] The distribution network resilience assessment method under hail disaster in this embodiment is as follows: Figure 1 As shown, it includes the following steps:

[0048] Step 1: Collect the number of hail disasters that occurred in the sixth and seventh months of the past year in the power distribution network as the historical hail disaster count A. Calculate the probability P(k) of the power distribution network experiencing k hail disasters in the past year using the following formula (1), where k is a natural number greater than or equal to 1.

[0049]

[0050] Step 2: Collect d physical characteristic data points of the first hail disaster that occurred in the distribution network in the past year as the first historical hail disaster physical characteristic data B1, where d is a natural number greater than or equal to 3; collect the number of component failures in the distribution network under the first hail disaster as the first historical hail disaster component failure data Y1 corresponding to the first historical hail disaster physical characteristic data B1; repeat the above steps to sequentially collect the physical characteristics of the distribution network from the second hail disaster to the last hail disaster that occurred in the past year as the second historical hail disaster physical characteristic data B2 to the nth historical hail disaster physical characteristic data B1. n The number of component failures in the distribution network from the second hailstorm to the last hailstorm was collected sequentially as B2 to B. n The corresponding second historical hail disaster component failure data Y2 to the nth historical hail disaster component failure data Y n ;

[0051] The physical characteristic data of the first historical hail disaster, B1, to the physical characteristic data of the nth historical hail disaster, B... n And the first historical hail disaster component failure data Y1 to the nth historical hail disaster component failure data Y n The collected data form a historical hail disaster dataset B, as shown in equation (2) below.

[0052]

[0053] In equation (2), ...arrive These are the first physical characteristic data to the dth physical characteristic data from the first hail disaster that occurred in the distribution network in the past year; ...arrive These are the first to the dth physical characteristic data points of the second hail disaster that occurred in the distribution network in the past year; ...arrive These are the first to the dth physical characteristic data points of the last hail disaster that occurred in the distribution network in the past year;

[0054] For example, the six physical characteristics of the first hail disaster in the past year are collected as the first historical hail disaster characteristic data B1. The six physical characteristics are the maximum hail diameter, hail duration, hail area, ambient temperature, maximum wind speed, and precipitation. The physical characteristics data B1 to B6 of the first historical hail disaster, along with the component failure data Y1 to Y6 of the first historical hail disaster, are collected to form the historical hail disaster dataset B, as shown in Table 1 below.

[0055]

[0056] Table 1

[0057] Step 3: Collect the first physical characteristic data from the first hail disaster to the last hail disaster in the past year for the distribution network in B to form the first physical characteristic data set. Repeat the above steps to obtain the second set of physical feature data. up to the d-th physical feature data set Will arrive Collected to form a set of physical characteristics of historical hail disasters, B ξ As shown in equation (3) below,

[0058]

[0059] Based on the data in Table 1, a set of physical characteristics of historical hail disasters, B, is formed. ξ As shown in Table 2 below,

[0060]

[0061] Table 2

[0062] Using the first physical characteristic of hail disasters as the first variable and the number of component failures under hail disasters as the dependent variable; based on and Y1 to Y n The data in the dataset were fitted using the least squares method to determine the relationship between the first variable and the dependent variable.

[0063] If the optimal parameters can be found to successfully fit the relationship between the first variable and the dependent variable, it indicates that the first physical characteristic of hail disaster represented by the first variable is an important physical characteristic and should be retained.

[0064] If the optimal parameters cannot be found to successfully fit the relationship between the first variable and the dependent variable, it indicates that the first physical characteristic of hail disaster represented by the first variable is not an important physical characteristic and should be eliminated.

[0065] Repeat the above steps to determine the importance of each of the d-th physical features of hail disasters. Then, filter the historical hail disaster dataset B using all the retained important physical features, keeping only the data that are important physical features and removing the data that are not, thus forming the historical hail disaster important feature dataset C, as shown in equation (4) below.

[0066]

[0067] In equation (4), C1 is the first historical hail disaster characteristic data B1 after filtering all important physical characteristics; b1′ 1 b1′ 2 ... to b1′u represent the first important physical feature data belonging to the first important feature to the uth important physical feature data belonging to the last important feature in the first historical hail disaster feature data B1; C2 is the important feature data of the second historical hail disaster after filtering all important physical features in the second historical hail disaster feature data B2; b2′ 1 b2′ 2 ...to b2′ u These are the data points from the first important physical feature belonging to the first important feature to the uth important physical feature belonging to the last important feature in the second historical hail disaster characteristic data B2; C n This is the nth historical hail disaster characteristic data B n Important feature data of historical hail disasters after filtering all important physical characteristics; b n ′ 1 b n ′ 2 ...to b n ′u are the characteristic data of the nth historical hail disaster, respectively. n From the first important physical feature data belonging to the first important feature to the uth important physical feature data belonging to the last important feature;

[0068] Based on the data in Tables 1 and 2, the relationship between the six physical characteristics and the number of component failures was fitted. It was found that the optimal parameters could be found for the three physical characteristics: maximum hail diameter, hail duration, and hail area. Therefore, these three physical characteristics were selected as important physical characteristics. The data in Table 1 was then filtered using these three important physical characteristics to obtain the historical hail disaster important characteristic dataset C, as shown in Table 3 below.

[0069]

[0070]

[0071] Table 3

[0072] Step 4: When the distribution network experiences a hail disaster, a detection period T is set, and real-time important physical characteristic data of the distribution network related to the hail disaster are collected in real time at time intervals of T throughout the entire hail disaster process. All the collected real-time important physical characteristic data are collected to form a real-time important physical characteristic data set D, as shown in the following formula (5).

[0073]

[0074] In equation (5), D1, D2, ... to D m These are the first real-time important physical characteristic data sequence to the m-th real-time important physical characteristic data sequence of the power distribution network during the entire hail disaster process, from the first detection period T to the last detection period T. ...arrive These are the first real-time important physical characteristic data of the power distribution network within the first detection period T during the entire hail disaster process, up to the u-th real-time important physical characteristic data. ...arrive These are the first real-time important physical characteristic data of the power distribution network within the second detection period T during the entire hail disaster process, up to the u-th real-time important physical characteristic data. ...arrive These are the first real-time important physical characteristic data of the distribution network within the last detection period T of the entire hail disaster process, up to the u-th real-time important physical characteristic data.

[0075] Step 5: Substitute the data from C and D into the following formula (6) to calculate the first impact probability R1 of the first important physical characteristic of the hail disaster affecting the fault of the distribution network components within the g-th detection period T during the entire hail disaster process.

[0076]

[0077] In equation (6), This refers to the mean of all data for the most important physical feature in the historical hail disaster important feature dataset C; min{b1′ 1 b2′ 1 ... b n ′ 1} refers to the minimum value among all data of the most important physical feature in the historical hail disaster important feature dataset C;

[0078] The probability R2 of the second important physical characteristic of the hail disaster affecting the failure of distribution network components during the g-th detection period T in the entire hail disaster process is calculated by the following formula (7).

[0079]

[0080] In equation (7), This refers to the mean of all data for the second most important physical feature in the historical hail disaster important feature dataset C; min{b1′ 2 b2′ 2 ... b n ′ 2} refers to the minimum value among all data points of the second most important physical feature in the historical hail disaster important feature dataset C;

[0081] Repeat the above steps to calculate the probability of the third important physical characteristic to the uth important physical characteristic affecting the fault of the distribution network components during the g-th detection period T of the hail disaster, from R3 to Rt. u The probability of influence of the power distribution network during the g-th detection period T of the entire hail disaster is calculated from the first influence probability R1 to the u-th influence probability R. u Substituting into equation (8), the component failure rate R of the distribution network in the g-th detection period T is calculated. g ,

[0082]

[0083] In equation (8), a1, a2, ... to a u These are the first proportional coefficients to the uth proportional coefficients of the first important physical characteristic of hail disaster, and all are empirical values.

[0084] Step 6: Calculate the time from the start of the hail disaster t0 to the end of the g-th detection period T during the hail disaster using the following formula (9). The critical load loss rate X of the distribution network g ,

[0085]

[0086] In equation (9), P d It is the key load change curve of the distribution network during hail disaster;

[0087] The following formula (10) is used to calculate the time from the start of the hail disaster t0 to the end of the g-th detection period T during the hail disaster in the distribution network. Mobile energy storage response rate Y g ,

[0088]

[0089] In equation (10), L d This is the curve showing the change in mobile energy storage during a hailstorm disaster in a power distribution network;

[0090] The distribution network resilience assessment index Q during the g-th detection period T in the entire hail disaster process is calculated using the following formula (11). g To conduct a resilience assessment of the power distribution network under hail disaster.

[0091]

[0092] In equation (11), L i It refers to the mobile energy storage deployed for disaster recovery by the power distribution network during the i-th detection period T of the entire hail disaster process; p i,t It is the load of the distribution network during the i-th detection period T in the entire hail disaster process.

[0093] If it is necessary to assess the distribution network resilience during other detection periods T throughout the entire hail disaster, then by repeating steps 5 to 6 based on the data in step 4, the distribution network resilience assessment index can be calculated and the distribution network resilience assessment can be carried out.

Claims

1. A method for assessing the resilience of a distribution network under hail disasters, characterized in that: Includes the following steps: Step 1: Collect the number of hail disasters that occurred in the sixth and seventh months of the past year in the power distribution network as the historical hail disaster count A. Calculate the probability P(k) of the power distribution network experiencing k hail disasters in the past year using the following formula (1), where k is a natural number greater than or equal to 1. Step 2: Collect d physical characteristic data of the first hail disaster that occurred in the distribution network in the past year as the first historical hail disaster physical characteristic data B1, where d is a natural number greater than or equal to 3; collect the number of component failures in the distribution network under the first hail disaster as the first historical hail disaster component failure data Y1 corresponding to B1; repeat the above steps to sequentially collect the physical characteristics of the distribution network from the second hail disaster to the last hail disaster that occurred in the past year as the second historical hail disaster physical characteristic data B2 to the nth historical hail disaster physical characteristic data B1. n The number of component failures in the distribution network from the second hail disaster to the last hail disaster is collected sequentially as B2 to B. n The corresponding second historical hail disaster component failure data Y2 to the nth historical hail disaster component failure data Y n ; The first historical hail disaster physical characteristic data B1 to the nth historical hail disaster physical characteristic data B n and the first historical hail disaster component failure data Y1 to the nth historical hail disaster component failure data Y n The collected data form a historical hail disaster dataset B, as shown in equation (2) below. In equation (2), ...arrive These are the first physical characteristic data to the dth physical characteristic data at the time of the first hail disaster that occurred in the power distribution network in the past year; ...arrive These are the first to the dth physical characteristic data points of the second hail disaster that occurred in the power distribution network in the past year; ...arrive These are the first to the dth physical characteristic data points of the last hail disaster that occurred in the power distribution network in the past year; Step 3: Collect the first physical characteristic data of the distribution network described in B from the first hail disaster to the last hail disaster in the past year to form the first physical characteristic data set. Repeat the above steps to obtain the second set of physical feature data. up to the d-th physical feature data set The arrive Collected to form a set of physical characteristics of historical hail disasters, B ξ As shown in equation (3) below, Using the first physical characteristic of hail disaster as the first variable and the number of component failures under hail disaster as the dependent variable; based on the above... and the Y1 to Y n The data in the dataset were fitted using the least squares method to fit the relationship between the first variable and the dependent variable. If the optimal parameters can be found to successfully fit the relationship between the first variable and the dependent variable, it indicates that the first physical characteristic of the hail disaster represented by the first variable is an important physical characteristic and should be retained. If the optimal parameters cannot be found to successfully fit the relationship between the first variable and the dependent variable, it indicates that the first physical characteristic of the hail disaster represented by the first variable is not an important physical characteristic and should be eliminated. Repeat the above steps to determine the importance of the second to dth physical features of hail disasters in turn; filter the historical hail disaster dataset B using all the important physical features that are ultimately retained, retain the data in B that belong to important physical features, and remove the data in B that do not belong to important physical features, to form the historical hail disaster important feature dataset C, as shown in the following formula (4). In equation (4), C1 is the first historical hail disaster characteristic data B1 after all important physical characteristics have been filtered; ...arrive These are, respectively, the first important physical feature data belonging to the first important feature to the uth important physical feature data belonging to the last important feature in the first historical hail disaster feature data B1; C2 is the second historical hail disaster important feature data after filtering all important physical features from the second historical hail disaster feature data B2; ...arrive These are, respectively, the first important physical feature data belonging to the first important feature to the uth important physical feature data belonging to the last important feature in the second historical hail disaster feature data B2; C n It is the nth historical hail disaster characteristic data B n Important physical feature data of the nth historical hail disaster; ...arrive These are the nth historical hail disaster characteristic data B. n From the first important physical feature data belonging to the first important feature to the uth important physical feature data belonging to the last important feature; Step 4: When the distribution network experiences a hail disaster, a detection period T is set, and real-time important physical characteristic data of the distribution network related to the hail disaster are collected in real time at time intervals of the detection period T throughout the entire hail disaster process. All the collected real-time important physical characteristic data are collected to form a real-time important physical characteristic data set D, as shown in the following formula (5). In equation (5), D1, D2, ... to D m These are the first real-time important physical feature data sequence to the m-th real-time important physical feature data sequence of the power distribution network during the entire hail disaster process, from the first detection period T to the last detection period T. ...arrive These are the first real-time important physical characteristic data of the power distribution network within the first detection period T of the entire hail disaster process, up to the u-th real-time important physical characteristic data. ...arrive These are the first real-time important physical characteristic data to the u-th real-time important physical characteristic data of the power distribution network during the second detection period T of the entire hail disaster process; ...arrive These are the first real-time important physical characteristic data to the u-th real-time important physical characteristic data within the last detection period T of the entire hail disaster process of the power distribution network; Step 5: Substitute the data from C and D into the following formula (6) to calculate the first impact probability R1 of the first important physical characteristic of the hail disaster affecting the fault of the distribution network components during the g-th detection period T of the entire hail disaster process. In equation (6), This refers to the mean of all data for the first important physical feature in the aforementioned historical hail disaster important feature dataset C; This refers to the minimum value among all data of the first important physical feature in the historical hail disaster important feature dataset C; The probability R2 of the second important physical characteristic of the hail disaster affecting the failure of the distribution network components during the g-th detection period T in the entire hail disaster process is calculated by the following formula (7). In equation (7), This refers to the mean of all data for the second most important physical feature in the aforementioned historical hail disaster important feature dataset C; This refers to the minimum value among all data of the second most important physical feature in the historical hail disaster important feature dataset C; Repeat the above steps to calculate the third probability R3 to the uth probability R of the hail disaster affecting the power distribution network component failure during the g-th detection period T of the entire hail disaster process. u The probability of influence of the distribution network from the first influence R1 to the uth influence R during the g-th detection period T of the entire hail disaster process. u Substituting into equation (8), the component failure rate R of the distribution network in the g-th detection period T is calculated. g , In equation (8), a1, a2, ... to a u These are the first proportional coefficients to the uth proportional coefficients of the first important physical characteristic to the uth important physical characteristic of the hail disaster, all of which are empirical values; Step 6: Calculate the time from the start of the hail disaster t0 to the end of the g-th detection period T during the hail disaster using the following formula (9). The critical load loss rate X of the distribution network g , In equation (9), P d This is the key load change curve of the power distribution network during a hail disaster; The following formula (10) is used to calculate the time from the start of the hail disaster t0 to the end of the g-th detection period T during the hail disaster in the distribution network. Mobile energy storage response rate Y g , In equation (10), L d This is the change curve of mobile energy storage when the power distribution network experiences a hail disaster; The distribution network resilience assessment index Q during the g-th detection period T in the entire hail disaster process is calculated using the following formula (11). g To conduct a resilience assessment of the power distribution network under hail disaster. In equation (11), L i p is the mobile energy storage deployed for disaster recovery in the distribution network during the i-th detection period T of the entire hail disaster process; i,t It is the load of the power distribution network during the i-th detection period T throughout the entire hail disaster.

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