A preventive partitioning method and system for an elastic power distribution network considering typhoon path uncertainty

Abstract

This invention discloses a preventive zoning method and system for resilient distribution networks considering the uncertainty of typhoon paths. First, a set of scenarios considering fault conditions under different typhoon paths is constructed. Then, through multi-round detection, the damage status of nodes and lines under different typhoon path scenarios is identified, and a node and line damage status detection model considering fault propagation is constructed. Next, combined with the node and line damage detection model, a preventive zoning model for resilient distribution networks considering the uncertainty of typhoon paths is constructed based on a stochastic optimization method. Finally, the constructed model is linearized to reduce the solution difficulty and then solved to obtain optimized distribution network islanding and node power supply strategies. The technical solution of this invention can properly handle the uncertainty of typhoon paths during distribution network islanding, effectively improving the risk resistance capability of post-disaster distribution network power supply restoration schemes.

Description

Technical Field

[0001] This invention belongs to the field of power grid technology, and in particular to a method and system for preventive zoning of flexible distribution networks that takes into account the uncertainty of typhoon paths. Background Technology

[0002] In recent years, frequent natural disasters have posed unprecedented challenges to the safe and reliable operation of power systems. Natural disasters often exceed the normal design standards of power systems, making power infrastructure prone to widespread physical failures and subsequent power outages. Against this backdrop, many scholars have proposed the concept of a "resilient distribution network" to mitigate the risk of power outage losses caused by extreme natural disasters. With the large-scale integration of distributed energy resources into the distribution system, a foundation has been laid for achieving resilient operation of the power grid after disasters. In areas within the distribution network where no faults have occurred, preventative zoning divides the distribution network into isolated microgrids powered by distributed energy sources. Restoring power to critical loads can effectively reduce the scale and duration of power outages.

[0003] Using typhoons as a typical natural disaster scenario, scholars have begun to focus on microgrid-based power restoration methods to ensure reliable power supply to critical loads after power outages. Extensive research has been conducted on microgrid structure optimization and resilient operation control. However, existing research primarily focuses on preventative zoning methods for distribution networks, with few studies considering the impact of the uncertainty of distribution network fault locations under different typhoon paths on preventative zoning schemes. In fact, current meteorological forecasting techniques struggle to accurately predict the impact on distribution networks throughout a typhoon disaster. Under different typhoon paths, the location of line faults within the distribution network also changes, making it difficult to obtain accurate fault locations. This leads to a decrease in the load restoration effectiveness of preventative zoning schemes and threatens the safety of power supply at nodes. Therefore, it is necessary to consider the uncertainty of line fault locations caused by typhoon path uncertainty when formulating preventative zoning strategies for resilient distribution networks, thereby improving the reliability of islanded operation of the distribution network after a disaster. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in the prior art by providing a resilient distribution network preventative zoning method and system that considers the impact of typhoon path uncertainty on internal lines and nodes of the distribution network. First, a set of scenarios considering fault conditions under different typhoon paths is constructed. Then, through multiple rounds of detection, the damage status of nodes and lines under different typhoon path scenarios is identified, and a node and line damage status detection model considering fault propagation is constructed. Next, combined with the node and line damage detection model, a resilient distribution network preventative zoning model considering typhoon path uncertainty is constructed based on a stochastic optimization method. Finally, the constructed model is linearized to reduce the solution difficulty and then solved to obtain optimized distribution network islanding and node power supply strategies. The technical solution of this invention can effectively handle the uncertainty of typhoon paths during the preventative zoning process of the distribution network, effectively improving the risk resistance capability of the resilient distribution network preventative zoning scheme.

[0005] The technical solution to achieve the objective of this invention is as follows: On the one hand, a preventive zoning method for a flexible distribution network considering the uncertainty of typhoon paths is provided, the method comprising the following steps:

[0006] Step 1: Construct a set of failure scenarios considering different typhoon paths;

[0007] Step 2: Through multiple rounds of detection, identify the damage status of nodes and lines under different typhoon path failure scenarios, and construct a node and line damage status detection model;

[0008] Step 3: Based on the model constructed in Step 2, construct a preventive zoning model for the resilient distribution network that considers the uncertainty of the typhoon path using a stochastic optimization method.

[0009] Step 4: Linearize the models from Steps 3 and 4 and solve them jointly to obtain the optimized distribution network preventive zoning and node power supply strategies.

[0010] Furthermore, step 1 constructs a set of scenarios considering fault conditions under different typhoon paths, specifically including:

[0011] Several fault scenarios are generated based on different typhoon paths. The fault location within the distribution network is different in each fault scenario, and each fault scenario has a different probability of occurrence, satisfying the following formula:

[0012]

[0013] In the formula, s represents a fault scenario, and S is the set of fault scenarios. It represents the probability of scenario s occurring.

[0014] Furthermore, step 2 involves identifying the damage status of nodes under different typhoon path failure scenarios through multiple rounds of detection, specifically including:

[0015] In the first round of testing, the node's damage status depends only on whether a remote control switch has been installed on the line near the node and whether the line connected to the node has failed in the current typhoon path failure scenario; in the t-th round of testing, the node's damage status depends on whether a remote control switch has been installed on the line connected to the node and the damage status of the node connected to the node in the previous round of testing in the current typhoon path failure scenario; t is greater than 1.

[0016] Specifically, it is expressed as follows:

[0017]

[0018]

[0019]

[0020] In the formula, T represents the total number of detection rounds; N represents the set of load nodes; and S represents the set of fault scenarios, where the location of the fault varies under different typhoon path fault scenarios. This is a 0-1 variable, representing the occurrence of line fault propagation between nodes i and j in the t-th detection under fault scenario s. A value of 0 indicates that a fault has propagated along the line. A value of 1 indicates that fault propagation did not occur or was blocked on that line; Indicates the number of lines connected to node i; The variable is 0-1, representing the damaged state of node i in the t-th detection under fault scenario s. A value of 1 indicates that node i is not damaged. A value of 0 indicates that node i is damaged; , Both are 0-1 variables, representing the damaged state of node i in the (t-1)th and Tthth detections under fault scenario s, respectively; It is a constant used to ensure that the constraint holds. It is a 0-1 variable, representing the damaged state of node i in the last round of detection under fault scenario s. A value of 1 indicates that node i was not damaged by fault propagation. A value of 0 indicates that node i is affected by fault propagation and power restoration is not allowed.

[0021] Furthermore, step 2 involves identifying the damage status of the lines under different typhoon path failure scenarios through multiple rounds of detection, specifically including:

[0022] The extent of damage to the line depends on whether it itself failed under the current typhoon path failure scenario and whether fault propagation occurred on that line during the last inspection; the occurrence of fault propagation depends on the fault condition of the line itself, the deployment of remote control switches, and the impact of fault propagation on the connected nodes, specifically as follows:

[0023]

[0024]

[0025]

[0026]

[0027] Where L is the set of lines; This is a 0-1 variable, representing the installation status of a remote control switch that can isolate faults on the line between nodes i and j, near node i. A value of 1 indicates that it has been installed. A value of 0 indicates that no device has been installed. This is a 0-1 variable, representing the installation status of a remote control switch that can isolate faults at the end of the line between nodes i and j, near node j. A value of 1 indicates that it has been installed. A value of 0 indicates that no device has been installed. This is a 0-1 variable, representing the installation status of remote control switches on the line between nodes i and j. A value of 1 indicates that the cable is installed at both ends or at least at one end. A value of 0 indicates that no remote control switch has been installed; This is a 0-1 variable, representing whether a fault occurs in the line between nodes i and j under fault scenario s. A value of 0 indicates that a fault has occurred on the line. A value of 1 indicates that there is no fault in the line; The variable is 0-1, representing the damaged state of node j in the t-th detection under fault scenario s. A value of 1 indicates that node j is not damaged. A value of 0 indicates that node j is damaged; It is a 0-1 variable, representing whether power can be allowed to pass through the line between nodes i and j after the fault scenario s is detected. A value of 1 indicates that power is allowed to pass. A value of 0 indicates that power cannot be allowed to pass through; This is a 0-1 variable, representing the occurrence of line fault propagation between nodes i and j in the T-th detection under fault scenario s. A value of 0 indicates that a fault has propagated along the line. A value of 1 indicates that the fault propagation did not occur or was blocked on that line.

[0028] Furthermore, step 3, combined with the model constructed in step 2, involves building a resilient distribution network preventative zoning model that considers the uncertainty of typhoon paths based on a stochastic optimization method. Specifically, this includes:

[0029] Step 3-1: Taking the maximum expected value of the weighted load restoration amount for different typhoon path fault scenarios during the fault duration as the power supply restoration objective, the objective function is constructed as follows:

[0030]

[0031] The load restoration amount is the difference between the load demand and the load shedding amount. This represents the load weight of node i; It represents the set of load nodes that are fault-free and unaffected by cascading failures, and is a subset of the load node set N; This represents the active power load demand of node i. This represents the amount of active power load cutoff at node i under fault scenario s. Let represent the probability of failure scenario s occurring; S represents the set of failure scenarios.

[0032] Step 3-2, establish constraints, including:

[0033] (1) A node belongs to at most one microgrid; at the same time, if a node is damaged by fault propagation, that node will not belong to any zone:

[0034]

[0035] Where M is a collection of microgrids; It is a 0-1 variable that represents the partition affiliation of node i with microgrid m. When it is 1, it means that node i belongs to microgrid m, and when it is 0, it means that node i does not belong to microgrid m.

[0036] (2) A distributed power source can only supply power to one node and can be deployed on one node:

[0037]

[0038]

[0039] Where K is the set of distributed power sources; It is a 0-1 variable that indicates whether the distributed power source k is deployed on node i. When it is 1, it means that the distributed power source k is deployed on node i, and when it is 0, it means that the distributed power source k is not deployed on node i.

[0040] (3) When a node is connected to a distributed power source with frequency and voltage support capabilities, the node must be powered by the zone where the distributed power source is located:

[0041]

[0042] Among them, K b It is a collection of distributed power sources with frequency and voltage support capabilities, and is a subset of K;

[0043] (4) Distributed power sources with frequency and voltage support capabilities are fixed at the initial node:

[0044]

[0045] in, It is a collection of distributed power nodes with frequency and voltage support capabilities, and is a subset of N;

[0046] (5) The relationship between the line and the microgrid:

[0047]

[0048] in, This represents the set of lines that have not experienced any faults. It is a 0-1 variable that represents the affiliation of the line between nodes i and j with microgrid m. When it is 1, it means that the line belongs to microgrid m, and when it is 0, it means that the line does not belong to microgrid m. It is a 0-1 variable that represents the partition affiliation of node j with microgrid m. When it is 1, it means that node j belongs to microgrid m, and when it is 0, it means that node j does not belong to microgrid m.

[0049] (6) The operating status of the remote control switch on the line is determined by the microgrid line ownership:

[0050]

[0051] Among them, b i,j It is a 0-1 variable that represents the closed state of the switch on the line between nodes i and j. A value of 1 indicates that the switch is closed, and a value of 0 indicates that the switch is open.

[0052] (7) Lines and load nodes that have already experienced faults or have been damaged due to fault propagation need to be excluded from the microgrid:

[0053]

[0054]

[0055] Where L is the set of lines;

[0056] (8) The generated microgrid topology needs to satisfy the radial constraint:

[0057]

[0058] in, This indicates the maximum number of branches allowed in a microgrid's radial topology;

[0059] (9) In the established microgrid island partitions, under each fault scenario, it is necessary to adjust the generator output and load shedding amount to schedule the microgrid; the power flow constraints in the microgrid need to be considered, and the model is as follows:

[0060]

[0061]

[0062]

[0063]

[0064] in, , These represent the active power and reactive power flowing from node i to node j on the line between nodes i and j under fault scenario s, respectively. , These represent the active power and reactive power flowing from node j to node i on the line between nodes i and j under fault scenario s, respectively. and These represent the active and reactive power outputs of the distributed power source on node i under fault scenario s, respectively. This represents the resistance of the line between nodes i and j; This represents the reactance of the line between nodes i and j; , These represent the amount of reactive and active loads to be removed from node i under fault scenario s, respectively. , V1 and V2 represent the voltages of node i and node j under fault scenario s, respectively; V0 is the reference voltage of the microgrid. As a slack variable, the constraint is guaranteed to hold when node i and node j do not belong to the same microgrid under fault scenario s; This represents the reactive power load demand of node i.

[0065] (10) In all fault scenarios, the voltage at the node where power is restored must be within a safe range:

[0066]

[0067] in, It is the maximum permissible voltage deviation percentage;

[0068] (11) Under all fault scenarios, the voltage of the node connected to the distributed power source with frequency voltage support capability shall be used as the reference voltage:

[0069]

[0070] (12) Under all fault scenarios, the output of distributed power sources connected to nodes within the microgrid must meet the upper / lower output limits:

[0071]

[0072]

[0073] in, , , and These represent the upper limit of active power output, the lower limit of active power output, the upper limit of reactive power output, and the lower limit of reactive power output of the distributed power source k, respectively.

[0074] (13) Under all fault scenarios, the load shedding amount on the load node must meet the following constraints: Nodes marked as unhealthy during the previous fault propagation detection process will shed all loads; the ratio of active / reactive load shedding amount to active / reactive load demand is consistent:

[0075]

[0076]

[0077] in, , Let represent the reactive load demand and active load demand of node i, respectively. This represents the amount of reactive load shelving at node i under fault scenario s;

[0078] (14) Under all fault scenarios, the active / reactive power flowing through the microgrid lines must be limited to the line capacity, and power is not allowed to pass through lines affected by fault propagation:

[0079]

[0080]

[0081] in, , These represent the maximum active power and maximum reactive power allowed to pass through the line between nodes i and j, respectively.

[0082] Furthermore, step 4 involves linearizing the models from steps 2 and 3 and solving them jointly to obtain optimized distribution network preventive zoning and node power supply strategies, specifically including:

[0083] Step 4-1 involves linearizing the constraints in the node and line damage state detection model that describe whether a line can be put into normal use under fault propagation conditions. Specifically:

[0084]

[0085] Step 4-2 involves linearizing the constraints in the node and line damage state detection model that describe the occurrence of fault propagation on the line. Specifically:

[0086]

[0087] in, It is an intermediate 0-1 variable generated after constraint linearization, representing the damage status of the line between nodes i and j in this round of detection under fault scenario s, and is only 1 when both nodes are undamaged;

[0088] Step 4-3: Linearize the constraints describing the active / reactive power limits flowing through microgrid lines in the resilient distribution network preventive zoning model that considers typhoon path uncertainty.

[0089]

[0090] in, It is a 0-1 variable used to constrain the linearization process;

[0091] Step 4-4: Based on the linearized constraints, solve the node and line damage status detection models considering fault propagation and the resilient distribution network preventive zoning model considering typhoon path uncertainty under different typhoon path fault scenarios after steps 4-1 to 4-3, to obtain the optimal resilient distribution network preventive zoning scheme, including the node affiliation. Operating status of remote control switch Ownership of distributed power sources Simultaneously, it obtains the node power supply strategies under different fault scenarios, including the active power output of distributed power sources. Unproductive efforts Active power reduction of load and reactive power reduction .

[0092] On the other hand, a resilient distribution network preventive zoning system considering the uncertainty of typhoon paths is provided, the system comprising:

[0093] The first module is used to build a set of scenarios that consider failure situations under different typhoon paths;

[0094] The second module is used to identify the damage status of nodes and lines under different typhoon path failure scenarios through multiple rounds of detection, and to build a node and line damage status detection model.

[0095] The third module is used to combine the model built in the second module to construct a preventive zoning model for a resilient distribution network that takes into account the uncertainty of the typhoon path based on the stochastic optimization method.

[0096] The fourth module is used to linearize the two models and solve them jointly to obtain optimized distribution network preventive zoning and node power supply strategies.

[0097] On the other hand, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the resilient distribution network preventive zoning method that takes into account the uncertainty of typhoon paths.

[0098] On the other hand, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the resilient distribution network preventive zoning method that takes into account the uncertainty of typhoon paths.

[0099] Compared with the prior art, the significant advantages of this invention are:

[0100] (1) When establishing the distribution network partitioning model, this invention considers the situation where the location of line faults is difficult to obtain accurately under the uncertainty of typhoon path; by modeling the distribution network islanding problem that considers the uncertainty of line fault location as a mathematical optimization model based on stochastic optimization method, the proposed model can effectively handle the uncertainty of line fault location and improve the risk resistance of the generated islanding method under typhoon disaster scenario.

[0101] (2) In establishing a preventive zoning model for a flexible distribution network that considers the uncertainty of typhoon paths, this invention models the distribution network islanding problem as a stochastic optimization model. First, a progressive detection method is used to identify whether load nodes and lines are affected by faults. Then, considering the uncertainty of typhoon paths, an islanding model based on a stochastic optimization method is established that considers the uncertainty of fault locations. Finally, the fault impact detection model and the islanding model are combined and solved synchronously. Compared with the traditional distribution network islanding method that first determines the fault location and then identifies the outage area, the method proposed in this invention establishes the fault impact detection process as a linear constraint that can be solved synchronously with the distribution network islanding model. This allows for the simultaneous handling of the uncertainty of fault locations and the problem of outage area expansion caused by fault propagation within the distribution network islanding model, thereby reducing the workload of distribution network control personnel and the complexity of the distribution network islanding problem.

[0102] (3) When establishing the preventive zoning model for the resilient distribution network, this invention considers the phenomenon of fault propagation leading to the expansion of the fault area, and can more accurately model the impact of faults caused by typhoons within the microgrid on the distribution network zoning scheme. This invention optimizes the distribution network zoning scheme by using a distribution network zoning model based on stochastic optimization to optimize variables such as the opening and closing status of remote control switches and the amount of load shedding. The optimized distribution network zoning scheme can accurately identify outage areas with high uncertainty, thereby reducing the impact of typhoon path uncertainty on the formed isolated microgrid, and ultimately improving the load recovery of the preventive zoning scheme for the resilient distribution network under the influence of typhoon path uncertainty.

[0103] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0104] Figure 1 This is a flowchart of a preventive zoning method for a resilient distribution network that takes into account the uncertainty of typhoon paths in one embodiment.

[0105] Figure 2 This is a schematic diagram of an IEEE 37-node test system in one embodiment.

[0106] Figure 3 This is a schematic diagram of the partitioning results of a power distribution network using this method in one embodiment.

[0107] Figure 4 This is a schematic diagram of the distribution network partitioning results in one embodiment without considering the uncertainty of the typhoon path. Detailed Implementation

[0108] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0109] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0110] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0111] In one embodiment, combined Figure 1 A preventive zoning method for resilient distribution networks considering the uncertainty of typhoon paths is provided. The method includes the following steps:

[0112] Step 1: Construct a set of failure scenarios considering different typhoon paths;

[0113] Step 2: Through multiple rounds of detection, identify the damage status of nodes and lines under different typhoon path failure scenarios, and construct a node and line damage status detection model;

[0114] Step 3: Based on the model constructed in Step 2, construct a preventive zoning model for the resilient distribution network that considers the uncertainty of the typhoon path using a stochastic optimization method.

[0115] Step 4: Linearize the models from Steps 3 and 4 and solve them jointly to obtain the optimized distribution network preventive zoning and node power supply strategies.

[0116] Furthermore, in one embodiment, step 1 involves constructing a set of scenarios considering fault conditions under different typhoon paths, specifically including:

[0117] Under the influence of typhoon disasters, the failure status of power lines needs to be estimated by combining vulnerability models and wind speed. Wind speed is related to the distance of the power line from the typhoon center. Therefore, it can be seen that the failure scenario of the power line under typhoon disaster is closely related to the typhoon path.

[0118] Several fault scenarios are generated based on different typhoon paths. The fault location within the distribution network is different in each fault scenario, and each fault scenario has a different probability of occurrence, satisfying the following formula:

[0119]

[0120] In the formula, s represents a fault scenario, and S is the set of fault scenarios. It represents the probability of scenario s occurring.

[0121] Furthermore, in one embodiment, step 2 involves identifying the damaged state of nodes under different typhoon path failure scenarios through multiple rounds of detection, specifically including:

[0122] Faults occurring on the line need to be isolated using remote control switches; otherwise, the power outage area will expand along lines without isolation switches. Therefore, in the first round of testing, the node's damage status depends only on whether a remote control switch is installed on the line closest to the node, and whether the line connected to the node has failed under the current typhoon path fault scenario. In the t-th round of testing, the node's damage status depends on the installation of a remote control switch on the line connected to the node, and the damage status of the node connected to the node in the previous round of testing under the current typhoon path fault scenario; t is greater than 1.

[0123] Specifically, it is expressed as follows:

[0124]

[0125]

[0126]

[0127] In the formula, T represents the total number of detection rounds; N represents the set of load nodes; and S represents the set of fault scenarios, where the location of the fault varies under different typhoon path fault scenarios. This is a 0-1 variable, representing the occurrence of line fault propagation between nodes i and j in the t-th detection under fault scenario s. A value of 0 indicates that a fault has propagated along the line. A value of 1 indicates that fault propagation did not occur or was blocked on that line; Indicates the number of lines connected to node i; The variable is 0-1, representing the damaged state of node i in the t-th detection under fault scenario s. A value of 1 indicates that node i is not damaged. A value of 0 indicates that node i is damaged; , Both are 0-1 variables, representing the damaged state of node i in the (t-1)th and Tthth detections under fault scenario s, respectively; It is a constant used to ensure that the constraint holds. It is a 0-1 variable, representing the damaged state of node i in the last round of detection under fault scenario s. A value of 1 indicates that node i was not damaged by fault propagation. A value of 0 indicates that node i is affected by fault propagation and power restoration is not allowed.

[0128] Furthermore, in one embodiment, step 2 involves identifying the damage status of the lines under different typhoon path failure scenarios through multiple rounds of detection, specifically including:

[0129] The extent of damage to the line depends on whether it itself failed under the current typhoon path failure scenario and whether fault propagation occurred on that line during the last inspection; the occurrence of fault propagation depends on the fault condition of the line itself, the deployment of remote control switches, and the impact of fault propagation on the connected nodes, specifically as follows:

[0130]

[0131]

[0132]

[0133]

[0134] Where L is the set of lines; This is a 0-1 variable, representing the installation status of a remote control switch that can isolate faults on the line between nodes i and j, near node i. A value of 1 indicates that it has been installed. A value of 0 indicates that no device has been installed. This is a 0-1 variable, representing the installation status of a remote control switch that can isolate faults at the end of the line between nodes i and j, near node j. A value of 1 indicates that it has been installed. A value of 0 indicates that no device has been installed. This is a 0-1 variable, representing the installation status of remote control switches on the line between nodes i and j. A value of 1 indicates that the cable is installed at both ends or at least at one end. A value of 0 indicates that no remote control switch has been installed; This is a 0-1 variable, representing whether a fault occurs in the line between nodes i and j under fault scenario s. A value of 0 indicates that a fault has occurred on the line. A value of 1 indicates that there is no fault in the line; The variable is 0-1, representing the damaged state of node j in the t-th detection under fault scenario s. A value of 1 indicates that node j is not damaged. A value of 0 indicates that node j is damaged; It is a 0-1 variable, representing whether power can be allowed to pass through the line between nodes i and j after the fault scenario s is detected. A value of 1 indicates that power is allowed to pass. A value of 0 indicates that power cannot be allowed to pass through; This is a 0-1 variable, representing the occurrence of line fault propagation between nodes i and j in the T-th detection under fault scenario s. A value of 0 indicates that a fault has propagated along the line. A value of 1 indicates that the fault propagation did not occur or was blocked on that line.

[0135] Furthermore, in one embodiment, step 3, which combines the model constructed in step 2, constructs a resilient distribution network preventive zoning model considering the uncertainty of typhoon paths based on a stochastic optimization method, specifically including:

[0136] Step 3-1: Taking the maximum expected value of the weighted load restoration amount for different typhoon path fault scenarios during the fault duration as the power supply restoration objective, the objective function is constructed as follows:

[0137]

[0138] The load restoration amount is the difference between the load demand and the load shedding amount. This represents the load weight of node i; It represents the set of load nodes that are fault-free and unaffected by cascading failures, and is a subset of the load node set N; This represents the active power load demand of node i. This represents the amount of active power load cutoff at node i under fault scenario s. Let represent the probability of failure scenario s occurring; S represents the set of failure scenarios.

[0139] Step 3-2, establish constraints, including:

[0140] (1) A node belongs to at most one microgrid; at the same time, if a node is damaged by fault propagation, that node will not belong to any zone:

[0141]

[0142] Where M is a collection of microgrids; It is a 0-1 variable that represents the partition affiliation of node i with microgrid m. When it is 1, it means that node i belongs to microgrid m, and when it is 0, it means that node i does not belong to microgrid m.

[0143] (2) A distributed power source can only supply power to one node and can be deployed on one node:

[0144]

[0145]

[0146] Where K is the set of distributed power sources; It is a 0-1 variable that indicates whether the distributed power source k is deployed on node i. When it is 1, it means that the distributed power source k is deployed on node i, and when it is 0, it means that the distributed power source k is not deployed on node i.

[0147] (3) When a node is connected to a distributed power source with frequency and voltage support capabilities, the node must be powered by the zone where the distributed power source is located:

[0148]

[0149] Among them, K b It is a collection of distributed power sources with frequency and voltage support capabilities, and is a subset of K;

[0150] (4) Distributed power sources with frequency and voltage support capabilities are fixed at the initial node:

[0151]

[0152] in, It is a collection of distributed power nodes with frequency and voltage support capabilities, and is a subset of N;

[0153] (5) The relationship between the line and the microgrid:

[0154]

[0155] in, This represents the set of lines that have not experienced any faults. It is a 0-1 variable that represents the affiliation of the line between nodes i and j with microgrid m. When it is 1, it means that the line belongs to microgrid m, and when it is 0, it means that the line does not belong to microgrid m. It is a 0-1 variable that represents the partition affiliation of node j with microgrid m. When it is 1, it means that node j belongs to microgrid m, and when it is 0, it means that node j does not belong to microgrid m.

[0156] (6) The operating status of the remote control switch on the line is determined by the microgrid line ownership:

[0157]

[0158] Among them, b i,j It is a 0-1 variable that represents the closed state of the switch on the line between nodes i and j. A value of 1 indicates that the switch is closed, and a value of 0 indicates that the switch is open.

[0159] (7) Lines and load nodes that have already experienced faults or have been damaged due to fault propagation need to be excluded from the microgrid:

[0160]

[0161]

[0162] Where L is the set of lines;

[0163] (8) The generated microgrid topology needs to satisfy the radial constraint:

[0164]

[0165] in, This indicates the maximum number of branches allowed in a microgrid's radial topology;

[0166] (9) In the established microgrid island partitions, under each fault scenario, it is necessary to adjust the generator output and load shedding amount to schedule the microgrid; the power flow constraints in the microgrid need to be considered, and the model is as follows:

[0167]

[0168]

[0169]

[0170]

[0171] in, , These represent the active power and reactive power flowing from node i to node j on the line between nodes i and j under fault scenario s, respectively. , These represent the active power and reactive power flowing from node j to node i on the line between nodes i and j under fault scenario s, respectively. and These represent the active and reactive power outputs of the distributed power source on node i under fault scenario s, respectively. This represents the resistance of the line between nodes i and j; This represents the reactance of the line between nodes i and j; , These represent the amount of reactive and active loads to be removed from node i under fault scenario s, respectively. , V1 and V2 represent the voltages of node i and node j under fault scenario s, respectively; V0 is the reference voltage of the microgrid. As a slack variable, the constraint is guaranteed to hold when node i and node j do not belong to the same microgrid under fault scenario s; This represents the reactive power load demand of node i.

[0172] (10) In all fault scenarios, the voltage at the node where power is restored must be within a safe range:

[0173]

[0174] in, It is the maximum permissible voltage deviation percentage;

[0175] (11) Under all fault scenarios, the voltage of the node connected to the distributed power source with frequency voltage support capability shall be used as the reference voltage:

[0176]

[0177] (12) Under all fault scenarios, the output of distributed power sources connected to nodes within the microgrid must meet the upper / lower output limits:

[0178]

[0179]

[0180] in, , , and These represent the upper limit of active power output, the lower limit of active power output, the upper limit of reactive power output, and the lower limit of reactive power output of the distributed power source k, respectively.

[0181] (13) Under all fault scenarios, the load shedding amount on the load node must meet the following constraints: Nodes marked as unhealthy during the previous fault propagation detection process will shed all loads; the ratio of active / reactive load shedding amount to active / reactive load demand is consistent:

[0182]

[0183]

[0184] in, , Let represent the reactive load demand and active load demand of node i, respectively. This represents the amount of reactive load shelving at node i under fault scenario s;

[0185] (14) Under all fault scenarios, the active / reactive power flowing through the microgrid lines must be limited to the line capacity, and power is not allowed to pass through lines affected by fault propagation:

[0186]

[0187]

[0188] in, , These represent the maximum active power and maximum reactive power allowed to pass through the line between nodes i and j, respectively.

[0189] Furthermore, in one embodiment, step 4, which involves linearizing the models from steps 2 and 3 and solving them jointly to obtain an optimized distribution network preventive zoning and node power supply strategy, specifically includes:

[0190] Step 4-1 involves linearizing the constraints in the node and line damage state detection model that describe whether a line can be put into normal use under fault propagation conditions. Specifically:

[0191]

[0192] Step 4-2 involves linearizing the constraints in the node and line damage state detection model that describe the occurrence of fault propagation on the line. Specifically:

[0193]

[0194] in, It is an intermediate 0-1 variable generated after constraint linearization, representing the damage status of the line between nodes i and j in this round of detection under fault scenario s, and is only 1 when both nodes are undamaged;

[0195] Step 4-3: Linearize the constraints describing the active / reactive power limits flowing through microgrid lines in the resilient distribution network preventive zoning model that considers typhoon path uncertainty.

[0196]

[0197] in, It is a 0-1 variable used to constrain the linearization process;

[0198] Step 4-4: Based on the linearized constraints, solve the node and line damage status detection models considering fault propagation and the resilient distribution network preventive zoning model considering typhoon path uncertainty under different typhoon path fault scenarios after steps 4-1 to 4-3, to obtain the optimal resilient distribution network preventive zoning scheme, including the node affiliation. Operating status of remote control switch Ownership of distributed power sources Simultaneously, it obtains the node power supply strategies under different fault scenarios, including the active power output of distributed power sources. Unproductive efforts Active power reduction of load and reactive power reduction .

[0199] In one embodiment, a resilient distribution network preventative zoning system considering typhoon path uncertainty is provided, the system comprising:

[0200] The first module is used to build a set of scenarios that consider failure situations under different typhoon paths;

[0201] The second module is used to identify the damage status of nodes and lines under different typhoon path failure scenarios through multiple rounds of detection, and to build a node and line damage status detection model.

[0202] The third module is used to combine the model built in the second module to construct a preventive zoning model for a resilient distribution network that takes into account the uncertainty of the typhoon path based on the stochastic optimization method.

[0203] The fourth module is used to linearize the two models and solve them jointly to obtain the optimized distribution network preventive zoning and node power supply strategies.

[0204] Specific limitations regarding the preventive zoning system for resilient distribution networks considering typhoon path uncertainty can be found in the limitations of the preventive zoning method for resilient distribution networks considering typhoon path uncertainty described above, and will not be repeated here. Each module in the aforementioned preventive zoning system for resilient distribution networks considering typhoon path uncertainty can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0205] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements:

[0206] Step 1: Construct a set of failure scenarios considering different typhoon paths;

[0207] Step 2: Through multiple rounds of detection, identify the damage status of nodes and lines under different typhoon path failure scenarios, and construct a node and line damage status detection model;

[0208] Step 3: Based on the model constructed in Step 2, construct a preventive zoning model for the resilient distribution network that considers the uncertainty of the typhoon path using a stochastic optimization method.

[0209] Step 4: Linearize the models from Steps 3 and 4 and solve them jointly to obtain the optimized distribution network preventive zoning and node power supply strategies.

[0210] For specific limitations on each step, please refer to the limitations on the preventive zoning method for resilient distribution networks considering the uncertainty of typhoon paths mentioned above, which will not be repeated here.

[0211] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program being implemented when executed by a processor:

[0212] Step 1: Construct a set of failure scenarios considering different typhoon paths;

[0213] Step 2: Through multiple rounds of detection, identify the damage status of nodes and lines under different typhoon path failure scenarios, and construct a node and line damage status detection model;

[0214] Step 3: Based on the model constructed in Step 2, construct a preventive zoning model for the resilient distribution network that considers the uncertainty of the typhoon path using a stochastic optimization method.

[0215] Step 4: Linearize the models from Steps 3 and 4 and solve them jointly to obtain the optimized distribution network preventive zoning and node power supply strategies.

[0216] For specific limitations on each step, please refer to the limitations on the preventive zoning method for resilient distribution networks considering the uncertainty of typhoon paths mentioned above, which will not be repeated here.

[0217] As a specific example, the invention will be further described and verified in detail in one embodiment.

[0218] Taking the improved IEEE-37 node test system as an example, the effectiveness of the proposed preventive zoning method for resilient distribution networks under typhoon path uncertainty is verified. The network topology is as follows: Figure 2 As shown.

[0219] Assume that after an extreme natural disaster, line 37-26 connecting the distribution network to the main grid has failed, causing a complete power outage in the distribution network. The system includes three fixed distributed power sources with frequency and voltage support capabilities, and two mobile emergency power sources to be configured. Power supply parameters are shown in Table 1, where "F" indicates that the power source has been pre-deployed and fixed at the node, while "M" indicates that the power source is mobile. The 0 / 1 status reflects the frequency and voltage support capability of the power supply equipment, where "1" indicates that the power source has frequency and voltage support capability, and "0" indicates that the power source does not have frequency and voltage support capability.

[0220] Table 1 Distributed Power Generation Parameters

[0221]

[0222] Considering the uncertainty of the typhoon's path, there are two additional faults with uncertain locations within the distribution network. This example randomly generated five fault scenarios, and their probabilities and the faulty lines within each scenario are shown in Table 2.

[0223] Table 2 Fault Scenario Parameters

[0224]

[0225] The active and reactive power demands of each node load in the system are randomly generated within the intervals [5,20] kW and [3,18] kVar, respectively. Based on the importance of the load, the loads are divided into three importance levels and assigned load node weight values ​​of 5 / 2 / 1, respectively.

[0226] The system includes fault isolation devices such as remote control switches installed on various lines, as follows: Figure 2 As shown. Some lines only have a disconnect switch installed at the end closest to the node, and the location of this installation end is... Figure 2 It has been marked in the text.

[0227] Through the optimization decision-making process of this invention, the preventive zoning results of the flexible distribution network are obtained as follows: Figure 3 As shown, the islanding method proposed in this invention can generate microgrids that conform to radial topology constraints, providing power supply to power outage loads within the distribution network as much as possible. Furthermore, mobile distributed power sources tend to be deployed near critical loads to facilitate their power support.

[0228] Figure 4The results of preventative zoning of a resilient distribution network without considering typhoon path uncertainty are presented. In this islanding method, the fault locations and outage areas of the lines are considered fixed because the uncertainty of the typhoon path is ignored. Table 3 shows a comparison of the expected weighted load recovery amounts for the two zoning methods. The expected load recovery amounts for the two islanding methods are obtained through the following steps: 1) Fix the islanding scheme obtained by the current model optimization; 2) Apply it to each fault scenario shown in Table 2 and calculate the weighted load recovery amount for each scenario; 3) Multiply the weighted load recovery amount for each scenario by its scenario probability and sum them.

[0229] Table 3 Comparison of Load Recovery Status

[0230]

[0231] As shown in Table 3, the resilient distribution network preventive zoning model considering typhoon path uncertainty yields a better expected weighted load recovery. This is because the proposed model uses a stochastic optimization method to address the uncertainty of fault locations and outage areas. In contrast, islanding methods that do not consider typhoon path uncertainty only generate zoning schemes applicable when fault locations and outage areas are fixed. Once the typhoon path changes, the expected fault locations also change, consequently altering the outage areas and severely impacting the effectiveness of the original islanding scheme. Therefore, the proposed resilient distribution network preventive zoning strategy considering typhoon path uncertainty better handles this uncertainty, effectively enhancing the resilience of post-disaster distribution network power restoration schemes.

[0232] The technical solution of this invention can properly handle the uncertainty of typhoon paths during the islanding process of power distribution networks, and effectively improve the risk resistance of the power supply restoration plan for power distribution networks after disasters.

[0233] The above embodiments illustrate and describe the basic principles and main features of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. A preventive zoning method for a flexible distribution network considering the uncertainty of typhoon paths, characterized in that, The method includes the following steps: Step 1: Construct a set of failure scenarios considering different typhoon paths; Step 2: Through multiple rounds of detection, identify the damage status of nodes and lines under different typhoon path failure scenarios, and construct a node and line damage status detection model; Step 3: Based on the model constructed in Step 2, construct a preventive zoning model for the resilient distribution network that considers the uncertainty of the typhoon path using a stochastic optimization method. Step 4: Linearize the models from Steps 3 and 4 and solve them jointly to obtain the optimized distribution network preventive zoning and node power supply strategies.

2. The preventive zoning method for flexible distribution networks considering typhoon path uncertainty according to claim 1, characterized in that, Step 1 involves constructing a set of scenarios that consider fault conditions under different typhoon paths, specifically including: Several fault scenarios are generated based on different typhoon paths. The fault location within the distribution network is different in each fault scenario, and each fault scenario has a different probability of occurrence, satisfying the following formula: In the formula, s represents a fault scenario, and S is the set of fault scenarios. It represents the probability of scenario s occurring.

3. The preventive zoning method for flexible distribution networks considering typhoon path uncertainty according to claim 1, characterized in that, Step 2 involves multiple rounds of detection to identify the damage status of nodes under different typhoon path failure scenarios, specifically including: In the first round of testing, the node's damage status depends only on whether a remote control switch has been installed on the line near the node and whether the line connected to the node has failed in the current typhoon path failure scenario; in the t-th round of testing, the node's damage status depends on whether a remote control switch has been installed on the line connected to the node and the damage status of the node connected to the node obtained in the previous round of testing in the current typhoon path failure scenario; t is greater than 1. Specifically, it is expressed as follows: In the formula, T represents the total number of detection rounds; N represents the set of load nodes; and S represents the set of fault scenarios, where the location of the fault varies under different typhoon path fault scenarios. This is a 0-1 variable, representing the occurrence of line fault propagation between nodes i and j in the t-th detection under fault scenario s. A value of 0 indicates that a fault has propagated along the line. A value of 1 indicates that fault propagation did not occur or was blocked on that line; Indicates the number of lines connected to node i; The variable is 0-1, representing the damaged state of node i in the t-th detection under fault scenario s. A value of 1 indicates that node i is not damaged. A value of 0 indicates that node i is damaged; , Both are 0-1 variables, representing the damaged state of node i in the (t-1)th and Tthth detections under fault scenario s, respectively; It is a constant used to ensure that the constraint holds. It is a 0-1 variable, representing the damaged state of node i in the last round of detection under fault scenario s. A value of 1 indicates that node i was not damaged by fault propagation. A value of 0 indicates that node i is affected by fault propagation and power restoration is not allowed.

4. The preventive zoning method for flexible distribution networks considering typhoon path uncertainty according to claim 3, characterized in that, Step 2 involves multiple rounds of detection to identify the damage status of the lines under different typhoon path failure scenarios, specifically including: The extent of damage to the line depends on whether it itself failed under the current typhoon path failure scenario and whether fault propagation occurred on that line during the last inspection; the occurrence of fault propagation depends on the fault condition of the line itself, the deployment of remote control switches, and the impact of fault propagation on the connected nodes, specifically as follows: Where L is the set of lines; This is a 0-1 variable, representing the installation status of a remote control switch that can isolate faults on the line between nodes i and j, near node i. A value of 1 indicates that it has been installed. A value of 0 indicates that no device has been installed. This is a 0-1 variable, representing the installation status of a remote control switch that can isolate faults at the end of the line between nodes i and j, near node j. A value of 1 indicates that it has been installed. A value of 0 indicates that no device has been installed. This is a 0-1 variable, representing the installation status of remote control switches on the line between nodes i and j. A value of 1 indicates that the cable is installed at both ends or at least at one end. A value of 0 indicates that no remote control switch has been installed; This is a 0-1 variable, representing whether a fault occurs in the line between nodes i and j under fault scenario s. A value of 0 indicates that a fault has occurred on the line. A value of 1 indicates that there is no fault in the line; The variable is 0-1, representing the damaged state of node j in the t-th detection under fault scenario s. A value of 1 indicates that node j is not damaged. A value of 0 indicates that node j is damaged; It is a 0-1 variable, representing whether power can be allowed to pass through the line between nodes i and j after the fault scenario s is detected. A value of 1 indicates that power is allowed to pass. A value of 0 indicates that power cannot be allowed to pass through; This is a 0-1 variable, representing the occurrence of line fault propagation between nodes i and j in the T-th detection under fault scenario s. A value of 0 indicates that a fault has propagated along the line. A value of 1 indicates that the fault propagation did not occur or was blocked on that line.

5. The preventive zoning method for flexible distribution networks considering typhoon path uncertainty according to claim 4, characterized in that, Step 3, combined with the model constructed in Step 2, involves building a resilient distribution network preventative zoning model that considers the uncertainty of typhoon paths based on stochastic optimization methods. Specifically, this includes: Step 3-1: Taking the maximum expected value of the weighted load restoration amount for different typhoon path fault scenarios during the fault duration as the power supply restoration objective, the objective function is constructed as follows: The load restoration amount is the difference between the load demand and the load shedding amount. This represents the load weight of node i; It represents the set of load nodes that are fault-free and unaffected by cascading failures, and is a subset of the load node set N; This represents the active power load demand of node i. This represents the amount of active power load cutoff at node i under fault scenario s. Let represent the probability of failure scenario s occurring; S represents the set of failure scenarios. Step 3-2, establish constraints, including: (1) A node belongs to at most one microgrid; at the same time, if a node is damaged by fault propagation, that node will not belong to any zone: Where M is a collection of microgrids; It is a 0-1 variable that represents the partition affiliation of node i with microgrid m. When it is 1, it means that node i belongs to microgrid m, and when it is 0, it means that node i does not belong to microgrid m. (2) A distributed power source can only supply power to one node and can be deployed on one node: Where K is the set of distributed power sources; It is a 0-1 variable that indicates whether the distributed power source k is deployed on node i. When it is 1, it means that the distributed power source k is deployed on node i, and when it is 0, it means that the distributed power source k is not deployed on node i. (3) When a node is connected to a distributed power source with frequency and voltage support capability, the node is powered by the zone where the distributed power source is located: Among them, K b It is a collection of distributed power sources with frequency and voltage support capabilities, and is a subset of K; (4) Distributed power sources with frequency and voltage support capabilities are fixed at the initial node: in, It is a collection of distributed power nodes with frequency and voltage support capabilities, and is a subset of N; (5) The relationship between the line and the microgrid: in, This represents the set of lines that have not experienced any faults. It is a 0-1 variable that represents the affiliation of the line between nodes i and j with microgrid m. When it is 1, it means that the line belongs to microgrid m, and when it is 0, it means that the line does not belong to microgrid m. It is a 0-1 variable that represents the partition affiliation of node j with microgrid m. When it is 1, it means that node j belongs to microgrid m, and when it is 0, it means that node j does not belong to microgrid m. (6) The operating status of the remote control switch on the line is determined by the microgrid line ownership: Among them, b i,j It is a 0-1 variable that represents the closed state of the switch on the line between nodes i and j. A value of 1 indicates that the switch is closed, and a value of 0 indicates that the switch is open. (7) Lines and load nodes that have already experienced faults or have been damaged due to fault propagation need to be excluded from the microgrid: Where L is the set of lines; (8) The generated microgrid topology needs to satisfy the radial constraint: in, This indicates the maximum number of branches allowed in a microgrid's radial topology; (9) In the established microgrid island partitions, under each fault scenario, it is necessary to adjust the generator output and load shedding amount to schedule the microgrid; the power flow constraints in the microgrid need to be considered, and the model is as follows: in, , These represent the active power and reactive power flowing from node i to node j on the line between nodes i and j under fault scenario s, respectively. , These represent the active power and reactive power flowing from node j to node i on the line between nodes i and j under fault scenario s, respectively. and These represent the active and reactive power outputs of the distributed power source on node i under fault scenario s, respectively. This represents the resistance of the line between nodes i and j; This represents the reactance of the line between nodes i and j; , These represent the amount of reactive and active loads to be removed from node i under fault scenario s, respectively. , V1 and V2 represent the voltages of node i and node j under fault scenario s, respectively; V0 is the reference voltage of the microgrid. As a slack variable, the constraint is guaranteed to hold when node i and node j do not belong to the same microgrid under fault scenario s; This represents the reactive power load demand of node i. (10) In all fault scenarios, the voltage at the node where power is restored must be within a safe range: in, It is the maximum permissible voltage deviation percentage; (11) Under all fault scenarios, the voltage of the node connected to the distributed power source with frequency voltage support capability shall be used as the reference voltage: (12) Under all fault scenarios, the output of distributed power sources connected to nodes within the microgrid must meet the upper / lower output limits: in, , , and These represent the upper limit of active power output, the lower limit of active power output, the upper limit of reactive power output, and the lower limit of reactive power output of the distributed power source k, respectively. (13) Under all fault scenarios, the load shedding amount on the load node must meet the following constraints: Nodes marked as unhealthy during the previous fault propagation detection process will shed all loads; the ratio of active / reactive load shedding amount to active / reactive load demand is consistent: in, , Let represent the reactive load demand and active load demand of node i, respectively. This represents the amount of reactive load shelving at node i under fault scenario s; (14) Under all fault scenarios, the active / reactive power flowing through the microgrid lines must be limited to the line capacity, and power is not allowed to pass through lines affected by fault propagation: in, , These represent the maximum active power and maximum reactive power allowed to pass through the line between nodes i and j, respectively.

6. The preventive zoning method for flexible distribution networks considering typhoon path uncertainty according to claim 5, characterized in that, Step 4 involves linearizing the models from steps 2 and 3 and solving them jointly to obtain optimized distribution network preventive zoning and node power supply strategies, specifically including: Step 4-1 involves linearizing the constraints in the node and line damage state detection model that describe whether a line can be put into normal use under fault propagation conditions. Specifically: Step 4-2 involves linearizing the constraints in the node and line damage state detection model that describe the occurrence of fault propagation on the line. Specifically: in, It is an intermediate 0-1 variable generated after constraint linearization, representing the damage status of the line between nodes i and j in this round of detection under fault scenario s, and is only 1 when both nodes are undamaged; Step 4-3: Linearize the constraints describing the active / reactive power limits flowing through microgrid lines in the resilient distribution network preventive zoning model that considers typhoon path uncertainty. in, It is a 0-1 variable used to constrain the linearization process; Step 4-4: Based on the linearized constraints, solve the node and line damage status detection models considering fault propagation and the resilient distribution network preventive zoning model considering typhoon path uncertainty under different typhoon path fault scenarios after steps 4-1 to 4-3, to obtain the optimal resilient distribution network preventive zoning scheme, including the node affiliation. Operating status of remote control switch Ownership of distributed power sources Simultaneously, it obtains the node power supply strategies under different fault scenarios, including the active power output of distributed power sources. Unproductive efforts Active power reduction of load and reactive power reduction .

7. A resilient distribution network preventive zoning system considering typhoon path uncertainty based on the method of any one of claims 1 to 6, characterized in that, The system includes: The first module is used to build a set of scenarios that consider failure situations under different typhoon paths; The second module is used to identify the damage status of nodes and lines under different typhoon path failure scenarios through multiple rounds of detection, and to build a node and line damage status detection model. The third module is used to combine the model built in the second module to construct a preventive zoning model for a resilient distribution network that takes into account the uncertainty of the typhoon path based on the stochastic optimization method. The fourth module is used to linearize the two models and solve them jointly to obtain the optimized distribution network preventive zoning and node power supply strategies.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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