A method and system for obtaining restoration priority of nodes of a power distribution network
By acquiring risk monitoring index data of distribution network nodes, constructing regional risk information and calculating a comprehensive risk index, the problem of direct mapping of distribution network fault recovery decision-making under extreme rainstorm conditions is solved, enabling rapid and objective emergency repair and dispatch support, and improving the safety and resilience of the distribution network.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN POWER SUPPLY BUREAU
- Filing Date
- 2026-04-13
- Publication Date
- 2026-07-14
AI Technical Summary
Under extreme rainstorm disaster conditions, existing technologies cannot comprehensively consider the impact of rainstorm disaster chains, distribution network topology, and user scale factors. This results in a lack of direct mapping relationship in distribution network fault recovery decisions, making it difficult to achieve rapid and objective emergency repair and dispatch support.
By acquiring risk monitoring index data of distribution network nodes, regional risk information is constructed, a regional comprehensive risk index is calculated, and the recovery priority of faulty nodes is determined by combining topological relationships and user scale, providing a quantitative basis for decision-making.
It enables rapid quantitative risk assessment and fault recovery priority determination of distribution networks under extreme rainstorm conditions, provides intuitive allocation of emergency repair resources and restoration sequence arrangement, and improves the safety and resilience of distribution networks.
Smart Images
Figure CN122393930A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system technology, and in particular to a method and system for obtaining the recovery priority of distribution network nodes. Background Technology
[0002] With the continuous increase in urbanization and electricity load density, urban power distribution networks are characterized by a large number of nodes, short power supply radius, complex load types, and high exposure of outdoor equipment. Their operational safety has a significant impact on urban production and daily life. At the same time, against the backdrop of global climate change, the frequency and intensity of extreme weather events have increased significantly, especially extreme precipitation events such as short-duration heavy rainfall and continuous rainstorms, which pose a severe challenge to the safe and stable operation of urban power distribution networks.
[0003] To address the aforementioned issues, researchers both domestically and internationally have proposed various algorithms to mitigate their negative impacts. First, some methods rely heavily on historical disaster samples and fault data. When the sample size is insufficient or the disaster pattern changes, the model's generalization ability is limited, making it difficult to adapt to rapidly evolving actual disaster situations. Second, existing risk assessment indicators often focus on single disaster factors, failing to comprehensively reflect the coupling relationship between rainstorms and the various secondary disasters they induce, leading to discrepancies between assessment results and actual operational risks. Furthermore, some assessment results are given only in the form of probability values or risk levels, lacking a direct mapping relationship with distribution network emergency repair and dispatch decisions. Dispatchers still need to rely on experience for judgment, hindering rapid and objective decision support. On the other hand, after a distribution network fault occurs, how to rationally determine the restoration sequence of faulty nodes under limited repair resources is a key issue affecting the scope, duration, and social impact of power outages. Existing fault recovery decision-making methods often only consider single factors such as whether a node is faulty or the number of users, failing to comprehensively consider regional environmental risks, network topology, and the scale of user impact, making it difficult to meet the actual needs of complex disaster scenarios.
[0004] Therefore, there is an urgent need for a method that can comprehensively consider the impact of rainstorm disaster chains, distribution network topology, and user scale factors to obtain the recovery priority of distribution network nodes. Without relying on complex prediction models, this method can achieve rapid quantitative assessment of distribution network operation risks and directly use the assessment results to guide fault recovery and emergency dispatch decisions, thereby improving the safety, reliability, and operational resilience of urban distribution networks under extreme rainstorm disaster conditions. Summary of the Invention
[0005] The technical problem to be solved by the embodiments of the present invention is to provide a method and system for obtaining the recovery priority of distribution network nodes, which can comprehensively consider the impact of rainstorm disaster chains, distribution network topology and user scale factors to determine the recovery priority of faulty nodes, provide quantitative decision-making basis for emergency dispatch of distribution networks, and thus improve the safety, reliability and operational resilience of urban distribution networks under extreme rainstorm disaster conditions.
[0006] To address the aforementioned technical problems, embodiments of the present invention provide a method for obtaining the recovery priority of distribution network nodes, the method comprising the following steps: S1. Determine all nodes in the designated distribution network, and based on multiple predefined risk monitoring indicators used to reflect the impact of rainstorms and their induced secondary disasters, obtain data for each risk monitoring indicator of all nodes from meteorological, hydrological and distribution network operation systems; S2. Based on the risk monitoring index data of all nodes obtained, construct each risk monitoring index to characterize the regional risk information of the operating environment of the specified distribution network under the action of the rainstorm disaster chain. S3. Calculate the regional comprehensive risk index for each node based on the regional risk information of each risk monitoring indicator constructed. S4. Based on the calculated regional comprehensive risk index of each node, and combined with the topology of the specified distribution network, as well as the node user scale and node operating status in the specified distribution network, determine the fault recovery priority of each node in the specified distribution network.
[0007] Specifically, step S2 includes: After all risk monitoring indicator data of all nodes are output by the disaster chain model and normalized, the entropy weight method is used to calculate the proportion of each risk monitoring indicator in each node, so as to further obtain the entropy value of the proportion of each risk monitoring indicator. Based on the entropy value of each risk monitoring indicator, the weight of each risk monitoring indicator is calculated, and the weight of each risk monitoring indicator is output as a corresponding regional risk information that characterizes the operating environment of the specified distribution network under the action of the rainstorm disaster chain.
[0008] Among them, through the formula The collected features are output as the state of the coupling effect of the disaster chain; whereby... represent Disaster indicators Disaster index amplification intensity Represents the chain of disaster propagation coefficient; Represents the total number of risk monitoring indicators; Through formula Each risk monitoring indicator data point was normalized; among them, Representative node The Data for each risk monitoring indicator; Through formula The weight of each risk monitoring indicator was calculated; among them, Representative node The Middle The proportion of each risk monitoring indicator; Through formula The entropy value of the proportion of each risk monitoring indicator is calculated; among which, Representing the The entropy value of the proportion of each risk monitoring indicator; Represents the total number of all nodes in the specified distribution network; ; Through formula The weight of each risk monitoring indicator is calculated; among them, Representing the The weight of each risk monitoring indicator.
[0009] Specifically, step S3 includes: Construct a triangular membership function matrix, and calculate the membership matrix of each node based on the proportion of each risk monitoring indicator in each node; Based on the membership matrix of each node, the weights of the corresponding risk monitoring indicators are selected from the weights of all risk monitoring indicators. After the weights of the selected risk monitoring indicators are weighted and calculated with the membership matrix of each node, the regional comprehensive risk index of each node is calculated in combination with the preset risk level of the selected risk monitoring indicators.
[0010] Wherein, the triangular membership function matrix is ,and ;in, Representative node The membership matrix; , Representative node The Middle Triangular membership degree of the proportion of each risk monitoring indicator; ; ; Through formula The weights of the selected risk monitoring indicators are weighted and calculated by respectively adding them to the membership matrix of each node; where, Representative node The result after weighted calculation; , Representative of the selected The weight of each risk monitoring indicator; Representative node The selected number Weight of each risk monitoring indicator With corresponding triangular membership degree The weighted value; Through formula The regional comprehensive risk index of each node was calculated; among them, Representative node The regional comprehensive risk index; represent The set matrix formed by the preset risk levels of each selected risk monitoring indicator.
[0011] Specifically, step S4 includes: The user scale and operating status of each node in the specified distribution network are obtained, and based on the topology of the specified distribution network, nodes with downstream nodes are selected. The user scale of each selected node, which includes all downstream nodes, is further calculated and updated. The operating status is either no power outage or power outage. The downstream node is the node that is cascaded down from the currently selected node along the power supply direction. Determine the user scale of all nodes after the update, and based on the regional comprehensive risk index and operating status of each node, introduce the preset node failure status factor to calculate the failure recovery priority value of each node. The calculated fault recovery priority values of each node are sorted from largest to smallest, and the fault recovery priority of each node in the specified distribution network is determined based on the sorting results.
[0012] Among them, through the formula Calculate and update the user size of each selected node; where, Represents the selected node Updated user base; Represents the selected node downstream nodes User scale at the location; Representative and selected node The set of all connected downstream nodes; ; Through formula , representing nodes The operating status is that there is no power outage; according to the formula , representing nodes The operating status is "power outage"; Through formula The fault recovery priority value of each node is calculated; among which, Representative node Fault recovery priority value; Representative node The result of normalizing the regional comprehensive risk index; Representative node The updated user scale after normalization; Representative node The result of normalizing the user scale; These represent two preset node fault state factors, both of which are located between (0,1).
[0013] The various risk monitoring indicators include rainfall intensity, short-term cumulative rainfall, water depth in urban flooding, river water level exceeding warning level, landslide and debris flow risk index, power equipment exposure, seawater intrusion factors, and reservoir water storage capacity.
[0014] This invention also provides a system for obtaining the recovery priority of distribution network nodes, comprising: The risk monitoring indicator data acquisition unit is used to identify all nodes in a specified distribution network and, based on multiple predefined risk monitoring indicators used to reflect the impact of rainstorms and their induced secondary disasters, acquire data for each risk monitoring indicator of all nodes from meteorological, hydrological, and distribution network operation systems. The regional risk information construction unit is used to construct regional risk information for each risk monitoring indicator, based on the risk monitoring indicator data of all nodes obtained, to characterize the operating environment of the specified power distribution network under the action of the rainstorm disaster chain. The regional comprehensive risk index calculation unit is used to calculate the regional comprehensive risk index of each node based on the regional risk information of each risk monitoring indicator constructed. The fault recovery priority determination unit is used to determine the fault recovery priority of each node in the specified distribution network based on the calculated regional comprehensive risk index of each node, combined with the topology of the specified distribution network, the number of node users and the node operating status in the specified distribution network.
[0015] The various risk monitoring indicators include rainfall intensity, short-term cumulative rainfall, water depth in urban flooding, river water level exceeding warning level, landslide and debris flow risk index, power equipment exposure, seawater intrusion factors, and reservoir water storage capacity.
[0016] Implementing the embodiments of the present invention has the following beneficial effects: This invention takes a holistic view of the disaster chain induced by rainstorms, comprehensively considering various risk factors such as heavy rainfall and its resulting urban flooding, river water level changes, and geological disasters. It does not rely on complex prediction models or a large number of historical fault samples, but rather uses real-time or near-real-time risk monitoring index data to rapidly quantify and assess the operational risks of the distribution network. Furthermore, it couples the regional comprehensive risk assessment results with the distribution network topology and user scale factors to establish a direct correlation between the risk assessment results and fault recovery decisions. This not only objectively and reasonably determines the recovery priority of distribution network nodes, but also provides an intuitive and effective decision-making basis for emergency dispatch, repair resource allocation, and recovery sequence arrangement of the distribution network under extreme rainstorm conditions. It is suitable for emergency scenarios where disasters evolve rapidly and information is incomplete. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.
[0018] Figure 1 A flowchart illustrating a method for obtaining the recovery priority of a distribution network node, provided in an embodiment of the present invention; Figure 2 is a simulation diagram of the IEEE-33 node distribution network topology when performing a method for obtaining the recovery priority of distribution network nodes provided in an embodiment of the present invention. Figure 3 is a heat map of the regional comprehensive risk index estimation experiment when a method for obtaining the recovery priority of distribution network nodes is simulated according to an embodiment of the present invention. Figure 4 is a heat map of the node fault priority determination experiment when a method for obtaining the recovery priority of distribution network nodes provided in an embodiment of the present invention is simulated. Figure 5 is a schematic diagram of a system for obtaining the recovery priority of a distribution network node according to an embodiment of the present invention. Detailed Implementation
[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.
[0020] like Figure 1 As shown in the figure, this is a method for obtaining the recovery priority of a distribution network node proposed in an embodiment of the present invention. The method includes the following steps: Step S1: Determine all nodes in the specified distribution network, and based on multiple predefined risk monitoring indicators used to reflect the impact of rainstorms and their induced secondary disasters, obtain data for each risk monitoring indicator of all nodes from the meteorological, hydrological, and distribution network operation systems. The specific process is as follows: First, based on the topology of the designated distribution network, all nodes in the designated distribution network are identified. Second, multiple risk monitoring indicators are predefined to reflect the impact of rainstorms and their induced secondary disasters, and data for each risk monitoring indicator for all nodes are obtained from meteorological, hydrological, and distribution network operation systems. These multiple risk monitoring indicators include, but are not limited to, rainfall intensity, short-term cumulative rainfall, water depth in urban flooding, river level exceeding warning levels, landslide and debris flow risk index, power equipment exposure, seawater intrusion factors, and reservoir storage capacity.
[0021] Understandably, based on the spatial zoning or node division method of the designated power distribution network, monitoring indicator data obtained from meteorological, hydrological and power distribution network operation systems are mapped to the corresponding nodes, thereby obtaining risk monitoring indicator data for each node.
[0022] Step S2: Based on the risk monitoring index data of all nodes obtained, construct each risk monitoring index to characterize the regional risk information of the operating environment of the specified distribution network under the action of the rainstorm disaster chain. The specific process is as follows: First, after all the risk monitoring indicator data of all nodes are output by the disaster chain model and normalized, the entropy weight method is used to calculate the proportion of each risk monitoring indicator in each node, so as to further obtain the entropy value of the proportion of each risk monitoring indicator.
[0023] For example, through formula The collected features are output as the state of the coupling effect of the disaster chain; whereby... represent Disaster indicators Disaster index amplification intensity Represents the chain of disaster propagation coefficient; This represents the total number of risk monitoring indicators.
[0024] Then through the formula Each risk monitoring indicator data point was normalized; among them, Representative node The Data for each risk monitoring indicator; Through formula The weight of each risk monitoring indicator was calculated; among them, Representative node The Middle The proportion of each risk monitoring indicator; Through formula The entropy value of the proportion of each risk monitoring indicator is calculated; among which, Representing the The entropy value of the proportion of each risk monitoring indicator; Represents the total number of all nodes in the specified distribution network; .
[0025] Secondly, based on the entropy value of the proportion of each risk monitoring indicator, the weight of each risk monitoring indicator is calculated, and the weight of each risk monitoring indicator is output as a corresponding regional risk information that characterizes the operating environment of the specified distribution network under the action of the rainstorm disaster chain.
[0026] For example, through formula The weight of each risk monitoring indicator is calculated; among them, Representing the The weight of each risk monitoring indicator.
[0027] Step S3: Calculate the regional comprehensive risk index for each node based on the regional risk information of each constructed risk monitoring indicator; The specific process is as follows: First, construct a triangular membership function matrix, and then calculate the membership matrix of each node based on the proportion of each risk monitoring indicator in each node.
[0028] For example, set an interval The triangular membership function is obtained as follows: ,in, Representative node The Middle Triangular membership degree of the proportion of each risk monitoring indicator; Applying the membership function results above to the nodes Calculate Each indicator corresponds to The membership degrees form a matrix. So that the triangular membership function matrix is ;in, Representative node The membership matrix; .
[0029] Secondly, based on the membership matrix of each node, the weights of the corresponding risk monitoring indicators are selected from the weights of all risk monitoring indicators. After the weights of the selected risk monitoring indicators are weighted and calculated with the membership matrix of each node, the regional comprehensive risk index of each node is calculated in combination with the preset risk level of the selected risk monitoring indicators.
[0030] For example, through formula The weights of the selected risk monitoring indicators are weighted and calculated by respectively adding them to the membership matrix of each node; where, Representative node The result after weighted calculation; , Representative of the selected The weight of each risk monitoring indicator; Representative node The selected number Weight of each risk monitoring indicator With corresponding triangular membership degree The weighted value; Through formula The regional comprehensive risk index of each node was calculated; among them, Representative node The regional comprehensive risk index; represent The set matrix formed by the preset risk levels of each selected risk monitoring indicator.
[0031] In one example, for node Calculate the five membership degrees corresponding to the eight indicators, forming a matrix as follows: Then, the weights of the selected risk monitoring indicators are combined with the membership matrix to obtain... The design level indicators, from high to low, are: ,get .
[0032] Step S4: Based on the calculated regional comprehensive risk index of each node, and combined with the topology of the specified distribution network, as well as the user scale and operating status of the nodes in the specified distribution network, determine the fault recovery priority of each node in the specified distribution network.
[0033] The specific process is as follows: First, the user scale and operating status of each node in the specified distribution network are obtained. Based on the topology of the specified distribution network, nodes with downstream nodes are selected. Then, the user scale of each selected node, including all its downstream nodes, is calculated and updated. The operating status is either "no power outage" or "power outage." Downstream nodes represent the nodes cascaded down the power supply direction from the currently selected node. It should be noted that for nodes without downstream nodes, their user scale remains unchanged after the update.
[0034] For example, through formula , representing nodes The operating status is that there is no power outage; according to the formula , representing nodes The operating status is "power outage"; Through formula Calculate and update the user size of each selected node; where, Represents the selected node Updated user base; Represents the selected node downstream nodes User scale at the location; Representative and selected node The set of all connected downstream nodes; .
[0035] Secondly, determine the user scale of all nodes after the update, and based on the regional comprehensive risk index and operating status of each node, introduce a preset node fault status factor to calculate the fault recovery priority value of each node.
[0036] For example, through formula The fault recovery priority value of each node is calculated; among which, Representative node Fault recovery priority value; Representative node The result of normalizing the regional comprehensive risk index; Representative node The updated user scale after normalization; Representative node The result of normalizing the user scale; These represent two preset node fault state factors, both of which are located between (0,1). At this time, This is a function for normalizing user scale.
[0037] Finally, the calculated fault recovery priority values of each node are sorted from largest to smallest, and the fault recovery priority of each node in the specified distribution network is determined based on the sorting results. That is, the larger the fault recovery priority value, the higher the priority value of the node should be restored.
[0038] In this embodiment of the invention, a method for obtaining the recovery priority of distribution network nodes proposed by the inventors is simulated, as follows: The IEEE-33 node distribution network was selected as the simulation object, as shown in Figure 2. The IEEE-33 node distribution network is a typical radial distribution network structure, containing 33 distribution nodes and multiple feeders. The positions of each node in the topology and the user scale of its downstream nodes vary significantly.
[0039] Based on the scenario assumption of rainstorm disaster chain, a high level of environmental risk is imposed on some nodes in the distribution network, and nodes 7 and 12 are further set as fault nodes to simulate the operation state of the distribution network under extreme rainstorm conditions where local faults occur.
[0040] Under the above simulation conditions, the operational risks of each region in the IEEE-33 node system were first quantitatively assessed based on multi-source risk information. The resulting regional comprehensive risk index distribution is shown in Figure 3 as a heat map. Figure 3 reflects the risk level distribution of each regional node when only considering the environmental impact of the rainstorm disaster chain. It can be seen that there are obvious risk differences between different regions, with high-risk areas mainly concentrated in areas where the impact of rainstorms and their induced secondary disasters is more severe.
[0041] Building upon this foundation, the coupling relationships of the distribution network topology, node user scale information, and node fault status are further incorporated to comprehensively determine the fault repair priority of nodes. The results are shown in Figure 4. Unlike Figure 3, which only reflects the regional risk level, the node fault repair priority shown in Figure 4 undergoes significant changes when considering topology coupling, user scale, and the fact that nodes 7 and 12 are faulty nodes. Specifically, the recovery priority levels of nodes 7 and 12 and their neighboring nodes are significantly increased. This indicates that the method of this invention can further highlight the faulty nodes and their cascading effects in the network structure based on regional risk assessment, making the recovery priority determination results more consistent with the actual needs of distribution network emergency repair.
[0042] Figure 5 shows a system for obtaining the recovery priority of a distribution network node provided in an embodiment of the present invention, comprising: The risk monitoring indicator data acquisition unit 110 is used to identify all nodes in a specified distribution network and, based on multiple predefined risk monitoring indicators used to reflect the impact of rainstorms and their induced secondary disasters, acquire data for each risk monitoring indicator of all nodes from meteorological, hydrological and distribution network operation systems. The regional risk information construction unit 120 is used to construct regional risk information for each risk monitoring indicator to characterize the operating environment of the specified power distribution network under the action of the rainstorm disaster chain based on the risk monitoring indicator data of each of all nodes obtained. The regional comprehensive risk index calculation unit 130 is used to calculate the regional comprehensive risk index of each node based on the regional risk information of each risk monitoring indicator constructed. The fault recovery priority determination unit 140 is used to determine the fault recovery priority of each node in the specified distribution network based on the calculated regional comprehensive risk index of each node, combined with the topology of the specified distribution network, as well as the node user scale and node operating status in the specified distribution network.
[0043] The various risk monitoring indicators include rainfall intensity, short-term cumulative rainfall, water depth in urban flooding, river water level exceeding warning level, landslide and debris flow risk index, power equipment exposure, seawater intrusion factors, and reservoir water storage capacity.
[0044] Implementing the embodiments of the present invention has the following beneficial effects: This invention takes a holistic view of the disaster chain induced by rainstorms, comprehensively considering various risk factors such as heavy rainfall and its resulting urban flooding, river water level changes, and geological disasters. It does not rely on complex prediction models or a large number of historical fault samples, but rather uses real-time or near-real-time risk monitoring index data to rapidly quantify and assess the operational risks of the distribution network. Furthermore, it couples the regional comprehensive risk assessment results with the distribution network topology and user scale factors to establish a direct correlation between the risk assessment results and fault recovery decisions. This not only objectively and reasonably determines the recovery priority of distribution network nodes, but also provides an intuitive and effective decision-making basis for emergency dispatch, repair resource allocation, and recovery sequence arrangement of the distribution network under extreme rainstorm conditions. It is suitable for emergency scenarios where disasters evolve rapidly and information is incomplete.
[0045] It is worth noting that the various system modules included in the above system embodiments are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional module are only for easy differentiation and are not used to limit the scope of protection of the present invention.
[0046] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as ROM / RAM, disk, optical disk, etc.
[0047] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.
Claims
1. A method for obtaining the recovery priority of distribution network nodes, characterized in that, The method includes the following steps: S1. Determine all nodes in the designated distribution network, and based on multiple predefined risk monitoring indicators used to reflect the impact of rainstorms and their induced secondary disasters, obtain data for each risk monitoring indicator of all nodes from meteorological, hydrological and distribution network operation systems; S2. Based on the risk monitoring index data of all nodes obtained, construct each risk monitoring index to characterize the regional risk information of the operating environment of the specified distribution network under the action of the rainstorm disaster chain. S3. Calculate the regional comprehensive risk index for each node based on the regional risk information of each risk monitoring indicator constructed. S4. Based on the calculated regional comprehensive risk index of each node, and combined with the topology of the specified distribution network, as well as the node user scale and node operating status in the specified distribution network, determine the fault recovery priority of each node in the specified distribution network.
2. The method for obtaining the recovery priority of distribution network nodes as described in claim 1, characterized in that, Step S2 specifically includes: After all risk monitoring indicator data of all nodes are output by the disaster chain model and normalized, the entropy weight method is used to calculate the proportion of each risk monitoring indicator in each node, so as to further obtain the entropy value of the proportion of each risk monitoring indicator. Based on the entropy value of each risk monitoring indicator, the weight of each risk monitoring indicator is calculated, and the weight of each risk monitoring indicator is output as a corresponding regional risk information that characterizes the operating environment of the specified distribution network under the action of the rainstorm disaster chain.
3. The method for obtaining the recovery priority of distribution network nodes as described in claim 2, characterized in that, Through formula The collected features are output as the state of the coupling effect of the disaster chain; whereby... represent Disaster indicators Disaster index amplification intensity Represents the chain of disaster propagation coefficient; Represents the total number of risk monitoring indicators; Through formula Each risk monitoring indicator data point was normalized; among them, Representative node The Data for each risk monitoring indicator; Through formula The weight of each risk monitoring indicator was calculated; among them, Representative node The Middle The proportion of each risk monitoring indicator; Through formula The entropy value of the proportion of each risk monitoring indicator is calculated; among which, Representing the The entropy value of the proportion of each risk monitoring indicator; Represents the total number of all nodes in the specified distribution network; ; Through formula The weight of each risk monitoring indicator is calculated; among them, Representing the The weight of each risk monitoring indicator.
4. The method for obtaining the recovery priority of distribution network nodes as described in claim 3, characterized in that, Step S3 specifically includes: Construct a triangular membership function matrix, and calculate the membership matrix of each node based on the proportion of each risk monitoring indicator in each node; Based on the membership matrix of each node, the weights of the corresponding risk monitoring indicators are selected from the weights of all risk monitoring indicators. After the weights of the selected risk monitoring indicators are weighted and calculated with the membership matrix of each node, the regional comprehensive risk index of each node is calculated in combination with the preset risk level of the selected risk monitoring indicators.
5. The method for obtaining the recovery priority of distribution network nodes as described in claim 4, characterized in that, The triangular membership function matrix is: ,and ;in, Representative node The membership matrix; , Representative node The Middle Triangular membership degree of the proportion of each risk monitoring indicator; ; ; Through formula The weights of the selected risk monitoring indicators are weighted and calculated by respectively adding them to the membership matrix of each node; where, Representative node The result after weighted calculation; , Representative of the selected The weight of each risk monitoring indicator; Representative node The selected number Weights of each risk monitoring indicator With corresponding triangular membership degree The weighted value; Through formula The regional comprehensive risk index of each node was calculated; among them, Representative node The regional comprehensive risk index; represent The set matrix formed by the preset risk levels of each selected risk monitoring indicator.
6. The method for obtaining the recovery priority of distribution network nodes as described in claim 5, characterized in that, Step S4 specifically includes: The user scale and operating status of each node in the specified distribution network are obtained, and based on the topology of the specified distribution network, nodes with downstream nodes are selected. The user scale of each selected node, which includes all downstream nodes, is further calculated and updated. The operating status is either no power outage or power outage. The downstream node is the node that is cascaded down from the currently selected node along the power supply direction. Determine the user scale of all nodes after the update, and based on the regional comprehensive risk index and operating status of each node, introduce the preset node failure status factor to calculate the failure recovery priority value of each node. The calculated fault recovery priority values of each node are sorted from largest to smallest, and the fault recovery priority of each node in the specified distribution network is determined based on the sorting results.
7. The method for obtaining the recovery priority of distribution network nodes as described in claim 6, characterized in that, Through formula Calculate and update the user size of each selected node; where, Represents the selected node Updated user base; Represents the selected node downstream nodes User scale at the location; Representative and selected node The set of all connected downstream nodes; ; Through formula , representing nodes The operating status is that there is no power outage; according to the formula , representing nodes The operating status is "power outage"; Through formula The fault recovery priority value of each node is calculated; among which, Representative node The fault recovery priority value; Representative node The result of normalizing the regional comprehensive risk index; Representative node The updated user scale after normalization; Representative node The result of normalizing the user scale; These represent two preset node fault state factors, both of which are located between (0,1).
8. The method for obtaining the recovery priority of distribution network nodes as described in claim 1, characterized in that, The multiple risk monitoring indicators include rainfall intensity, short-term cumulative rainfall, water depth in urban flooding, river water level exceeding warning level, landslide and debris flow risk index, power equipment exposure, seawater intrusion factors, and reservoir water storage capacity.
9. A system for obtaining the recovery priority of distribution network nodes, characterized in that, include: The risk monitoring indicator data acquisition unit is used to identify all nodes in a specified distribution network and, based on multiple predefined risk monitoring indicators used to reflect the impact of rainstorms and their induced secondary disasters, acquire data for each risk monitoring indicator of all nodes from meteorological, hydrological, and distribution network operation systems. The regional risk information construction unit is used to construct regional risk information for each risk monitoring indicator, based on the risk monitoring indicator data of all nodes obtained, to characterize the operating environment of the specified power distribution network under the action of the rainstorm disaster chain. The regional comprehensive risk index calculation unit is used to calculate the regional comprehensive risk index of each node based on the regional risk information of each risk monitoring indicator constructed. The fault recovery priority determination unit is used to determine the fault recovery priority of each node in the specified distribution network based on the calculated regional comprehensive risk index of each node, combined with the topology of the specified distribution network, the number of node users and the node operating status in the specified distribution network.
10. The system for obtaining the recovery priority of distribution network nodes as described in claim 9, characterized in that, The multiple risk monitoring indicators include rainfall intensity, short-term cumulative rainfall, water depth in urban flooding, river water level exceeding warning level, landslide and debris flow risk index, power equipment exposure, seawater intrusion factors, and reservoir water storage capacity.