A power distribution network risk weak point identification method and system

Abstract

The application provides a power distribution network risk weak point identification method and system, comprising the following steps: calculating the utility coupling degree of each node in the power distribution network based on the operation state of the power distribution network; calculating the comprehensive risk index of each node in the power distribution network according to the utility coupling degree; and identifying the weak point in the power distribution network according to the comprehensive risk index of each node. The application can find the risk weak point of the power distribution network by calculating the utility coupling degree of each node in the power distribution network, improve the reliability of the power distribution network, and reduce the probability of a large-scale power outage.

Description

Technical Field

[0001] This invention relates to the field of power systems and their automation, specifically to a method and system for identifying weak points in power distribution networks. Background Technology

[0002] With the continuous advancement of smart grid construction, the power system's reliance on control, communication, and information technology is constantly increasing. Simultaneously, a large number of heterogeneous units, such as distributed power sources, flexible loads, energy storage, and advanced metering devices, are being integrated into smart distribution networks, significantly altering their function and structure. Distribution networks are no longer merely the ends of lines; they have become input points for various renewable energy sources and data collection points for user information, possessing powerful functions and complex structures. The uncertainty of power flow and the complex interactions between nodes lead to numerous emergent behaviors and nonlinear characteristics, significantly increasing the vulnerability of distribution networks and posing risks to their control and operation. Once a critical link breaks down or is subjected to severe interference, it can lead to successive failures of line components, causing large-scale power outages. Currently, there are no effective means to identify the vulnerabilities of distribution networks. Summary of the Invention

[0003] To address the problem that existing technologies lack effective means for identifying vulnerabilities in power distribution networks, making it difficult to locate and determine these vulnerabilities, this invention provides a method and system for identifying vulnerabilities in power distribution networks. This method and system can effectively and accurately locate vulnerabilities in power distribution networks, thereby reducing operational risks and improving power supply reliability.

[0004] The technical solution provided by this invention is:

[0005] An improved method for identifying vulnerable points in a power distribution network includes:

[0006] Calculate the utility coupling degree of each node in the distribution network based on the operating status of the distribution network;

[0007] Calculate the comprehensive risk index of each node in the distribution network based on the aforementioned utility coupling degree;

[0008] Weak points in the power distribution network are identified based on the comprehensive risk indicators of each node.

[0009] The first preferred technical solution provided by the present invention is improved in that the calculation of the utility coupling degree of each node in the distribution network based on the operating status of the distribution network includes:

[0010] Obtain the topology corresponding to the operating state of the distribution network, and obtain the heterogeneous dependent network of the distribution network and the resistance parameters between each node in the distribution network.

[0011] Calculate the correlation value between each node based on the impedance parameters between each node;

[0012] The utility coupling degree of each node is calculated based on the correlation values ​​between nodes and the interdependencies between nodes in the heterogeneous dependency network.

[0013] The second preferred technical solution provided by the present invention is improved in that, after calculating the utility coupling degree of each node in the distribution network, it further includes:

[0014] Determine if the state of the distribution network has changed. If it has changed, recalculate the utility coupling degree of each node in the distribution network.

[0015] The third preferred technical solution provided by the present invention is improved in that the formula for calculating the correlation value is as follows:

[0016]

[0017] In the formula S ij Z represents the association value between node i and node j. ii Z is the self-impedance of node i. jj Z is the self-impedance of node j. ij Let be the mutual impedance between node i and node j.

[0018] The fourth preferred technical solution provided by the present invention is improved in that the formula for calculating the utility coupling degree is as follows:

[0019] T i =a d ×λ i d +a b ×λ i b

[0020] In the formula T i Let a be the utility coupling degree of node i. d a is the weight of the direct coupling degree of the node. b λ is the weight of the inter-node coupling degree. i d Let λ be the direct coupling degree of node i. i b Let be the degree of inter-node coupling.

[0021] The fifth preferred technical solution provided by the present invention is improved in that the direct coupling degree λ of node i is... i d The calculation formula is as follows:

[0022]

[0023] The inter-node coupling degree λ i b The calculation formula is as follows:

[0024]

[0025] Where k is the node directly connected to node i. Let i be the set of all nodes directly connected to node i. This represents the association value between node i and its directly connected inflow node k. This represents the association value between node i and its directly connected outflow node k. This is the sum of the correlation values ​​between all directly connected inflow nodes in the current distribution network. This represents the sum of correlation values ​​between all directly connected outgoing nodes in the current distribution network, where j is the node connected to node i. Let i be the set of all nodes connected to node i. This represents the association value between node i and the indirect inflow node j. This represents the association value between node i and the indirectly connected outflow node j. This is the sum of the correlation values ​​between all indirect inflow nodes in the current distribution network. It is the sum of the correlation values ​​between all indirect outflow nodes in the current distribution network.

[0026] The sixth preferred technical solution provided by the present invention is improved in that the calculation of the comprehensive risk index of each node in the distribution network based on the calculation of the distribution network operation status includes:

[0027] The over-limit risk value and the under-load risk value are calculated based on the utility coupling degree of each node and the obtained voltage over-limit risk probability and under-load risk probability of each node, respectively.

[0028] Calculate the comprehensive risk index for each node based on the over-limit risk value and the underload risk value.

[0029] The seventh preferred technical solution provided by the present invention is improved in that the calculation formula for the voltage over-limit risk value is as follows:

[0030] R ui =F′ ui ·P(u i )

[0031] In the formula, R ui P(u) represents the voltage over-limit risk value of node i. i F' represents the probability that node i will exceed its voltage limit. ui The normalized voltage limit severity is obtained by normalizing the voltage limit severity of node i.

[0032] The formula for calculating the severity of voltage over-limit at node i is as follows:

[0033] F ui =T i ·S(u i )

[0034] In the formula F ui T represents the severity of voltage exceedance at node i. i Let S(u) be the utility coupling degree of node i. i ) represents the severity of voltage offset at node i.

[0035] The eighth preferred technical solution provided by the present invention is improved in that the calculation formula for the loss-of-load risk value of the node is as follows:

[0036] R Bi =F′ Bi ·P(B i )

[0037] In the formula, R Bi P(B) represents the load loss risk value of node i. i F' represents the probability of node i losing its load. Bi The normalized load loss risk index is obtained by normalizing the load loss risk index of node i.

[0038] The formula for calculating the load loss risk index of node i is as follows:

[0039] F Bi =T i ·U(B i )

[0040] In the formula F Bi T is the load loss risk index for node i. i Let U(B) be the utility coupling degree of node i. i ) represents the severity of load loss at node i.

[0041] The ninth preferred technical solution provided by the present invention is improved in that the calculation formula of the comprehensive risk index is as follows:

[0042] R i =b1R ui +b2R Bi ;

[0043] In the formula: b1 is the weight of voltage exceeding the limit, b2 is the weight of node load loss, and R ui R represents the voltage over-limit risk value of node i. Bi This represents the load loss risk value of node i.

[0044] Based on the same inventive concept, the present invention also provides a distribution network risk vulnerability identification system, which is improved in that it includes: a utility coupling degree module, a comprehensive risk index module and an identification module;

[0045] The utility coupling degree module is used to calculate the utility coupling degree of each node in the distribution network based on the operating status of the distribution network.

[0046] The comprehensive risk index module is used to calculate the comprehensive risk index of each node in the distribution network based on the utility coupling degree.

[0047] The identification module is used to identify weak points in the power distribution network based on the comprehensive risk indicators of each node.

[0048] The tenth preferred technical solution provided by the present invention is improved in that the utility coupling degree module includes: a data acquisition unit, an association value unit, and a coupling degree unit;

[0049] The data acquisition unit is used to acquire the topology corresponding to the operating state of the distribution network, and to obtain the heterogeneous dependent network corresponding to the distribution network and the resistance parameters between each node in the distribution network.

[0050] The correlation value unit is used to calculate the correlation value between each node based on the impedance parameters between each node.

[0051] The coupling degree unit is used to calculate the utility coupling degree of each node based on the correlation value between each node and the mutual dependence relationship between nodes in the heterogeneous dependency network.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] This invention provides a method and system for identifying weak points in a power distribution network, comprising: calculating the utility coupling degree of each node in the power distribution network based on the network's operating status; calculating the comprehensive risk index of each node based on the utility coupling degree; and identifying weak points in the power distribution network based on the comprehensive risk index of each node. This invention can find weak points in the power distribution network by calculating the utility coupling degree of each node, thereby improving the reliability of the power distribution network and reducing the probability of major power outages. Attached Figure Description

[0054] Figure 1 A schematic diagram of a method for identifying weak points in a power distribution network provided by the present invention;

[0055] Figure 2 This is a schematic diagram illustrating the direct and indirect connections between nodes provided in an embodiment of the present invention;

[0056] Figure 3 This is a flowchart illustrating the risk vulnerability identification method for a power distribution network according to an embodiment of the present invention.

[0057] Figure 4 This invention provides a schematic diagram of the basic structure of a power distribution network risk vulnerability identification system.

[0058] Figure 5 This invention provides a detailed structural diagram of a power distribution network risk vulnerability identification system. Detailed Implementation

[0059] To better understand this invention, the following description, in conjunction with the accompanying drawings and examples, will further illustrate the invention.

[0060] Example 1:

[0061] The technical solution of this invention to solve the above problems is: to establish a power grid topology, define directly connected nodes and indirectly connected nodes in the power grid, analyze the coupling degree of different nodes, use the distribution network operation risk assessment system to assess the risk indicators of each node in the network, and identify the risk weaknesses in the distribution network.

[0062] The present invention provides a method for identifying weak points in a power distribution network, as follows: Figure 1 As shown, it includes:

[0063] Step 1: Calculate the utility coupling degree of each node in the distribution network based on the operating status of the distribution network;

[0064] Step 2: Calculate the comprehensive risk index of each node in the distribution network based on the utility coupling degree;

[0065] Step 3: Identify the weak points in the power distribution network based on the comprehensive risk indicators of each node.

[0066] The aforementioned method for identifying weak points in distribution networks based on node coupling degree categorizes nodes into two types: directly connected nodes and indirectly connected nodes. In a network with multiple node sets, nodes are considered directly connected if there is a direct path between them. If there is no direct path between nodes, but a change in the state of one node causes a change in the state of another, then these two nodes are defined as indirectly connected nodes. Power systems contain different types of nodes, such as power source nodes, load nodes, and connection nodes. These nodes may be directly or indirectly connected, collectively forming the nodes of the power system.

[0067] The aforementioned method for identifying vulnerable points in distribution networks based on node coupling degree includes both direct and indirect coupling degree. When the state of a node in the network changes, the path relationships between nodes affect the operating states of other nodes in the network. Therefore, node coupling degree can be used to more comprehensively reflect the impact of network nodes on network operational stability and other aspects.

[0068] The aforementioned method for identifying distribution network risk vulnerabilities based on node coupling degree includes risk indicators based on node coupling degree, such as voltage over-limit risk, load shedding risk, and comprehensive node risk indicators. Power system risk refers to the probability and severity of harm caused. Identifying risk vulnerabilities enables operators to assess the potential disasters that could occur based on the system's operating status, thereby allowing them to take appropriate safety measures. Risk is generally defined as the product of the probability of an accident and its severity. Its expression is:

[0069] R = P × S

[0070] In the formula: R represents the risk value; P represents the probability of an accident; and S represents the severity of an accident.

[0071] The aforementioned voltage exceedance risk is a novel voltage deviation metric proposed by applying node coupling to voltage exceedance. Based on the hazard-based exponential utility function, the traditional node i voltage deviation severity is...

[0072]

[0073] Among them, U i This represents the actual per-unit voltage value of the node. Combining node coupling with traditional node voltage offset, the severity index of voltage exceedance at node i is obtained as follows:

[0074] F ui =T i ·S(u i )

[0075] The voltage exceedance severity index of node i is normalized to obtain F′. ui At this point, the voltage over-limit risk value of node i is...

[0076] R ui =F′ ui ·P(u i )

[0077] In the formula, P(u i ) represents the probability that node i will exceed the voltage limit.

[0078] The aforementioned load loss risk incorporates node coupling into the risk indicator of node load loss. When a line fault occurs, the traditional node i load loss severity is...

[0079]

[0080] Among them, E(P) i Let be the expected active power of node i. Combining node coupling degree with the traditional node load loss severity, the load loss risk index of node i is obtained as follows:

[0081] FBi =T i ·U(B i )

[0082] The load risk index of node i is normalized to obtain F′. Bi At this point, the risk of load loss at node i is...

[0083] R Bi =F′ Bi ·P(B i )

[0084] In the formula, P(B) i ) represents the probability that node i loses its load.

[0085] The aforementioned node comprehensive risk index is represented by a weighted average of node voltage over-limit and underload risks. Its expression is:

[0086] R i =b1R ui +b2R Bi

[0087] In the formula: b1 and b2 represent the weighting systems for the two risk events, which can be adjusted according to different risks.

[0088] The aforementioned method for identifying vulnerable points in distribution networks based on node coupling degree is derived by assessing the risk indicators of each node in the network based on a distribution network operation risk assessment system. Specifically, it includes the following steps:

[0089] Step 101: Obtain the distribution network topology, define the directly connected nodes and indirectly connected nodes of the distribution network according to the power flow relationship between each node, and determine the inflow and outflow nodes of each node for direct connection and inflow and outflow nodes for indirect connection based on the inflow and outflow direction of the power flow between nodes.

[0090] Step 102: Calculate the correlation value S between each node using the impedance parameters between nodes. ij

[0091]

[0092] Step 103: Calculate the coupling degree T of each node in the network. i ;

[0093] Step 104: Obtain the current network operating status; if the network has changed, return to step 101, otherwise proceed to step 105;

[0094] Step 105: Based on the current network, obtain the probability of voltage exceeding the limit and the probability of load loss for each node;

[0095] Step 106: Based on the node coupling degree in Step 103 and the node risk event probability in Step 105, calculate the comprehensive risk index of each node and output the results.

[0096] The coupling degree T of the above node i i It consists of direct coupling and indirect coupling, where the direct coupling and indirect coupling of a node are respectively composed of the inflow and outflow coupling of that node.

[0097] T i =a d ×λ i d +a b ×λ i b

[0098] In the formula: a d a b These represent the weights of direct and indirect node coupling, respectively, and can be adjusted according to different power distribution network systems.

[0099]

[0100]

[0101] Equation (1) represents the direct coupling degree of node i, where node k represents the node directly connected to node i. Let i represent the set of all nodes directly connected to node i. This represents the association value between node i and its directly connected inflow node k. This represents the association value between node i and its directly connected outflow node k. This represents the sum of correlation values ​​between all directly connected inflow nodes in the current distribution network. This represents the sum of the correlation values ​​between all directly connected outgoing nodes in the current distribution network;

[0102] Equation (2) represents the degree of indirect coupling of node i, where node j represents the node connected to node i. Let represent the set of all nodes connected to node i. This represents the association value between node i and the indirect inflow node j. This represents the association value between node i and the indirectly connected outflow node j. This represents the sum of correlation values ​​between all indirect inflow nodes in the current distribution network. This represents the sum of the correlation values ​​between all indirect outflow nodes in the current distribution network.

[0103] The aforementioned method for identifying vulnerable points in distribution networks based on node coupling degree analyzes the coupling degree of nodes and ranks the comprehensive risk indicators of each node under power grid fault risk, thereby analyzing and evaluating the risk value of each node. A higher comprehensive risk indicator value indicates a weaker node. When the comprehensive risk indicator value T of a node is greater than δ, it is considered a vulnerable point in the power grid. The value of δ can be adjusted according to different needs and situations; in this invention, δ is set to 0.95. This method plays an important role in improving the power supply reliability of distribution networks and reducing the probability of major power outages.

[0104] The present invention has the following advantages:

[0105] This invention can identify the weak points in the power distribution network, improve the power supply reliability of the power distribution network, and reduce the probability of major power outages.

[0106] Example 2:

[0107] This invention provides a method for identifying risk vulnerabilities in distribution networks based on node coupling degree, such as... Figure 3 As shown, the specific steps include:

[0108] Step 201: Obtain the distribution network topology, define the directly connected nodes and indirectly connected nodes of the distribution network according to the power flow relationship between each node, and determine the inflow and outflow nodes of each node for direct connection and inflow and outflow nodes for indirect connection based on the inflow and outflow direction of the power flow between nodes.

[0109] Step 202: Calculate the correlation value S between each node using the impedance parameters between nodes. ij

[0110]

[0111] Step 203: Calculate the degree coupling T of each node in the network. i ;

[0112] The coupling degree T of the above node i i It consists of direct coupling and indirect coupling, where the direct coupling and indirect coupling of a node are respectively composed of the inflow and outflow coupling of that node.

[0113] T i =a d ×λ i d +a b ×λ i b

[0114] In the formula: a d a bThese represent the weights of direct and indirect node coupling, respectively, and can be adjusted according to different power distribution network systems.

[0115]

[0116]

[0117] Equation (1) represents the direct coupling degree of node i, where node k represents the node directly connected to node i. Let i represent the set of all nodes directly connected to node i. This represents the association value between node i and its directly connected inflow node k. This represents the association value between node i and its directly connected outflow node k. This represents the sum of correlation values ​​between all directly connected inflow nodes in the current distribution network. This represents the sum of the correlation values ​​between all directly connected outgoing nodes in the current distribution network;

[0118] Equation (2) represents the degree of indirect coupling of node i, where node j represents the node connected to node i. Let represent the set of all nodes connected to node i. This represents the association value between node i and the indirect inflow node j. This represents the association value between node i and the indirectly connected outflow node j. This represents the sum of correlation values ​​between all indirect inflow nodes in the current distribution network. This represents the sum of the correlation values ​​between all indirect outflow nodes in the current distribution network.

[0119] Step 204: Obtain the current network operating status; if the network has changed, return to step 201; otherwise, proceed to step 205.

[0120] Step 205: Based on the current network, obtain the probability of voltage exceeding the limit and the probability of load loss for each node;

[0121] Among them, voltage exceedance risk is a novel voltage deviation index proposed by applying the utility coupling degree of heterogeneous dependent nodes to voltage exceedance. Based on the risk-prone exponential utility function, the traditional node i voltage deviation severity is:

[0122]

[0123] In the formula, U i This represents the actual per-unit voltage value of the node. Combining node coupling with traditional node voltage offset, the voltage exceedance severity index for node i is obtained as follows:

[0124] F ui =T i ·S(ui )

[0125] The voltage exceedance severity index of node i is normalized to obtain F′. ui At this point, the voltage over-limit risk value for node i is:

[0126] R ui =F′ ui ·P(u i )

[0127] In the formula, P(u i ) represents the probability that node i will exceed the voltage limit.

[0128] Among them, load loss risk is a risk indicator that incorporates node coupling degree into node load loss. When a line fault occurs, the traditional node i load loss severity is...

[0129]

[0130] In the formula, E(P) i Let be the expected active power of node i. Combining node coupling degree with the traditional node load loss severity, the load loss risk index of node i is obtained as follows:

[0131] F Bi =T i ·U(B i )

[0132] The load risk index of node i is normalized to obtain F′. Bi At this point, the risk of load loss at node i is...

[0133] R Bi =F′ Bi ·P(B i )

[0134] In the formula, P(B) i ) represents the probability that node i loses its load.

[0135] Step 206: Based on the coupling degree of the nodes in Step 203 and the probability of risk events of the nodes in Step 205, calculate the comprehensive risk index of each node. The larger the node risk index, the more susceptible the node is to the impact of risk events. The point with the largest risk index is identified as the weakest point in terms of risk. Output the results.

[0136] The node comprehensive risk index is represented by a weighted average of node voltage over-limit and load shedding risks. Its expression is:

[0137] R i =b1R ui +b2R Bi

[0138] In the formula: b1 and b2 represent the weighting systems for the two risk events, which can be adjusted according to different risks.

[0139] Figure 2 The diagram shows direct and indirect connections between nodes. Solid arrows indicate direct connections between nodes, while dashed arrows indicate indirect connections.

[0140] When outputting results, this invention also sorts the comprehensive risk indicators of each node to facilitate viewing the risk order of nodes and further prevent the impact caused by risk points.

[0141] Example 3:

[0142] The following is a novel embodiment of a distribution network risk vulnerability identification method based on node coupling degree. This method applies complex network theory, defines heterogeneous dependent nodes in the power grid, analyzes the utility coupling degree of these nodes, and uses a distribution network operation risk assessment system to evaluate the risk indicators of each node based on its utility coupling degree, thereby identifying the risk vulnerabilities in the distribution network.

[0143] The concept of heterogeneous dependent nodes arises from the existence of heterogeneous dependent networks. Heterogeneous dependent networks can be considered a form evolved from heterogeneous networks. In complex network theory, a heterogeneous network refers to a complex network with elements possessing different attributes. Heterogeneous networks refine the relationships between nodes and edges based on the types of elements within the network, forming complex networks containing multiple types of nodes or edges. In terms of data structure representation, heterogeneous networks can be described as a special type of directed graph, where the types of internal nodes or edges are clearly distinguished.

[0144] For a heterogeneous network G, if the intersection of the set of nodes and the set of branches in the network is empty, and if there exists a set T... a Its state f(T) a Instability (or failure) will cause T b state f(T) b If a heterogeneous network tends towards an unstable state or fails, it is defined as a heterogeneous dependent network, as shown in the equation.

[0145]

[0146] Where, ξ min ,ξ max The threshold for stable node operation.

[0147] The heterogeneous dependency nodes are divided into two types: direct dependency and indirect dependency. In a heterogeneous dependency network with multiple types of node element sets, a direct dependency relationship exists if there are direct connecting paths between elements of different types. If the pairwise intersection of the element sets of different types is empty, and the pairwise dependency path set between each set is non-empty, then when the state of an element in one element set changes, the states of some elements in the other set also change accordingly, as shown in the equation.

[0148]

[0149] Then define T a Elements in a set have indirect dependencies, and these element nodes are heterogeneous indirectly dependent nodes. In a power system, there are different types of nodes, such as power source nodes, load nodes, and connection nodes. These nodes may have direct or indirect dependencies, together constituting the heterogeneous dependent nodes of the power system.

[0150] The node utility coupling degree includes direct dependency coupling degree and indirect dependency coupling degree. When the state of a node in a heterogeneous dependency network changes, it directly and completely affects the fully dependent nodes, and influences the operating state of other nodes in the network through dependency paths. Therefore, by analyzing the utility coupling degree of heterogeneous dependent nodes, we can more comprehensively reflect the impact of network node failure on network stability, integrity, and the survival strength of remaining nodes.

[0151] Figure 2 The diagram illustrates the dependencies between nodes. Solid arrows indicate direct dependencies, while dashed arrows indicate indirect dependencies. Therefore, the utility coupling degree of node vi can be composed of both its direct and indirect dependency coupling degrees. For node k, which is directly dependent on node i, it also has a direct dependency on node h. Considering both utility coupling and dependency relationships, the weight ω between nodes is obtained. ki for

[0152]

[0153] Among them, S ki and S kh These are the utility values ​​between node k and nodes i and h, respectively. Let k be the set of nodes that node k depends on. Let i be the set that depends on node i.

[0154] Similarly, the weight ω between node i and its indirectly dependent node j ij for

[0155]

[0156] in, Let i be the set of nodes that node i indirectly depends on.

[0157] Normalizing the utility values ​​between nodes yields the following results:

[0158]

[0159] Entropy is an indicator that reflects the degree of disorder in a system; its magnitude is related to the degree of disorder. Applying the entropy theorem to the utility between nodes yields the direct dependency coupling degree of node vi. and indirect dependency coupling for

[0160]

[0161] Therefore, the utility coupling degree T of node i i =(1-∑P ki lnP ki )∑ω ki +(1-∑P ij ln P ij )∑ω ij .

[0162] The risk indicators based on node utility coupling include voltage over-limit risk, load shedding risk, and comprehensive node risk indicators. Power system risk refers to the probability and severity of harm. Identifying risk vulnerabilities enables operators to assess the potential disasters caused by accidents based on the system's operating status, thereby taking appropriate safety measures. Risk is generally defined as the product of the probability of an accident and its severity. Its expression is:

[0163] R = P × S

[0164] In the formula: R represents the risk value; P represents the probability of an accident; and S represents the severity of an accident.

[0165] The aforementioned method for identifying vulnerable points in distribution networks based on node utility coupling degree analyzes the utility coupling degree of heterogeneous dependent nodes, ranks the utility coupling degree of each node in relation to grid fault risk, and analyzes and evaluates the risk value of each node to identify vulnerable points in the power grid. This method plays an important role in improving the power supply reliability of distribution networks and reducing the probability of major power outages.

[0166] Example 4:

[0167] Based on the same inventive concept, this invention also provides a distribution network risk vulnerability identification system. Since the principle of these devices in solving technical problems is similar to that of the distribution network risk vulnerability identification method, the repetitions will not be repeated.

[0168] A schematic diagram of the basic structure of the power distribution network risk vulnerability identification system is shown below. Figure 4 As shown, it includes: a utility coupling degree module, a comprehensive risk indicator module, and an identification module;

[0169] Among them, the utility coupling degree module is used to calculate the utility coupling degree of each node in the distribution network based on the operating status of the distribution network;

[0170] The comprehensive risk index module is used to calculate the comprehensive risk index of each node in the distribution network based on the utility coupling degree.

[0171] The identification module is used to identify weak points in the power distribution network based on the comprehensive risk indicators of each node.

[0172] A detailed structural diagram of the power distribution network risk vulnerability identification system is shown below. Figure 5 As shown.

[0173] The utility coupling module includes: a data acquisition unit, an association value unit, and a coupling degree unit;

[0174] The data acquisition unit is used to acquire the topology corresponding to the operating status of the distribution network, and to obtain the heterogeneous dependent network of the distribution network and the resistance parameters between each node in the distribution network.

[0175] The correlation value unit is used to calculate the correlation value between nodes based on the impedance parameters between each node.

[0176] The coupling degree unit is used to calculate the utility coupling degree of each node based on the correlation value between nodes and the interdependence between nodes in the heterogeneous dependency network.

[0177] The distribution network risk vulnerability identification system also includes a status judgment module;

[0178] The status judgment module is used to determine whether the status of the distribution network has changed. If it has changed, the utility coupling degree of each node in the distribution network is recalculated.

[0179] The comprehensive risk indicator module includes: a two-indicator calculation unit and a comprehensive risk indicator unit;

[0180] Two index calculation units are used to calculate the over-limit risk value and the under-load risk value respectively based on the utility coupling degree of each node and the obtained voltage over-limit risk probability and under-load risk probability of each node;

[0181] The comprehensive risk index unit is used to calculate the comprehensive risk index of each node based on the over-limit risk value and the load loss risk value.

[0182] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0183] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0184] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0185] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit its protection scope. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this application, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims pending approval.

Claims

1. A method for identifying vulnerable points in a power distribution network, characterized in that, include: Calculate the utility coupling degree of each node in the distribution network based on the operating status of the distribution network; Calculate the comprehensive risk index of each node in the distribution network based on the aforementioned utility coupling degree; Identify weak points in the power distribution network based on the comprehensive risk indicators of each node; The calculation of the utility coupling degree of each node in the distribution network based on the operating status of the distribution network includes: Obtain the topology corresponding to the operating state of the distribution network, and obtain the heterogeneous dependent network of the distribution network and the resistance parameters between each node in the distribution network. Calculate the correlation value between each node based on the impedance parameters between each node; Based on the correlation values ​​between nodes and the interdependencies between nodes in the heterogeneous dependency network, the utility coupling degree of each node is calculated. The formula for calculating the utility coupling degree is as follows: In the formula T i Let be the utility coupling degree of node i. The weights represent the direct coupling degree of the nodes. The weights represent the inter-node coupling degree. Let be the direct coupling degree of node i. Let be the degree of coupling between nodes i; The direct coupling degree of node i The calculation formula is as follows: ； The inter-node coupling degree The calculation formula is as follows: Where k is the node directly connected to node i. Let i be the set of all nodes directly connected to node i. This represents the association value between node i and its directly connected inflow node k. This represents the association value between node i and its directly connected outflow node k. This is the sum of the correlation values ​​between all directly connected inflow nodes in the current distribution network. This represents the sum of correlation values ​​between all directly connected outgoing nodes in the current distribution network, where j is the node connected to node i. Let i be the set of all nodes connected to node i. This represents the association value between node i and the indirect inflow node j. This represents the association value between node i and the indirectly connected outflow node j. This is the sum of the correlation values ​​between all indirect inflow nodes in the current distribution network. This is the sum of the correlation values ​​between all indirect outflow nodes in the current distribution network; The formula for calculating the correlation value is as follows: In the formula The association value between node i and node j. Let be the self-impedance of node i. Let J be the self-impedance of node j. Let be the mutual impedance between node i and node j.

2. The method as described in claim 1, characterized in that, After calculating the utility coupling degree of each node in the distribution network, the method further includes: Determine if the state of the distribution network has changed. If it has changed, recalculate the utility coupling degree of each node in the distribution network.

3. The method as described in claim 1, characterized in that, The calculation-based distribution network operation status calculation of the comprehensive risk index of each node in the distribution network includes: The over-limit risk value and the under-load risk value are calculated based on the utility coupling degree of each node and the obtained voltage over-limit risk probability and under-load risk probability of each node, respectively. Calculate the comprehensive risk index for each node based on the over-limit risk value and the underload risk value.

4. The method as described in claim 3, characterized in that, The formula for calculating the voltage over-limit risk value is as follows: In the formula, R ui This represents the voltage over-limit risk value for node i. Represents a node i The probability of voltage exceeding the limit. For nodes i The normalized voltage limit severity is obtained by normalizing the voltage limit severity. The node i The formula for calculating the severity of voltage over-limit is as follows: In the formula For nodes i The severity of voltage exceeding the limit, T i Let be the utility coupling degree of node i. The severity of voltage offset at node i.

5. The method as described in claim 3, characterized in that, The formula for calculating the risk of load loss at the node is as follows: In the formula, R Bi This represents the load loss risk value of node i. Represents a node i The probability of load failure, For nodes i The normalized loss-of-load risk index is obtained by normalizing the loss-of-load risk index. The formula for calculating the load loss risk index of node i is as follows: In the formula T is the load loss risk index for node i. i Let be the utility coupling degree of node i. For nodes i Severity of load loss.

6. The method as described in claim 3, characterized in that, The formula for calculating the comprehensive risk index is as follows: ； In the formula: b1 is the weight of voltage exceeding the limit, and b2 is the weight of node load loss. This represents the voltage over-limit risk value for node i. This represents the load loss risk value of node i.

7. A system for identifying vulnerable points in a power distribution network, characterized in that, include: The module consists of a utility coupling degree module, a comprehensive risk index module, and an identification module. The utility coupling degree module is used to calculate the utility coupling degree of each node in the distribution network based on the operating status of the distribution network. The comprehensive risk index module is used to calculate the comprehensive risk index of each node in the distribution network based on the utility coupling degree. The identification module is used to identify weak points in the power distribution network based on the comprehensive risk indicators of each node. The utility coupling module includes: a data acquisition unit, an association value unit, and a coupling unit; The data acquisition unit is used to acquire the topology corresponding to the operating state of the distribution network, and to obtain the heterogeneous dependent network corresponding to the distribution network and the resistance parameters between each node in the distribution network. The correlation value unit is used to calculate the correlation value between each node based on the impedance parameters between each node. The coupling degree unit is used to calculate the utility coupling degree of each node based on the correlation value between each node and the mutual dependence relationship between nodes in the heterogeneous dependency network. The formula for calculating the utility coupling degree is as follows: In the formula T i Let be the utility coupling degree of node i. The weights represent the direct coupling degree of the nodes. The weights represent the inter-node coupling degree. Let be the direct coupling degree of node i. Let be the degree of coupling between nodes i; The direct coupling degree of node i The calculation formula is as follows: ； The inter-node coupling degree The calculation formula is as follows: Where k is the node directly connected to node i. Let i be the set of all nodes directly connected to node i. This represents the association value between node i and its directly connected inflow node k. This represents the association value between node i and its directly connected outflow node k. This is the sum of the correlation values ​​between all directly connected inflow nodes in the current distribution network. This represents the sum of correlation values ​​between all directly connected outgoing nodes in the current distribution network, where j is the node connected to node i. Let i be the set of all nodes connected to node i. This represents the association value between node i and the indirect inflow node j. This represents the association value between node i and the indirectly connected outflow node j. This is the sum of the correlation values ​​between all indirect inflow nodes in the current distribution network. This is the sum of the correlation values ​​between all indirect outflow nodes in the current distribution network; The formula for calculating the correlation value is as follows: In the formula The association value between node i and node j. Let be the self-impedance of node i. Let J be the self-impedance of node j. Let be the mutual impedance between node i and node j.

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